Special Set Linear Algebra

Special Set Linear Algebra - Cover:Layout 1

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SPECIAL SET LINEAR ALGEBRA AND SPECIAL SET FUZZY LINEAR ALGEBRA

W. B. Vasantha Kandasamy e-mail: vasanthakandasamy@gmail.com web: http://mat.iitm.ac.in/home/wbv/public_html/ www.vasantha.in Florentin Smarandache e-mail: smarand@unm.edu K Ilanthenral e-mail: ilanthenral@gmail.com

Editura CuArt 2009

This book can be ordered in a paper bound reprint from:

Editura CuArt Strada Mânastirii, nr. 7 Bl. 1C, sc. A, et. 3, ap. 13 Slatina, Judetul Olt, Romania Tel: 0249-430018, 0349-401577 Editor: Marinela Preoteasa

Peer reviewers: Dr. Arnaud Martin, ENSIETA E3I2-EA3876, 2 Rue Francois Verny, 29806 Brest Cedex 9, France Prof. Mihàly Bencze, Department of Mathematics Áprily Lajos College, Bra ov, Romania Prof. Nicolae Ivaschescu, Department of Mathematics, Fratii Buzesti College, Craiova, Romania.

Copyright 2009 by Editura CuArt and authors Cover Design and Layout by Kama Kandasamy

Many books can be downloaded from the following Digital Library of Science: http://www.gallup.unm.edu/~smarandache/eBooks-otherformats.htm

ISBN-10: 1-59973-106-1 ISBN-13: 978-159-97310-6-3 EAN: 9781599731063

Standard Address Number: 297-5092 Printed in the Romania

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CONTENTS

Dedication

5

Preface

6

Chapter One

BASIC CONCEPTS

7

Chapter Two

SPECIAL SET VECTOR SPACES AND FUZZY SPECIAL SET VECTOR SPACES AND THEIR PROPERTIES 2.1 2.2 2.3 2.4

Special Set Vector Spaces and their Properties Special Set Vector Bispaces and their Properties Special Set Vector n-spaces Special Set Fuzzy Vector Spaces

3

33 33 66 103 156

Chapter Three

SPECIAL SEMIGROUP SET VECTOR SPACES AND THEIR GENERALIZATIONS 207 3.1 Introduction to Semigroup Vector Spaces 3.2 Special Semigroup Set Vector Spaces and Special Group Set Vector Spaces

207 213

Chapter Four

SPECIAL FUZZY SEMIGROUP SET VECTOR SPACES AND THEIR GENERALIZATIONS 4.1 Special Fuzzy Semigroup Set Vector Spaces and their properties 4.2 Special Semigroup set n-vector Spaces

295

295 313

Chapter Five

SUGGESTED PROBLEMS

423

FURTHER READING

457

INDEX

462

ABOUT THE AUTHORS

467

4

DEDICATION

(15-09-1909 to 03-02-1969) We dedicate this book to late Thiru C.N.Annadurai (Former Chief Minister of Tamil Nadu) for his Centenary Celebrations. He is fondly remembered for legalizing self respect marriage, enforcing two language policy and renaming Madras State as TamilNadu. Above all he is known for having dedicated his rule to Thanthai Periyar.

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PREFACE This book for the first time introduces the notion of special set linear algebra and special set fuzzy linear algebra. This is an extension of the book set linear algebra and set fuzzy linear algebra. These algebraic structures basically exploit only the set theoretic property, hence in applications one can include a finite number of elements without affecting the systems property. These new structures are not only the most generalized structures but they can perform multi task simultaneously; hence they would be of immense use to computer scientists.

This book has five chapters. In chapter one the basic concepts about set linear algebra is given in order to make this book a self contained one. The notion of special set linear algebra and their fuzzy analogue is introduced in chapter two. In chapter three the notion of special set semigroup linear algebra is introduced. The concept of special set n- vector spaces, n greater than or equal to three is defined and their fuzzy analogue is given in chapter four. The probable applications are also mentioned.

The final chapter suggests 66 problems. Our thanks are due to Dr. K. Kandasamy for proof-reading this book. We also acknowledge our gratitude to Kama and Meena for their help with corrections and layout.

W.B.VASANTHA KANDASAMY FLORENTIN SMARANDACHE K.ILANTHENRAL

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Chapter One

BASIC CONCEPTS

In this chapter we just introduce the notion of set linear algebra and fuzzy set linear algebra. This is mainly introduced to make this book a self contained one. For more refer [60].

DEFINITION 1.1: Let S be a set. V another set. We say V is a set vector space over the set S if for all v V and for all s S; vs and sv V. Example 1.1: Let V = {1, 2, ... , f} be the set of positive integers. S = {2, 4, 6, ... , f} the set of positive even integers. V is a set vector space over S. This is clear for sv = vs V for all s S and v V. It is interesting to note that any two sets in general may not be a set vector space over the other. Further even if V is a set vector space over S then S in general need not be a set vector space over V. For from the above example 1.1 we see V is a set vector space over S but S is also a set vector space over V for we see for every s S and v V, vs = sv S. Hence the above

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example is both important and interesting as one set V is a set vector space another set S and vice versa also hold good inspite of the fact S z V. Now we illustrate the situation when the set V is a set vector space over the set S. We see V is a set vector space over the set S and S is not a set vector space over V. Example 1.2: Let V = {Q+ the set of all positive rationals} and S = {2, 4, 6, 8, … , f}, the set of all even integers. It is easily verified that V is a set vector space over S but S is not a set 7 7 V and 2 S but .2 S. Hence vector space over V, for 3 3 the claim.

Now we give some more examples so that the reader becomes familiar with these concepts. °­§ a b · ®¨ ¸ a, b, c, d (set of all °¯© c d ¹ positive integers together with zero)} be the set of 2 u 2 matrices with entries from N. Take S = {0, 2, 4, 6, 8, … , f}.

Clearly M2x2 is a set vector space over the set S.

Example 1.3: Let M 2u2

Example 1.4: Let V = {Z+ u Z+ u Z+} such that Z+ = {set of positive integers}. S = {2, 4, 6, …, f}. Clearly V is a set vector space over S. Example 1.5: Let V = {Z+ u Z+ u Z+} such that Z+ is the set of positive integers. S = {3, 6, 9, 12, … , f}. V is a set vector space over S. Example 1.6: Let Z+ be the set of positive integers. pZ+ = S, p any prime. Z+ is a set vector space over S. Note: Even if p is replaced by any positive integer n still Z+ is a set vector space over nZ+. Further nZ+ is also a set vector space

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over Z+. This is a collection of special set vector spaces over the set.

Example 1.7: Let Q[x] be the set of all polynomials with coefficients from Q, the field of rationals. Let S = {0, 2, 4, …, f}. Q[x] is a set vector space over S. Further S is not a set vector space over Q[x].

Thus we see all set vector spaces V over the set S need be such that S is a set vector space over V. Example 1.8: Let R be the set of reals. R is a set vector space over the set S where S = {0, 1, 2, … , f}. Clearly S is not a set vector space over R. Example 1.9: ^ be the collection of all complex numbers. Let

Z+ {0} = {0, 1, 2, … , f} = S. ^ is a set vector space over S but S is not a set vector space over ^.

At this point we propose a problem.

Characterize all set vector spaces V over the set S which are such that S is a set vector space over the set V. Clearly Z+ {0} = V is a set vector space over S = p Z+ {0}, p any positive integer; need not necessarily be prime. Further S is also a set vector space over V. Example 1.10: Let V = Z12 = {0, 1, 2, … , 11} be the set of integers modulo 12. Take S = {0, 2, 4, 6, 8, 10}. V is a set vector space over S. For s S and v V, sv { vs (mod 12).

Example 1.11: Let V = Zp = {0, 1, 2, … , p – 1} be the set of integers modulo p (p a prime). S = {1, 2, … , p – 1} be the set.

V is a set vector space over S.

In fact V in example 1.11 is a set vector space over any proper subset of S also.

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This is not always true, yet if V is a set vector space over S. V need not in general be a set vector space over every proper subset of S. Infact in case of V = Zp every proper subset Si of S = {1, 2, …, p – 1} is such that V is a set vector space over Si. Note: It is important to note that we do not have the notion of linear combination of set vectors but only the concept of linear magnification or linear shrinking or linear annulling. i.e., if v V and s is an element of the set S where V is the set vector space defined over it and if sv makes v larger we say it is linear magnification.

Example 1.12: If V = Z+ {0} = {0, 1, 2, …, f} and S = {0, 2, 4, …, f} we say for s = 10 and v = 21, sv = 10.21 is a linear magnification.

Now for v = 5 and s = 0, sv = 0.5 = 0 is a linear annulling.

Here we do not have the notion of linear shrinking.

So in case of any modeling where the researcher needs only linear magnification or annulling of the data he can use the set vector space V over the set S. Notation: We call the elements of the set vector space V over the set S as set vectors and the scalars in S as set scalars.

Example 1.13: Suppose Q+ {0} = {all positive rationals} = Q* = V and 1 1 1 ­ ½ S = ®0, 1, , 2 , 3 , ... ¾ .

2 2 2 ¯ ¿

Then V is a set vector space over S. We see for 6 Q* = V and 1 1 S .6 3 . This is an instance of linear shrinking.

2 2 21 21 Suppose s = 0 and v then sv = 0.

0 is an instance of 3 3 linear annulling.

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Now we see this vector space is such that there is no method to get a linearly magnified element or the possibility of linear magnification.

Now we have got a peculiar instance for 1 S and 1.v = v for every v V linear neutrality or linearly neutral element. The set may or may not contain the linearly neutral element. We illustrate yet by an example in which the set vector space which has linear magnifying, linear shrinking, linear annulling and linearly neutral elements.

Example 1.14: Let Q+ {0} = V = {set of all positive rationals with zero}.

1 1 ­ ½ S = ®0, 1, , 2 , ..., 2, 4,6, ..., f ¾ 3 3 ¯ ¿ be the set. V is a set vector space over the set S. We see V is such that there are elements in S which linearly magnify some elements in V; for instance if v = 32 and s = 10 then sv = 10.32 = 320 is an instance of linear magnification. Consider v = 30 1 1 and s = then sv = u 30 = 10 V. This is an instance of 3 3 linear shrinking. Thus we have certain elements in V which are linearly shrunk by elements of the set S. Now we have 0 S such that 0 v = 0 for all v V which is an instance of linear annulling. Finally we see 1 S is such that 1.v = v for all v V; which is an instance of linearly neutral.

Thus we see in this set vector space V over the set S given in example 1.14, all the four properties holds good. Thus if a researcher needs all the properties to hold in the model he can take them without any hesitation. Now we define yet another notion called linearly normalizing element of the set vector space V. Suppose v V is a set vector and s S is a set scalar and 1 V which is such that 1.v = v.1 = v and s.1 = 1.s = s for all v V and for all s S; we call the element scalar s in S to

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be a linearly normalizing element of S if we have v V such that sv = vs = 1.

For example in the example 1.14, we have for 3 V and S;

1

3

1 1 3 = 1 S. Thus is a linearly normalizing element of S. 3 3

It is important to note that as in case of linearly annulling or linearly neutral element the scalar need not linearly normalize every element of the set vector space V. In most cases an element can linearly normalize only one element.

Having seen all these notions we now proceed on to define the new notion of set vector subspace of a set vector space V. DEFINITION 1.2: Let V be a set vector space over the set S. Let W V be a proper subset of V. If W is also a set vector space over S then we call W a set vector subspace of V over the set S.

We illustrate this by a few examples so that the reader becomes familiar with this concept.

Example 1.15: Let V = {0, 1, 2, …, f} be the set of positive integers together with zero. Let S = {0, 2, 4, 6, …, f}, the set of positive even integers with zero. V is a set vector space over S. Take W = {0, 3, 6, 9, 12, …, f} set of all multiples of 3 with zero. W V; W is also a set vector space over S. Thus W is a set vector subspace of V over S. Example 1.16: Let Q[x] be the set of all polynomials with coefficients from Q; the set of rationals. Q[x] is a set vector space over the set S = {0, 2, 4, … , f}. Take W = {0, 1, …, f} the set of positive integers with zero. W is a set vector space over the set S. Now W Q Q[x]; so W is a set vector subspace of V over the set S.

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Example 1.17: Let V = Z+ u Z+ u Z+ u Z+ a set of vector space over the set Z+ = {0, 1, 2, …, f}. Let W = Z+ u Z+ u {0} u {0}, a proper subset of V. W is also a set vector space over Z+, i.e., W is a set vector subspace of V. Example 1.18: Let V = 2Z+ u 3Z+ u Z+ be a set, V is a set vector space over the set S = {0, 2, 4, …, f}. Now take W = 2Z+ u {0} u 2Z+ V; W is a set vector subspace of V over the set S. Example 1.19: Let

­§ a d · °¨ ¸ V = M3u2 = ®¨ b e ¸ a, b, c, d, e, f Z+ {0}} °¨ c f ¸ ¹ ¯© be the set of all 3 u 2 matrices with entries from the set of positive integers together with zero. V is a set vector space over the set S = Z+ {0}. Take ­§ a 0 · °¨ ¸ W = ®¨ b 0 ¸ °¨ c 0 ¸ ¹ ¯©

a, b, c Z+ {0}} V;

W is a set vector subspace of V over the set S. Now having defined set vector subspaces, we proceed on to define the notion of zero space of a set vector space V over the set S. We as in the case of usual vector spaces cannot define set zero vector space at all times. The set zero vector space of a set vector space V exists if and only if the set vector space V over S has {0} in V i.e., {0} is the linearly annulling element of S or 0 V and 0 S in either of the two cases we have the set zero subspace of the set vector space V.

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Example 1.20: Let Z+ = V = {1, 2, …, f} be a set vector space over the set S = {2, 4, 6, …, f}; V is a set vector space but 0 V, so V does not have a set vector zero subspace.

It is interesting to mention here that we can always adjoin the zero element to the set vector space V over the set S and this does not destroy the existing structure. Thus the element {0} can always be added to make the set vector space V to contain a set zero subspace of V. We leave it for the reader to prove the following theorem.

THEOREM 1.1: Let V be a set vector space over the set S. Let W1, …, Wn be n proper set vector subspaces of V over S. Then n

W

i

n

is a set vector subspace of V over S. Further

i 1

W

i

I

i 1

can also occur, if even for a pair of set vector subspaces Wi and Wj of V we have Wi Wj = I (i z j).

Clearly even if 0 V then also we cannot say

n

W

i

= {0}

i 1

as 0 need not be present in every set vector subspace of V.

We illustrate the situation by the following example.

Example 1.21: Let Z+ = {1, 2, …, f} be a set vector space over the set S = {2, 4, 6, …, f}. Take

W1 = {2, …, f}, W2 = {3, 6, …, f}, …, and Wp = {p, 2p, 3p, …, f}.

We see Wi Wj z I for every i, j (i z j).

Will Wi = I if i = 1, 2, … , f ?

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n

Will

W z I i

if i = 1, 2, … , n; n < f?

i 1

Example 1.22: Let V = {1, 2, …, f} be a set vector space over S = {2, , …, f}. W = {2, 22, …, 2n …} is set vector subspace of V over the set S. W1 = {3, 32, 33, …, f} is a proper subset of V but W1 is not a set vector subspace of V as W1 is not a set vector space over S as 2.3 = 6 W1 for 2 S and 3 W1. Thus we by this example show that in general every proper subset of the set V need not be a set vector space over the set S.

Now having seen the set vector subspaces of a set vector space we now proceed to define yet another new notion about set vector spaces.

DEFINITION 1.3: Let V be a set vector space over the set S. Let T be a proper subset of S and W a proper subset of V. If W is a set vector space over T then we call W to be a subset vector subspace of V over T.

We first illustrate this situation by examples before we proceed on to give more properties about them.

Example 1.23: Let V = Z+ {0} = {0, 1, 2, …, f} be a set vector space over the set S = {2, 4, 6, …, f}. Consider W = {3, 6, 9, … , } V. Take T = {6, 12, 18, 24, …} S. Clearly W is a set vector space over T; so W is a subset vector subspace over T. Example 1.24: Let V = Z+ u Z+ u Z+ be a set vector space over Z+ = S. Take W = Z+ u {0} u {0} a proper subset of V and T = {2, , 6, …, f} Z+ = S. Clearly W is a set vector space over T i.e., W is a subset vector subspace over T.

Now we show all proper subsets of a set vector space need not be a subset vector subspace over every proper subset of S. We illustrate this situation by the following examples.

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Example 1.25: Let V = Z+ u Z+ u Z+ be a set vector space over Z+ = S = {0, 1, 2, ..., f}. Let W = {3, 32, …, f} u {5, 52, …} u {0} V. Take T = {2, 4, 6, …} S.

Clearly W is not a set vector space over T. That is W is not a subset vector subspace of V over T. Thus we see every subset of a set vector space need not in general be a subset vector subspace of the set vector space V over any proper subset T of the set S. We illustrate this concept with some more examples.

Example 1.26: Let V = 2Z+ u 3Z+ u 5Z+ = {(2n, 3m, 5t) | n, m, t Z+}; V is a set vector space over the set S = Z+ = {1, 2, …, f}. Take W = {2, 22, …, f} u {3, 32, … } u {5, 52, …} V. W is not a subset vector subspace over the subset T = {2, 4, …, f} Z+. Further W is not even a set vector subspace over Z+ = S. Take W = {2, 22, …} u {0} u {0} a proper subset of V. Choose T = {2, 23, 25, 27}. W is a subset vector subspace of V over the subset T S. Example 1.27: Let

­°§ a b · + V = ®¨ ¸ a, b, c, d Z {0} = {0, 1, 2, …}} °¯© c d ¹ be the set of all 2 u 2 matrices with entries from Z+ {0}. V is a set vector space over the set S = Z+ = {1, 2, …, f}. Take °­§ x W = ®¨ °¯© z

y· + ¸ x, y, z, w 2Z = {2, 4, 6, …}} V. w¹

W is a subset vector subspace over the subset T = {2, 4, ..., f}.

Example 1.28: Let

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°­§ a b c d · V = ®¨ ¸ a, b, c, d, e, f, g and h Z} °¯© e f g h ¹ be the set of all 2 u 4 matrices with entries from the set of integers. V is a set vector space over the set S = Z+ = {1, 2, …, f}. Take ­°§ a b c d · + W = ®¨ ¸ a, b, c, d, e, f, g and h Z } V. °¯© e f g h ¹ Clearly W is a subset vector subspace over T = {2, 4, …, f} S = Z +.

Now as in case of vector spaces we can have the following theorem.

THEOREM 1.2: Let V be a set vector space over the set S. Suppose W1, …, Wn be a set of n subset vector subspaces of V n

over the same subset T of S. Then either

Wi = I or i 1

n

W

i

is a

i 1

subset vector subspace over T or if each Wi contains 0 then n

W

i

= {0}, the subset vector zero subspace of V over T.

i 1

The proof is left as an exercise for the reader as the proof involves only simple set theoretic techniques.

Note: We cannot say anything when the subset vector subspaces of V are defined over different subsets of S. We illustrate this situation by some more examples.

Example 1.29: Let V = Z+ u Z+ u Z+ be a set vector space over a set S = Z+ = {1, 2, …, f}. W = {2, 4, 6, 8, …} u {I} u {I} is a subset vector subspace of V over the subset T = {2, 4, 8, 16, …}. W1 = {3, 6, 9, …} u I u I V, is a subset subvector space over the subset T1 = {3, 32, …}. Clearly we cannot define W 17

W1 for we do not have even a common subset over which it can be defined as T T1 = I. From this example a very natural question is if T T1 z I and if W1 W is not empty can we define some new structure. For this we make the following definition.

DEFINITION 1.4: Let V be a set vector space over the set S. Suppose Wi is a subset vector subspace defined over the subset Ti of S for i = 1, 2, …, n; n < f and if W = Wi z I and T = Ti z I; then we call W to be a sectional subset vector sectional subspace of V over T.

Note: We call it a sectional for every subset vector subspace contributes to it.

We give illustration of the same.

Example 1.30: Let V = Z+ u Z+ u Z+ u Z+ be a set vector space over the set Z+ = {1, 2, …, f}. Let W1 = {2, 4, 6, …} u {2, 4, 6, …} u Z+ u Z+ be a subset vector subspace over T1 = {2, 4, …, f}. W2 = {Z+} u {2, 4, 6, ...} u Z+ u Z+ be a subset vector subspace over T2 = {2, 22, …, f}. Let W3 = {2, 22, …, f} u {Z+} u {2, 4, 6, …} u {2, 22, …, f} be a subset vector subspace over T3 = {2, 23, 25, …, f}. Consider W1 W2 W3 = {2, 22, ..., f} u {2, 4, 6, ...} u {2, 4, 6, ..., f} u {2, 22, ..., f} = W. Now T = T1 T2 T3 = {2, 23, 25, …}; W is a sectional subset vector sectional subspace of V over T.

We see a sectional subset vector sectional subspace is a subset vector subspace but a sectional subset vector sectional subspace in general is not a subset vector subspace.

We prove the following interesting theorem.

THEOREM 1.3: Every sectional subset vector sectional subspace W of set vector space V over the set S is a subset vector subspace of a subset of S but not conversely.

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n

Proof: Let V be the set vector space over the set S. W =

W

i

i 1

n

be a sectional subset vector sectional subspace over T =

T

i

i 1

where each Wi is a subset vector subspace over Ti for i = 1, 2, …, n with Wi z Wj and Ti z Tj; for i z j, 1 d i, j d n. We see W is a sectional subset vector subspace over Ti ; i = 1, 2, …, n.

We illustrate the converse by an example.

Example 1.31: Let V = {2, 22, …} be a set vector space over the set S = {2, 22}. W1 = {22, 24, …} is a subset vector subspace of V over the set T1 = {2} and W2 = {23, 26, …, f} is a subset vector subspace of over the set T2 = {22}. Now W1 W2 z I but T1 T2 = I. We do not and cannot make W1 or W2 as sectional subset vector sectional subspace.

That is why is general for every set vector space V over the set S we cannot say for every subset vector subspace W( V) over the subset T( S) we can find atleast a subset vector subspace W1( V) over the subset T1( S) such that W1 W2 z I and T T1 z I. Now before we define the notion of basis, dimension of a set S we mention certain important facts about the set vector spaces.

1. All vector spaces are set vector spaces but not conversely.

The converse is proved by giving counter examples.

Take V = Z+ = {0, 1, 2, …, f}, V is a set vector space over the set S = {2, 4, 6, …, f}. We see V is not an abelian group under addition and S is not a field so V can never be a vector space over S. But if we have V to be a vector space over the field F we see V is a set vector space over the set F as for every c F and v V we have cv V. Thus every vector space is a set vector space and not conversely.

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2. All semivector spaces over the semifield F is a set vector space but not conversely.

We see if V is a semivector space over the semifield F then V is a set and F is a set and for every v V and a F; av V hence V is trivially a set vector space over the set F. However take {–1, 0, 1, 2, …, f} = V and S = {0, 1} V is a set vector space over S but V is not even closed with respect to ‘+’ so V is not a group. Further the set S = {0, 1} is not a semifield so V is not a semivector space over S. Hence the claim.

Thus we see the class of all set vector spaces contains both the collection of all vector spaces and the collection of all semivector spaces. Thus set vector spaces happen to be the most generalized concept.

Now we proceed on to define the notion of generating set of a set vector space V over the set S. DEFINITION 1.5: Let V be a set vector space over the set S. Let B V be a proper subset of V, we say B generates V if every element v of V can be got as sb for some s S and b B. B is called the generating set of V over S. Example 1.32: Let Z+ {0} = V be a set vector space over the set S = {1, 2, 4, …, f}. The generating set of V is B = {0, 1, 3, 5, 7, 9, 11, 13, …, 2n + 1, …} B is unique. Clearly the cardinality of B is infinite.

Examples 1.33: Let V = Z+ {0} be a set vector space over the set S = {1, 3, 32, …, f}. B = {0, 1, 2, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, …, 80, 82, …, 242, 244, …, 36 – 1, …, 36 + 1, …, f} V is the generating set of V. Example 1.34: Let V = {0, 3, 32, …, 3n, …} be a set vector space over S = {0, 1, 3}. B = {0, 3} is a generating set for 3.0 = 0, 3.1 = 3, 3.3 = 32, 3.32 = 33 so on.

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Example 1.35: Let

­°§ a b · + V = ®¨ ¸ a, b, c, d Z = {1, 2, …, f}} c d ¹ °¯© be the set vector space over the set S = Z+. The generating set of V is infinite.

Thus we see unlike in vector space or semivector spaces finding the generating set of a set vector space V is very difficult.

When the generating set is finite for a set vector space V we say the set vector space is finite set hence finite cardinality or finite dimension, otherwise infinite or infinite dimension.

Example 1.36: Let Z+ = V = {1, 2, …, f} be a set vector space over the set S = {1}. The dimension of V is infinite.

Example 1.37: Let V = {1, 2, …, f} be a set vector space over the set S = {1, 2, …, f} = V. Then dimension of V is 1 and B is uniquely generated by {1}. No other element generates V.

Thus we see in case of set vector space we may have only one generating set. It is still an open problem to study does every set vector space have one and only one generating subset?

Example 1.38: Let V = {2, 4, 6, …} be a set vector space over the set S = {1, 2, 3, …}. B = {2} is the generating set of V and dimension of V is one.

Thus we have an important property enjoyed by set vector spaces. We have a vector space V over a field F of dimension one only if V = F, but we see in case of set vector space V, dimension V is one even if V z S. The example 1.38 gives a set vector space of dimension one where V z S. Example 1.39: Let V = {2, 4, 6, …, f} be a set vector space over the set S = {2, 22, …, f}. B = {2, 6, 10, 12, 14, 18, 20, 22, 24, …, 30, 34, …} is a generating set of V.

21

Now in case of examples 1.38 and 1.39 we see V = {2, 4, …, f} but only the set over which they are defined are different so as in case of vector spaces whose dimension is dependent on the field over which it is defined are different so as in case of vector spaces whose dimension is dependent on the field over which it is defined so also the cardinality of the generating set of a vector space V depends on the set over which V is defined. This is clear from examples 1.38 and 1.39.

Now having defined cardinality of set vector spaces we define the notion of linearly dependent and linearly independent set of a set vector space V over the set S. DEFINITION 1.6: Let V be a set vector space over the set S. B a proper subset of V is said to be a linearly independent set if x, y B then x z sy or y z s'x for any s and s' in S. If the set B is not linearly independent then we say B is a linearly dependent set.

We now illustrate the situation by the following examples.

Example 1.40: Let Z+ = V = {1, 2, ..., f} be a set vector space over the set S = {2, 4, 6, …, f}. Take B = {2, 6, 12} V; B is a linearly dependent subset for 12 = 6.2, for 6 S. B = {1, 3} is a linearly independent subset of V. Example 1.41: Let V = {0, 1, 2, …, f} be a set vector space over the set S = {3, 32, …}. B = {1, 2, 4, 8, 16, …} is a linearly independent subset of V. As in case of vector spaces we can in case of set vector spaces also say a set B which is the largest linearly dependent subset of V? A linearly independent subset B of V which can generate V, then we say B is a set basis of V or the generating subset of V and cardinality of B gives the dimension of V. DEFINITION 1.7: Let V and W be two set vector spaces defined over the set S. A map T from V to W is said to be a set linear transformation if T (v) = w

22

and T(sv) = sw = sT(v) for all v V, s S and w W. Example 1.42: Let V = Z+ = {0, 1, 2, …, f} and W = {0, 2, 4, …, f} be set vector spaces over the set S = {0, 2, 22, 23, …}.

T:VoW T(0) = 0, T(1) = 2, T(2) = 4, T(3) = 6 and so on. T (2np) = 2n (2p) for all p V. Thus T is a set linear transformation from V to W. As in case of vector spaces we will not be always in a position to define the notion of null space of T in case of set vector spaces. For only if 0 V as well as W; we will be in a position to define null set of a set linear transformation T. Now we proceed on to define the notion of set linear operator of a set vector space V over the set S. DEFINITION 1.8: Let V be a set vector space over the set S. A set linear transformation T from V to V is called the set linear operator of V. Example 1.43: Let V = Z+ = {1, 2, …, f} be a set vector space over the set S = {1, 3, 32, …, f}. Define T from V to V by T(x) = 2x for every x V. T is a set linear operator on V.

Now it is easy to state that if V is a set vector space over the set S and if OS(V) denotes the set of all set linear operators on V then OS(V) is also a set vector space over the same set S. Similarly if V and W are set vector spaces over the set S and TS (V, W) denotes the set of all linear transformation from V to W then TS (V,W) is also a set vector space over the set S. For if we want to prove some set V is a set vector space over a set S it

23

is enough if we show for every s S and v V; sv V. Now in case of OS (V) = {set of all set linear operators from V to V}.

OS (V) is a set and clearly for every s S and for every T OS(V), sT Os (V) i.e., sT is again a set linear operator of V. Hence OS (V) is a set vector space over the set S. Likewise if we consider the set TS (V, W) = {set of all set linear transformations from V to W}, then the set TS (V, W) is again a set vector space over S. For if s S and T TS (V, W) we see sT is also a set linear operator from V to W. Hence the claim.

Now we can talk about the notion of invertible set linear transformation of the set vector spaces V and W defined over the set S. Let T be a set linear transformation from V into W. We say T is set invertible if there exist a set linear transformation U from W into V such that UT and TU are set identity set maps on V and W respectively. If T is set invertible, the map U is called the set inverse of T and is unique and is denoted by T–1.

Further more T is set invertible if and only if (1) T is a one to one set map that is TD = TE implies D = E. (2) T is onto that is range of T is all of W. We have the following interesting theorem.

THEOREM 1.4: Let V and W be two set vector spaces over the set S and T be a set linear transformation from V into W. If T is invertible the inverse map T–1 is a set linear transformation from W onto V.

Proof: Given V and W are set vector spaces over the set S. T is a set linear transformation from V into W. When T is a one to one onto map, there is a uniquely determined set inverse map T–1 which set maps W onto V such that T–1T is the identity map on V and TT–1 is the identity function on W. Now what we want to prove is that if a set linear transformation T is set invertible then T–1 is also set linear.

24

Let x be a set vector in W and c a set scalar from S. To show T–1 (cx) = cT–1 (x).

Let y = T–1(x) that is y be the unique set vector in V such that Ty = x. Since T is set linear T(cy) = cTy. Then cy is the unique set vector in V which is set by T into cx and so T–1 (cx) = cy = c T–1 (x) and T is set linear !

–1

Suppose that we have a set invertible set linear transformation T from V onto W and a set invertible set linear transformation U from W onto Z. Then UT is set invertible and (UT)–1 = T–1 U–1.

To obtain this it is enough if we verify T–1 U–1 is both left and right set inverse of UT. Thus we can say in case of set linear transformation; T is one to one if and only if TD = TE if and only if D = E. Since in case of set linear transformation we will not always be in a position to have zero to be an element of V we cannot define nullity T or rank T. We can only say if 0 V, V a set vector space over S and 0 W, W also a set vector space over S then we can define the notion of rank T and nullity T, where T is a set linear transformation from V into W. We may or may not be in a position to have results of linear transformation from vector spaces.

Also the method of representing every vector space V over a field F of dimension n as V # F u ... u F may not be feasible

n times

in case of set vector spaces. Further the concept of representation of set linear transformation as a matrix is also not possible for all set vector spaces. So at this state we make a note of the inability of this structure to be always represented in this nice form.

Next we proceed on to define the new notion of set linear functionals of a set vector space V over a set S.

25

DEFINITION 1.9: Let V be a set vector space over the set S. A set linear transformation from V onto the set S is called a set linear functional on V, if f:VoS f (cD) = cf (D) for all c S and D V. Example 1.44: Let V = {0, 1, 2, ..., f} and S = {0, 2, 4, …, f}. f : V o S defined by f(0) = 0, f(1) = 2, f(2) = 4, ..., is a set linear functional on V. Example 1.45: Let V = Z+ u Z+ u Z+ be a set vector space over Z+. A set linear functional f : V o Z+ defined by f(x, y, z) = x + y + z.

Now we proceed onto define the new notion of set dual space of a set vector space V. DEFINITION 1.10: Let V be a set vector space over the set S. Let L(V,S) denote the set of all linear functionals from V into S, then we call L(V,S) the set dual space of V. Infact L(V, S) is also a set vector space over S.

The study of relation between set dimension of V and that of L(V, S) is an interesting problem.

Example 1.46: Let V = {0, 1, 2, …, f} be a set vector space over the set S = {0, 1, 2, ..., f}. Clearly the set dimension of V over S is 1 and this has a unique generating set B = {1}. No other proper subset of B can ever generate V.

What is the dimension of L(V, S)?

We cannot define the notion of set hyperspace in case of set vector space using set linear functionals. We can define the concept of set annihilator if and only if the set vector space V and the set over which it is defined contains the zero element, otherwise we will not be in a position to define the set annihilator.

26

DEFINITION 1.11: Let V be a set vector space over the set S, both V and S has zero in them. Let A be a proper subset of the set V, the set annihilator of A is the set Ao of all set linear functionals f on V such that f(D) = 0 for every D in A. DEFINITION 1.12: A fuzzy vector space (V, K) or KV is an ordinary vector space V with a map K : V o [0, 1] satisfying the following conditions;

1.

2.

3.

4.

K (a + b) > min {K (a), K (b)} K (â&#x20AC;&#x201C; a) = K (a) K (0) = 1 K (ra) > K (a)

for all a, b, Â? V and r Â? F where F is the field.

We now define the notion of set fuzzy vector space or VK or VK or KV. DEFINITION 1.13: Let V be a set vector space over the set S. We say V with the map K is a fuzzy set vector space or set fuzzy vector space if K: V o [0, 1] and K (ra) t K(a) for all a Â?V and r Â? S. We call VK or VK or KV to be the fuzzy set vector space over the set S.

We now illustrate this situation by the following example.

Example 1.47: Let V = {(1 3 5), (1 1 1), (5 5 5), (7 7 7), (3 3 3), (5 15 25), (1 2 3)} be set which is a set vector space over the set S = {0, 1}.

Define a map K: V o [0, 1] by

§x y z¡ K(x, y, z) = ¨ ¸ � > 0,1@ Š 50 š

for (x, y, z) Â? V. VK is a fuzzy set vector space.

27

Example 1.48: Let V = Z+ the set of integers. S = 2Z+ be the set.

V is a set vector space over S. Define K: V o [0, 1] by, for 1 . KV is a set fuzzy vector space or fuzzy every v Â? V; K(v) = v set vector space.

Example 1.49: Let V = {(aij) | aij Â? Z+; 1 d i, j d n} be the set of all n u n matrices with entries from Z+.

Take S = 3Z+ to be the set. V is a fuzzy set vector space where K: V o [0, 1] is defined by ­ 1 ° K(A = (aij)) = Ž 5 | A | ° 1 ¯

if | A | z 0 if | A |

0.

VK is the fuzzy set vector space.

The main advantage of defining set vector spaces and fuzzy set vector spaces is that we can include elements x in the set vector spaces V provided for all s Â? S, sx Â?V this cannot be easily done in usual vector spaces. Thus we can work with the minimum number of elements as per our need and work with them by saving both time and money.

We give yet some more examples.

Example 1.50: Let V = 2Z+ u 5Z+ u 7Z+ be a set vector space over the set Z+; with K: V o [0, 1] defined by

K((x, y, z)) =

1 x y z

makes, KV a fuzzy set vector space.

Now we define the notion of set fuzzy linear algebra.

28

DEFINITION 1.14: A set fuzzy linear algebra (or fuzzy set linear algebra) (V, K) or KV is an ordinary set linear algebra V with a map such K: V o [0, 1] such that K(a + b) t min (K(a), K(b)) for a, b Â? V. Since we know in the set vector space V we merely take V to be a set but in case of the set linear algebra V we assume V is closed with respect to some operation usually denoted as â&#x20AC;&#x2DC;+â&#x20AC;&#x2122; so the additional condition K(a + b) t min (K(a), K(b)) is essential for every a, b Â? V.

We illustrate this situation by the following examples.

Example 1.51: Let V = Z+[x] be a set linear algebra over the set S = Z+; K: V o [0, 1].

1 ­ ° K(p(x)) = Ž deg(p(x)) °1 if p(x) is a constant. ¯ Clearly VK is a set fuzzy linear algebra.

Example 1.52: Let

­°§ a b ¡ ½ ° V = Ž¨ ¸ a, b,d,c � Z ž °¯Š c d š ¿° be set linear algebra over 2Z+ = S. Define

1 ­ §§a b¡¡ ° K ¨¨ ¸ ¸ = Ž | ad bc | ŠŠ c dšš ° 0 ¯

if ad z bc if ad

bc

for every a, b, c, d Â? Z+. Clearly VK is a fuzzy set linear algebra.

29

Example 1.53: Let V = Z+ be a set linear algebra over Z+.

1 Define K : V o [0, 1] as K(a) = . VK is a fuzzy set linear a algebra.

Now we proceed onto define the notion of fuzzy set vector subspace and fuzzy set linear subalgebra.

DEFINITION 1.15: Let V be a set vector space over the set S. Let W Â? V be the set vector subspace of V defined over S. If K: W o [0, 1] then WK is called the fuzzy set vector subspace of V.

We illustrate this by the following example.

Example 1.54: Let V = {(1 1 1), (1 0 1), (0 1 1), (0 0 0), (1 0 0)} be a set vector space defined over the set {0, 1}. Define K : V o [0, 1] by

K(x y z) =

(x y z) 9

(mod 2) .

So that K (0 0 0) = 0 1 K (1 1 1) = 9 K (1 0 1) = 0 1 K (1 0 0) = 9 K (0 1 1) = 0.

VK is a set fuzzy vector space. Take W = {(1 1 1), (0 0 0), (0 1 1)} Â? V. W is a set vector subspace of V. K: W o [0, 1].

K (0 0 0) = 0 1 K (111) = 9 K (011) = 0.

30

WK is the fuzzy set vector subspace of V. Example 1.55: Let V = {(111), (1011), (11110), (101), (000), (0000), (0000000), (00000), (1111111), (11101), (01010), (1101101)} be a set vector space over the set S = {0,1}.

Let W = {(1111111), (0000000), (000), (00000), (11101), (01010) (101)} Â? V. Define K: W o [0, 1] by 1 .

K(x1, x2, â&#x20AC;Ś, xr) = 12 KW is a fuzzy set vector subspace.

We now proceed on to define the notion of fuzzy set linear subalgebra.

DEFINITION 1.16: Let V be a set linear algebra over the set S. Suppose W is a set linear subalgebra of V over S. Let K : W o [0, 1], KW is called the fuzzy set linear subalgebra if K (a + b) > min {K (a), K (b)} for a, b, Â? W.

We give some examples of this new concept.

Example 1.56: Let V = Z+ u Z+ u Z+ be a set linear algebra over the set S = 2Z+. W = Z+ u 2Z+ u 4Z+ is a set linear subalgebra over the set S = 2Z+. Define K: W o [0, 1]

K (a b c) = 1

1 . a b c

Clearly K (x, y) t min {K (x), K (y)} where x = (x1, x2, x3) and y = (y1, y2, y3); x, y Â? W. WK is a fuzzy set linear subalgebra.

Example 1.57: Let ½ °­§ a b ¡ ° V = Ž¨ ¸ a, b,c,d,� Z ž . ¯°Š c d š ¿°

31

V is a set linear algebra over the set S = {1, 3, 5, 7} Â&#x17D; Z+. Let ½ °­§ a a ¡ ° W = Ž¨ ¸ aÂ?Z ž ¯°Š a a š ¿° be the set linear subalgebra of V. Define K: W o [0, 1] by §a a¡ 1 K¨ ¸ 1 . a Ša aš WK or WK is a set fuzzy linear subalgebra.

For more about set linear algebra and set fuzzy linear algebra please refer [60].

32

Chapter Two

SPECIAL SET VECTOR SPACES AND FUZZY SPECIAL SET VECTOR SPACES AND THEIR PROPERTIES

In this chapter we for the first time introduce the notion of special set vector spaces and fuzzy special set vector spaces.

This chapter has four sections. Section one introduces the notion of special set vector spaces and describes some of their properties. In section two special set vector bispaces are introduced and their properties are studied. Section three generalizes the notion of special set vector bispaces to special set vector n-spaces. The final section introduces the notion of fuzzy special set vector spaces and fuzzy special set n-vector spaces.

2.1 Special Set Vector Spaces and their Properties In this section for the first time we define the notion of special set vector space and special set linear algebra and describe some of their properties.

33

DEFINITION 2.1.1: Let V = {S1, S2, …, Sn} be a set of collection of sets, such that each Si is distinct i.e., Si Sj or Sj Si if i z j; 1d i, j d n. Suppose P is any set which is nonempty such that each Si is a set vector space over the set P; for each i = 1, 2, …, n then we call V = {S1, S2, …, Sn} to be a special set vector space over the set P. We illustrate this situation by some simple examples.

Examples 2.1.1: Let V = (V1, V2, V3, V4) where V1 = {(1 1 1 1), (0 0 0 0), (1 1 0 0), (0 0 1 1), (1 1), (0 1), (1 0), (0 0)},

­ §a a· °§ a a a · ¨ ¸ V2 = ®¨ ¸ , ¨ a a ¸ | a Z2 °© a a a ¹ ¨ a a ¸ © ¹ ¯

½ ° {0, 1}¾ , ° ¿

V3 = {Z2 × Z2 × Z2, Z2 × Z2 × Z2 × Z2 × Z5} and

V4

§1 ­§ 1 0 0 · § 1 0 0 · § 1 1 1 · ¨ °¨ ¸ ¨ ¸ ¨ ¸ 1 ®¨ 0 1 0 ¸ , ¨ 0 1 0 ¸ , ¨ 0 0 0 ¸ , ¨¨ °¨ 0 0 0 ¸ ¨ 0 0 0 ¸ ¨ 1 1 1 ¸ ¨ 0 ¹ © ¹ © ¹ ¯© ©0 § 1 1 1 1· § 0 0 0 0 · ½ ¨ ¸ ¨ ¸° ¨ 0 1 1 1¸ , ¨ 0 0 0 0 ¸ °¾ . ¨ 0 0 1 1¸ ¨ 0 0 0 0 ¸ ° ¨ ¸ ¨ ¸ © 0 0 0 1¹ © 0 0 0 0 ¹ °¿

0 1 0 0

0 0 1 0

0· ¸ 0¸ , 1¸ ¸ 1¹

Clearly V is a special set vector space over the set P = {0, 1}.

Example 2.1.2: Let V = {V1, V2, V3, V4, V5} where

V1 = {all polynomial of degree 3 and all polynomials of even degree with coefficients from Z+ {0}},

34

V2 = {3Z+ Â&#x2030; {0} Ă&#x2014; 5Z+ Â&#x2030; {0} Ă&#x2014; 7Z+ Â&#x2030; {0}, 8Z+ Â&#x2030; {0} Ă&#x2014; 9Z}, ­ §a a a¡ °§ a a ¡ ¨ ¸ 0 V3 = Ž¨ ¸, ¨ a a a ¸ a Â? Z a a š ¨ °Š ¸ Ša a aš ÂŻ

V4

­§ a °¨ °¨ 0 Ž¨ °¨ 0 °Š 0 ¯

b c d¡ §a ¸ ¨ e f g¸ ¨b , 0 h i ¸ ¨c ¸ ¨ 0 0 jš Šd

½ ° Z Â&#x2030;{0} ž ° Âż

0 0 0¡ ¸ e 0 0¸ f g 0¸ ¸ p h cš

a, b, c, d, e, f, g, h, i, p, j Â? 2Z+ Â&#x2030; {0}} and

­ ° ° ª a1 °°  a 3 V5 = Ž  °a 5 °  a 7 ° ¯°

½ ÂŞ a1 Âş ° ÂŤ Âť a 2 Âş ÂŤa 2 Âť ° °° a 4  ÂŤ a 3 Âť , ÂŤ Âť a i Â? 3Z Â&#x2030; {0}, 1 d i d 8 ž . a 6 Âť ÂŤa 4 Âť ° Âť ° a8 Âź ÂŤa 5 Âť ÂŤ Âť ° ÂŤÂŹ a 6 Ÿ ¿°

V is a special set vector space over the set S = Z0 = Z+ Â&#x2030; {0}.

Example 2.1.3: V = {Z10, Z15, Z11, Z19, Z22, Z4} = {V1, V2, V3, V4, V5, V6} is a special set vector space over the set S = {0, 1}.

Example 2.1.4: Let V = {V1, V2, V3, V4} where V1 = {(1 1 1 1 1), (0 0 0 0 0), (1 1 1 0 0), (1 0 1 0 1), (0 1 1 0 1), (1 1 1), (0 0 0), (1 0 0), (0 0 1)}, V2 = {(1 1), (0 0), (1 1 1 1 1 1), (0 0 0 0 0 0), (0 1 0 1 0 1), (1 0 1 0 1 0), (1 1 1), (0 0 0)}, V3 = {(1 1 1 1), (0 0 0 0), (1 1 0 1), (0 1 1 1), (1 1 1 1 1 1 1), (0 0 0 0 0 0 0), (1 1 0 0 0 1 0)} and V4 = {(1 1 1 1 1 1 1), (0 0 0 0 0 0), (1 1 1), (0 0 0), (1 1 1 1), (0 0 0 0), (1 1), (0 0)} be a special set vector space

35

over the set S = Z2 = {0, 1}. Note we see Si Sj and Sj Si if i z j but Si Sj z I, that is in general is nonempty if i z j, 1 d i, j d 4.

Now having seen special set vector spaces now we proceed onto define the notion of special set vector subspace.

DEFINITION 2.1.2: Let V = (V1, V2, …, Vn) be a special set vector space over the set S. Take W = (W1, W2, …, Wn) (V1, V2, …, Vn) such that Wi Vi, 1d i d n and Wi z Wj and Wj Wi, if i z j, 1 d i, j d n. Further if for every s S and wi Wi, swi W for i = 1, 2, …, n then we define W = (W1, …, Wn) to be the special set vector subspace of V over the set S. Clearly every subset of V need not in general be a special set vector subspace of V.

We illustrate this by some simple examples.

Example 2.1.5: Let V = (V1, V2, V3, V4, V5) where ­°§ a V1 = ®¨ °¯© a ­§ a a °¨ V2 = ®¨ a a °¨ a a ¯©

½° a a a a· ¸ a Z10 ¾ , a a a a¹ °¿

½ a· ° ¸ §a a · a ¸, ¨ ¸ a, d Z10 ¾ , ©a d¹ ° a ¸¹ ¿

V3 = {Z10 × Z10 × Z10, (1 1 1 1 1), (0 0 0 0 0), (2 2 2 2 2), (6 6 6 6 6 6), (3 3 3 3 3), (4 4 4 4 4), (5 5 5 5 5), (7 7 7 7 7), (8 8 8 8 8), (9 9 9 9 9)},

V4

­ ° ªa ° «a °« ® ° «a ° ¬« a °¯

½ ª a1 º aº « » ° a2 » » ° « a» ° , « a 3 » a, b,a i Z10 , 1d i d 5¾ a» « » ° » a4 ° a¼ « » «¬ a 5 »¼ °¿

36

and V5 = {all polynomials of even degree and all polynomials of degree less than or equal to three with coefficients from Z10} be a special set vector space over the set Z10. Take W = (W1, W2, â&#x20AC;Ś, W5) with Wi Â&#x17D; Vi, 1 d i d 5; where W1

­°§ a a a a a ¡ ½° Ž¨ ¸ a Â? {0, 2, 4, 6, 8}ž Â&#x17D; V1 , °¯Š a a a a a š °¿ ­°§ a a ¡ ½° W2 = Ž¨ ¸ a, d Â? Z10 ž Â&#x17D; V2, °¯Š a d š ¿°

W3 = {Z10 Ă&#x2014; Z10 Ă&#x2014; Z10} Â&#x17D; V3,

W4

­ ª a1 º °  ° a 2  ° Ž  a 3  a i � Z10 , 1d i d 5 ° a  ° 4  °¯  a 5 Ÿ

½ ° ° ° ž Â&#x17D; V4 ° ° °¿

and W5 = {all polynomials of even degree with coefficients from Z10} Â&#x17D; V5. W = (W1, W2, â&#x20AC;Ś., W5) Â&#x17D; (V1, V2, â&#x20AC;Ś, V5) = V is clearly a special set vector subspace of V over the set Z10.

We give yet another example.

Example 2.1.6: Let V = (V1, V2, V3, V4) where V1 = {Z+[x]},

V2 = {Z+ u 2Z+ Ă&#x2014; 3Z+ Ă&#x2014; 5Z+, Z+ Ă&#x2014; Z+}, ­ § a1 °§ a b ¡ ¨ V3 = Ž¨ ¸, ¨ a4 °Š c d š ¨ a Š 7 ÂŻ

a2 a5 a8

37

½ a3 ¡ ° ¸

a 6 ¸ a i �Z , 1 d i d 9ž ° a 9 ¸š ¿

and

­ ½ ÂŞa a a Âş ° ° ÂŤa a a Âť ° ° ÂŤ Âť ° ° ÂŤa a a Âť ° ÂŞa a a a a Âş ÂŤ Âť ° V4 = ÂŽ ÂŤ Âť , ÂŤa a a Âť a Â? Z ž ° ÂŹa a a a a Âź ÂŤa a a Âť ° ° ° ÂŤ Âť ° ° ÂŤa a a Âť ° ° ÂŤa a a Âť ÂŹ Âź ÂŻ Âż be a special set vector space over the set S = 2Z+. Take W = (W1, W2, W3, W4) Â&#x17D; (V1, V2, V3, V4) (Wi Â&#x17D; Vi; i = 1, 2, 3, 4) where W1 = {all polynomials of degree less than or equal to 6 with coefficients from Z+} Â&#x17D; V1, W2 = {Z+ Ă&#x2014; Z+} Â&#x17D; V2, ­§ a1 °¨ W3 = Ž¨ a 4 °¨ a ¯Š 7

a2 a5 a8

½ a3 ¡ ° ¸

a 6 ¸ a i Â? Z 1 d i d 9 ž Â&#x17D; V3 ° a 9 ¸š Âż

and ­ ªa ° ° a ° a ° W4 = Ž  a ° a ° ° a ° ¯ a

a a a a a a a

½ aº °  a ° ° a ° 

a Âť a Â? Z ž Â&#x17D; V4 ° aÂť ° Âť aÂť ° ° Âť aÂź Âż

to be the subset of V. It is easily verified that W = (W1, W2, W3, W4) Â&#x17D; (V1, V2, V3, V4) is a special set vector subspace of V. Now we give yet another example.

Example 2.1.7: Let V = (V1, V2, V3, V4, V5) where V1 = {Z9}, V2 = {Z14}, V3 = {Z16} V4 = {Z18} and V5 = {Z8} be a special

38

set vector space over the set S = {0, 1}. Take W = (W1, W2, W3, W4, W5) where Wi Vi, i = 1, 2, 3, 4, 5 and W1 = {0, 3, 6} V1, W2 = {0, 2, 4, 6, 8, 10, 12} Z14 = V2, W3 = {0, 4, 8, 12} V3, W4 = {0, 9} V4 and W5 = {0, 2, 4, 6) V5. Clearly W = (W1, W2, W3, W4, W5) V is a special set vector subspace of V. One of the natural question which is pertinent is that, will every proper subset of a special set vector space be a special set vector subspace of V? The answer is no and we prove this by a simple example.

Example 2.1.8: Let V = (V1, V2, V3, V4) where

V1 = {(1 1 1 1 1 1), (0 0 0 0 0 0), (1 1 1 1 1), (0 0 0 0 0), (0 0 0 0), (1 1 1 1)},

V2

­§ a b c · ½ °¨ ° ¸ ®¨ d e f ¸ a, b, c, d, e, f , g, h, i Z5 ¾ , °¨ g h i ¸ ° ¹ ¯© ¿ V3

­° ª a a a a a º ½° a, b Z10 ¾ ®« » °¯ ¬ b b b b b ¼ °¿

and

V4

­ ªa °« ° «a ° «a °« ® «a ° «a °« ° «a °« ¯ ¬a

½ aº ° » a» ° ° a» ° » a » a Z7 ¾ ° a» ° » a» ° ° » a¼ ¿

a a a a a a a

be a special set vector space over the set S = {0, 1}. Take W = (W1, W2, W3, W4) where W1 = {(1 1 1 1 1 1), (1 1 1 1), (0 0 0 0 0)} V1,

39

W2

°­ ª 1 W3 = ® « °¯ ¬ 2 ª5 5 «2 2 ¬

­§ a a a · ½ °¨ ° ¸ ®¨ a a a ¸ a Z5 \{0} ¾ V2, °¨ a a a ¸ ° ¹ ¯© ¿ 1 1 1 1 1º ª3 , 2 2 2 2 2 »¼ «¬ 4 5 5 5 5 º ª7 7 , 2 2 2 2 »¼ «¬ 1 1

3 3 3 3 3º , 4 4 4 4 4 »¼ 7 7 7 7º , 1 1 1 1 »¼

ª 2 2 2 2 2 2 º ½° « 9 9 9 9 9 9 » ¾ V3 ¼ °¿ and

W4

­ ªa °« ° «a ° ® «a ° «a °« °¯ «¬ a

½ aº ° » a» ° ° a » a {1, 2, 5} Z7 ¾ V4 . » ° a» ° a »¼ °¿

Clearly W = (W1, W2, W3, W4) is a proper subset of V but W is not a special set vector subspace of V. For take (1 1 1 1), (1 1 1 1 1 1) W1; 0(1 1 1 1) = (0 0 0 0) W1 and 0(1 1 1 1 1 1) = (0 0 0 0 0 0) W1 hence W1 is not a set vector subspace of V1 over S = {0, 1}. Also take §a a a· § a a a · § 0 0 0· ¨ ¸ ¨ ¸ ¨ ¸ ¨ a a a ¸ W2 , 0 ¨ a a a ¸ ¨ 0 0 0 ¸ W2 . ¨a a a¸ ¨ a a a ¸ ¨ 0 0 0¸ © ¹ © ¹ © ¹ So W2 is also not a set vector subspace over the set S = {0, 1}.

Likewise in W3 we see ª1 1 1 1 1 1 º « 2 2 2 2 2 2 » W3 ¼

40

but ª1 1 1 1 1 1 º ª0 0 0 0 0 0º 0. « » « » W3 . ¬ 2 2 2 2 2 2¼ ¬0 0 0 0 0 0¼ So W3 too is not a set vector subspace of V3. Finally ªa a º «a a » « » « a a » W4 « » «a a » «¬ a a »¼ but ªa a º ª0 0º «a a » «0 0» » « » « 0 «a a » = «0 0» » « » « «a a » «0 0» «¬ a a »¼ «¬ 0 0 »¼ is not in W4. Thus W4 too is not a set vector subspace of V4. So W = (W1, W2, W3, W4) (V1, V2, V3, V4) is a proper subset of V but is not a special set vector subspace of V. Now we want to make a mention that in the subset W = (W1, W2, W3, …, Wn) even if one of the Wi Vi is not a set vector subspace of Vi, 1 d i d n, then also W = (W1, W2, …, Wn) (V1, V2, … Vn) will not be a special set vector subspace of V. Now we proceed onto define the notion of generating special subset of a special set vector space V over the set S over which V is defined.

DEFINITION 2.1.3: Let V = (V1, V2, …, Vn) be a special set vector space over the set S. Let (P1, P2, …, Pn V = (V1, V2, …, Vn) where each Pi Vi is such that Pi generates Vi over the set S; for 1d i d n. Then we call P to be the special generating subset of V. However it may so happen that at times for some Pi Vi; Pi = Vi, i {1, 2, … , n}.

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We now give an example of a special generating subset of V. Example 2.1.9: Let V = (V1, V2, V3, V4, V5) where

V1 = {Z+ Â&#x2030; {0}}, ­§ a a a ¡ ½ °¨ ° ¸

V2 = Ž¨ a a a ¸ a Â? Z Â&#x2030; {0} ž , °¨ a a a ¸ ° š ¯Š Âż ­ ÂŞa ° ° ÂŤa ° V3 = ÂŽ ÂŤ a ° ÂŤa ° °¯ ÂŤÂŹ a

½ aÂş ° Âť aÂť ° ° a Âť a Â? Z Â&#x2030;{0}ž , Âť ° aÂť ° a Ÿ ¿°

V4 = {(a a a a a), (a a a a a a a) | a Â? Z+ Â&#x2030; {0}} and V5 = {all polynomials of the form n(1 + x + x2 + x3 + x4 + x5) | n Â? Z+ Â&#x2030; {0}}.

Clearly V = (V1, V2, V3, V4, V5) is a special set vector space over the set S = Z+ Â&#x2030; {0}. Take P = (P1, P2, P3, P4, P5) Â&#x17D; V = (V1, V2, V3, V4, V5); where P1 = {1}, ÂŞ1 1Âş ÂŤ1 1Âť § 1 1 1¡ ÂŤ Âť ¨ ¸ P2 = ¨ 1 1 1¸ , P3 = ÂŤ1 1Âť , ÂŤ Âť ¨ 1 1 1¸ Š š ÂŤ1 1Âť ÂŤÂŹ1 1Ÿ P4 = {(1 1 1 1 1), (1 1 1 1 1 1 1)} and P5 = {(1 + x + x2 + x3 + x4 + x5)}. It is easily verified that P is the special generating subset of V over the set S = Z+ Â&#x2030; {0}. Further we say in this case the special set vector space V is finitely generated over S. The n-

42

cardinality of V is denoted by (|P1|, |P2|, ..., |Pn|) is called the ndimension of V. Hence |P| = (1, 1, 1, 1, 1) is the 5-dimension. However it is pertinent to mention as in case of vector spaces the special set vector spaces are also depend on the set over which it is defined.

For if we take in the above example instead of S = Z+ {0}, T = {0, 1}, still V = (V1, V2, V3, V4, V5) is a special set vector space over T = {0, 1} however now the special generating subset of V over T is infinite. Thus the n-dimension becomes infinite even if one of the generating subset of V say Pi of (P1, P2, ..., Pn) = P is infinite; we say the n-dimension of V is infinite.

Now we give yet another example.

Example 2.1.10: Let V = (V1, V2, V3, V4, V5) where V1 = (Z3 × Z3), V2 = {Z2 × Z2 × Z2}, V3 = Z11, V4 = {S × S × S | S = {0, 2, 4} Z6} and V5 = Z7, be a special set vector space over the set S = {0, 1}.

Take P = (P1, P2, P3, P4, P5) where P1 = P2 = P3 = P4 =

P5 =

{(1 1), (1 2), (2 1), (2 2), (0 1), (0 2), (2 0), (1 0)} V1, {(1 1 1), (1 1 0), (0 1 1), (1 0 1), (1 0 0), (0 1 0), (0 0 1)} V2 , {1, 2, 3, 4, 5, 6, 7, 8, 9, 10} V3, {(0 2 4), (0 4 2), (2 2 2), (4 4 4), (0 2 2), (0 4 4), (4 2 0), (2 4 0), (2 0 4), (4 0 2), (2 0 2), (2 2 0), (4 0 4), (4 4 0), (2 2 4), (2 4 2), (4 2 2), (2 4 4), (4 2 4), (4 4 2), (0 0 2), (0 0 4), (0 2 0), (0 4 0), (4 0 0), (2 0 0)} V4 and {1, 2, 3, 4, 5, 6} Z7.

Clearly P = (P1, P2, P3, P4, P5) is a special generating subset of V over S = {0, 1}. Further V is finite 5-dimensional. The 5dimension of V is |P| = (|P1|, |P2|, |P3|, |P4|, |P5|) = (8, 7, 10, 26, 6).

We now give an example of an infinite dimensional special set vector space.

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Example 2.1.11: Let V = (V1, V2, V3, V4) where

V1 = {Z[x]; all polynomials in the variable x with coefficients from Z}, V2 = (Z+ Â&#x2030; {0}) Ă&#x2014; (Z+ Â&#x2030; {0}) Ă&#x2014; (Z+ Â&#x2030; {0}), ­§ a1 °¨ V3 = Ž¨ a 4 °¨ a ¯Š 7

a2 a5 a8

a3 ¡ ¸ a 6 ¸ a i Â? Z Â&#x2030;{0}, 1 d i d 9} a 9 ¸š

and V4

­° ÂŞa a a a a Âş ½° a Â? Z Â&#x2030;{0}ž ÂŽÂŤ Âť °¯ ÂŹa a a a a Âź °¿

be a special set vector space over the set S = {0, 1}. Now consider the special generating subset P = (P1, P2, P3, P4) of V = (V1, V2, V3, V4) where P1 = {every non zero polynomial p(x) in Z[x]} Â&#x17D; Z[x] and P1 = [Z \ {0}][x], P2 = {¢x y z²} Â&#x17D; V2 and x, y, z Â? Z+, that is P2 = V2 \ {(0 0 0)}, ­§ a b c ¡ °¨ ¸ P3 = Ž¨ d e f ¸ °¨ g h i ¸ š ¯Š ­§ 0 0 °¨ Ž¨ 0 0 °¨ 0 0 ¯Š

½ ° a, b, c, d, e, f , g, h, i Â? Z Â&#x2030;{0}ž \ ° Âż 0 ¡ ½ ­§ 0 0 0 ¡ ½ ¸ ° °¨ ¸° 0 ¸ ž \ Ž¨ 0 0 0 ¸ ž Â&#x17D; V3 0 ¸š °¿ ¯°¨Š 0 0 0 ¸š ¿°

and ­° ÂŞ a a a a a Âş ½° P4 = ÂŽ ÂŤ a Â? Z ž Â&#x17D; V4 . Âť °¯ ÂŹ a a a a a Âź °¿ We see every subset Pi of Vi is infinite. Thus the special set vector space is only infinitely generated and n-dimension of V is (f, f, f, f). If we replace the set S = {0, 1} by the set S = Z+

44

{0} still the special set vector space is of infinite dimension but not the special generating subset of V given by T = (T1, T2, T3, T4) is such that T1 is infinite set of polynomials, T2 too is an infinite subset of V2, T3 too is an infinite subset of V3 but different from P3 and ­°§1 1 1 1 1· ½° T4 = ®¨ ¸¾ . °¯©1 1 1 1 1¹ ¿° Thus |T| = (|T1|, |T2|, |T3|, |T4|) = (f, f, f, 1) but it is important and interesting to observe that the infinite of Ti is different from the infinite of Pi, 1 d i d 4.

Let us now define the notion of special set linear algebra.

DEFINITION 2.1.4: Let V = (V1, V2,…, Vn) be a special set vector space over the set S. If at least one of the Vi of V is a set linear algebra over S then we call V to be a special set linear algebra; i {(1, 2, …, n)}.

We see every special set linear algebra is a special set vector space and not conversely; i.e., a special set vector space in general is not a special set linear algebra.

We now illustrate this definition by some examples.

Example 2.1.12: Let V = (V1, V2, V3, V4) where

V1 = Z5 × Z5 × Z5, V2 = {(a a a a), (a a a) | a Z+ {0}}, ­°§ a b · ½° V3 = ®¨ ¸ a, b,c,d {0,1}¾ °¯© c d ¹ ¿°

and

V4

­ §a °¨ ° ¨a °¨ ® a ° ¨a °¨ °¯ ¨© a

a· ¸ a¸ a¸, ¸ a¸ a ¸¹

½° ªa a a a a º « a a a a a » a, b,c {0,1}¾ ¼ °¿

45

be a special set linear algebra over the set S = {0 1}. We see V1 and V3 are set linear algebras over the set S = {0, 1}. However on V4 and V2 we cannot define any compatible operation. So V2 and V4 are not set linear algebras over S = {0, 1} they are only just set vector spaces over S. We give yet another example.

Example 2.1.13: Let V = (V1, V2, V3, V4, V5) where

V1

­°§ a b ¡ Ž¨ ¸ a, b,c,d� {2, 3, 5, 7, 9, 14, 18, 22, 15}}, °¯Š c d š

V2 = {(1 1 1 1 1), (0 0 0 0 0), (1 1 1), (0 0 0), (1 0 1), (1 1 1 1 1), (0 0 0 0), (1 1 0 0), (1 0 0 1)}, V3

­°§ a a a ¡ ½°

Ž¨ ¸ a Â? Z Â&#x2030; {0}ž ¯°Š a a a š ¿°

and

V4

­ ªa º ½ °  ° ° a  ° ° ° Ž  a  a � {0,1}ž ° a  ° °  ° °¯  a Ÿ ¿°

be a special set linear algebra over the set S = {0, 1}. Clearly V1 and V2 are not set linear algebras over the set S = {0, 1}, but V3 and V4 are set linear algebras over the set S = {0, 1}.

Now we proceed onto define some substructures of these special set linear algebras.

DEFINITION 2.1.5: Let V = (V1, V2, â&#x20AC;Ś, Vn) be a special set linear algebra over the set S. If W = (W1, W2, â&#x20AC;Ś, Wn) Â&#x17D; (V1, V2, â&#x20AC;Ś, Vn) with Wi Â&#x17D; Vi where at least one of the Wi Â&#x17D; Vi is a set

46

linear subalgebra of a set linear algebra over S and if W = (W1, W2, …, Wn) (V1, V2, …, Vn) is also a special set vector subspace of V over S then we call W to be a special set linear subalgebra of V over S.

We now illustrate this by a simple example.

Example 2.1.14: Let V = (V1, V2, V3, V4, V5) where V1 = {Z3[x] | all polynomials of degree less than or equal to 4 with coefficients from Z3}, V2 = {(a a a a), (a a) | a Z5},

­§ a1 °¨ °¨ a 3 ° V3 = ®¨ a 5 °¨ a °¨ 7 °¯¨© a 9

½ a2 · ° ¸ a4 ¸ ° ªa a a a º ° a6 ¸, « a, a Z ;1 i 10

d d ¾, i 7 » ¸ ¬a a a a ¼ ° a8 ¸ ° a10 ¸¹ °¿

­ ½ §a a· °§ a b c · ¨ ° ¸ V4 = ®¨ ¸ , ¨ a a ¸ a, b,c,d,e,f Z11 ¾ d e f ¹ ¨ °© ° ¸ ©a a¹ ¯ ¿ and V5

½° ­°§ a b · ®¨ ¸ a, b,c,d, Z6 ¾ ¯°© c d ¹ °¿

be a special set linear algebra over the set S = {0, 1}. Take W = (W1, W2, …, W5) with Wi Vi, 1 d i d 5, where W1 = {0, (1 + x + x2 + x3 + x4), 2(1 + x + x2 + x3 + x4)} V1, W2 = {(a a a a) | a Z5}, V2, ­° ª a a a a º ½° W3 = ® « a Z7 ¾ V3 , » ¯° ¬ a a a a ¼ ¿° W4

½° °­§ a b c · ®¨ ¸ a, b,c, d,e,f Z11 ¾ V4 ¯°© d e f ¹ ¿°

47

and ­°§ a a · ½° ®¨ ¸ a Z6 ¾ V5 . °¯© a a ¹ °¿

W5

W is clearly a special set vector subspace of V as well as W is a special set linear subalgebra of V over S = {0 1}.

We give yet another example.

Example 2.1.15: Let V = (V1, V2, V3, V4) where

V1 = {Z7 × Z7 × Z7}, ½° °­§ a b · V2 = ®¨ ¸ a, b,c,d Z7 ¾ , ¯°© c d ¹ ¿° V3 = {(a a a a a a), (a a a a a) | a Z7} and ­ ªa °« ° «a V4 = ® ° «a ° «¬ a ¯

½ aº ° » a » ªa a a º ° , a Z7 ¾ a » «¬a a a »¼ ° » ° a¼ ¿

be a special set linear algebra over the set S = Z7. Take W = (W1, W2, W3, W4) where Wi Vi, 1 < i < 4 and W1 = {(a a a) | a Z7} V1, ­°§ a a · W2 = ®¨ ¸ a Z7 °¯© a a ¹

½° ¾ V2 , °¿

W3 = {(a a a a a a) | a Z7) V3 and

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­° ª a a a º ½° W4 = Ž  a � Z7 ž  °¯  a a a Ÿ ¿° is a special set linear subalgebra of V over Z7.

Now we define yet another new algebraic substructure of V. DEFINITION 2.1.6: Let V = (V1, V2, â&#x20AC;Ś, Vn) be a special set linear algebra over the set S. If W = (W1, W2, â&#x20AC;Ś, Wn) Â&#x17D; (V1, V2, â&#x20AC;Ś, Vn) with each Wi Â&#x17D; Vi is only a set vector subspace of Vi and never a set linear subalgebra even if one Vi is a set linear algebra 1 d i d n, then we call W to be a pseudo special set vector subspace of the special set linear algebra V over S.

We illustrate this by the following example.

Example 2.1.16: Let V = (V1, V2, ..., Vn), where

V1 = {Zo[x] where Zo = Z+ Â&#x2030; {0}}, V2 = {(a a a a a), (a a a a a a) | a Â? Z+ Â&#x2030; {0}}, ½° °­§ a b ¡ § a a a a ¡

V3 = Ž¨ ¸, ¨ ¸ a, b,c,d Â?Z Â&#x2030;{0}ž ¯°Š c d š Š b b b b š ¿° and ­§ a °¨ ° 0 V4 = Ž¨ °¨¨ 0 °Š 0 ÂŻ

½ b c d¡ ° ¸ e f g¸ °

a, b,c,d,e,f ,g, h,i, j Â? Z Â&#x2030; {0}ž 0 h i¸ ° ¸ ° 0 0 jš Âż

be a special set linear algebra over the set S = Zo = Z+ Â&#x2030; {0}.

Clearly V1, V4 are set linear algebras over S. Take W = (W1, W2, W3, W4) Â&#x17D; V; where W1 = {n(5x3 + 3x + 1), n(7x7 + 8x6 + 5x + 7), s(3x + 7), t(9x3 + 49x + 1)} Â&#x17D; V1; s, t, n Â? Z+ Â&#x2030; {0}}.

49

W2 = {(2a, 2a, 2a, 2a, 2a), (5a, 5a, 5a, 5a, 5a) | a Â? Z+ Â&#x2030; {0}} Â&#x17D; V2, ­°§ a a a a ¡ ½°

W3 = Ž¨ ¸ a, b Â? 2Z Â&#x2030;{0}ž Â&#x17D; V3 ¯°Š a a a a š ¿° and

W4

­ §1 ° ¨ ° ¨0 Žn ¨ ° ¨0 ° Š0 ¯

½ 1 1 1¡ ° ¸ 1 1 2¸ °

n Â? Z Â&#x2030;{0}ž Â&#x17D; V4 0 1 2¸ ° ¸ ° 0 0 2š Âż

is a special set vector subspace of V which is not a special set linear subalgebra of V; so W is a pseudo special set vector subspace of V. Now having given example of pseudo special set vector subspace we make a mention of the following result. A pseudo special set vector subspace of V is never a special set linear subalgebra of V. Now we proceed onto define the notion of special set linear transformation of V onto W, V and W are special set vector spaces over the same set S. DEFINITION 2.1.7: Let V = (V1, V2, â&#x20AC;Ś, Vn) be a special set vector space over the set S. W = (W1, W2, â&#x20AC;Ś, Wn) be another special set vector space over the same set S. A special set linear map T = (T1, â&#x20AC;Ś, Tn) where Ti: Vi o Wi; 1 d i d n such that Ti( v) = Ti(v) for all Â? S and v Â? Vi, 1 d i d n is called the special set linear transformation of V into W. S HomS(V, W) = {HomS(V1, W1), â&#x20AC;Ś, HomS(Vn, Wn)} where HomS(Vi, Wi) denotes the set of all set linear transformations of the vector space Vi into the vector space Wi, 1 d i d n.

It is easy to verify SHomS (V, W) is again a special set vector space over the set S. We shall illustrate this by some examples.

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Example 2.1.17: Let V = (V1, V2, V3, V4) and W = (W1, W2, W3, W4) be special set vector spaces over the set S = {0, 1}, here V1 = {Z2 × Z2 × Z2},

­°§ a b · ½° V2 = ®¨ ¸ a, b, c, d {0, 1}¾ , °¯© c d ¹ ¿° V3 = {Z2[x]} all polynomials of degree less than or equal to 5, with coefficients from Z2 = {0, 1}} and ­° ª a a 2 a 3 a 7 º ½° V4 = ® « 1 a1 ,! ,a 8 Z2 ¾ » °¯ ¬ a 4 a 5 a 6 a 8 ¼ ¿° are set vector spaces of the special set vector space V over the set S = {0, 1}. Now ­°§ a b · ½° W1 = ®¨ ¸ a, b,c Z2 ¾ , °¯© 0 c ¹ ¿° W2 = Z2 × Z2 × Z2 × Z2, ­°§ a W3 = ®¨ 1 °¯© a 4

a2 a5

½° a3 · ¸ a i Z2 , 1d i d 6 ¾ a6 ¹ ¿°

and ­ ª a1 °« ° a W4 = ® « 2 °«a 3 ° «¬ a 4 ¯

½ a5 º ° » a6 » ° a Z2 , 1 d i d 8¾ a7 » i ° » ° a8 ¼ ¿

are set vector space of the special set vector space W over the set S = {0, 1}. Define a special set linear map T = (T1, T2, T3, T4) from V = (V1, V2, V3, V4) into W = (W1, W2, W3, W4) such that Ti : Vi o Wi; 1 d i d 4 where 51

§a b· T1: V1 o W1 is defined as T1(a b c) = ¨ ¸, ©0 c¹ §a b· T2: V2 o W2; T2 ¨ ¸ = (a, b, c, d), ©0 c¹ T3:V3 o W3;

§a T3(a0 + a1x + a2x2 + a3x3 + a4x4 + a5x5) = ¨ 0 © a5

a1 a4

a2 · ¸ a5 ¹

and

§ ªa T4: V4 o W4 as T4 ¨ « 1 © ¬a 5

a2

a3

a6

a7

a4 º · ¸ a 8 ¼» ¹

ª a1 «a « 2 «a 3 « ¬a 4

a5 º a 6 »» . a7 » » a8 ¼

It is easily verified that T = (T1, T2, T3, T4) is a special set linear transformation of V into W. We give yet another example.

Example 2.1.18: Let V = (V1, V2, V3, V4, V5) be a special set vector space over the set S = {0, 1} where V1 = {Z7 × Z7}, V2 = Z14 × Z14 × Z14; V3 = Z19, V4 = {(a a a a a) | a Z19} and V5 = {Z7 × Z12 × Z14} is a special set vector space over the set S = {0 1}. Let W = (W1, W2, W3, W4, W5) where

W1 = {(a a) | a Z7},

½° ­°§ a a · W2 = ®¨ a, b, c

Z 14 ¾ , ¸ °¯© b c ¹ °¿ W3 = Z19 ×Z19,

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½° °­§ a b c · W4 = ®¨ a, b, c, d, e

Z 19 ¾ ¸ °¯© 0 d e ¹ °¿ and W5 {(a a a) | a Z7} be the special set vector space over the set S = {0, 1}. Define T = (T1, T2, T3, T4, T5) from V = (V1,V2,V3, V4, V5) into W = (W1, W2, W3, W4, W5) by T1: V1 o W1; by T1(a, b) = (a a), §a a· T2: V2 o W2; T2(a b c) = ¨ ¸; ©b c¹ T3: V3 o W3 ; by T3(a) = (a, a),

§a a a· T4: V4 o W4; T4(a a a a a) = ¨ ¸ ©0 a a¹ and T5: V5 o W5 as T5(a b c) = (a a a).

It is clear that T = (T1, T2, T3, T4, T5) is a special set linear transformation of V into W. Now we proceed onto define the notion of special set linear operator on V. DEFINITION 2.1.8: Let V = (V1 V2, …, Vn) be a special vector space over the set S. A special set linear map T = (T1, T2, …,Tn) from V = (V1, V2, V3, …, Vn) into V = (V1, V2, V3, …, Vn) is defined by Ti: Vi o Vi, i = 1, 2, …, n such that Ti( Vi) = T(Vi) for every S and Vi Vi. We call T = (T1, …, Tn) to be a special set linear operator on V. If we denote by SHoms (V, V) = {Homs (V1, V1), Homs (V2, V2), …, Homs (Vn, Vn)} then it can be verified SHoms(V,V) is a special set linear algebra over S under the composition of maps.

53

We illustrate this by some simple examples.

Example 2.1.19: Let V = {V1, V2, V3, V4} where V1 = Z3 × Z3 × Z3,

½° °­§ a b c d · a, b, c, d, e, f, g, h

Z V2 = ®¨ 5 ¾, ¸ °¯© e f g h ¹ °¿ ­°§ a V3 = ®¨ 1 °¯© a 3

½° b2 · ¸ ai Z7, 1 d i d Z4 ¾ a4 ¹ ° ¿

and ­§ a °¨ ° 0 V4 = ®¨ °¨¨ 0 °© 0 ¯

b e 0 0

c d· ¸ f g¸ a, b, c, d, e, f, g, h, i, l Z9 h i¸ ¸ 0 e¹

½° ¾ °¿

be a special set vector space over the set S = {0 1}. Define a special set linear operator T = (T1, T2, T3, T4) : V = (V1, V2, V3, V4) o (V1, V2, V3, V4) = V where Ti: Vi o Vi such that T1(a b c) = (c b a), §a b c d· T2: V2 o V2 as T2 ¨ ¸ ©e f g g¹ §a T3: V3 o V3 defined by T3 ¨ 1 © a3

§e f g h· ¨ ¸, ©a b c d¹ a2 · ¸ a4 ¹

§ a1 ¨ © a2

a4 · ¸ a3 ¹

and §a ¨ 0 T4: V4 o V4 is such that T4 ¨ ¨0 ¨ ©0

54

b e 0 0

c d· §a ¸ ¨ f g¸ ¨0 = h i ¸ ¨0 ¸ ¨ 0 l ¹ ©0

b e 0 0

0 0· ¸ f 0¸ .

0 h¸ ¸ 0 l¹

It is easily verified that T = (T1, T2, T3, T4) is a special set linear operator on V. Define another special linear operator P = (P1, P2, P3, P4) from V to V as Pi: Vi o Vi; i = 1, 2, 3, 4 given by P1: V1 o V1 P1 (a b c) = (a a a), §a b c d· P2: V2 o V2 given by P2 ¨ ¸ ©e f g h¹ §a P3: V3 o V3 defined by P3 ¨ 1 © a3 and §a b c ¨ 0 e f P4: V4 o V4 by P4 ¨ ¨0 0 h ¨ ©0 0 0

a2 · ¸ a4¹

§a b c d· ¨ ¸, ©0 0 0 0¹

§ a2 ¨ © a2

d· §d ¸ ¨ g¸ ¨0 = i ¸ ¨0 ¸ ¨ l ¹ ©0

a2 · ¸ a2¹

g i l· ¸ c f h¸ .

0 b e¸ ¸ 0 0 a¹

P = (P1, P2, P3, P4) is a special set linear operator on V. Define PoT

= =

(P1, P2, P3, P4) o (T1, T2, T3, T4) (P1 o T1, P2 o T2, P3 o T3, P4 o P4)

as P1 o T1: V1 o V1 as P1 o T1(a b c) = P1(c b a) = (c c c).

P2 o T2: V2 o V2 given by §a b c d· §e f g h· P2 o T2 ¨ ¸ P2 ¨ ¸ ©e f g h¹ ©a b c d¹ P3 o T3: V3 o V3 defined by § a a2 · § a1 P3 o T3 ¨ 1 ¸ P3 ¨ © a3 a 4 ¹ © a2 and P4 o T4: V4 o V4 gives

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§e f g h· ¨ ¸. ©0 0 0 0¹

a4 · § a4 ¸ ¨ a 3 ¹ © a4

a4 · ¸ a4¹

§a ¨ 0 P4T4 ¨ ¨0 ¨ ©0

b e 0 0

c d· §a ¨ ¸ 0 f g¸ = P4 ¨ ¨0 h i¸ ¸ ¨ 0 l¹ ©0

b e 0 0

0 0· §0 0 ¸ ¨ f 0¸ ¨0 0 0 h¸ ¨0 0 ¸ ¨ 0 l ¹ ©0 0

h l· ¸ f 0¸ . b e¸ ¸ 0 a¹

It is easily verified P o T = (P1 o T1, P2 o T2, P3 o T3, P4 o T4) is a special set linear operator on V. Now find T o P = (T1 o P1, T2 o P2, T3 o P3, T4 o P4): V o V. T1 o P1(a b c) = T1 (a a a) = (a a a) z P1 o T1.

T2 o P2: V2 o V2 is such that §a b c d· §a b c d· §0 0 0 0· T2 o P2 ¨ ¸ T2 ¨ ¸ ¨ ¸ ©e f g h¹ ©0 0 0 0¹ ©a b c d¹ z P2 o T2.

T3 o P3: V3 o V3 is defined by § a a2 · § a2 a2 · § a2 a2 · T3 o P3 ¨ 1 ¸ T3 ¨ ¸ ¨ ¸. © a2 a2 ¹ © a2 a2 ¹ © a3 a4 ¹ Thus T3 o P3 z P3 o T3 and now T4 o P4: V4 o V4 given by §a ¨ 0 T4 o P4 ¨ ¨0 ¨ ©0

b e 0 0

c d· §d ¸ ¨ f g¸ ¨0 T4 h i ¸ ¨0 ¸ ¨ 0 l ¹ ©0

g i l · §d ¸ ¨ c f g¸ ¨0 0 b e¸ ¨0 ¸ ¨ 0 0 a¹ ©0

g c 0 0

0 f 0 0

0· ¸ 0¸ . e¸ ¸ a¹

Here also T4 o P4 z P4 o T4. Thus we see T o P z P o T but both T o P and P o T are special set linear operator on V. Thus we can prove SHoms(V, V) = (Homs(V1, V1), …, Homs(Vn, Vn)) is a special set linear algebra over the set S.

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Now we give yet another example of a special set linear operator on V. Example 2.1.20: Let V = {V1, V2, V3, V4} where

V1 = Z9× Z9 × Z9 × Z9, ­°§ a b · V2 = ®¨ ¸ a, b, c, d Z8}, °¯© a a ¹

½° °­ ª a b c d e º a, b, c, d, e, f, g, h

Z V3 = ® « 6 ¾ » °¯ ¬ f g h i j ¼ °¿ and V4 = Z12 × Z12 × Z12 be a special set linear algebra over the set S = {0, 1}. Define T = (T1, T2, T3, T4): V o V = (V1, V2, V3, V4) where Ti: Vi o Vi where T1(a b c d) = (b c d a), §a b· T2 ¨ ¸ ©a a ¹

§d b· ¨ ¸ ©b d¹

where T2: V2 o V2, T3: V3 o V3 is such that § ªa b c d e º · T3 ¨ « »¸ © ¬f g h i j¼ ¹

ªa 0 c 0 e º «f 0 h 0 j» ¼

and T4: V4 o V4 is defined as T4 (a b c) = (a c a).

Clearly T = (T1, T2, T3, T4) is a special set linear operator on V. Now we find out T o T = (T1 o T1, T2 o T2, T3 o T3, T4 o T4) from V o V = (V1, V2, V3, V4) where Ti o Ti; Vi o Vi; 1 d i d 4 T1 o T1 o T1 o T1 (a b c d) = T1 o T1 o T1 (b c d a) = T1 o T1 (c d a b) = T1 (d a b c)

57

=

(a d c d).

Thus T1 o T1 o T1 o T1 = I1: V1 o V1. I1 the identity special linear operator on V. §a b· §d b· §d b· T2 o T2 ¨ ¸ T2 ¨ ¸ ¨ ¸. ©c d¹ ©b d¹ ©b d¹ Thus T2 o T2 = T2 on V2. ªa T3 o T3 « ¬f ªa 0 c 0 = T3 « ¬f 0 h 0

b c d eº g h i j »¼ e º ªa 0 c 0 eº . j »¼ «¬ f 0 h 0 j »¼

Thus T3 o T3 = T3 on V3. Finally T4 o T4 o T4 (a b c) = T4 o T4 (a c a) = T4 (a a a) = (a a a). Thus T4 o T4 o T4 = T4 o V4. Further we see T o T = (T1 o T1, T2 o T2, T3 o T3, T4 o T4) is yet another special set linear operator on V. Now we proceed onto define the notion of special set idempotent operator on V = (V1, V2, …, Vn).

DEFINITION 2.1.9: Let V = (V1, V2, …, Vn) be a special set vector space over the set S. Suppose T = (T1, T2, …, Tn): V o V = (V1, V2, …, Vn) where Ti: Vi o Vi, i d i d n be a special set linear operator on V and if T o T = (T1 o T1, T2 o T2, …, Tn o Tn) = (T1, T2, …, Tn) then we call T to be a special set linear idempotent operator on V.

We illustrate this by a simple example.

Example 2.1.21: Let V = (V1, V2, V3, V4, V5) be a special set vector space over the set S = {0, 1} where V1 = {Z2 × Z2 × Z2 × Z2},

58

­° ª a b º ½° V2 = ® « a, b, c, d Z2 ¾ , » °¯ ¬ c d ¼ °¿ V3

­°§ a1 ®¨ °¯© a 6

a2 a7

V4

­ ª a1 °« ° «a 2 ° ®«a3 ° «a °« 4 °¯ «¬ a 5

a3 a8

a4 a9

a5 · °½ ¸ a i Z2 ;1d i d 10 ¾ , a10 ¹ ¿°

½ a6 º ° » a7 » ° ° a 8 » a i Z2 ; 1d i d10 ¾ » ° a9 » ° a10 »¼ ¿°

and V5 = {(1 1 1 1 1 1 1), (0 0 0 0 0 0 0), (1 1 1 1), (0 0 0 0), (0 0 0), (1 1 1), (1 1 1 1 1), (0 0 0 0 0), (1 1), (0 0)}.

Define T = (T1, T2, T3, T4, T5) : V = (V1, V2, V3, V4, V5) o V = (V1, V2, …, V5) with Ti: Vi o Vi, 1 d i d 5 such that each Ti is a set linear operator of the set vector space Vi, i = 1, 2, …, 5. T1: V1 o V1 is such that here T1(a b c d) = (a a a a), T1 o T1 (a b c d) = T1 ( a a a a) = (a a a a). Thus T1 o T1 = T1 on V1 i.e., T1 is a set idempotent linear operator on V1.

T2: V2 o V2, with §a b· §d d· T2 ¨ ¸ ¨ ¸ ©c d¹ ©d d¹ so that §a b· §d d· T2 o T2 ¨ ¸ = T2 ¨ ¸. ©c d¹ ©d d¹ Hence T2 o T2 = T2 on V2 hence T2 is a set idempotent linear operator on V2. T3: V3 o V3 defined by T3

§ a1 ¨ © a6

a2 a7

a3 a8

a4 a9

a5 · § a2 ¸ = T3 ¨ a10 ¹ © a7

59

a2 a7

a3 a8

a3 a8

a3 · ¸. a8 ¹

Thus T3 o T3 = T3 on V3 i.e. T3 is also a set idempotent linear operator on V3. Finally T4: V4 o V4 defined by T4(a1, â&#x20AC;Ś, ai) = (a1, â&#x20AC;Ś, ai) for every (a1, â&#x20AC;Ś, ai) Â? V4. We see T4 o T4 = T4, i.e., T4 is also a set idempotent operator on V4. Thus T o T = (T1 o T1, T2 o T2, T3 o T3, T4 o T4) = (T1, T2, T3, T4) = T. Hence T: V o V is a special set idempotent linear operator on V. Now we proceed onto define the notion of pseudo special set linear operator on a special set vector space.

DEFINITION 2.1.10: Let V = (V1, V2, â&#x20AC;Ś, Vn) be a special set vector space over the set S. Let T = (T1, T2, â&#x20AC;Ś, Tn): V = (V1, V2, â&#x20AC;Ś, Vn) o V = (V1, V2, â&#x20AC;Ś, Vn) be a special set linear transformation from V to V where Ti = Vi o Vj where i z j for at least one Ti; 1d i, j d n. Then we call T = (T1, â&#x20AC;Ś, Tn) to be a pseudo special set linear operator on V. Clearly SHoms(V, V) = {Homs (Vi1 ,Vi2 )! Homs (Vin ,Vi j ) } is only a special set vector space over S and not a special set linear algebra in general.

We illustrate this by an example.

Example 2.1.22: Let V = (V1, V2, V3, V4) be a special set vector space over the set S = Z+ Â&#x2030; {0}; where V1 = {S Ă&#x2014; S Ă&#x2014; S Ă&#x2014; S}, ­° § a b c d ¡ ½°

V2 = ÂŽ ¨ ¸ a, b,c,d,e,f ,g, h Â? Z Â&#x2030; {0}ž ; °¯ Š e f g h š °¿

V3 = {[Z+ Â&#x2030; {0}] [x] set of all polynomials of degree less than or equal to three} and ­ªa ° ° b V4 = ÂŽ ÂŤ ° c ° ÂŤÂŹ d ÂŻ

½ eº °  f °

a, b,c,d,e,f ,g, h Â? Z Â&#x2030;{0}ž . gÂť ° Âť ° nÂź Âż

60

Define a special map T = (T1, T2, T3, T4): V = (V1, V2, V3, V4) o (V1, V2, V3, V4) by Ti: Vi o Vj for at least one i z j, 1 d i, j d 4. Let T1: V1 o V3 T2: V2 o V4 T3: V3 o V2 T4: V4 o V1 be defined on Vi’s as follows: T1(a b c d) = a + bx + cx2 + dx3.

§a b c d· T2 ¨ ¸ ©e f g h¹

ªa «b « «c « ¬d

§a T3 ((a0 + a1x + a2x2 + a3x3)) = ¨ 0 ©0

eº f »» ; g» » h¼ a1 a 2 0

0

a3 · ¸ 0¹

and § ªa ¨« b T4 ¨ « ¨ «c ¨¨ « © ¬d

eº· ¸ f »» ¸ g» ¸ »¸ h ¼ ¸¹

a b c d .

T = (T1, T2, T3, T4) is a pseudo special set linear operator on V. Now we proceed onto define special direct sum in special set vector spaces.

DEFINITION 2.1.11: Let V = (V1, …, Vn) be a special set vector space over the set S. If each Vi of V can be written as Vi = W1i ! Wnii where Wt i Wki = I or zero element if t z k

where it, ik {i1, i2, …, in} and each Wki is a set vector subspace of Vi. If this is true for each i = 1, 2, …, n then

61

V = ( W11 ! Wn11 ,W12 ! Wn22 , !, W1n ! Wnn ) is called the special set direct sum of the special set vector space V = (V1, V2, …, Vn).

We first illustrate this situation by an example.

Example 2.1.23: Let V = (V1, V2, V3, V4) where

V1 = {(1 1 1 1), (0 1), (1 0), (0 0), (0 0 0 0)}, ­ ªa ° « °§ a a · «a V2 = ®¨ ¸, °© a a ¹ «a « ° ¬a ¯

½ aº ° » a» ° a {0, 1}¾ , a» ° » ° a¼ ¿

V3 = {Z2 × Z2 × Z2} and V4 = {1 + x2, 0, 1 + x + x2, 1 + x, x3 + 1, x2 + x, x3 + x, x3 + x2, 1 + x3 + x2 + x} be a special set vector space over the set S = {0, 1}. Write V1 = {(1 1 1 1), (0 0 0 0)} {(0 1), (1 0), (0 0)} = W11 W21 . ­ ªa °« °­§ a a · °½ ° «a V2 = ®¨ ¸¾ ® °¯© a a ¹ °¿ ° «a ° «¬a ¯

aº½ ° a »» ° ¾ a»° » a ¼ °¿

W12 W22 ,

V3 = {Z2 × {0} × Z2 × {0}} {{0} × {0} × {0} × Z2} {{0} × Z2 × {0} × {0}} = W13 W22 W33 and V4 = {1 + x3 + x2 + x, 1 + x3 x2 + x, 0) + {x2 + x + 1, 1 + x2, 1 + x, 0} + {0, x3 + x, x3 + x2} = W14 W24 W34

62

with Wj4 WK4

(0), if i z j.

Now V = (V1, V2, V3, V4) = ^W W , W W22 , W13 W23 W33 , W14 W24 W34 ` 1 1

1 2

2 1

is the special direct sum of special set vector subspace of V. However it is interesting to note that the way of writing V as a special direct sum is not unique.

Now we define special set projection operator of V. Further if V = (W11 W21 , W12 W22 , W13 W23 W33 , W14 W24 W34 ) then W any special set vector subspace of V is (Wi11 , Wi22 ,! , Winn ) (V1, V2, …, Vn), i.e.; Witt Vt; 1 d t d n. So any way of combinations of Witt will give special set vector subspaces of V. DEFINITION 2.1.12: Let V = (V1, V2, …, Vn) be a special set vector space over the set S. Let W = (W1, W2, …, Wn) (V1, V2, …, Vn) = V where Wi Vi; 1 d i d n be a special set vector subspace of V. Define T = (T1, T2, …, Tn) a special linear operator on V such that Wi is invariant under Ti for i = 1, 2, …, n; i.e., Ti : Vi o Vi with Ti(Wi) Wi then T is called the special set projection of V.

We illustrate this by one example.

Example 2.1.24: Let V = (V1, V2, V3, V4) where

V1 = {Z5 × Z5 × Z5 × Z5}, °­§ a b · °½ V2 = ®¨ ¸ a, b,c,d Z7 ¾ , ¯°© c d ¹ ¿° ­°§ a a a a · ½° V3 = ®¨ ¸ a Z12 ¾ °¯© a a a a ¹ ¿°

63

and ­ ª a1 °« ° a V4 = ® « 4 °« a 7 ° «¬ a10 ¯

a2 a5 a8 a11

½ a3 º ° » a6 » ° a i Z10 , 1d i d12 ¾ a9 » ° » ° a12 ¼ ¿

be a special set vector space over the set S = {0, 1}. Take W = (W1, W2, W3, W4) V where W1 = {Z5 × Z5 × {0} u {0}} V1, ­°§ a a · ½° W2 = ®¨ ¸ a Z7 ¾ V2 , °¯© a a ¹ °¿ ­°§ a a a a · ½° W3 = ®¨ ¸ a {0, 2, 4, 6, 8, 10}¾ V3 ¯°© a a a a ¹ ¿°

and ­ ªa °« ° a W4 ® « ° «a ° «¬ a ¯

a a aº a a a »» a Z10 a a a» » a a a¼

½ ° ° ¾ V4 ° ° ¿

be a special set vector subspace of V over S. Define a special set linear operator T on V by T = (T1, T2, T3, T4): V = (V1, V2, V3, V4) o V = (V1, V2, V3, V4) such that Ti: Vi o Vi, i = 1, 2, 3, 4.

T1: V1 o V1 is such that T1(a b c d) = {(a b 0 0)}, T2: V2 o V2 is such that §a b· §a a· T2 ¨ ¸ ¨ ¸, ©c d¹ ©a a¹ T3: V3 o V3 is defined by §a a a a· T3 ¨ ¸ ©a a a a¹ and

°­ § b b b b · °½ ®¨ ¸ b {0, 2, 4, 6, 8, 10}¾ ¯° © b b b b ¹ ¿°

64

T4: V4 o V4 is such that § ª a1 a 2 a 3 º · ¨« ¸ a 4 a 5 a 6 »» ¸ ¨ « T4 ¨ « a 7 a8 a9 » ¸ ¨¨ « » ¸¸ © ¬a10 a11 a12 ¼ ¹

ª a1 «a « 1 « a1 « ¬ a1

a1 a 1 º a1 a1 »» a1 a 1 » » a1 a 1 ¼

such that a1 Z10. T = (T1, T2, T3, T4) is easily seen to be a special set linear operator on V. Further it can be verified that T is a special set projection operator on V. For T o T = (T1 o T1, T2 o T2, T3 o T3, T4 o T4) where Ti o Ti: Vi o VI given by T1 o T1 (a b c d) = T1 (a b 0 0) = (a b 0 0), i.e.; T1 o T1 = T1 on V1, i.e., T1 is a set projection operator on V1. We see T2 o T2 : V2 o V2 gives §§a b·· T2 o T2 ¨ ¨ ¸¸ ©© c d¹¹

§§a a ·· T2 ¨ ¨ ¸¸ ©©a a ¹¹

§a a· ¨ ¸, ©a a¹

i.e., T2 o T2 = T2 is again a set projection operator on V2.

Consider T3 o T3 : V3 o V3 given by §§a a a a ·· §§ b b b b·· T3 o T3 ¨ ¨ ¸ ¸ T3 ¨ ¨ ¸¸ ©©a a a a ¹¹ ©© b b b b¹¹ such that b {0, 2, 4, 6, 8, 10} and §§ b b b b·· T3 ¨ ¨ ¸¸ ©© b b b b¹¹

§b b b b· ¨ ¸ ©b b b b¹

then by making T3 is set projector operator on V3; i.e., T3 o T3 = T3. Finally T4 o T4: V4 o V4 gives

65

§ ª a1 ¨« a T4 o T4 ¨ « 4 ¨ « a7 ¨¨ « © ¬ a10

a2 a5 a8 a11

a3 º · ªa1 ¸ » «a a6 » ¸ T4 « 1 «a1 a9 » ¸ » ¸¸ « a12 ¼ ¹ ¬ a1

a1 a1 º ª a1 » a1 a1 » «« a1 = a1 a1 » « a1 » « a1 a1 ¼ ¬ a1

a1 a1 º a1 a1 »» a1 a1 » » a1 a1 ¼

there by making T4 o T4 = T4; thus T4 is a set projection operator on V4.

Thus T = (T1, T2, T3, T4) is such that T o T = (T1 o T1, T2 o T2, T3 o T3, T4 o T4) leaving W = (W1, W2, W3, W4) to be invariant under T. T(W) = (T1(W1), T2(W2), T3(W3), T4(W4)) (W1,W2, W3, W4) = W (V1, V2, V3,V4) = V. We see T o T = T (T1, T2, T3, T4).

Interesting results in this direction can be obtained by interested readers.

2.2 Special Set Vector Bispaces and their Properties

In this section we introduce the notion of special set vector bispaces and study some of their properties. Now we proceed onto define the notion of special set vector bispaces.

DEFINITION 2.2.1: Let V = (V1 V2) where V1 = ( V11 ,V21 ,! ,Vn1 ) and V2 = ( V12 ,V22 ,!,Vn22 ) are distinct special set

vector spaces over the same set S i.e., V1 V2 or V2 V1. Then we call V = V1 V2 to be a special set bivector space or special set vector bispace over the set S. We give some examples of special set vector bispaces.

Example 2.2.1: Let

V = V1 V2 = { V11 , V21 , V31 , V41} {V12 , V22 , V32 , V42 , V52 } where V11 {Z2 × Z2},

66

­°§ a b · °½ ®¨ ¸ a, b,c,d Z5 ¾ , °¯© c d ¹ ¿°

V21

­° ª a a a a a º ½° a Z7 ¾ ®« » °¯ ¬ a a a a a ¼ ¿°

V31 and V41

°­ ªa a a a a a º °½ a Z10 ¾ ®« » ¯° ¬a a a a a a ¼ ¿°

so that V1 = ( V11 , V21 , V31 , V41 ) is a special set vector space over the set S = {0, 1}. Here in V2 = (V12 , V22 , V32 , V42 , V52 ) we have V12 = Z7 × Z7 × Z7 × Z7, V22 {Z5[x] is set all polynomials in the variable x of degree less than or equal to 4},

V32

­§ a1 °¨ ®¨ a 4 °¨ a ¯© 7

a2 a5 a8

½ a3 · ° ¸ a 6 ¸ a i Z9 ; 1 d i d 9 ¾ , ° a 9 ¸¹ ¿

V42 = {(a a a), (a a), (a a a a a) | a Z11} and

V52

­ ª a1 °« °«a 3 ® °«a 5 ° «¬ a 7 ¯

½ a2 º ° » a4 » § a a · ° ,¨ a,a i Z8 , 1d i d 8¾ ¸ a6 » © a a ¹ ° » ° a8 ¼ ¿

is a special set vector space over the set S = {0, 1}. Thus V = V1 V2 = (V11 , V21 , V31 , V41 ) (V12 , V22 , V32 , V42 , V52 ) is a special set vector bispace over the set S = {0, 1}.

Example 2.2.2: Let V = V1 V2 = (V11 , V21 , V31 ) (V12 , V22 , V32 ) where

67

­°§ a b ¡ ½° o

V11 Ž¨ ¸ a , b,c,d Â? S Z Â&#x2030; {0}ž , °¯Š c d š °¿ V21 {So Ă&#x2014; So},

V31

V12

­ªa º ½ °  ° °b  ° o

a, b,c,d Â?S Z Â&#x2030;{0}ž , ÂŽ ÂŤ Âť ° c ° ° ÂŤÂŹ d Ÿ ° ÂŻ Âż

{(a a a a a), (a a a a) | a Â? Z+ Â&#x2030; {0} = So}, V22 {So u 2So u 3So u 4So}

and

V32

­ ½ ªa º ° °   ° ªa º a  ° °  o°   , a S � a a Ž  ž. ° a  a  ° ° Ÿ   °  a Ÿ °¯ ¿°

Clearly V1 and V2 are special vector spaces over the same set So. Thus V = V1 Â&#x2030; V2 is a special set vector bispace over the set So .

Now we proceed onto define the notion of special set linear bialgebra over the set S. DEFINITION 2.2.2: Let V = V1 Â&#x2030; V2 be a special set vector bispace over the set S. If both V1 and V2 are special set linear algebras on the set S, then we call V = V1 Â&#x2030; V2 to be a special set linear bialgebra.

It is important to mention here certainly all special set linear bialgebras are special ser vector bispaces however all special set vector bispaces in general are not special set linear bialgebras.

68

We first illustrate this by a simple example.

Example 2.2.3: Let V = V1 V2 = (V11 , V21 , V31 ) (V12 , V22 , V32 , V42 ) where V11 = {(a a a a), (a a)| a Z2 = {0 1}}, 1 2

V

­§ a a · ½ °¨ ° ¸ §a a a· ®¨ a a ¸ , ¨ ¸ a Z2 {0,1} ¾ a a a ¹ °¨ a a ¸ © ° ¹ ¯© ¿

and V31

{0, x2 + 1, x3 + x + 1, x + 1, x4 + x3 + x + 1, x2 + x5 + x4 + 1, x7 + 1}.

Clearly V1 = (V11 , V21 , V31 ) is not a special set linear algebra for none of the Vi1 ’s are set linear algebras; i =1, 2, 3,. Now even if V2 = (V12 , V22 , V32 , V42 ) is a special set linear algebra over the set {0, 1}, still V = V1 V2 is not a special set linear bialgebra over the set {0, 1}.

In view of this example we have the following new notion.

DEFINITION 2.2.3: Let V = V1 V2 where V1 = (V11 ,V21 ,!,Vn11 )

is a special set linear algebra over the set S and V2 = (V12 ,V22 ,!,Vn12 ) is a special set vector space over the set S which is not a special set linear algebra. We call V = V1 V2 to be a quasi special set linear bialgebra.

Now we proceed onto describe the substructures.

DEFINITION 2.2.4: Let V = (V1 V2) = (V11 ,V21 ,!,Vn11 ) (V12 ,V22 ,! ,Vn22 )

where both V1 and V2 are special set vector spaces over the set S. Thus V = V1 V2 is a special set bivector space over the set S. Now consider a proper biset

69

W = W1 W2 = (W11 ,W21 ,!,Wn11 ) (W12 ,W22 ,!,Wn22 ) V1 V2 such that Wt i Vt i ; i = 1, 2 and 1 d t d n1 or n2 and each Wt i is a set vector subspace of Vt i , i = 1, 2 and t = 1, 2, … n1 and t = 1, 2, …, n2, we call W1 W2 = (W11 ,!,Wn11 ) (W12 ,!,Wn22 ) as a special set vector bisubspace of V = V1 V2.

We illustrate this situation by some simple examples.

Example 2.2.4: Let V = V1 V2 = {V11 , V21 , V31} {V12 , V22 , V32 , V42 } where V11 = {Z2 × Z2 × Z2},

­°§ a b · ®¨ ¸ a, b,c,d Z2 ¯°© c d ¹

V21

½° ¾ ¿°

and V31

°­§ a a a a · § a a a · °½ ®¨ ¸, ¨ ¸ a Z2 ¾ ¯°© a a a a ¹ © a a a ¹ ¿°

is a special set vector space over the set S = {0, 1}.

V21 V22

{Z9 × Z9 × Z9},

­°§ a b c · ®¨ ¸ a, b,e,c,d,f Z6 ¯°© c d f ¹

V32

­ ªa º ° ªa º « » ° «a » «a » °« » ® , «a » a Z12 ° «a » «a » ° «¬a »¼ « » °¯ ¬«a ¼»

and

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½ ° ° ° ¾ ° ° ¿°

½° ¾, ¿°

V42

­°§ a a a a a · § a a a a a a · ½° ®¨ ¸,¨ ¸ a Z10 ¾ . °¯© a a a a a ¹ © a a a a a a ¹ °¿

Clearly V2 = {V12 , V22 , V32 , V42 } is a special set vector space over the same set S = {0, 1}. We see V = V1 V2 is a special set vector bispace over the set S = {0, 1}. Take W = W1 W2 = (W11 , W21 , W31 ) (W12 , W22 , W32 , W42 ) V1 V2 where W11 V11 and W11 = {Z2 × Z2 × {0}} V11 , °­§ a a · °½ 1 W21 ®¨ ¸ a Z2 ¾ V2 , and a a ¹ ¯°© ¿° W31

Thus W1

­°§ a a a · ½° 1 ®¨ ¸ a Z2 ¾ V3 . a a a ¹ °¯© ¿°

(W11 , W21 , W31 ) (V 11, V21 , V31 ) V1 is a special set vector

subspace of V1. Consider W2 = (W12 , W22 , W32 , W32 ) where W12 = {Z9 × Z9 u {0}} V12 , W22

­°§ a a a · ½° 2 ®¨ ¸ a Z6 ¾ V2 , a a a ¹ ¯°© ¿°

W32

­ ªa º ½ °« » ° ° «a » ° a Z12 ¾ V32 ® ° «a » ° ° «¬a »¼ ° ¯ ¿

and ½° °­§ a a a a a a · 2 W42 ®¨ ¸ a Z10 ¾ V4 . a a a a a a ¹ ¯°© ¿°

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Thus W2 = (W12 , W22 , W32 , W42 ) (V12 , V22 , V32 , V42 ) is a special set vector subspace of V2. Thus W1 W2 = (W11 , W21 , W31 ) (W 12 , W22 , W32 , W42 ) V1 V2 =V is a special set vector bisubspace of V. We give yet another example.

Example 2.2.5: Let V = V1 V2 =

V ,V ,V ,V

2 1

2 2

2 3

2 4

(V11 , V21 , V31 , V41 )

be a special set vector bispace of V over the

set S = Z+ {0} = Zo. Here V1 = (V11 , V21 , V31 , V41 ) where ½ °­§ a b · o° V11 ®¨ ¸ a, b,c,d Z ¾ , ¯°© c d ¹ ¿° V21 = {Zo × Zo × Zo},

V31 = {(a a a a), (a a), (a a a a a a) | a Zo} and V41 = {Zo[x] set of all polynomials of degree less than or equal to four with coefficients from Zo}.

V1 = ( V11 , V21 , V31 , V41 ) is a special set vector space over the set S = Zo. Now V2 = ( V12 , V22 , V32 , V42 ) where ­§ a1 a 2 a 3 · ½ °¨ ° ¸ 2 o V1 ®¨ a 4 a 5 a 6 ¸ a i Z ,1 d i d 9 ¾ , °¨ a a a ¸ ° 8 9¹ ¿ ¯© 7 2 2 7 9 6 2 V2 = {n(x + 1), n(x + 3x + 9x + 1), n(x + 3x + 6x3 + 9) | n a positive integer n Zo},

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V32

­ ½ ªa º ° ° « » °§ a · « a » ° °¨ ¸ o° « » ®¨ a ¸ , a a Z ¾ °¨ a ¸ « a » ° °© ¹ « » ° «¬ a »¼ °¯ ¿°

and V42 = {Zo × Zo × Zo × Zo × Zo}, V2 = (V12 , V22 , V32 , V42 ) is a special set vector space over the set Zo. Clearly V = (V12 , V22 , V32 , V42 ) is a special set vector bispace over the set Zo. Take W = W1 W2 = { W11 , W21 , W31 , W41} {W12 , W22 , W32 , W42 } V1 V2 with ­°§ a a · ½ o° 1 W11 ®¨ ¸ a Z ¾ V1 , a a ¹ ¯°© ¿° W21 = Zo × Zo × {0} V21 , {(a a a a), (a a) | a Zo} V31

W31

and W41 = {n(1+x+x2 +x3+x4)| n Zo} V41 ; W1 = (W11 , W21 , W31 , W41 ) V1 is a special set vector subspace of V1 over the set Zo. W2 = (W12 , W22 , W32 , W42 ) V2 where 2 1

W

­§ a a a · ½ °¨ ¸ o° 2 ®¨ a a a ¸ a Z ¾ V1 , °¨ a a a ¸ ° ¹ ¯© ¿

W22 ={n(x2 + 1) | n Zo) V22 ,

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2 3

W

­ ªa º ½ °« » o° 2 ® «a » a Z ¾ V3 ° «a » ° ¯¬ ¼ ¿

and W42 = {Zo × {0} × Zo × {0} × Zo} V42 .

W2 = (W12 , W22 , W32 , W42 ) V2 is a special set vector subspace of V2 over Zo. Thus W = W1 W2 = { W11 , W21 , W31 , W41} {W12 , W22 , W32 , W42 } V1 V2 = {V11 , V21 , V31 , V41} { V12 , V22 , V32 , V42 } is a special set vector subbispace of V = V1 V2.

Now we proceed onto define the notion of special set linear subbialgebra of V = V1 V2.

DEFINITION 2.2.5: Let V = V1 V2 = (V11 ,V21 ,!,Vn11 ) (V12 , V22 ,!,Vn22 )

be a special set linear bialgebra over the set S. Suppose W = W1 W2 = (W11 ,W21 ,!,Wn11 ) (W12 ,W22 ,!,Wn22 ) V1 V2 is such that Wi is a special set linear algebra of Vi over the set S for i = 1, 2 then we call W = W1 W2 to be a special set linear subbialgebra of V = V1 V2 over the set S.

We illustrate this by some examples.

Example 2.2.6: Let V = (V11 , V21 , V31 , V41 ) (V12 , V22 , V32 , V42 ) = V1 V2 be a special set linear bialgebra over the set S = {0, 1}.

Here V1 = (V11 , V21 , V31 , V41 ) is a special set linear algebra over the

set S = {0, 1} where V11 = {Z2 × Z2 × Z2 × Z2 × Z2},

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V21

­°§ a b · ½° ®¨ ¸ a, b,c,d Z5 ¾ , °¯© c d ¹ ¿°

V31 = {(a a a a a), (a a a a) | a Z7}

and V41 = {Z2[x] all polynomials of degree less than or equal to 10}.

V2 = (V12 , V22 , V32 , V42 ) is a special set linear algebra over the set S = {0, 1} where V12 = {Z10 × Z10 × Z10 × Z10 × Z10}, ½° °­§ a b c d · ®¨ ¸ a, b,c,d,e,f ,g, h Z9 ¾ , ¯°© e f g h ¹ ¿°

V22

V32

­ ªa °« ° «a ® ° «a ° «¬ a ¯

½ aº ° » a » ªa a a a a º ° , a Z12 ¾ a » «¬ a a a a a »¼ ° » ° a¼ ¿

and V42 = {(a a a a a a a a) | a Z+ {0}} is also take W = =

where

a special set linear algebra over the set S = {0, 1}. Now W1 W2 { {W11 , W21 , W31 , W41} { W12 , W22 , W32 , W42 } V1 V2 W11 V11 , W21 V21 , W31 V31 and W41 V41 ;

similarly Wi2 Vi2 ; 1 d i d 4.

Here W11 = {Z2 × Z2 × Z2 × {0} × {0}} V11

75

­°§ a a · ½° 1 W21 ®¨ ¸ a Z5 ¾ V2 , °¯© a a ¹ °¿ W31 = {(a a a a a) | a Z7}

and W41 = {all polynomials of degree less than or equal to 5 with coefficients from S = {0, 1}}.

Clearly W1 = (W11 , W21 , W31 , W41 ) (V11 , V21 , V31 , V41 ) = V1 is a special set linear subalgebra of V1.

W2 = (W12 , W22 , W32 , W42 ) is such that W12 = {Z10 × Z10 × {0} × {0} × Z10} V12 , W22

2 3

W

­°§ a a a a · ½° 2 ®¨ ¸ a Z9 ¾ V2 , a a a a ¹ °¯© °¿ ­ ªa a a a a º ½ °« ° » 2 ® «a a a a a » a Z12 ¾ V3 ° «a a a a a » ° ¼ ¯¿

and W42 = {(a a a a a a a a) | a 5Z+ {0}} V42 .

Clearly W2 = ( W12 , W22 , W32 , W42 ) V2 (V12 , V22 , V32 , V42 ) is a special set linear subalgebra of V2. Thus W1 W2

=

=

(W11 , W21 , W31 , W41 ) (W12 , W22 , W32 , W42 ) V1 V2 V.

V is a special set linear bisubalgebra of V = V1 V2.

We give another example of special set linear bisubalgebra.

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Example 2.2.7: Let V = (V1 Â&#x2030; V2) = {V11 , V21 , V31 , V41 , V51}Â&#x2030;

{ V12 , V22 , V32 } where

°­§ a b ¡ o V11 Ž¨ ¸ a, b,c,d� Z c d š ¯°Š

°½ Z Â&#x2030; {0}ž , ¿°

V21 = {(a a a a a), (a a) | a Â? Zo}, V31 = {Zo[x] of all polynomials in the variable x of degree less than or equal to 8 with coefficients from Zo}, V41 {Zo Ă&#x2014; Zo Ă&#x2014; Zo Ă&#x2014; Zo} and V51 = {all n Ă&#x2014; n upper triangular matrices with entries from Zo}. Thus V1 = (V11 , V21 , V31 , V41 , V51 ) is a special set linear algebra over the set Zo = Z+ Â&#x2030; {0}. Now V2 = ( V12 , V22 , V32 ) where V12 = {(a a a) | a Â? Zo}, V22 = {all 10 u 10 matrices with entries from Zo} and V32 = {Zo Ă&#x2014; 2Zo Ă&#x2014; 5Zo Ă&#x2014; 7Zo Ă&#x2014; 8Zo Ă&#x2014; 9Zo} is a special set linear algebra over the set Zo. Thus V = V1 Â&#x2030; V2 is a special set linear bialgebra over the set Zo. Now consider

W = W1 Â&#x2030; W2 = {W11 , W21 , W31 , W41 , W51} Â&#x2030; {W12 , W22 , W32 } Â&#x17D; V1 Â&#x2030; V2 = (V11 , V21 , V31 , V41 , V51 ) Â&#x2030; (V12 , V22 , V32 ) where W11

­°§ a a ¡ ½° o 1 Ž¨ ¸ a Â? Z Â&#x2030; {0}ž Â&#x17D; V1 , ¯°Š a a š ¿°

W21 ={(a a a a a) | a Â? 5Zo} Â&#x17D; V21 , W31 = {Zo4 [x] all polynomials of degree less than or equal to 4 with coefficients from Zo} Â&#x17D; V31 , W41 = {Zo Ă&#x2014; {0}Ă&#x2014; Zo{0}} Â&#x17D; V41 and W51 = {all n Ă&#x2014; n upper triangular matrices with entries from 5Zo} Â&#x17D; V51 .

Clearly W1 = ( W11 , W21 , W31 , W41 , W51 ) Â&#x17D; V = ( V11 , V21 , V31 , V41 , V51 )

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is a special set linear subalgebra of V1. Now W12

{(a a a) | a

7Z } V , W = {all 10 × 10 matrices with entries from 5Zo} o

2 1

2 2

and W32 = {Zo × {0} × 5Zo × 7Zo × {0} × {0}} V32 . It is easily verified that W2 = (W12 , W22 , W32 ) V2 (V12 , V22 , V32 ) is a special set linear subalgebra of V2. Thus W1 W 2

= {W11 , W21 , W31 , W41 , W51 )} (W12 , W22 , W32 )} V1 V2

is a special set linear subbialgebra of V over the set Zo. Now we proceed onto define yet another new substructure.

DEFINITION 2.2.6: Let V (V11 ,V21 ,!,Vn11 ) (V12 ,V22 ,! ,Vn22 ) = V1 V2 be a special set linear bialgebra defined over the set S. Suppose W = W1 W2 = (W11 ,W21 ,!,Wn11 ) (W12 ,W22 ,!,Wn22 )

=

V1 V2 (V11 ,V21 ,! ,Vn11 ) (V12 ,V22 ,!,Vn22 )

is such that W1 = (W11 ,W21 ,!,Wn11 ) V1 is also a pseudo special set vector subspace of V1 over S and W2 = (W12 ,W22 ,!,Wn22 ) V2 is only a pseudo special set vector subspace of V2 over S. Then we call W = W1 W2 V1 V2 to be a pseudo special set vector bisubspace of V over S. Now if in this definition one of W1 or W2 is taken to be a special set linear subalgebra of Vi (i = 1 or 2) then we call W = W1 W2 V1 V2 to be a pseudo special set linear subbialgebra of V over the set S. We illustrate this set up by one example.

Example 2.2.8: Let V = V1 V2 = {V11 , V21 , V31 , V41} {V12 , V22 , V32 } be a special set linear bialgebra over the set S = {0, 1}, where

78

V11 = {Z2 × Z2 × Z2 × Z2}, °­§ a b · V21 = ®¨ ¸ a, b, c, d Z20}, °¯© c d ¹

V31 = {All 5×5 matrices with entries from the set {-1, 0 1}} and ­ ª a1 º ½ °« » ° ° «a 2 » ° 1 V4 ® a i Z10 ,1 d i d 4 ¾ . °«a 3 » ° ° «¬ a 4 »¼ ° ¯ ¿ V1 (V11 , V21 , V31 , V41 ) is a special set linear algebra over the set S = {0, 1}.

Now V12 = {Z5 × Z5 × Z5}, V22 = {all 4 × 4 matrices with entries from Z2 = {0, 1}; under matrix addition using the fact 1 + 1 { 0 mod 2, is a semigroup}, V32 = {Z2[x], all polynomials of degree less than or equal to 15 with entries from the set {0, 1}}.

Clearly V2 = (V12 , V22 , V32 ) is a special set linear algebra over the set S = {0, 1}. Thus V1 V2 is the given special set linear bialgebra over the set S = {0, 1}. Take W = =

W1 W2 (W11 , W21 , W31 , W41 ) (W12 , W22 , W32 )

V1 V2 where W11 = {(1 1 0 0), (0 1 1 1), (1 1 1 1), (0 0 0 0)} V11 ; W11 is just a pseudo special set vector subspace of V11 , ½° °­§ a 0 · § a1 a 2 · 1 W21 ®¨ ¸¨ ¸ a1 , a 2 , a, b Z20 ¾ V2 , b 0 0 0 ¹© ¹ ¯°© ¿° clearly W21 is also a pseudo special set vector subspace of V21 .

Now

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­§ 0 °¨ °¨ 0 ° 1 W3 = ®¨ 0 °¨ 0 °¨ ¨ ¯°© 0

§1 ¨ ¨1 ¨1 ¨ ¨1 ¨1 ©

1 1 1 1 1

1 1 1 1 1

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

1 1 1 1 1

1· § 0 ¸ ¨ 1¸ ¨ 1 1¸ , ¨ 0 ¸ ¨ 1¸ ¨ 1 1¸¹ ¨© 0

0· ¸ 0¸ 0¸, ¸ 0¸ 0 ¸¹

§ 1 1 0 ¨ ¨1 1 0 ¨1 1 1 ¨ ¨ 1 1 0 ¨ 0 0 1 ©

1 0 1 1 0

1· ¸ 1¸ 1¸ , ¸ 0¸ 0 ¸¹

1 1 0 1· ½ ¸° 0 0 1 0 ¸ ° ° 0 1 0 1 ¸ ¾ V31 ¸ 0 1 0 1 ¸° ° 1 1 0 0 ¸¹ °¿

W31 is only just a pseudo special set vector subspace of V31 .

Now ­ ª 0 º ª a1 º ª 0 º ½ °« » « » « » ° a 0 ° 0 ° W41 ® « » , « » , « 1 » a1 ,a 2 Z10 ¾ V41 ° «0 » «a 2 » « 0 » ° ° «¬0 »¼ «¬ 0 »¼ «¬ a 2 »¼ ° ¯ ¿ is only a pseudo special set vector subspace of V41 . Thus W1 = (W11 , W21 , W31 , W41 ) V1 is only a pseudo special set vector subspace of V1 over the set S = {0, 1}. We choose W12 = {(1 2 3), (4 1 0), (1 1 2), (0 0 0), (4 1 1)} V12 is only a pseudo set vector subspace of V12 over the set S = {0, 1},

W22

­ ª1 °« ° «0 ® ° «1 ° «¬0 ¯

1 1 0º ª0 0 0 0 »» ««0 , 1 1 1 » «0 » « 0 0 0¼ ¬0

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0 0 0º 0 0 0 »» , 0 0 0» » 0 0 0¼

ª1 «0 « «0 « ¬0

1 1 1º ª1 1 1 1»» ««1 , 0 1 1» «1 » « 0 0 1¼ ¬1

1 1 1 º ª1 0 0 0 »» ««1 , 0 0 0 » «1 » « 0 0 0 ¼ ¬1

0 0 0º ½ ° 1 0 0 »» ° 2 ¾ V2 0 0 0» ° » 0 0 1 ¼ °¿

is only a pseudo set vector subspace of V22 over the set S = {0, 1}. Finally W32 = {x3 + 1, 0, x7 + 1 + x, x2 + x + x3 + x4 + 1, 1 + x10 + x9, x10 + x2 + x6 + x8 + x4 + 1} V32 , W32 is only a pseudo set vector subspace of V32 . Thus W2 = (W12 , W22 , W32 ) V2 is only a pseudo special set vector subspace of V2. So W = W1 W2 = (W11 , W21 , W31 , W41 ) (W12 , W22 , W32 ) V1 V2 is a pseudo special set vector bi subspace V = V1 V2 over the set S = {0, 1}.

Now we show that V = V1 V2 has also pseudo special set linear bi subalgebras. To this end we define W = W1 W2 = {W11 , W21 , W31 , W41} {W12 , W22 , W32 } V1 V2 as follows : W11 = {Z2 × Z2 × {0} × {0}} V11 is a set linear subalgebra of V11 , ­°§ a a · ½° 1 W21 ®¨ ¸ a Z20 ¾ V2 °¯© a a ¹ °¿ is again a set linear subalgebra of V21 , W31 ={5 × 5 lower

triangular matrices with entries from {0, 1}} V31 is just a set vector subspace of V31 .

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W41

­ ªa º ½ °« » ° ° «a » ° 1

a Z ® 10 ¾ V4 « » ° a ° ° «¬a »¼ ° ¯ ¿

is again a set linear subalgebra of V41 . Thus W1 = (W11 , W21 , W31 , W41 ) V1 is a special set linear subalgebra of V1. Take W2 = (W12 , W22 , W32 ) as above so that W2 is only a pseudo special set vector subspace of V2. Thus W = W1 W2 V1 V2 is a pseudo special set linear subbialgebra of V = V1 V2.

Now we define the special bigenerator set of the special set vector bispace.

DEFINITION 2.2.7: Let V = V1 V2 = {V11 ,V21 ,! ,Vn11 } {V12 ,V22 ,!,Vn22 }

be a special set vector bispace over the set S. If X = X1 X2 = ( X 11 , X 21 ,!, X n11 ) ( X 12 , X 22 , !, X n22 ) V1 V2, i.e., X ipi V pii 1d id 2 with i = 1, 2; 1 d p1 d n1 and 1d p2 d n2 is such that each X i1 set generates the set vector space Vi1 over S, 1 d i d n1 and X i2 set generates the set vector space Vi 2 over S, 1 d i d n2; then

we define X = X1 X2 = ( X 11 , X 21 ,!, X n11 ) ( X 12 , X 22 ,! , X n22 ) V1 V2 to be the special bigenerator subset of V. If each X tii is finite, 1d i d 2 with 1 d t1 d n1 and 1 d t2 d n2 then we say V = V1 V2 is bigenerated finitely over S. Even if one of the sets X ti is of infinite cardinality then we say V is bigenerated infinitely.

The bicardinality of X is given by |X| = |X1| |X2| = (| X 11 |,| X 21 |,!,| X n11 | ) (| X 12 |,| X 22 |,!,| X n22 |) .

In case V = V1 V2 is a special set linear bialgebra then the special bigenerating biset X = X1 X2 is such that

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( X 11 , X 21 ,!, X n11 ) is a special generator of the special set linear algebra V1 over S and ( X 12 , X 22 ,!, X n22 ) is the special set bigenerator of V2 over S as a special set linear algebra then we call X = X1 X2 V1 V2 as the special bigenerator set of the special set linear bialgebra over S.

We illustrate this by some examples.

Example 2.2.9: Let V = V1 V2 = {V11 , V21 , V31} {V12 , V22 } where ­°§ a a · ½° V11 ®¨ ¸ a Z2 ¾ , °¯© a a ¹ ¿° 1 1 V 2 = Z2 × Z2 and V3 = {all polynomials of the form 1 + x, 1 +

x2, 0, x2, x + x2, 1 + x + x2, x}. Clearly V1 = {V11 , V21 , V31} is a special set vector space over the set S = {0, 1}. Now take

2 1

V

­§ a a · ½ °¨ ° ¸ ®¨ a a ¸ a Z2 {0,1}¾ °¨ ° ¸ ¯© a a ¹ ¿

and V22 = {(1 1 1), (0 0 0), (1 1 0), (0 1 1)}. V2 = (V12 , V22 ) is also a special set vector space over the set S = {0, 1}. Thus V = V1 V2 is a special set vector bispace over the set S = {0, 1}.

Take X = X1 X2 °­§1 1· = ®¨ ¸ , {(1 1), (0 1), (1 0)}, °¯©1 1¹ (x, x2, x + x2, 1 + x, 1+x2 1 + x + x2)} ­ ª1 1º °« » ® «1 1» , ((1 1 0), (1 1 1), (0 1 1))} ° «1 1» ¼ ¯¬

V1 V2.

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It is easily verified that X = X1 X2 special bigenerates V1 V2. Now the special bidimension of V = V1 V2 is |X| = |X1| |X2| = (1, 6, 1) (1, 3).

We give yet another example.

Example 2.2.10: Let V = V1 V2 = {V11 , V21 , V31 , V41} {V12 , V22 , V32 , V42 , V52 } be a special set vector bispace over the set Z+ {0} = S. Here V11 = {S × S}, V21 = {(a a a) | a S},

­°§ a a · ½° V31 ®¨ ¸ a S¾ °¯© a a ¹ ¿° and ­ ªa º °« » ° «a » ® , ° «a » ° «¬a »¼ ¯

V41

ªa a a a a º «a a a a a » ¼

½ ° o° a S ¾ . ° ° ¿

Now X1 = (X11 , X12 , X13 , X14 ) V1 (V11 , V21 , V31 , V41 ) , i.e., Vi1 contains X1i as a subset for i = 1, 2, 3, 4. X1 is a special set generator of V1 over S where X11 = {an infinite set of the form (x, y)}; °­§ 1 1· °½ 1 X12 = {(1 1 1)} V21 , X13 ®¨ ¸ ¾ V3 °¯© 1 1¹ °¿ and

X14

­ ª1º ½ °« » ° ° «1» ª1 1 1 1 1º ° 1 ® ,« » ¾ V4 . « » 1 1 1 1 1 1 ¼° ° ° «¬1»¼ ° ¯ ¿

84

Now X1 = (X11 , X12 , X13 , X14 ) is a special set generator of V1.

Let X2 = (X12 , X 22 , X 32 , X 42 , X 52 ) Â&#x17D; V2 = (V12 , V22 ,! , V52 ) ; X i2 Â&#x17D; Vi2 ; 1 d i d 5.

2 1

V

­§ a a a ¡ °¨ ¸ o Ž¨ a a a ¸ a �S °¨ a a a ¸ š ¯Š

½ ° Z Â&#x2030;{0}ž , ° Âż

­°§ a b ¡ ½ o° ¸ a, b,c,d �S ž , ¯°Š c d š ¿°

V22 = {(a a a a a) | a � So}, V32 Ž¨

V42

­ ½ ªa º ° °   ° ªa º a  ° °° a  a  ° o°   , a S �   Ž ž ° a  a  ° ° a Ÿ a  °   ° ° °¯ a Ÿ ¿°

and V52 = {So (x) = (Z+ Â&#x2030; {0}) (x), all polynomials in x with coefficients from So}. V2 = (V12 , V22 , V32 , V42 , V52 ) is a special set vector space over So. Now X2 = (X12 , X 22 , X 32 , X 42 , X 52 ) Â&#x17D; (V12 , V22 , ! , V52 ) be the special set generator of V2 over So, where

2 1

X

­§1 1 1¡ ½ °¨ ¸° 2 Ž¨1 1 1¸ ž Â&#x17D; V1 , °¨1 1 1¸ ° š¿ ¯Š

X 22 = {(1 1 1 1)} Â&#x17D; V22 , X 32 = {infinite set of matrices with elements from So} Â&#x17D; V32 ,

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X 24

­ ÂŞ1Âş ½ ° ÂŤ ° ° ÂŞ1Âş ÂŤ1Âť ° °° ÂŤ1Âť ÂŤ1Âť °° 2 ÂŽ ÂŤ Âť , ÂŤ Âť ž Â&#x17D; V4 ° ÂŤ1Âť ÂŤ1Âť ° ° ÂŤÂŹ1Ÿ ÂŤ1Âť ° ÂŤ ° ° ÂŤÂŹ1Ÿ °¿ °¯

and X 52 = {infinite set of polynomials}.

Clearly X2 = (X12 , X 22 ,! , X 52 ) special generates V2. Now we see |X1 Â&#x2030; X2| = |X1| Â&#x2030; |X2| = {(f, 1, 1, 2) Â&#x2030; (1, 1, f, 2, f)}. We see V = V1 Â&#x2030; V2 is a bigenerated by the special biset X = X1 Â&#x2030; X2.

We have just seen examples of special set bivector spaces which are finite dimensional as well as examples of infinite dimensional.

Now we wish to record if V = V1 Â&#x2030; V2 is a special set vector bispace and if it is made into a special set linear bialgebra then we see the special bidimension of V as a special set vector bispace is always greater than or equal to the special bidimension of V as a special set linear bialgebra.

We illustrate this by an example as every special set linear bialgebra is a special set vector bispace.

Example 2.2.11: Let V = V1 Â&#x2030; V2 = (V11 , V21 , V31 , V41 ) Â&#x2030; (V12 , V22 , V32 , V42 , V52 ) be a special set linear bialgebra over the set S = Z+ Â&#x2030; {0}. Here V1 = (V11 , V21 , V31 , V41 ) where V11 V21

½° °­§ a b ¡

Ž¨ ¸ a, b,c,dÂ? Z Â&#x2030;{0}ž , ¯°Š 0 d š ¿°

{S Ă&#x2014; S Ă&#x2014; S Ă&#x2014; S}, V31

and

86

{Z+ Â&#x2030; {0} Ă&#x2014; Z+ Â&#x2030; {0}}

1 4

V

­ ª a1 º ° « » ª a1 ® «a 2 » , « ° « a » ¬a 4 ¯ ¬ 3¼

a2 a5

½ a3 º ° a i S;1 d i d 6 ¾ » a6 ¼ ° ¿

is a special set linear algebra over S = Z+ {0}.

Let X1 = (X11 , X12 , X13 , X14 ) (V11 , V21 , V31 , V41 ) where °­§ 1 0 · § 0 1 · § 0 0 · °½ 1 X11 ®¨ ¸, ¨ ¸, ¨ ¸ ¾ V1 °¯© 0 0 ¹ © 0 0 ¹ © 0 1 ¹ °¿ set generates V11 as a linear algebra over Z+ {0}, X12 = {(1 0 0 0), (0 1 0 0), (0 0 1 0), (0 0 0 1)} V21 set generates V21 as a set linear algebra over S = Z+ {0}, X13 = {(1 0), (0 1)} V31 set generates V31 as a set linear algebra over S. Finally V41 is generated infinitely by X14 = {this is an infinite set}.

So X1 = (X11 , X12 , X13 , X14 ) is a special generator of V1 and |X1| = ( | X11 |,| X12 |,| X13 |,| X14 |) = {3, 4, 2, f} as a special set linear algebra. Now let V2 = (V12 , V22 , V32 , V42 , V52 ) be given by V12 = {Zo × Zo × Zo} where Zo = Z+ {0} = S, ­ ªa º ½ °« » ° ° «a » 2 o° V2 ® such that a Z ¾ , ° «a » ° ° «¬a »¼ ° ¯ ¿

2 3

V

­ ªa a a º ½ ªa a a a a º °« » o° ® «a a a » , « » a Z ¾ , ° «a a a » ¬a a a a a ¼ ° ¼ ¯¿ V42 = {Zo[x] all polynomials of degree less than or equal to three}

87

and

V52

­ ªa °« ° «a ® ° «a ° «¬a ¯

½ 0 0 0º ° » a 0 0» ° o a Z {0}¾ . a a 0» ° » ° a a a¼ ¿

Thus V2 = (V12 , V22 , V32 , V42 , V52 ) is a special set linear algebra over the set S = Zo = Z+ {0}. Consider X2 = (X12 , X 22 , X 32 , X 42 , X 52 ) (V12 , V22 , V32 , V42 , V52 ) where X12 {(1 0 0), (0 1 0), (0 0 1)} V12 generates V12 as a set linear algebra over S = Z0, ­ ª1º ½ °« » ° °1 ° X 22 ® « » ¾ V22 ° «1» ° ° «¬1»¼ ° ¯ ¿ is the set generator of the set linear algebra V22 . °­ ª1 1 1º ª1 1 1 1 X 32 ® « »,« °¯ ¬1 1 1¼ ¬1 1 1 1 is the set generator of the set vector space

1º °½ 2 ¾ V3 » 1¼ °¿ V32 ,

X 24 = {1, x, x2, x3} V42

is a set generator of the set linear algebra V42 over Zo.

X 52

­ ª1 °« ° «1 ® ° «1 ° «¬1 ¯

0 0 0º 1 0 0 »» V52 1 1 0» » 1 1 1¼

is the set generator of the set linear algebra V52 over Zo.

88

Thus X2 = (X12 , X 22 , X 32 , X 42 , X 52 ) is the special set generator of the special set linear algebra over Zo. Now |X2| = {3, 1, 2, 4, 1} is the set dimension of V2 as a special set linear algebra over Zo. We see V = V1 V2 as a special set linear algebra is special set bigenerated by X = =

X1 X2 { X11 , X12 , X13 , X14 } {X12 , X 22 , X 32 , X 42 , X 52 } V1 V2

and the special set bicardinality of X = |X1| |X2| = (3, 4, 2, f) (3, 1, 2, 4, 1)}.

Now we will find the special set bigenerator of the special set vector bispace V = V1 V2, i.e.; we are treating the same V = V1 V2 now only as a special set vector bispace over Zo = S. Now the biset which is the special bigenerator is given by X = X1 X2 where X1 = (X11 , X12 , X13 , X14 , X15 ) with X11 = {An infinite set of upper singular 2 × 2 matrices with entries from Zo} V11 ;X12 = {An infinite set of 4-tuples with entries from Zo} V21 , X13 = {an infinite set of pairs with entries from Zo} V31 ; X14 = {An infinite set of 3 × 1 matrix and an infinite set of 2 × 3 matrix with entries from Zo} V41 . X1 = (X11 , X12 , X13 , X14 ) set generates V1 = ( (V11 , V21 , V31 , V41 ) infinitely as a special set vector space and the special dimension of V1 is |X1| = (| X11 |,| X12 |,| X13 |,| X14 |) (f,f,f,f). Thus we see the difference between the special set dimensions in case of the special set vector space V1 and the same special set linear algebra V1 defined over the same set special dimension of V1 as a special set vector space over S = Z+ {0}, is |X1| = (f,f,f,f) and the special dimension of V1 as a special set linear algebra over the set S = Z+ {0} is (3, 4, 2, f).

89

Now we obtain the special set dimension of the special set vector space V2 = (V12 , V22 , V32 , V42 , V52 ) over the set Zo. Let X2 = ( X12 , X 22 , X 32 , X 42 , X 52 ) (V12 , V22 , V32 , V42 , V52 ) where X12 = {is an infinite set of triples with entries from Zo} V12 ;

­ ª1º ½ °« » ° °1 ° X 22 = ® « » ¾ V22 , ° «1» ° ° «¬1»¼ ° ¯ ¿

X

2 3

­ ª1 1 1º °« » ® «1 1 1» , ° «1 1 1» ¼ ¯¬

ª1 1 1 1 1º °½ 2 «1 1 1 1 1» ¾ V3 , ° ¼¿

X 24 = {an infinite set of polynomial in x with coefficients from Zo of degree less than or equal to three} V42 and

X 52

­ ª1 °« ° «1 ® ° «1 ° «¬1 ¯

0 0 0º ½ ° 1 0 0 »» ° 2 ¾ V5 .

1 1 0» ° » 1 1 1 ¼ °¿

Thus X2 = (X12 , X 22 , X 32 , X 42 , X 52 ) is a special set generator of the special set vector space V2 = (V12 , V22 , V32 , V42 , V52 ) . Now the special dimension of V2 as a special set vector space over Zo is |X2| = (f, 1, 2, f, 1) > (3, 1, 2, 4, 1) which is the special set dimension V2 as a special set linear algebra over Zo. Thus we see as a special set linear algebra V2 over Zo is finite dimensional whereas V2 as a special set vector space over Zo is infinite dimension. Thus V = V1 V2 which we utilized as a special set linear bialgebra over Zo is of special set bidimension |X| = |X1| |X2| = (3, 4, 2, f) (3, 1, 2, 4, 1) but 90

the same V = V1 V2 treated as a special set vector bispace over the set Zo is of special set bidimension |X| = |X1| |X2| = (f, f, f, f) (f, 1, 2, f, 1).

Now having defined the special set bidimension we proceed to define special set linear bitransformation and special set linear bioperator.

DEFINITION 2.2.8: Let V = V1 V2 = (V11 ,V21 ,! ,Vn11 ) (V12 ,V22 ,! ,Vn22 )

be a special set vector bispace over the set S. Let W = W1 W2 = (W11 , W21 ,! ,Wn11 ) (W12 ,W22 ,!Wn22 ) be a special set vector bispace over the set S. Let T = T1 T2 = ( (T11 , T21 ,!, Tn11 ) (T12 , T22 ,!, Tn22 ) be a special set bimap from V1 V2 o W1 W2, i.e., (T11 , T21 ,! , Tn11 ): V1 o W1 and (T12 , T22 ,! , Tn22 ): V2 o W2 such that Ti1 :Vi1 Wi1 with Ti1 (DQ i1 ) D Ti1 (Q i1 ) for all S and

Q i1 Vi1 true for i = 1, 2, ..., n1, and Ti 2 :Vi 2 o Wi 2 such that Ti 2 (DQ i2 ) D Ti 2 (Q i2 ) true for i = 1, 2,..., n2 for Q i2 Vi 2 , and for all S. We call T = T1 T2 = (T11 , T21 ,! , Tn1 ) (T12 , T22 ,! , Tn2 ) : 1

2

V = (V ,!,V ) (V ,!,V ) 1 1

1 n1

2 1

2 n2

o (W11 ,W21 ,!,Wn11 ) (W12 ,W22 ,!,Wn22 ) W a special set linear bitransformation of the special set bivector space V = V1 V2 into the special set bivector space W = W1 W2 both V and W defined over the same set S. The following observations are important.

1. Both V = V1 V2 and W = W1 W2 must be defined over the same set S. 2. Clearly both V1 and W1 must have same number of set vector spaces; likewise V2 and W2 must also have the same number of set vector spaces.

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3. If SHomS(V, W) = S HomS (V1, W1) SHomS (V2, W2) = {HomS (V11 ,W11 ), HomS (V21 ,W21 ),! , HomS (Vn11 ,Wn11 )} {HomS (V12 ,W12 ), HomS (V22 ,W22 ),!, HomS (Vn22 ,Wn22 )}

then, SHomS (V,W) is also a special set vector bispace over the set S.

We illustrate this by the following examples.

Example 2.2.12: Let V = V1 V2 = {V11 , V21 , V31} {V12 , V22 , V32 , V42 } and W = W1 W2 = {W11 , W21 , W31} {W12 , W22 , W32 , W42 } be two special set vector bispaces over the set Zo = Z+ {0} where V1 = (V11 , V21 , V31 ) is such that V11 = {Zo × Zo × Zo}, ½° °­ ª a b º V21 ® « a, b,c,d Zo ¾ » ¯° ¬ c d ¼ ¿°

and ­° ªa a a a º ½° a Zo ¾ ®« » °¯ ¬a a a a ¼ ¿° is a special set vector space over Zo. Now (V12 , V22 , V32 , V42 ) is such that V12 Zo × Zo × Zo × Zo,

V31

V22

­°§ a1 ®¨ °¯© a 4

V32

a2 a5

­ ª a1 °« °«a 3 ® °«a 5 ° «¬ a 7 ¯

½° a3 · o ¸ a i Z , 1 d i d 6¾ , a6 ¹ °¿ ½ a2 º ° » a4 » ° o a Z ; 1d i d 8 ¾ a6 » i ° » ° a8 ¼ ¿

92

V2

and V42 = {all 4 × 4 upper triangular matrices with entries from Zo} is a special set vector space over the set Zo. Define W = W1 W2 where W1 = (W11 , W21 , W31 ) is such that ­°§ a b · ½ o° ®¨ ¸ a, b, c Z ¾ , °¯© c 0 ¹ ¿°

W11

{Zo × Zo × Zo × Zo}

W21 and

­ ªa °« ° «a ® ° «a °« ¯ ¬a

½ aº ° » a» ° W31 a Zo ¾ a» ° » ° a¼ ¿ is also a special set vector space over the set Zo. Now W2 = (W12 , W22 , W32 , W42 ) is such that W12

2 2

W

2 3

W

­ ªa1 a 2 °« ®« 0 0 °« 0 0 ¯¬

­°§ a b · ½ o° ®¨ ¸ a, b,c,d Z ¾ , ¯°© c d ¹ ¿°

­ ª a1 °« ® «a 3 ° «a ¯¬ 5 a3 a5 a8

½ a2 º ° » o a 4 » a i Z , 1 d i d 6¾ , ° a 6 »¼ ¿ a4 º a 6 »» such that ai Zo; 1 < i < 8 a 7 ¼»

½ ° ¾ ° ¿

and W42 = {all 4 × 4 matrices with entries from the set Zo}.

Clearly W2 = (W12 , W22 , W32 , W42 ) is a special set vector space over the set Zo. Thus V = V1 V2 and W = W1 W2 are special set vector bispaces over the set Zo. Now define the special set bimap

93

T = T1 T2 = (T11 , T21 , T31 ) (T12 ,T22 ,T32 ,T42 ) : V (V11 , V21 , V31 ) (V12 , V22 , V32 , V42 ) o

W = (W11 , W21 , W31 ) (W12 , W22 , W32 , W42 ) such that T11 : V11 o W11 T21 : V21 o W21

T31 : V31 o W31 and T12 : V12 o W12 T22 : V22 o W22 T32 : V32 o W32

T42 : V42 o W42

and given by

§a b· T11 (a b c) = ¨ ¸, ©c 0¹ §a b· T21 ¨ ¸ = (a b c d) ©c d¹

and §a a a a · T31 ¨ ¸ ©a a a a ¹

§a ¨ ¨a ¨a ¨ ©a

a· ¸ a¸ . a¸ ¸ a¹

Now T12 (a b c d)

§a T ¨ 1 © a4 2 2

a2 a5

a3 · ¸ a6 ¹

94

§a b· ¨ ¸, ©c d¹ § a1 ¨ ¨ a3 ¨a © 5

a2 · ¸ a4 ¸ , a 6 ¸¹

§ a1 ¨ a T32 ¨ 3 ¨ a5 ¨ © a7

a2 · ¸ a4 ¸ a6 ¸ ¸ a8 ¹

§ a1 a 2 ¨ ¨0 0 ¨0 0 ©

a3 a5 a7

a4 · ¸ a6 ¸ a 8 ¸¹

and

§a ¨ 2 ¨0 T4 ¨0 ¨ ©0

b c d· §a ¸ ¨ e f g¸ ¨b 0 i j¸ ¨c ¸ ¨ 0 0 k¹ ©d

b c d· ¸ e f g¸ . f c j¸ ¸ g j k¹

Clearly T = T1 T2 is the special set linear bitransformation of V = V1 V2 into W = W1 W2.

Example 2.2.13: Let V = V1 V2 and W = W1 W2 be two special set vector bispaces over the set S = {0, 1}. Here V = V1 V2 = {V11 , V21} {V12 , V22 , V32 }

and with

W = W1 W2 = {W11 , W21} {W12 , W22 , W32 } V11 = {Z2 × Z2 × Z2 × Z2},

­°§ a b c · ½° ®¨ ¸ a, b, c, d, e, f Z2 ¾ , °¯© d e f ¹ ¿°

V21

V12

^ª¬a º¼ ij

4u 4

`

,a ij Z4 , V22

{Z7} and V32 \ {Z8}.

W1 = (W11 , W21 ) where W11

­°§ a b · ½° ®¨ ¸ a, b,c,d Z2 ¾ °¯© c d ¹ ¿°

and 1 2

W

­ ªa b º ½ °« ° » ® « c d » a, b, c, d, e, f Z2 ¾ . ° «e f » ° ¼ ¯¿

95

W2 = {W12 , W22 , W32 } where W12

­°§ a1 ®¨ ¯°© a 5

a2 a6

½° a4· ¸ a i Z4 ;1 d i d 8¾ , a8 ¹ ¿°

a3 a7

W22 = {(Z7)} and W32 = {0, 1, 2, …, 23 = Z24}.

Define

T = T1 T2 = T11 ,T21 T12 ,T22 ,T32

as T11 :V11 o W11 where §a b· T11 (a b c d) = ¨ ¸, ©c d¹ T21 : V21 o W21 given by §a b c· T ¨ ¸ ©d e f ¹ 1 2

Now T12 : V12 o W12 ; § a1 a 2 ¨ a a6 T12 ¨ 5 ¨ a 9 a10 ¨ © a13 a14

a3 a7 a11 a15

ªa b º «c d » . « » «¬ e f »¼

a4 · ¸ a 8 ¸ § a1 ¨ a12 ¸ © a 5 ¸ a16 ¹

a2

a3

a6

a7

a4 · ¸, a8 ¹

T22 :V22 o W22 ; T22 (a) = a, T32 : V32 o W32 ; T32 (a) = a Z24 i.e.,

T32 (0)

0, T32 (1) (3),

T32 (2) T32 (4) T32 (6)

6, T32 (3) 9, 12, T32 (5) 15,

18 and T32 (7)

21.

Thus T = T1 T2 : V o W = V1 V2 o W1 W2 is (T11 , T21 ) (T12 ,T22 ,T32 ): (V11 , V21 ) (V12 , V22 , V32 ) o (W11 , W21 ) (W12 , W22 , W32 )

96

is a special set linear bitransformation of V into W. Now are proceed onto define the notion of special set linear bioperators on V. DEFINITION 2.2.9: Let V = V1 V2 = (V11 , V21 , ! ,Vn11 ) (V12 ,V22 , ! , Vn22 )

is a special set vector bispace over the set S. Let T = T1 T2 = (T11 , T21 ,! , Tn11 ) (T12 , T22 ,!, Tn22 ) : V1 V2 = (V11 ,V21 ,!,Vn11 )

(V12 ,V22 ,! ,Vn22 ) o V1 V2 such that Ti1 :Vi1 o Vi1 , 1 d id n1 and Ti 2 : Vi 2 o Vi 2 , 1d i d n2. If T1 T2 = T is a special set linear bitransformation of V into V then we call T = T1 T2 to be the special set linear bioperator on V; i.e., if in the definition of a special set linear bitransformation the domain space coincides with the range space then we call T to be a special set linear bioperator on V.

We illustrate this by a simple example.

Example 2.2.14: Let V = V1 V2 where V1 = (V11 , V21 , V31 , V41 )

and V2 = (V12 , V22 , V32 , V42 , V52 ) be a special set vector bispace over the set Zo = Z+ {0}. Here V11 = Zo × Zo × Zo × Zo,

V31

­° ª a1 ®« °¯ ¬ a 6

V21

½ °­§ a b · o° ®¨ ¸ a, b,c,d Z ¾ , ¯°© c d ¹ ¿°

a2 a7

a3 a8

a4 a9

½° a 5 º ª a1 º , « » a i Zo ;1d i d 10 ¾ , » a10 ¼ ¬a 2 ¼ °¿

and V41 = {all polynomials in the variable x with coefficients from Zo of degree less than or equal to 4}. Now

97

2 1

V

­§ a b c · ½ °¨ ¸ o° ®¨ 0 d e ¸ a, b, c, d, e, f Z ¾ , °¨ 0 0 f ¸ ° ¹ ¯© ¿ V22

{Zo × Zo × Zo × Zo × Zo},

V32 {all polynomial in x of degree less than or equal to 6 with coefficients from Zo}, ª a1 V = {(a1 a2 a3 a4 a5 a6), «« a 3 «¬ a 5

a2 º a 4 »» a i Zo , 1 < i < 6} a 6 »¼

2 4

and V52 = {4 × 4 matrices with entries from Zo}.

V= V1 V2 is a special set vector bispace over the set Zo. Define T = T1 T2: V o V by T1 T2 = (T11 , T21 , T31 , T41 ) (T12 , T22 , T32 ,T42 ,T52 ) : V = V1 V2 1 1 1 = (V1 , V2 , V3 , V41 ) (V12 , V22 , V32 , V42 , V52 ) o (V11 , V21 , V31 , V41 ) (V12 , V22 , V32 , V42 , V52 ) as

Ti1 :Vi1 o Vi1 , 1 d i d 4

and

Ti2 :Vi2 o Vi2 , 1 d i d 5.

T11 :V11 o V11 T11 (a b c d) = (b c d a),

T21 :V21 o V21 defined by §a b· T21 ¨ ¸ ©c d¹ T31 :V31 o V31

98

§a c· ¨ ¸, ©b d¹

defined by §a T31 ¨ 1 © a6 and

a2

a3

a4

a7

a8

a9

a5 · ¸ a10 ¹

ªa º T31 « 1 » ¬a 2 ¼

§ a6 ¨ © a1

a7

a8

a9

a2

a3

a4

a10 · ¸ a5 ¹

ªa 2 º «a » . ¬ 1¼

T41 :V41 o V41 defined by T41 (a0 + a1x + a2x2 + a3x3 + a4x4) = (a1x + a3 x3).

T1 = (T11 , T21 , T31 , T41 ) is a special set linear operator on V1 = ( V11 , V21 , V31 , V41 ) . Define T2 = (T12 ,T22 ,T32 ,T42 ,T52 ) from V2 o V2 = (V12 , V22 , V32 , V42 , V52 ) by T12 :V12 o V12 ; §a b c· ¨ ¸ T ¨0 d e¸ ¨0 0 f ¸ © ¹ 1 2

§a d e· ¨ ¸ ¨0 b c¸ , ¨0 0 f ¸ © ¹

T22 :V22 o V22 by

T22 (a b c d e) = (a c b e d), T32 :V32 o V32 is defined by T32 (a0 + a1x + a2x2 + a3x3 + a4 x4 + a5 x5 + a6x6) = a1x6 + a1x5 + a2x4 + a3x3 + a4x2 + a5x + a6.

T42 :V42 o V42 is given by ª a1 T (a1 a2 a3 a4 a5 a6) = «« a 3 «¬ a 5 2 4

and

99

a2 º a 4 »» a 6 »¼

ª a1 T «« a 3 «¬ a 5 2 4

Finally T52 :V52 o V52 § a11 a12 ¨ a a 22 T52 ¨ 21 ¨ a 31 a 32 ¨ © a 41 a 42

a2 º a 4 »» = (a1 a2 a3 a4 a5 a6). a 6 »¼

is defined as a13 a 14 · § a11 ¸ ¨ a 23 a 24 ¸ ¨ a12 = a 33 a 34 ¸ ¨ a13 ¸ ¨ a 43 a 44 ¹ © a14

a 21 a 22

a 31 a 32

a 23 a 24

a 33 a 34

a 41 · ¸ a 42 ¸ . a 43 ¸ ¸ a 44 ¹

Clearly T2 = (T12 ,T22 ,T32 ,T42 ,T52 ) is a special set linear operator on V2. Thus T1 T2 = (T11 ,T21 ,T31 ,T41 ) (T12 ,T22 ,T32 ,T42 ,T52 ) is a special set linear bioperator on V = V1 V2.

Now we can also define as in case of special set vector spaces the notion of special set pseudo linear bioperator on V = V1 V2.

DEFINITION 2.2.10: Let V = V1 V2 = (V11 ,V21 ,!,Vn11 ) (V12 ,V22 ,!,Vn22 )

be a special set vector bispace over a set S. Let T = T1 T2 = (T11 , T21 ,! , Tn11 ) (T12 , T22 ,! , Tn22 ) be a special set vector transformation such Ti1 :Vi1 o V j1 , (i z j); 1 d i, j d n1 for some i and j.

Similarly Ti 2 :Vi 2 o V j2 , i z j for some i and j; 1 d i, j d n2 we call this T = T1 T2 to be a special set pseudo linear bioperator on V. Clearly SHomS (V,V) = {HomS (Vi11 ,V j11 )! HomS (Vin11 ,V jn1 1 )}

{HomS (Vi22 ,V j22 ) … HomS (Vin22 ,V jn2 2 )} is only a special set vector bispace over the set S.

We illustrate this by an example.

100

Example 2.2.15: Let V = V1 Â&#x2030; V2 = {V11 , V21 , V31 , V41} Â&#x2030;{V12 , V22 , V32 } where °­§ a b ¡ °½

V11 Ž¨ ¸ a, b,c,d Â? Z Â&#x2030; {0}ž , ¯°Š c d š ¿° V21

{(Z+ Â&#x2030; {0}) Ă&#x2014; (Z+ Â&#x2030; {0}) Ă&#x2014; Z+ Â&#x2030; {0} Ă&#x2014; (Z+ Â&#x2030; {0})}, V31 = {(Z+ Â&#x2030; {0}) [x], all polynomials of degree less than or equal to 5}

and ­°§ a V41 Ž¨ 1 ¯°Š a 4

V12

­§ a °¨ °¨ 0 Ž¨ °¨ 0 °Š 0 ¯

a2 a5

a 3 ¡ ½° + ¸ ž such that ai Â? Z Â&#x2030; {0}; 1 < i < 6}. a 6 š ¿°

½ d¡ ° ¸ e f g¸ °

a, b,c,d,e,f ,g, h,i, k Â? Z Â&#x2030; {0}ž , ¸ 0 h i ° ¸ ° 0 0 kš Âż b c

V22 = {(Z+ Â&#x2030; {0}) [x]; all polynomials of degree less than or equal to 9} and

V32

­§ a1 °¨ °¨ a 4 Ž¨ °¨ a 7 °Š a10 ¯

a2 a5 a8 0

½ a3 ¡ ° ¸ a6 ¸ °

a i Â? Z Â&#x2030;{0};1d i d 9 ž ; a9 ¸ ° ¸ ° 0š Âż

thus V = V1 Â&#x2030; V2 = (V11 , V21 , V31 , V41 ) Â&#x2030; (V12 , V22 , V32 ) is a special set vector bispace over the set S = Z+ Â&#x2030; {0}. Now define a special set linear bitransformation T from V to V by T = T1 Â&#x2030; T2 = { T11 , T21 ,T31 ,T41} Â&#x2030; {T12 , T22 , T32 }; V = V1 Â&#x2030; V2 = (V11 , V21 , V31 , V41 ) Â&#x2030; (V12 , V22 , V32 ) o V = V1 Â&#x2030; V2 = (V11 , V21 , V31 , V41 ) Â&#x2030; (V12 , V22 , V32 )

101

as follows : T11 :V11 o V21 defined by §a b¡ T11 ¨ ¸ = (a, b, c, d), Šc dš T21 : V21 o V11 by §a b¡ T21 (a b c d) ¨ ¸, Šc dš T31 :V31 o V41 by ªa T31 (a 0 a1x a 2 x 2 a 3 x 3 a 4 x 4 a 5 x 5 ) =  0 a 3 1 1 1 and T4 :V4 o V3 by

ÂŞa T41 ÂŤ 1 ÂŹa 4

a2 a5

a1 a4

a2 º a 5 Ÿ

a3 º = a1x5 + a2x4 + a3x3 + a4x2 + a5x + a6. a 6 Ÿ

Thus T1 = (T11 ,T21 ,T31 ,T41 ): V1 o V1 is only a pseudo set linear operator on V. Now define T12 : V12 o V22 by §a b ¨ e 2 ¨0 T1 ¨0 0 ¨ Š0 0

c d¡ ¸ f g¸ = (a + bx + cx2 + dx3 h i¸ ¸ 0 jš + ex4 + fx5 + gx6 + hx7 + ix8 + jx9),

T22 : V22 o V32 by § a0 ¨ a T22 = (a0 + a1x + a2x2 + â&#x20AC;Ś + a9x9) = ¨ 3 ¨ a6 ¨ Š a9 and

102

a1 a4 a7 0

a2 ¡ ¸ a5 ¸ a8 ¸ ¸ 0š

T32 :V32 o V22 by § a1 a 2 a 3 · ¨ ¸ a a5 a6 ¸ T32 ¨ 4 = a1x9 + a2x8 + a3x7 + x4x6 ¨ a7 a 8 a9 ¸ ¨ ¸ © a10 0 0 ¹ + a5x6 + a5x5 + a6x4 + a7x3 + a8x2 + a9x + a10.

Clearly T2 = ( T12 ,T22 ,T32 ) : V2 = ( V12 , V22 , V32 ) o V2

(V12 , V22 , V32 )

is a pseudo set linear operator on V2.

Thus T = T1 T2 = (T11 ,T21 ,T31 ,T41 ) (T12 ,T22 ,T32 ) is a pseudo special set linear bioperator from V = V1 V2 into V = V1 V2.

2.3 Special Set Vector n-spaces

Now we proceed onto generalize the notion of special set vector bispaces to special set vector n-spaces; n > 3.

DEFINITION 2.3.1: Let V = (V1 V2 … Vn) where each Vi = (V1i ,V2i ,!,Vnii ) is a special set vector space over the same set

S; i = 1, 2, …, n and Vi z Vj; if i z j, i.e.; Vi’s are distinct, Vi Vj and Vj Vi, 1 d i, j d n. Then we call V = (V1 V2 …

Vn) = ( V11 ,V21 ,! ,Vn11 ) (V12 ,V22 ,! ,Vn22 ) … ( V1n ,V2n ,! ,Vnnn ) to be a special set vector n-space over the set S. If n = 2 then V = V1 V2 is the special set vector bispace that is why we assume nt 3; when n = 3 we get the special set vector trispace.

We illustrate this by some simple examples.

Example 2.3.1.: Let V = V1 V2 V3 V4 = (V11 , V21 , V31 ) (V12 , V22 , V32 , V42 )

(V13 , V23 , V33 , V43 ) (V14 , V24 , V34 )

103

be a special set vector 4-space over set S = Z+ {0}. Here V1 = (V11 , V21 , V31 ) where ­°§ a b · ½° V11 ®¨ ¸ a, b,c,d S¾ , ¯°© c d ¹ ¿°

V21

{S × S ×S}

and

V31 = {(a a a a a a) | a S} is a special set vector space over the set S. Now V2 = (V12 , V22 , V32 , V42 ) where V12 = (S × S × S × S × S) ­§ a1 °¨ ®¨ a 4 °¨ a ¯© 7

2 2

V

a2 a5 a8

½ a3· ° ¸ a 6 ¸ a i S,1d i d 9 ¾ , ° a 9 ¸¹ ¿

V32 = {(a a a a) | a S} and

V42

­ ª a1 °« °«a 3 ® °«a 5 ° «¬ a 7 ¯

½ a2 º ° » a4 » ° a i S;1d i d 8¾ ; a6 » ° » ° a8 ¼ ¿

V2 = (V12 , V22 , V32 , V42 ) is a special set vector space over the set S = Z+ {0}. Now V3 = (V13 , V23 , V33 , V43 ) where V13 = {4 × 4 upper triangular matrices with entries from S}, V23 = {(Zo {0}) [x] all polynomials is the variable x with coefficients from S of degree less than or equal to 4}, V33 = {7 ×7 diagonal matrices with entries from S} and ­§ a b c · ½ °¨ ° ¸ °¨ d e f ¸ ° 3 V4 ® a, b,c,d,e,f ,g, h,i, j, k,l S¾ . °¨¨ g h i ¸¸ ° °© j k l ¹ ° ¯ ¿

104

Thus V3 is a special set vector space over the set S. Now consider V4 = (V14 , V24 , V34 ) °­§ a b ¡ § a a a ¡ o V14 Ž¨ ¸,¨ ¸ a, b,c,d� Z c d a a a š Š š ¯°Š

°½ Z Â&#x2030; {0}ž , ¿°

½ ÂŞa Âş ° ÂŤa Âť ° ÂŤ Âť ° V24 = {(a a a a), ÂŤ a Âť a Â? Z Â&#x2030;{0} Sž ÂŤ Âť ° ÂŤa Âť ° ÂŤÂŹ a Ÿ °¿ and V34 = {all 5 Ă&#x2014; 5 lower triangular matrices with entries from S = Zo = Z+ Â&#x2030; {0}}.

V4 = (V14 , V24 , V34 ) is a special set vector space over the set S. Thus V = V1 Â&#x2030; V2 Â&#x2030; V3 Â&#x2030; V4 is a special set vector 4-space over the set S. We give another example of a special set n-vector space over a set S n t 3.

Example 2.3.2: Let V = V1 Â&#x2030; V2 Â&#x2030; V3 = (V11 , V21 , V31 , V41 , V51 ) Â&#x2030; (V12 , V22 , V32 , V42 ) Â&#x2030; (V13 , V23 , V33 ) be a special vector trispace

over the set S = {0 1}. V1 = (V11 , V21 , V31 , V41 , V51 ) where V11 = {all 3 Ă&#x2014; 3 matrices with entries from S}, V21 = {S u S u S u S u S u S}, V31 = {all polynomials of degree less than or equal to 6},

V41

­ ªa °  ° ªa a a º a , Ž  ° a a a Ÿ a  ° a ¯

105

½ aÂş ° Âť aÂť ° a Â? 3Z Â&#x2030;{0}ž Âť a ° Âť ° aÂź Âż

and V51 = {all 7 Ă&#x2014; 7 upper triangular matrices with entries from S}. V1 = (V11 , V21 , V31 , V41 , V51 ) is a special set vector space over the set S. V2 = (V12 , V22 , V32 , V42 ) where V12 {S Ă&#x2014; S Ă&#x2014; S Ă&#x2014; S}, V22 = {all 8 Ă&#x2014; 8 matrices with entries from S}, V32 = {(a a a a a), (a a), (a a a a a a a) | a Â? Z+ Â&#x2030; {0}} and

V42

­ ªa ° ° a Ž ° a ° a ¯

½ aº °  a  ªa a a a º °

,ÂŤ a Â? Z Â&#x2030;{0}ž . Âť a Âť ÂŹa a a a Âź ° Âť ° aÂź Âż

V2 = (V12 , V22 , V32 , V42 ) is again a special set vector space over S. Finally V3 = (V13 , V23 , V33 ) is such that V13 = {S Ă&#x2014; S}, ­°§ a b ¡ ½° V23 Ž¨ ¸ a, b,c,dÂ? Sž ¯°Š c d š ¿° and V33 = {all 5 Ă&#x2014; 5 lower triangular matrices}. We see V3 is again a special set vector space over the set S. Thus V = V1 Â&#x2030; V2 Â&#x2030; V3 is a special set trivector space over the set S = {0, 1}.

Now we proceed onto define the notion of special set nvector subspace.

DEFINITION 2.3.2. Let V = (V1 Â&#x2030; V2 Â&#x2030; V3 Â&#x2030;â&#x20AC;ŚÂ&#x2030; Vn) = ( V11 ,V21 ,!,Vn11 )Â&#x2030; (V12 ,V22 ,...,Vn22 ) Â&#x2030;â&#x20AC;ŚÂ&#x2030; ( V1n ,V2n ,...,Vnnn )

be a special set n-vector space over the set S. Suppose W = W1 Â&#x2030; W2 Â&#x2030;â&#x20AC;ŚÂ&#x2030; Wn = ( W11 ,W21 ,! ,Wn11 )Â&#x2030; (W12 ,W22 ,...,Wn22 ) Â&#x2030;â&#x20AC;ŚÂ&#x2030; (W1n ,W2n ,! ,Wnnn )

Â&#x17D;

V1 Â&#x2030; V2 Â&#x2030;â&#x20AC;ŚÂ&#x2030; Vn be such that (W1i ,W2i ,! ,Wnii ) Â&#x17D; (V1i ,V2i ,...,Vnii ),

106

i.e.; Wt i Vt i is a set vector subspace of Vt i , 1d t d ni so that Wi = ( W1i ,W2i ,...,Wnii ) is a special set vector subspace of Vi = ( V1i ,! ,Vnii ); true for i = 1, 2, …, n then we define W = W1 …

Wn to be a special set vector n-subspace of V over the set S.

We will illustrate this by some simple examples.

Example 2.3.3. Let V = V1 V2 V3 V4 = ( V11 , V21 , V31 ) (V12 , V22 , V32 ) (V13 , V23 , V33 , V43 ) (V14 , V24 , V34 , V44 )

be a special set 4-vector space over the set S = {0, 1}. Here V1 = (V11 , V21 , V31 ) is such that V11 = (S × S × S ×S), ­ ªa a º °« » ° a a » ªa a a a a º V21 ® « ,« a Z+ {0}} » « » ° a a ¬a a a a a ¼ ° «¬a a »¼ ¯ and V13 = {all 5 × 5 upper triangular matrices with entries from Z+ {0}}; V1 is a special set vector space over the set S = {0, 1}. V2 = (V12 , V22 , V32 ) where

2 1

V

­§ a b c · °¨ ¸ ®¨ d e f ¸ a, b, …, i Z2 = {0, 1}}, °¨ g h i ¸ ¹ ¯© V22 {Zo u Zo u Zo | Zo = Z+ {0}}

and

V32

°­ ª a1 ®« °¯ ¬a 4

a2 a5

ª a1 a 3 º «« a 3 , a 6 »¼ « a 5 « ¬a 7

107

a2 º a 4 »» ai Z2 = {0, 1}} a6 » » a8 ¼

is also a special set vector space over the set S = {0, 1}.

V3 (V13 , V23 , V33 , V43 ) where V13 = {Zo × Zo × Zo × Zo | Zo = Z+ {0}}, ­§ a1 a 2 a 3 · °¨ ¸ + V ®¨ a 4 a 5 a 6 ¸ ai Z {0}}, °¨ a a a ¸ 8 9¹ ¯© 7 °­ ª a a a a º , (a a a a a a) | a Zo = Z+ {0}} ®« » °¯ ¬ a a a a ¼ 3 2

V33 and

V43

­ ªa º °« » ° «a » ªa a a a a º ° « » + ® « a » , « a a a a a » a Z {0}} « » ° a «a a a a a » ¼ °« » °¯ «¬ a »¼

is again a special set vector space over the set S = {0, 1}.

Finally V4 = {V14 , V24 , V34 , V44 } is such that V14 = {S × S × S × S × S};

V

­§ a b c · °¨ ¸ + ®¨ 0 d e ¸ a, b, c, d, e, f Z {0}}, °¨ 0 0 f ¸ ¹ ¯©

V34

ªa «a °­ ªa a a a º « ®« » , «a ¯° ¬a a a a ¼ «a « «¬a

4 2

108

aº a »» a » a Z+ {0}} » a» a »¼

and ªa º V = {(a a a a a a), «« a »» a Z+ {0}} «¬ a »¼ is a special set vector space over the set S = {0,1}. Thus V = V1 V2 V3 V4 is a special set vector 4-space over the set S = {0,1}. Take 4 4

W = =

W1 W2 W3 W4 (W11 , W21 , W31 ) (W12 , W22 , W32 ) ( W13 , W23 , W33 , W43 ) (W14 , W24 , W34 , W44 )

(V11 , V21 , V31 ) (V12 , V22 , V32 ) ( V13 , V23 , V33 , V43 ) (V14 , V24 , V34 , V44 )

where W11 V11 and W11 = S × S × {0} × {0} V11 , ­ ªa °« ° a 1 W2 = ® « ° «a ° «¬ a ¯

aº a »» a Z+ {0}} V21 » a » a¼

and W31 = {5 × 5 upper triangular matrices with entries from 3Z+ {0}} V31 .

Thus (W11 , W21 , W31 ) (V11 , V21 , V31 ) is a special set vector subspace of V1. Now ­§ a a a · °¨ ¸ 2 W1 ®¨ a a a ¸ a {0, 1} V12 , °¨ a a a ¸ ¹ ¯© 2 o W2 = {Z × Zo × {0}} V22 and

109

W32

­ ª a1 °« °«a 3 ® °«a 5 ° «¬ a 7 ¯

a2 º a 4 »» ai Z2 = {0,1} 1d i d 8} V32 . » a6 » a8 ¼

Thus W2 = (W12 , W22 , W32 ) ( V12 , V22 , V32 ) V2 is a special set vector subspace of V2 over 5 = {0, 1}. Take W13

3 2

W

{Zo × {0} × Zo {0}} V13 ,

­§ a a a · °¨ ¸ + 3 ®¨ a a a ¸ a Z {0}} V2 , °¨ a a a ¸ ¹ ¯©

W33 = {(a a a a a a) | a Z+ {0}} V33

and

W43

­ ªa º °« » ° «a » ° + 3 ® « a » a Z {0}} V4 ° «a » °« » °¯ «¬ a »¼

so W3 = ( W13 , W23 , W33 , W43 ) ( V13 , V23 , V33 , V43 ) = V3 is a special set vector subspace of V3 over the set S = {0, 1}. Finally W14 = {S × S × S × S u {0}} V14 is a set vector subspace of V14 over S = {0, 1}. ­§ a a a · °¨ ¸ 4 W2 ®¨ 0 a a ¸ a Z+ {0}} V24 °¨ 0 0 a ¸ ¹ ¯© over the set S = {0,1}.

110

­° ª a a a a º a Z+ {0}} V34 W34 ® « » °¯ ¬ a a a a ¼ is a set vector subspace of V34 over the set S = {0, 1} and ­ ªa º °« » + 4 W ® «a » a Z {0}} V4 ° «a » ¯¬ ¼ is a set vector subspace of V44 over the set S = {0,1}. Thus 4 4

W4 = ( W14 , W24 , W34 , W44 ) (V14 , V24 , V34 , V44 ) =V4 is a special set vector subspace of V4 over the set S = {0,1}.

Hence W = W1 W2 W3 W4 = ( W11 , W21 , W31 ) ( W12 , W22 , W32 ) ( W13 , W23 , W33 , W43 ) (W14 , W24 , W34 , W44 )

(V11 , V21 , V31 ) ( V12 , V22 , V32 ) ( V13 , V23 , V33 , V43 )

=

( V14 , V24 , V34 , V44 ) V1 V2 V3 V4 = V

is a special set vector 4-subspace of V over set S = {0,1}.

Example 2.3.4: Let V = V1 V2 V3 = { V11 , V21 , V31 , V41} {V12 , V22 , V32 , V42 , V52 } {V13 , V23 , V33 , V43 } be a special set vector 3-space over the set S = Z+ {0}. Here V1 = { V11 , V21 , V31 , V41} is such that V11 = {S×S×S}, V21 = {2 × 2 matrices with entries from S}, ­ ªa º ° « » ° a a a a a «a » º °ª V31 = ® « » , «a » a S} a a a a a ¼ « » ° «a » ° «¬a »¼ °¯

111

and ­ ªa a º °« » ° a a» V41 ® « , [a a a a a a] | a S}. ° «a a » ° «¬ a a »¼ ¯ V1 is a special set vector space over the set S. Take W1 = (W11 , W21 , W31 , W41 ) ( V11 , V21 , V31 , V41 ) where W11 = {S×S×{0}} V11 , W21

W31

­°§ a a · 1 ®¨ ¸ a S} V2 , °¯© a a ¹

­° ªa a a a a º 1 ®« » a S} V3 , and a a a a a ¼ ¯° W41 = {[a a a a a a] | a S} V41 .

Thus W1 is a special set vector subspace of V1 over the set S. Now V2 (V12 , V22 , V32 , V42 , V52 ) where V12 = {S × S × S × S × S}, ­°§ a a a a · V22 ®¨ ¸ , (a a a a a) | a S}, ¯°© a a a a ¹ V32 = {3 × 3 matrices with entries from S}, V42 = {s[x] polynomials in x with coefficients from S of degree less than or equal to 5} and V52 = {4 × 4 upper triangular matrices with entries from S}. Take W2 = (W12 , W22 , W32 , W42 , W52 )} ( V12 , V22 , V32 , V42 , V52 ) where W12 = S × S × S × {0} × {0} V12 ,

­°§ a a a a · + 2 ®¨ ¸ a Z {0}} V2 , a a a a ¹ °¯© 2 W3 = {3 × 3 matrices with entries from 5Z+ {0}} V32 , W22

112

W42 = {All polynomials of degree less than or equal to 3 with coefficients from S} V42 and W52 = {4 × 4 upper triangular matrices with entries from 3Z+ {0}} V52 .

Thus W2 = (W12 , W22 , W32 , W42 , W52 ) (V12 , V22 , V32 , V42 , V52 ) = V2 is a special set vector subspace of V2 over S. Let V3 = (V13 , V23 , V33 , V43 ) where V13 = {all lower triangular 5 × 5 matrices with entries from S = Z+ {0}}, V23 = {[a a a a a], [a a], (a a a) | a S},

V33

­ ªa °« ° a ®« ° «a ° «¬ a ¯

aº a »» , a» » a¼

ªa a a a a º « a a a a a » a S} and ¼

V43 = {All polynomials in the variable x with coefficients from S of degree less than or equal to 7}.

Clearly V3 = ( V13 , V23 , V33 , V43 ) is a special set vector space over the set S = Z+ {0}. Take W3 = ( W13 , W23 , W33 , W43 ) where W13 = {all lower triangular 5 × 5 matrices with entries from 3Z+ {0}} V13 , W23 ={[a a a a], [a a] | a S} V23 ,

113

W33

­ ªa °« ° «a ® ° «a ° «¬ a ¯

aº a »» a 3Z+ {0}} V33 » a » a¼

and W43 = {all polynomials in the variable x with

coefficients from x of degree less than or equal to 5} V43 .

W3 = ( W13 , W23 , W33 , W43 ) V3 = ( V13 , V23 , V33 , V43 ) is a special set vector subspace of V3 over the set S. Thus W = W1 W2 W3 V1 V2 V3 is a special set 3-vector subspace of V over the set S. Now we proceed on to define the notion of special set nlinear algebra or special set linear n-algebra over a set S. DEFINITION 2.3.3: Let V = (V1 V2 … Vn) 1 1 1 = ( V1 ,V2 ,! ,Vn1 ) (V12 ,V22 ,! ,Vn22 ) … (V1n ,V2n ,! ,Vnnn )

be a special set n vector space over the set S. If each Vi = ( V1i ,V2i ,! ,Vnii ) is a special set linear algebra over the set S for each i, i = 1, 2, …, n then we call V = (V1 V2 … Vn) to be a special set linear n-algebra over the set S.

We illustrate this by the following example.

Example 2.3.5: Let V = (V1 V2 V3 V4) = {V11 , V21 , V31 , V41 ) { V12 , V22 , V32 }

{V13 , V23 , V33 , V43 , V53 } {V14 , V24 , V34 } be a special set linear 4-algebra over the set S = Z+ {0}. Here each Vi is a special set linear algebra over S, 1 d i d 4. Now V1 (V11 , V21 , V31 , V41 ) is such that

114

V11 = {All 3 × 3 matrices with entries in S}, V21

{(a a a a a) | a S},

­ ªa a º °« » ª a a a º a S} ® «a a » , « » ° «a a » ¬a a a ¼ ¼ ¯¬

1 3

V and

V41 = {S[x] all polynomials in the variable x with coefficients form S of degree less than or equal to 4}.

Clearly V1 = (V11 , V21 , V31 , V41 ) is special set linear algebra over the set S. Now V2 = (V12 , V22 , V32 ) where V12 = {all 4 × 4 upper triangular matrices with entries from the set S},

V22

­ ªa a º ° ªa a a a º « » ®« » , «a a » a S} a a a a ¼ « °¬a a »¼ ¯

and ­° ªa b º ®« » a, b, c, d S}. ¯° ¬ c d ¼ V2 is a special set linear algebra over the set S. V32

V3 = (V13 , V23 , V33 , V43 , V53 ) where V13 = S × S × S × S, §a· ¨ ¸ a V23 = {(a a a a a a), ¨ ¸ a S}, ¨a¸ ¨ ¸ ©a¹ V33 = {all 4 × 4 matrices with entries from S},

V43 = {all diagonal 5 × 5 matrices with entries from S} and

115

3 5

V

§a a a· ­°§ a a · ¨ ¸ ®¨ ¸ , ¨ a a a ¸ a S}. a a ¹ ¨ ¯°© ¸ ©a a a¹

V3 = (V13 , V23 , V33 , V43 , V53 ) is a special set linear algebra over the set S. Finally V4 = (V14 , V24 , V34 ) is such that V14 = S × S × S, ªa º V = {(a a a a a a), «« a »» «¬ a »¼ 4 2

a S}

and V34 = {all 7 × 7 matrices with entries from S}.

V4 = ( V14 , V24 , V34 ) is a special set linear algebra over the set S. Thus V = V1 V2 V3 V4 is a special set linear 4-algebra over the set S. Example 2.3.6: Let V = V1 V2 V3 be a special set linear trialgebra over the set S = {0, 1}. Here V1 = (V11 , V21 , V31 , V41 , V51 ),

V2 = (V12 , V22 , V32 ) and V3 = ( V13 , V23 ) where V1, V2 and V3 are special set linear algebras over the set S = {0, 1}. V1 = ( V11 , V21 , V31 , V41 , V51 ) is given by V11 V21

V31

S × S × S × S.

°­§ a b · ®¨ ¸ °¯© c a ¹

a, b, c, d Z2},

­ ªa ° « ° ªa a a a a º ª a a a º «a ®« », « », ° ¬a a a a a ¼ ¬ a a a ¼ «a « ° ¬a ¯

116

aº a »» a Z+ {0}}, » a » a¼

V41

{All polynomials with coefficients from S of degree less

than or equal to 4} and V51 = {All 3 Ă&#x2014; 5 matrices with elements from S}. V1 = ( V11 , V12 , V31 , V41 , V51 ) is a special set linear algebra over the set S. We take V2 = ( V12 , V22 , V32 ) where V12 = (S u S u S u S),

V22

­°§ a a ¡ ½°

Ž¨ ¸ a Â? Z Â&#x2030; {0}ž ¯°Š a a š ¿°

and V32 = {(a a a a a), (a a), (a a a) | a Â? Z+ Â&#x2030; {0}};

V2 = V12 , V22 , V32 is a special set linear algebra over the set S = {0, 1}. Now V3 = V13 , V23 where V13 = S u S u S u S u S and

V23 = {(a a a a a), (a a a), (a a a a a a) | a Â? Z+ Â&#x2030; {0}}. Thus V3

= V13 , V23 is a special set linear algebra over S = {0, 1}. Hence V = (V1 Â&#x2030; V2 Â&#x2030; V3) is a special set linear trialgebra over the set S = {0, 1}.

Now we proceed onto define the notion of special set linear n-subalgebra.

DEFINITION 2.3.4: Let V = (V1 Â&#x2030; V2 Â&#x2030; â&#x20AC;Ś Â&#x2030; Vn) = V11 , V21,...,Vn11 Â&#x2030; V12 , V22 ,...,Vn22 Â&#x2030;â&#x20AC;ŚÂ&#x2030; V1n , V2n ,...,Vnnn

be a special set linear n-algebra over the set S. Suppose W = (W1 Â&#x2030; W2 Â&#x2030; â&#x20AC;Ś Â&#x2030; Wn) = W11 , W 21,...,Wn11 Â&#x2030; W12 , W 22,...,Wn22 Â&#x2030;â&#x20AC;ŚÂ&#x2030; W1n , W 2n ,...,Wnnn

Â&#x17D; (V1 Â&#x2030; V2 Â&#x2030; Â&#x2030; Vn) = V and if each Wi = W1i , W 2i ,...,Wni Â&#x17D; Vi is a special set i

linear subalgebra of Vi over the set S, for i = 1, 2, â&#x20AC;Ś, n, then we call W = W1 Â&#x2030; W2 Â&#x2030; â&#x20AC;Ś Â&#x2030; Wn to be a special set linear nsubalgebra of V over the set S.

117

Now in case if in W = W1 … Wn some of the Wi’s are not special set linear subalgebra of Vi’s but only a special set vector subspace of Vi’s, 1 d i d n then we call W = (W1 W2 … Wn) V = (V1 V2 … Vn) to be a pseudo special set linear n-subalgebra of V over the set S. If in W = (W1 W2 … Wn) all the Wi’s are only special set vector subspaces of Vi’s for each i = 1, 2, …, n then we call W = W1 W2 …

Wn to be a pseudo special set vector subspace of V over the set S. Now we illustrate these concepts by some simple examples.

Example 2.3.7: Let V = (V1 V2 V3 V4

V11 , V21, V31 ) ( V12 , V22 , V32 ) V13 , V23 )

( V14 , V24 , V34 , V44 ) be a special set linear 4-algebra over the set S = Z+ {0}. Here V1 = ( V11 , V21, V31 ) where ­°§ a b · ½° V11 = ®¨ ¸ a, b,c,d S¾ , °¯© c d ¹ ¿° 1 V2 = S u S u S u S u S,

V31 = {(a a a a a a), (a a a) | a S}.

Take W1 = ( W11 , W21, W31 ) V11 , V21, V31 ) where ­°§ a a · ½° 1 W11 = ®¨ ¸ a S¾ V1 , a a ¹ ¯°© ¿°

W21 = {(a a a a a) | a S} V21 and W31 = {(a a a a a a), (a a a) | a 5Z+ {0}} V31 .

118

Clearly W1 = ( W11 , W21, W31 ) Â&#x17D; V11 , V21, V31 ) is a special set linear subalgebra of V1 over the set S = Z+ Â&#x2030; {0}.

Consider V2 = ( V12 , V22 , V32 ) where V12 = {all 3 u 3 upper triangular matrices with entries from S}, ­ §a ° ¨ °§ a a a ¡ ¨ a 2 V2 = Ž¨ ¸, °Š a a a š ¨¨ a ° Ša ¯

½ a¡ ° ¸ a¸ ° a � Sž a¸ ° ¸ ° aš ¿

and V32 = {S[x] all polynomials of degree less than or equal to 4 with coefficients from S}. Take in V2 a special subset W2 = ( W12 , W22 , W32 ) Â&#x17D; ( V12 , V22 , V32 ) = V2 where W12 = {all upper triangular 3 u 3 matrices with entries from 3Z+ Â&#x2030; {0}} Â&#x17D; V12 , ­ §a ° ¨ °§ a a a ¡ ¨ a 2 W2 = Ž¨ ¸, °Š a a a š ¨¨ a ° Ša ÂŻ

½ a¡ ° ¸ a¸ °

a Â? 5S Â&#x2030; {0}ž Â&#x17D; V22 ¸ a ° ¸ ° aš Âż

and W32 = {S[x] all polynomials of degree less than or equal to two with coefficients from S}. W2 = ( W12 , W22 , W32 ) Â&#x17D; V2 = ( V12 , V22 , V32 ) is a special set sublinear algebra of V2. Set V3 = ( V13 , V23 ) as V13 = {set of all 5 u 5 matrices with entries from S} and V23 = {(a a a),(a a)| aÂ? S}. In V3 take W3 = ( W13 , W23 ) where W13 = {set of all 5 u 5 matrices with entries from the set 5Z+ Â&#x2030;

{0} Â&#x17D; V13 and W23 = {(a a a),(a a)| a Â? 7Z+ Â&#x2030; {0}} Â&#x17D; V23 . Clearly

W3 = W13 , W23 is a special set linear subalgebra of V3 over the

set S. Finally assume V4 = V14 , V24 , V34 , V44 as V14 = {S u S u S u S u S u S},

119

­§ a ¡ ½ °¨ ¸ § a ¡ ° ° a ¨ ¸ §a ¡ ° V24 = Ž¨ ¸ , ¨ a ¸ , ¨ ¸ a � Sž , ¨ ¸ °¨ a ¸ ¨ a ¸ Š a š ° °Š a š Š š ° ¯ ¿ V34 = {All upper triangular 2 u 2 matrices with entries S} and

V44 = {(a a a a a), (a a a) | a Â? S} where

W4 = W14 , W24 , W34 , W44 Â&#x17D; V14 , V24 , V34 , V44 = V4

as a special set linear subalgebra of V4. Thus W = =

(W1 Â&#x2030; W2 Â&#x2030; W3 Â&#x2030; W4) W11 , W21, W31 Â&#x2030; W12 , W22 , W32 Â&#x2030;

W , W Â&#x2030; W , W , W , W

3 1

3 2

4 1

4 2

4 3

4 4

Â&#x17D; (V1 Â&#x2030; V2 Â&#x2030; V3 Â&#x2030; V4) is a special set 4-linear subalgebra of V over S. Example 2.3.8: Let V = (V1 Â&#x2030; V2 Â&#x2030; V3 be a special set linear trialgebra over the set S = {0,1} where V1 = ( V11 , V21, V31 , V41 ), V2

= ( V12 , V22 ) and V3 = (V13 , V23 , V33 ) with ­°§ a b ¡ ½°

V11 = Ž¨ ¸ a, b,c,d Â? Z Â&#x2030; {0}ž , ¯°Š c d š ¿° V21 = {(a a a a), (a a a)| a Â? Z+ Â&#x2030; {0}}, ­ §a ° ¨ °§ a a a a a ¡ ¨ a V31 = Ž¨ , ¸ °Š a a a a a š ¨¨ a ° Ša ÂŻ and

120

½ a¡ ° ¸ a¸ ° a Â? Z Â&#x2030; {0}ž a¸ ° ¸ ° aš Âż

V41 = {all polynomials in the variable x with coefficients from Z+ Â&#x2030; {0} of degree less than or equal to 5}.

Take W1 = W11 , W21, W31 , W41

where ­°§ a a ¡ ½°

1 W11 = Ž¨ ¸ a Â? Z Â&#x2030; {0}ž Â&#x17D; V1 °¯Š a a š ¿° is a set linear subalgebra of V11 , W21 = {(a a a)| a Â? Z+ Â&#x2030; {0}} Â&#x17D; V21 is a set linear subvector space of V21 . ½° °­§ a a a a a ¡

1 W31 = Ž¨ ¸ a Â? Z Â&#x2030; {0}ž Â&#x17D; V3 a a a a a š ¯°Š ¿° is a set linear subvector space of V31 and W41 = {all polynomials in the variable x of degree less than or equal to 3} Â&#x17D; V41 is a set

linear subalgebra of V41 . Thus W1 = W11 , W21, W31 , W41 Â&#x17D; V1 is a special set linear subalgebra of V1 over the set S = {0, 1}. Now consider V2 = V12 , V22 where

­§ a a a ¡ ½ °¨ ° ¸

V = Ž¨ a a a ¸ a Â? Z Â&#x2030; {0}ž °¨ a a a ¸ ° š ¯Š Âż 2 1

and

­ §a °§ a ¡ ¨ °¨ a ¸ ¨ a ° 2 V2 = Ž¨ ¸ , ¨ a °¨¨ a ¸¸ ¨ a °Š a š ¨ ¨ °¯ Ša

½ a¡ ° ¸ a¸ ° °

¸ a a Â? Z Â&#x2030; {0}ž . ¸ ° a¸ ° a š¸ ¿°

Take a subset W2 = W12 , W22 of V2 as

121

­§ a a a ¡ ½ °¨ ° ¸

W = Ž¨ a a a ¸ a Â? 3Z Â&#x2030; {0}ž Â&#x17D; V12 °¨ a a a ¸ ° š ¯Š Âż 2 1

is set linear subalgebra of V12 and

­§ a ¡ ½ °¨ ¸ ° °¨ a ¸ ° 2

W2 = ÂŽ a Â? 5Z Â&#x2030; {0}ž °¨¨ a ¸¸ ° °Š a š ° ÂŻ Âż is a set vector subspace of V22 . Thus W2 = W12 , W22 Â&#x17D; V2 is a special set linear subalgebra of V2 over the set S = {0, 1}.

Define V3 = V13 , V23 , V33 where V13 = {all 4u4 upper triangular matrices with entries from Z+ Â&#x2030; {0}}, V23 = Zo u Zo u Zo | Zo = Z+ Â&#x2030; {0}} and

­§ a a ¡ ½ °¨ ¸ §a a a a a¡ o° V = Ž¨ a a ¸ , ¨ ¸ a�Z ž . °¨ a a ¸ Š a a a a a š ° š ¯Š ¿ 3 3

Take W3 = W13 , W23 , W33 Â&#x17D; V3 = V13 , V23 , V33 such that

­§ a °¨ ° 0 3 W1 = Ž¨ °¨¨ 0 °Š 0 ¯

½ a a a¡ ° ¸ a a a¸ °

a Â? Z Â&#x2030; {0}ž Â&#x17D; V13 0 a a¸ ° ¸ ° 0 0 aš Âż

122

is a set linear subalgebra of V13 , W23 = {Zo u Zo u {0}} Â&#x17D; V23 is again a set linear subalgebra of V23 and

­§ a a ¡ ½ °¨ ¸ o° W = Ž¨ a a ¸ a � Z ž °¨ a a ¸ ° š ¯Š ¿ 3 3

is a set vector subspace of V33 . Hence W3 =

W , W , W

3 1

3 2

3 3

Â&#x17D; V3 is a special set linear subalgebra of V3.

Thus W = (W1 Â&#x2030; W2 Â&#x2030; W3) Â&#x17D; (V1 Â&#x2030; V2 Â&#x2030; V3) = V is a special set linear sub trialgebra of V over the set S = {0, 1}.

Now we proceed onto illustrate a pseudo special set vector n subspace of V over the set S. Example 2.3.9: Let V = (V1 Â&#x2030; V2 Â&#x2030; V3 Â&#x2030; V4) where V1 = ( V11 , V21 ),

V2 = V12 , V22 , V32 ,

V3 = V13 , V23 and V4 = V14 , V24 , V34 , V44

be a special set linear 4-algebra over the set S = Z+ Â&#x2030; {0}.

Take ­°§ a b ¡ ½°

V11 = Ž¨ ¸ a, b,c,d Â? Z Â&#x2030; {0}ž ¯°Š c d š ¿° and V21 = {(a a a a), (a a), (a a a a) such that a Â? S}.

Take ­°§ 2a 2a ¡ § 3a 3a ¡ § 7a 7a ¡ ½°

1 W11 = Ž¨ ¸,¨ ¸,¨ ¸ a Â? Z Â&#x2030; {0}ž Â&#x17D; V1 , 2a 2a 3a 3a 7a 7a š Š š Š š ¯°Š ¿° W11 is only a pseudo set vector subspace of V11 . W21 = {(a a a a), (a a)| a Â? S} Â&#x17D; V21 is again only a pseudo set vector subspace of

123

V21 . Hence W1 =

W , W

1 1

1 2

is a pseudo special set vector

subspace of V over the set S. Consider V2 = V12 , V22 , V32 where V12 = {all 4 u 5 matrices with entries in S}, V22 = {2 u 7 matrices with entries in S} and V32 = {(a a a a a), (a a) | a Â? S}. Now take W12 = {all 4 u 5 matrices with entries from 2Z+ and all 4 u 5 matrices with entries from 3Z+ Â&#x2030; {0}} Â&#x17D; V12 is a pseudo set vector subspace

of V12 . W22 = {2 u 7 matrices with entries from 5Z+ Â&#x2030; {0} and 2 u 7 matrices with entries from 7Z+ Â&#x2030; {0}} Â&#x17D; V22 is only a pseudo set vector subspace of V22 over S = Z+ Â&#x2030; {0}. W32 = {(a a a a a), (a a)| a Â? 3Z+ Â&#x2030; {0}} Â&#x17D; V32 is only a set vector

subspace of V32 . Thus W2 = W12 , W22 , W32 is a pseudo special set vector subspace of V2 over the set S = Z+ Â&#x2030; {0}. We take V3 = V13 , V23 where V13 = {set of all 3 u 5 matrices with entries from Z+ Â&#x2030; {0}} and V23 = {(a a a a a), (a a a), (a a), (a a a a)| a

Â? Z+ Â&#x2030; {0}}. Consider W13 = {set of all 3 u 5 matrices with entries from the set 5Z+ Â&#x2030; {0} and 3 u 5 matrices with entries form the set 3Z+ Â&#x2030; {0}} Â&#x17D; V13 . Take W23 = {(a a), (a a a a)| a Â? Z+ Â&#x2030; {0}} Â&#x17D; V23 ; Clearly W23 is only a set vector subspace of W23 . Take W3 = W13 , W23 is only a pseudo special set vector

subspace of V3.

Finally consider V4 = V14 , V24 , V34 , V44 where V14 = {2u4 matrices with entries from Z+ Â&#x2030; {0}},

­§ a a ¡ § a a a a a a ¡ ½ °¨ ° ¸ ¨ ¸

V = Ž¨ a a ¸ , ¨ a a a a a a ¸ a Â? Z Â&#x2030; {0}ž , °¨ a a ¸ ¨ a a a a a a ¸ ° š Š š ¯Š Âż 4 2

124

V34 = {set of all polynomial in the variable x with coefficients from Z+ Â&#x2030; {0} of degree less than or equal to 9} and V44 = {6u6 upper triangular matrices with entries from Z+ Â&#x2030; {0}}. Take W4 = W14 , W24 , W34 , W44 Â&#x17D; V14 , V24 , V34 , V44 where W14 = {2 u 4 matrices with entries from 7Z+ Â&#x2030; {0} and 2 u 4 matrices with entries from 11Z+ Â&#x2030; {0}} Â&#x17D; V14 is only a pseudo set vector subspace of V14 over the set Z+ Â&#x2030; {0},

­§ a a ¡ § a a a a a a ¡ ½ °¨ ° ¸ ¨ ¸

W = Ž¨ a a ¸ , ¨ a a a a a a ¸ a Â? 3Z Â&#x2030; {0}ž Â&#x17D; V24 , °¨ a a ¸ ¨ a a a a a a ¸ ° š Š š ¯Š Âż 4 2

W24 is only a subspace of V24 . Take W34 = {set of all polynomials n (x6 + x5 + 2x4 + 3x3 + 5x +1); n Â? Z+ Â&#x2030; {0} and m(3x9 + 5x8 + 4x7 + 3x2 â&#x20AC;&#x201C; 7) such that m Â? 7Z+ Â&#x2030; {0}} Â&#x17D; V34 ;

clearly W34 is a pseudo set vector subspace of V34 over the set Z+ Â&#x2030; {0}. Hence W4 = W14 , W24 , W34 , W44 is a pseudo special set vector subspace of V4 over the set Z+ Â&#x2030; {0}. Now W = (W1 Â&#x2030; W2 Â&#x2030; W3 Â&#x2030; W4) Â&#x17D; (V1 Â&#x2030; V2 Â&#x2030; V3 Â&#x2030; V4) = V is a pseudo special set vector 4-subspace of V defined over the set Z+ Â&#x2030; {0}.

Now we proceed onto define the new notion of pseudo special set linear n subalgebra of V over the set S. Example 2.3.10: Let V = (V1 Â&#x2030; V2 Â&#x2030; V3 Â&#x2030; V4) where V1 = V11 , V21, V31 ,

V2 = V12 , V22 ,

V3 = V13 , V23 , V33

and

V4 = V14 , V24 , V34 , V44

125

be a special set linear 4-algebra over the set S = {0, 1}. Choose in V1 = V11 , V21, V31 ; V11 = {2 u 2 matrices with entries from Zo = Z+ {0}}, V21 = {Zo u Zo u Zo u Zo }, V31 = {(a a a a a a), (a a a) / a Zo} so that V1 is a special set linear algebra over the set S = {0,1}. Let V2 = V12 , V22 such that V12 = {all 3 u matrices with entries from Zo} and V22 = {set of all 2 u 5 matrices with entries from Zo and the set of all 4 u 3 matrices with entries from Zo} so that V2 is also a special set linear algebra over S = {0, 1}. V3 = ( V13 , V23 , V33 ) is such that V13 = {4 u 4 upper triangular matrices with entries from Zo}, V23 = {(a a a a), (a a), (a a a a a) | a Zo}

and V33 = {Zo[x] all polynomials in the variable x of degree less than or equal to 6 with coefficients from Zo}.

Thus V3 = V13 , V23 , V33 is a special set linear algebra over the set S = {0, 1}. Now take V4 = V14 , V24 , V34 , V44 where V14 = {Zo

u Zo}, V24 = {upper triangular 2 u 2 matrices with entries from Zo}, V34 = {2 u 3 matrices with entries from 3Z+ {0} and 2 u 3 matrices with entries from 5Z+ {0}} and

­ ªa ° « ªa º «a ° ° V44 = ® a a a a a , ««a »» , «a « ° ¬«a ¼» «a ° «¬a °¯

½ aº ° » a» ° ° » a a Z¾ . » ° a» ° a »¼ °¿

V4 = V14 , V24 , V34 , V44 is a special set linear algebra over the set S = {0, 1}.

126

Take W = (W1 W2 W3 W4) with W1 = W11 , W21, W31

V11 , V21, V31 such that

½ °­§ a a · o° 1 W11 = ®¨ ¸ a Z ¾ V1 , a a ¹ ¯°© ¿° W21 = {Zo u Zo u ^ ` u ^ `} V21 , W31 = {(a a a a a a), (a a a)|

a Zo} V31 . W1 is a special set linear subalgebra of V1 over S = {0, 1}. W2 = W12 , W22 V2 where W12 = {3 u matrices

with entries from 3Zo; 3 u matrices with entries from 5Zo} V12 , W22 = {set of all 2 u 5 matrices with entries from 3Zo and the set of all 4 u 3 matrices with entries from 5Zo} V22 . Thus W2 = W12 , W22 is only a pseudo special vector subspace of V2

over the set S = {0, 1}. W3 = W13 , W23 , W33 V3 where W13 =

{4 u 4 upper triangular matrices with entries from 3Zo and 4 u 4 upper triangular matrices with entries from 5Zo} V13 , W23 = {(a a), (a a a a) | a Zo} V23 and W33 = {Zo[x] all polynomials of degree less than or equal to 3 and all polynomials of the form n (1 + x + x2 + x3 + x4 + x5 + x6) such that n Zo}} V33 . Clearly

W3 = W13 , W23 , W33 V3 is only a pseudo special set vector subspace of V3 over the set S = {0, 1}. W4 = W14 , W24 , W34 , W44

V4 is such that W14 = {3Zou Zo} V14 , ½ °­§ a a · o° 4 W24 = ®¨ ¸ a Z ¾ V2 , a a ¹ ¯°© ¿° W34 = {2 u 3 matrices with entries from 6Zo {0} and 2 u 3 matrices with entries from 10Zo {0}} V34 and

127

­ ½ ªa º ° « » o° W = ® a a a a a , « a » a Z ¾ V44 . ° ° «¬ a »¼ ¯ ¿ 4 4

Thus W4 =

W , W , W , W V4 = V , V , V , V

4 1

4 2

4 3

4 4

4 1

4 2

4 3

4 4

is a

special set linear subalgebra of V4 over S = {0, 1}. Thus W = (W1 W2 W3 W4) is only a pseudo special set linear 4subalgebra of V over the set {0, 1}.

Now we proceed on to define the new notion of special set n-dimension generator of a special set n-vector space over the set S. DEFINITION 2.3.5: Let V = (V1 V2 … Vn) 1 1 1 = V1 , V2 ,...,Vn1 V12 , V22 ,..., Vn22 V1n , V2n ,..., Vnnn

be a special set vector n-space over the set S. Suppose X = (X1 X2 … Xn) X 11 , X 21 ,..., X n11 X 12 , X 22 ,..., X n22 … =

X

V , V ,...,V V n 1

, X 2n ,..., X nnn 1 1

1 2

1 n1

2 1

, V22 ,..., Vn22

V

n 1

, V2n ,..., Vnnn

such that X ti Vt i ; 1 d i d n and 1 d t d n1, n2, …, nn, is such that each X ti generates Vt i over the set S, i = 1, 2, …, n, then we call X to be the special set n-dimension generator of V over the set S. If each X ti is of finite cardinality, 1 d t d n1, n2, …, nn, i = 1, 2, …, n, then we say the special set n-vector space over S is finite n-dimensional. If even one of the X ti is of infinite cardinality then we say the special set n-vector space over S is infinite n-dimensional.

We shall describe this by the following example:

128

Example 2.3.11: Let V = (V1 V2 V3 V4) be a special set 4 vector space over the set S = Z+ {0} = Zo. Here V1 = V11 , V21, V31 , V41 ,

V2 = V12 , V22 ,

V3 = V13 , V23 , V33

V4 = V14 , V24

and where

­°§ a b · ½° o V11 = ®¨ ¸ a, b,c,d Z S¾ , °¯© c d ¹ ¿° o o 1 1 V2 = {(a a a a), (a a a) | a Z }; V3 = {Z [x]; all polynomials of degree less than or equal to 5} and ­ ½ ªa a º °§ a a a a · « » o° V = ®¨ ¸ , «a a » a Z ¾ . a a a a © ¹ « ° ° ¬ a a »¼ ¯ ¿ 1 4

V1 = V11 , V21, V31 , V41 has a subset

X1 = X11 , X 12 , X13 , X14

­°§ 1 0 · § 0 1 · § 0 0 · § 0 0 · ½° = ®¨ ¸,¨ ¸,¨ ¸,¨ ¸ ¾ , {(1 1 1 1), (1 1 1)}, ¯°© 0 0 ¹ © 0 0 ¹ © 1 0 ¹ © 0 1 ¹ °¿ {an infinite set of elements which are polynomials}, ­ §1 1· ½ °§1 1 1 1· ¨ ¸° 1 1 1 1 ®¨ ¸ , ¨1 1¸ ¾ V1 , V2 , V3 , V4

1 1 1 1 ¹ ¨ °© ¸° ©1 1¹ ¿ ¯ is a special set generator of V1 and |X1| = (4, 2, f, 2) … I. Now V2 = V12 , V22 where

129

­§ a a a · ½ °¨ ¸ o° V = ®¨ a a a ¸ such that a Z ¾ , °¨ a a a ¸ ° ¹ ¯© ¿ 2 1

­ ªa ° « ° ªa a a º «a , V22 = ® « » ° ¬a a a ¼ «a « ° ¬a ¯

½ aº ° » a» o° a Z ¾ a» ° » ° a¼ ¿

with a special set generator § ­ ª1 ¨ ° ­°§1 1 1· ½° °§ 1 1 1· ««1 X2 = X12 , X 22 = ¨¨ ®¨ ¸ ¾ , ®¨ ¸, °©1 1 1¹ ¿° °© 1 1 1¹ «1 ¯ ¨ « ¨ ° ¬1 ¯ © and |X2| = (1, 2) … II.

1º ½ · °¸ 1»» ° ¸ ¾ 1» ° ¸ » ¸ 1¼ ¿° ¹¸

V3 = V13 , V23 , V33 where V13 = {n(1 + x + x2 + x3 + x4 + x5)| n Zo}, ­§ a b c · ½ °¨ ¸ o° V = ®¨ 0 d e ¸ a, b,c,d,e,f ,g Z ¾ , °¨ 0 0 g ¸ ° ¹ ¯© ¿ 3 2

V33 = {(a a a a a a), (a a), (a a a) | a Zo}. Let X3 = X13 , X 32 , X 33

be the special set generating V3, then X3 = ({(1 + x + x2 + x3 + x4 + x5)}, {an infinite collection of 3 u 3 upper triangular matrices}, {(1 1 1 1 1 1), (1 1), (1 1 1)} V3 = V13 , V23 , V33 .

Thus |X3| = (1, f , 3) … III. Finally V4 = V14 , V24 where

130

­ ½ §a a a¡ °§ a a ¡ ¨ ¸§ a a a ¡ o° V = Ž¨ ¸¨ 0 a a ¸¨ ¸ a�Z ž a a a a a Š š Š š ° ° ¨0 0 a¸ Š š ¯ ¿ 4 1

and V24 = {n(1+ x + x2), n(x3 + 3x2 + x + 3), n(x7 + 7) | n Â? Zo}.

The special set generator subset X4 = X14 , X 42 Â&#x17D; V14 , V24 is given by ­­ ½ § 1 1 1¡ °°§1 1¡ ¨ ¸ § 1 1 1¡ ° X4 = ŽŽ¨ ¸ , ¨ 0 1 1¸ , ¨ ¸ž , °°Š1 1š ¨ 0 0 1¸ Š 1 1 1š ° Š š Âż ÂŻÂŻ

{(1 + x + x2, x7 + 7, x3 + 3x2 + x + 3}} Â&#x17D; V14 , V24 and |X4| = (3, 3) â&#x20AC;Ś IV. X4 is the special set generator of V4. Thus we see X = (X1 Â&#x2030; X2 Â&#x2030; X3 Â&#x2030; X4) Â&#x17D; (V1 Â&#x2030; V2 Â&#x2030; V3 Â&#x2030; V4) = V is a special set 4-generator of V over S and the 4-dimension of V is |X| = (|X1|, |X2|, |X3|, |X4|) = {(4, 2, f 2), (1, 2), (1, f,3), (3, 3)}.

Thus V is an infinite dimensional, special set 4-vector space over S. Now we proceed on to define the notion of special set linear ntransformation of special set n-vector spaces defined over the same set S. DEFINITION 2.3.6: Let V = (V1 Â&#x2030; V2 Â&#x2030; â&#x20AC;Ś Â&#x2030; Vn) and W = (W1 Â&#x2030; W2 Â&#x2030; â&#x20AC;Ś Â&#x2030; Wn) be two special set n-vector spaces defined over the same set S; such that in both V and W each Vi in V and Wi in W have same number of sets which are set vector spaces, i.e., Vi

= V1i ,..., Vnii and Wi = W1i ,..., Wnii

true for i = 1, 2, â&#x20AC;Ś, n.

That is

1 1

1 2

1 n1

= V , V ,...,V

V = (V1 Â&#x2030; V2 Â&#x2030; â&#x20AC;Ś Â&#x2030; Vn) Â&#x2030; V12 , V22 ,..., Vn22 Â&#x2030; â&#x20AC;Ś Â&#x2030; V1n , V2n ,..., Vnnn

and W = (W1 Â&#x2030; W2 Â&#x2030; Â&#x2030; Wn)

131

= W11 , W21,...,Wn11 W12 , W22 ,...,Wn22 … W1n , W 2n ,...,Wnnn

be such special set n-vector spaces over the set S. Let T : V o W such that T = (T1 T2 … Tn) 1 1 1 = T1 , T2 ,..., Tn1 T12 , T22 ,..., Tn22 … T1n , T2n ,..., Tnn1 :

(V1 V2 … Vn) o (W1 W2 Wn) where each Ti : Vi o Wi is T1i , T2i ,..., Tnii : Vi = V1i , V2i ,...,Vnii o Wi = W1i , W2i ,...,Wnii

with T ji : V ji o W ji ; 1 d j d ni; i = 1, 2, …, n; i.e., each Ti: Vi

o Wi is a special set linear transformation of the special set vector space Vi to special set vector space Wi over S, for i = 1, 2, …, n. Then we call T = (T1 T2 … Tn): V o W as the special set linear n-transformation.

SHomS (V, W) = {SHomS (V1, W1) SHomS (V2, W2) …

SHomS (Vn, Wn)} where SHomS (Vi, Wi) = { HomS V1i ,W1i ,

HomS V2i ,W2i ,..., HomS Vnii ,Wnii } true for i = 1, 2, …, n. Thus

SHomS = (V, W) is again a special set vector n space over the set S.

We illustrate this by a simple example.

Example 2.3.12: Let V = (V1 V2 V3 V4) 1 1 = V1 , V2 V12 , V22 , V32 V13 , V23 V14 , V24 , V34 , V44

be a special set 4-vector space over the set S = Zo {0}.

W = (W1 W2 W3 W4) 1 1 = W1 , W2 W12 , W22 , W32 W13 , W23 W14 , W24 , W34 , W44

be a special set 4-vector space over the same set S = Z+ {0}.

Here V1 = V11 , V21 with ½° °­§ a b · o V11 = ®¨ ¸ a, b,c,d Z {0}¾ ¯°© c d ¹ ¿° 132

and V21 = SuS, such that V1 is a special set vector space over Zo {0}. V2 = V12 , V22 , V32 where V12 = S u S u S, ­° ª a V22 = ® « 1 ¯° ¬ a 4

a2 a5

½° a3 º a i Zo {0},1 d i d 6 ¾ » a6 ¼ ¿°

and ­ ª a1 º ½ ° ° V32 = ® «« a 2 »» a i S,1 d i d 3¾ °«a » ° ¯¬ 3 ¼ ¿ is a special set vector space over S. V3 = V13 , V23 where V13 = {All polynomials of degree less than or equal to 3 with coefficients from S} and ­§ a b c · ½ °¨ ° ¸ V = ®¨ 0 d e ¸ a, b,c,d,e,f S¾ ; °¨ 0 0 f ¸ ° ¹ ¯© ¿ 3 2

V3 is again a special set vector space over S. Finally V4 = V14 , V24 , V34 , V44 is such that V14 = SuS ­°§ a b · ½° o V24 = ®¨ ¸ a, b,c,d Z {0}¾ , °¯© o d ¹ ¿°

V34 = {all polynomials in x of degree less than or equal to 4 with coefficients from S} and ­°§ a b c d · ½° o V44 = ®¨ ¸ a, b,c,d,e,f ,g, h S Z {0}¾ . °¯© e f g h ¹ °¿

V4 is again a special set vector space over S.

133

Now W1 = W11 , W21 where W11 = S u S u S u S and ½° °­§ a ¡ W21 = Ž¨ ¸ a, b � Sž ; ¯°Š b š ¿°

W1 is special set vector space over Z+ Â&#x2030;{0}.W2 = W12 , W22 , W32

where ­°§ a b ¡ ½°

W12 = Ž¨ ¸ a, b,c Â? Z Â&#x2030; {0}ž , °¯Š 0 c š ¿°

W22 = S u S u S u S u S u S and W32 = S u S u S; W is a special

set vector space over Z+ Â&#x2030; {0}; W3 = W13 , W23 where W13 = S

u S u S u S and W23 = {all polynomials of degree less than or equal to 5 with coefficients from S}, so that W3 is again a special set vector space over S. W4 = W14 , W24 , W34 , W44 where ­°§ a ¡ ½° W14 = Ž¨ ¸ a, b � Sž , °¯Š b š ¿° ­°§ a 0 ¡ ½° W24 = Ž¨ ¸ a, b,c,d � Sž , °¯Š b d š ¿° W34 = S u S u S u S u S, ­ ª a1 ° ° a W44 = Ž  3 °a 5 °  a 7 ¯

½ a2 º °  a4  ° a i � S,1 d i d 8ž . a6  °  ° a8 Ÿ ¿

W4 is a special set vector space over S. Now define T = (T1 Â&#x2030; T2 Â&#x2030; T3 Â&#x2030; T4)

134

= T11 , T21 T12 ,T22 ,T32 T13 ,T23 T14 ,T24 ,T34 ,T44 : = V , V 1 1

1 2

V = (V1 V2 V3 V4) V , V22 , V32 V13 , V23 V14 , V24 , V34 , V44

2 1

o W = (W1 W2 W3 W4) = W11 , W21 W12 , W22 , W32 W13 , W23 W14 , W24 , W34 , W44

as follows:

§a b· T11 : V11 o W11 defined by T11 ¨ ¸ = (a b c d). ©c d¹ ªa º T21 : V21 o W21 is given by T21 a b « » ; ¬b ¼

thus T1 = T11 ,T21 :V1 o W1 is a special set linear transformation over S. Now define T2 = T12 ,T22 ,T32 : V2 o W2 by

§a T22 ¨ 1 © a4

§a b· T12 a b c ¨ ¸. ©0 c¹ a 2 a3 · ¸ a1 ,a 2 ,a 3 ,a 4 ,a 5 ,a 6 , a5 a6 ¹ § a1 · ¨ ¸ T ¨ a2 ¸ ¨a ¸ © 3¹ 2 3

a1 ,a 2 ,a 3 .

Thus T2: V2 o W2 is a special set linear transformation over S. T3 : V3 o W3; T3 = T , T : V13 , V23 o W13 , W23

3 1

3 2

given by T13 : V13 o W13 T13 (a0 + a1x + a2x2 + a3x3) = (a0, a1, a2, a3)

135

T23 : V23 o W23 is defined as § a1 · ¨ ¸ T ¨ a 2 ¸ = (a1, a2, a3). ¨a ¸ © 3¹ 3 2

Thus T3: V3 o W3 is a special set linear transformation on S. T4 = T ,T ,T ,T 4 1

4 2

4 3

4 4

T4: V4 o W4 i.e., : V14 , V24 , V34 , V44 o W14 , W24 , W34 , W44

is given by ªa º «b» ; ¬ ¼ 4 T2 : V24 o W24

T14 a b

T14 : V14 o W14 is given by

§a b· § a 0· T24 ¨ ¸ ¨ ¸. ©0 d¹ ©b d¹ T34 : V34 o W34

is such that T34 (a0 + a1x + a2x2 + a3x3 + a4x4) = (a0, a1, a2, a3, a4) and T44 : V44 o W44 is such that ªa e º « » § a b c d · «b f » T44 ¨ . ¸ © e f g h ¹ «c g» « » ¬d h ¼

Thus T4 = T14 ,T24 ,T34 ,T44 is a special set linear transformation of V4 to W4 over S. Thus T = (T1, T2, T3, T4): V = (V1 V2 V3 V4) o (W1 W2 W3 W4) = W is a special set linear 4-transformation over the set S.

136

Now we proceed on to define the notion of special set linear operator of a special set vector space V over a set S. DEFINITION 2.3.7: Let V = (V1 Â&#x2030; â&#x20AC;Ś Â&#x2030; Vn)

= V11 , V21,...,Vn11 Â&#x2030; V12 , V22 ,..., Vn22

Â&#x2030; Â&#x2030; V

n 1

, V2n ,..., Vnnn

be a special set n-vector space over the set S. Suppose T = (T1 Â&#x2030; T2 Â&#x2030; â&#x20AC;Ś Â&#x2030; Tn) be a special set linear operator on V; i.e., T = (T1 Â&#x2030; T2 Â&#x2030; â&#x20AC;Ś Â&#x2030; Tn) 1 1 1 = T1 , T2 ,..., Tn1 Â&#x2030; T12 , T22 ,..., Tn22 Â&#x2030; â&#x20AC;Ś Â&#x2030; T1n , T2n ,..., Tnnn :

V = {V1 Â&#x2030; V2 Â&#x2030; â&#x20AC;Ś Â&#x2030; Vn} = V , V ,...,Vn11 Â&#x2030; â&#x20AC;Ś Â&#x2030; V1n , V2n ,..., Vnnn o

1 1

1 2

V = {V1 Â&#x2030; V2 Â&#x2030; â&#x20AC;Ś Â&#x2030; Vn} is a special set linear n-operator on V if T is a special set linear transformation from V to V. Let SHomS (V,V) = {SHomS (V1,V1) Â&#x2030; â&#x20AC;Ś Â&#x2030; SHomS (Vn,Vn)} where SHomS (Vi, Vi) = {HomS V1i , V1i ,â&#x20AC;Ś, HomS Vnii ,Vnii } for i = 1,

2, â&#x20AC;Ś, n.

We can verify each HomS Vnii ,Vnii

is a set linear algebra

over S. Thus SHomS (Vi, Vi) is a special set linear algebra over the set S under the composition of mappings. Thus SHomS (V,V) = (SHomS (V1, V1) Â&#x2030; SHomS (V2, V2) Â&#x2030; â&#x20AC;Ś Â&#x2030; SHomS (Vn, Vn)} is a special set linear n-algebra over the set S. Now we illustrate this by a simple example.

Example 2.3.13: Let

V = (V1 Â&#x2030; V2 Â&#x2030; V3) = V , V , V , V41 Â&#x2030; V12 , V22 Â&#x2030; V13 , V23 , V33

1 1

1 2

1 3

where ­°§ a b ¡ ½°

V11 = Ž¨ ¸ a, b,c,d Â? Z Â&#x2030; {0}ž , °¯Š c d š ¿° V21 ={S u S u S u S u S where S = Z+ Â&#x2030; {0}}, V31 ={S[x] all polynomials of degree less than or equal to four}

137

and ­ ½ §a a¡ ° ° ¨ ¸ °§ a a a a ¡ ¨ a a ¸ ° 1

V4 = Ž¨ a Â? Z Â&#x2030; {0}ž , ¸, °Š a a a a š ¨¨ a a ¸¸ ° ° ° a a Š š ÂŻ Âż V1 = V11 , V21, V31, V41 is a special set vector space over the set S.

Consider V2 = V12 , V22 where

­§ a b c ¡ ½ °¨ ° ¸ V = Ž¨ d e f ¸ a, b,c,d,e,f ,g, h,i � Sž , °¨ g h i ¸ ° š ¯Š ¿ 2 1

V22

­ ½ ªa º ° ° a  ° °   ªa a º °° °° a   a a a § ¡ 

ÂŽ> a a a a a a @ , ÂŤ Âť , ÂŤa a Âť , ¨ ¸ a Â? Z Â&#x2030; {0} Sž , ÂŤa Âť ÂŤa a Âť Š a a a š ° ° Âź ÂŤa Âť ÂŹ ° ° ÂŤ Âť ° ° °¯ °¿ ÂŹÂŤa Ÿ

thus V2 = V12 , V22 is a special set vector space over the set S =

Z+ Â&#x2030; {0}. V3 = V13 , V23 , V33 where ­° ÂŞ a V13 = ÂŽ ÂŤ 1 ¯° ÂŹ a 5

a2 a6

a3 a7

½° a4 º a i � S;1 d i d 8ž  a8 Ÿ ¿°

V23 = {All polynomials in x with coefficients from S of degree less than or equal to 5} and V33 = {All upper triangular 4 u 4 matrices with entries from S and all lower triangular matrices

138

with entries from S}. Hence V3 = V13 , V23 , V33 is a special set vector space over S. Thus V = (V1 V2 V3) is a special set vector 3-space over S. Define T = (T1 T2 T3) 1 1 1 = T1 , T2 , T3 , T41 T12 , T22 T13 , T23 , T33

from V = (V1 V2 V3) into V = (V1 V2 V3) as follows: T1: V1 = V1 such that T1 = T11 , T21,T31,T41 : V11 , V21, V31, V41 into V11 , V21, V31, V41

defined by T11 : V11 o V11 where §a b· § c d· T11 ¨ ¸ ¨ ¸. © c d¹ ©a b¹ T21 : V21 o V21 such that T21 (a b c d e) = (d e c a b) T31 : V31 o V31 by

T31 (a0 + a1x + a2x2 + a3x3 + a4x4) = (a0 + a2x2 + a4x4) and T41 : V41 o V41 defined as §a a a a· T41 ¨ ¸ ©a a a a¹

ªa «a « «a « ¬a

aº a »» a» » a¼

and

ªa «a T41 « «a « ¬a

aº a »» § a a a a · ¨ ¸. a» © a a a a ¹ » a¼

Thus T1 = T11 , T21,T31,T41 :V1 oV1 is a special set linear operator on V1. Now T2 = T12 , T22 : V2 = V12 , V22 into V2 = V12 , V22 .

139

T2 : V2 o V2 is T12 : V12 o V12 is such that defined as

§a b c· §a b c· ¨ ¸ ¨ ¸ T ¨d e f ¸ ¨0 e f ¸ ¨g h i ¸ ¨0 0 i ¸ © ¹ © ¹ 2 2 2 and T2 : V2 o V2 is given by 2 1

T22 a a a a a a

§a· ¨ ¸ ¨a¸ ¨a¸ ¨ ¸ ¨a¸ ¨a¸ ¨¨ ¸¸ ©a¹

and §a· ¨ ¸ ¨a¸ ¨a¸ T22 ¨ ¸ ¨a¸ ¨a¸ ¨¨ ¸¸ ©a¹

a

a a a a a .

§a a· ¨ ¸ §a a a· T ¨a a¸ ¨ ¸ ¨a a¸ ©a a a¹ © ¹ 2 2

and §a a· §a a a· ¨ ¸ T ¨ ¸ ¨a a¸ . ©a a a¹ ¨a a¸ © ¹ 2 1

Thus T2 = T12 , T22 is a special set linear operator on V2.

Now consider T3 = T13 , T23 ,T33 : V3 = V13 , V23 , V33 into V3 =

V , V , V defined as follows.

3 1

3 2

3 3

140

T13 : V13 o V13 §a T13 ¨ 1 © a5

a2 a6

a3 a7

a4 · § a5 ¸ ¨ a 8 ¹ © a1

a6 a2

a7 a3

a8 · ¸, a4 ¹

T23 : V23 o V23 by T23 (a0 + a1x + a2x2 + a3x3 + a4x4+ a5x5) = (a0 +a1x + a3x3 + a5x5) and T33 : V33 o V33 is given by §a b c d· § a 0 0 0· ¨ ¸ ¨ ¸ 0 e f g¸ ¨ b e 0 0¸ T33 ¨ ¨0 0 h i ¸ ¨ c f h 0¸ ¨ ¸ ¨ ¸ ©0 0 0 j¹ ©d g i j¹ and § a 0 0 0· § a b c d · ¨ ¸ ¨ ¸ b e 0 0¸ ¨ 0 e f g ¸ T33 ¨ ¨ c f h 0¸ ¨ 0 0 h i ¸ ¨ ¸ ¨ ¸ ©d g i j¹ ©0 0 0 j¹

Thus T3 = T13 , T23 ,T33 : V3 o V3 is also a special set linear operator on V3. Hence T = (T1, T2, T3) = T11 , T21,T31,T41 T12 , T22 T13 , T23 ,T33

is a special set linear 3-operator on V = (V1 V2 V3).

Now we proceed to define the new notion of pseudo special set linear n-operator on V. DEFINITION 2.3.8: Let V = (V1 V2 … Vn) 1 1 1 = V1 , V2 ,...,Vn1 V12 , V22 ,..., Vn22 V1n , V2n ,..., Vnnn

be a special set n-vector space over the set S. Take T = (T1 T2 … Tn) 1 1 1 = T1 , T2 ,..., Tn1 T12 , T22 ,..., Tn22 T1n , T2n ,..., Tnnn :

141

V = (V1 V2 … Vn) o (V1 V2 … Vn) such that T is a special set linear n-transformation on V; i.e., Ti: Vi o Vj where i z j and ni d nj, j, i = 1, 2, …,n.

Then we call T = (T1 T2 … Tn) to be a pseudo special set linear n-operator on V. Further SHomS (V, V) = {SHomS (V1, Vj1) … SHomS (Vn,Vjn)} here if i z ji then ni d nji,1d ji d n, i = 1, 2,…, n; where SHomS(Vt, Vjt) = {Special set linear transformation from Vt into Vjt}. {HomS V1i ,Vt1i , HomS V2i ,Vt i2 ,…, HomS Vnii ,Vtni i } = SHomS(Vi,

Vi) where 1d t1, t2, …, tni d ni, this is true for each i, i = 1, 2, …, n. So that SHomS(V, V) is a special set n-vector space over the set S. We now illustrate this definition by some examples.

Example 2.3.14: Let V = (V1 V2 V3 V4) be a special set vector 4-space over the set S = {0, 1}. Here V1 = V11 , V21, V31 ,

V2 = V12 , V22 , V32 , V42 , V3 = V13 , V23 , V33

and

V4 = V14 , V24 , V34 , V44

are described in the following. ­°§ a b e · ½° V11 = ®¨ ¸ a, b,c,d,e,f S¾ ¯°© c d f ¹ ¿°

V21 = S u S u S u S, V31 = {All polynomials of degree less than or equal to 5 with entries from S}.

V1 = V11 , V21, V31 is a special set vector space over S. Now

142

­°§ a b ¡ ½°

V12 = Ž¨ ¸ a, b,c,d Â? Z Â&#x2030; {0}ž , °¯Š c d š ¿° V22 = {(a a a a a), (a a a) | a Â? Z+ Â&#x2030; {0}}; V32 = {(Z+ Â&#x2030; {0})[x],

polynomials of degree less than or equal to four} and V42 = {All 4 u 4 upper triangular matrices with entries from S}; Clearly V2 = V12 , V22 , V32 , V42 is a special set vector space over the set S. In V3, take ­°§ a b ¡ ½° V13 = Ž¨ ¸ a, b,c,d � Sž , °¯Š c d š ¿° V23 = {S u S u S u S u S u S}, ­§ a b ¡ ½ °¨ ° ¸ V = Ž¨ c d ¸ a, b,c,d,e,f � Sž °¨ e f ¸ ° š ¯Š ¿ 3 3

so that V3 is again a special set vector space over S. Consider V4 = V14 , V24 , V34 , V44 here V14 = {(Z+ Â&#x2030; {0})[x] all polynomials of degree less than or equal to 8}, ­ ½ ÂŞa Âş ° ° ÂŤ Âť ° ÂŞa Âş ÂŤa Âť ° ° ° V24 = ÂŽ ÂŤÂŤ a  , ÂŤ a Âť a Â? Z Â&#x2030; {0}ž , ° ÂŤa Âť ÂŤa Âť ° ° Âź ÂŤ Âť ° ÂŤÂŹ a Ÿ °¯ ¿° V34 = {Zo u Zo u Zo u Zo where Z0 = Z+ Â&#x2030; {0}} and V44 = {All 4 u 4 lower triangular matrices with entries from S}; V4 = V14 , V24 , V34 , V44 is a special set vector space over the set S.

143

Now V = (V1 V2 V3 V4) is a special set vector 4-space over S. Define T : V o V; i.e., T = (T1 T2 T3 T4) : V = (V1 V2 V3 V4) o (V1 V2 V3 V4) by T1 : V1 o V3 that is T1 = T11 , T21,T31 : V11 , V21, V31 = V1 o V13 , V23 , V33

defined by T11 : V11 o V33 T21 : V21 o V13

T31 : V31 o V23 as follows §a b e· T ¨ ¸ ©c d f ¹ 1 1

ªa b º «c d » , « » ¬« e f »¼

ªa b º T21 a b c d « » ¬c d¼ and T31 (a0 + a1x + a2x2 + a3x3 + a4x4 + a5x5) = (a0, a1, a2, a3, a4, a5).

Thus T1 = T11 , T21,T31 : V1 o V3 is a pseudo special set linear transformation of V1 into V3.

Define T2 : V2 o V4 where T2 = T12 , T22 ,T32 ,T42 :

V2 = V12 , V22 , V32 , V42 o V14 , V24 , V34 , V44 = V4

as follows: T12 : V12 o V34 as §a b· T12 ¨ ¸ ©c d¹ and T22 : V22 o V24 by

a

144

b c d

T22 a a a a a

§a · ¨ ¸ ¨a ¸ ¨a ¸ ¨ ¸ ¨a ¸ ¨a ¸ © ¹

and ªa º T (a a a) = «« a »» , «¬ a »¼ 2 2

T32 : V32 o V14 by T32 (a0 + a1x + a2x2 + a3x3 + a4x4) = (a0 + a1x2 +a2x4 + a3x6 + a4x8) and T42 : V42 o V44 by §a ¨ 0 T42 ¨ ¨0 ¨ ©0

b e 0 0

c d· § a 0 0 0· ¸ ¨ ¸ f g¸ ¨ b e 0 0¸ . h i ¸ ¨ c f h 0¸ ¸ ¨ ¸ 0 j¹ ©d g i j¹

Thus T2 = T12 , T22 ,T32 ,T42 is a pseudo special set linear transformation of V2 into V4.

Define T3 = T13 , T23 ,T43 : V3 o V1 as follows.

T13 : V13 o V21 such that §a b· T13 ¨ ¸ = (a b c d), ©c d¹ T23 : V23 o V11 defined as §a b e· T23 (a b c d e f) = ¨ ¸ ©c d f ¹ and T33 : V33 o V31 is defined by

145

ªa 0 T [a0 + a1x + a2x + a3x + a4x + a5x ] = «« a 2 «¬ a 4 3 3

2

3

4

5

a1 º a 3 »» . a 5 »¼

Thus T3 : V3 o V1 is again a pseudo special set linear transformation. Finally T4 : V4 o V2 is defined as follows.

T4 = T14 , T24 ,T34 ,T44 : V4 = V14 , V24 , V34 , V44 o V2 = V12 , V22 , V32 , V42

such that T14 : V14 o V32 is defined by T14 (a0 + a1x + a2x2 + a3x3 + a4x4 + a5x5+ a6x6 + a7x7 + a8x8) = (a0 + a1x + a2x2 + a3x3 + a4x4), T24 : V24 o V22 is defined as

ªa º «a » « » T24 « a » « » «a » «¬ a »¼

a

a a a a

and ªa º T ««a »» = (a a a), «¬a »¼ 4 2

T34 : V34 o V12 is defined by

§a b· T34 a b c d ¨ ¸ ©c d¹ and T44 : V44 o V42 is defined as

146

§a ¨ b T44 ¨ ¨c ¨ ©d Thus T4 =

0 0 0· §a ¸ ¨ e 0 0¸ ¨0 f h 0¸ ¨0 ¸ ¨ g i j¹ ©0

T , T ,T ,T

4 1

4 2

4 3

4 4

b e 0 0

c d· ¸ f g¸ . h i¸ ¸ 0 j¹

is a pseudo special linear

transformation of V44 into V42 .

Hence T = (T1 T2 T3 T4) 1 1 1 = T1 , T2 ,T3 T12 , T22 ,T32 ,T42 T13 , T23 ,T43

T14 , T24 ,T34 ,T44

: V = (V1 V2 V3 V4) = V , V , V31 V12 , V22 , V32 , V42 V13 , V23 , V43

1 1

1 2

V14 , V24 , V34 , V44

o (V1 V2 V3 V4) is a pseudo special linear 4-operator from V into V. Now as in case of special set linear bioperators in case of special set linear n-operators also we can define n-projection and idempotent special set linear n-operators.

Now we proceed on to define the notion of special set direct sum and of special set vector n-subspaces of V. DEFINITION 2.3.9: Let V = (V1 V2 … Vn) 1 1 1 = V1 , V2 ,...,Vn1 V12 , V22 ,..., Vn22 V1n , V2n ,..., Vnnn

be a special set vector n space over the set S. Suppose each Vt ii is a direct summand of subspaces Wt1i ... Wtri for each i = 1, 2, …, n and 1 d ti d ni then we call V = (V1 V2 … Vn) be the special set direct n union of subspaces.

We illustrate this by a simple example.

147

Example 2.3.15: Let

V = (V1 Â&#x2030; V2 Â&#x2030; V3) = V , V , V31 Â&#x2030; V12 , V22 Â&#x2030; V13 , V23 , V33

1 1

1 2

where ½° °­§ a b ¡

V11 = Ž¨ ¸ a, b,c,d Â? Z Â&#x2030; {0} Sž , ¯°Š c d š ¿°

­ ½ ªa º ° °  

V = ÂŽ a a a , ÂŤa Âť , > a a @ a Â? S Z Â&#x2030; {0}ž ° ° ÂŤÂŹa Ÿ ÂŻ Âż 1 2

V31 = {SuSuS},

­° ½° ªa º V12 = Ž a a a a ,   , > a a a @ a � Sž a Ÿ °¯ °¿ ­§ a1 °¨ V = Ž¨ a 4 °¨ a ¯Š 7 2 2

a2 a5 a8

½ a3 ¡ ° ¸ a 6 ¸ such that a i � S; 1 d i d 9 ž , ° a 9 ¸š ¿

V13 = {S u S u S u S}, V23 = {S[x]; polynomials of degree less than or equal to 2}

and

­§ a1 ¡ ªa °¨ ¸ a °¨ a 2 ¸ 3 , >a a a a a a @ ,  V3 = Ž ¨ ¸ a a 3 °¨ ¸  °Š a 4 š a ¯ 148

½ aº °  a ° a,a i � S; 1 d i d 4 ž . a °  ° aŸ ¿

Now we express each Vtii as a direct union of vector subspaces. ­°§ a b ¡ ½°

V11 = Ž¨ ¸ a, b,c,d Â? 3Z Â&#x2030; {0}ž Â&#x2020; ¯°Š c d š ¿° ½° °­§ a b ¡

Ž¨ ¸ a, b,c,d Â? 5Z Â&#x2030; {0}ž Â&#x2020; ¯°Š c d š ¿° ­°§ a b ¡ ½ 1 1 1

° Ž¨ ¸ a, b,c,d Â? S \ {5Z Â&#x2030; 3Z }ž = W11 Â&#x2020; W21 Â&#x2020; W31 . °¯Š c d š ¿° ­ ÂŞa Âş ½ ° ° V = ^ a a a ` Â&#x2020; ÂŽ ÂŤÂŤa  ž Â&#x2020; {> a a @} ° ÂŤa Âť ° ÂŻÂŹ Âź Âż 1 2

1 1 Â&#x2020; W32 = W121 Â&#x2020; W22

(a Â? S).

V31 = ({S u S u ^ ` Â&#x2030; ^ ` Â&#x2020; ({0} u{0} u S u S) 1 = W111 Â&#x2020; W21 Â&#x2020; W311 .

­° °­§ a ¡ °½ °½ V12 = ÂŽ^ a a a a ` Â&#x2020; Ž¨ ¸ ž Â&#x2020; ^ a a a ` | a Â? Sž °¯Š a š °¿ °¯ °¿ 1 1 Â&#x2020; W32 = W121 Â&#x2020; W22

­§ a1 °¨ V = Ž¨ a 4 °¨ a ¯Š 7 2 2

­§ a1 °¨ Ž¨ a 4 °¨ a ¯Š 7

a2 a5 a8

a2 a5 a8

½ a3 ¡ ° ¸

a 6 ¸ a i Â? 3Z Â&#x2030; {0}; 1 d i d 9 ž Â&#x2020; ° a 9 ¸š Âż

½ a3 ¡ ° ¸

a 6 ¸ a i Â? 7S \ 3Z Â&#x2030; {0}; 1 d i d 9 ž ° a 9 ¸š Âż 149

= W122 W222 .

We represent V13 = {SuSu^ `u^ `} {^ `u^ `u SuS} 1 = W131 W23

V23 = {ax + b | a, b 3Z+ {0}} {ax+b | a, b 5Z+ {0}} {ax+b | a, b S – {3Z+ 5Z+}} = W132 W232 W332 .

­§ a1 · ½ °¨ ¸ ° °¨ a 2 ¸ ° 3

V3 = ® a i 9Z {0} 1 d i d 4 ¾ °¨¨ a 3 ¸¸ ° °© a 4 ¹ ° ¯ ¿ ­§ a1 · ½ °¨ ¸ ° °¨ a 2 ¸ °

®¨ ¸ a i S \ 9Z {0} 1 d i d 4 ¾ °¨ a 3 ¸ ° °© a 4 ¹ ° ¯ ¿ ­ ªa °« a >a a a a a a @ a S ®° ««a ° ° «¬a ¯

^

`

½ aº ° » a» ° a S¾ a» ° » ° a¼ ¿

= W133 W233 W333 W433 .

Thus V = V1 V2 V3 V4 = W W W311 , W112 W212 W312 , W113 W213 W313

^

1 11

1 21

W

1 12

1 W22 W321 , W122 W222

150

= V11 , V21, V31 Â&#x2030; V12 , V22 Â&#x2030; V13 , V23 , V33 .

Now it is pertinent to mention here that where V = (V1 Â&#x2030; V2 Â&#x2030; â&#x20AC;Ś Â&#x2030; Vn) happen to be a special set linear n-algebra we can replace in the definition the special set direct n union by special set direct n-sum.

We shall represent this by one example when V is a special set linear n-algebra.

Example 2.3.16: Let V = (V1 Â&#x2030; V2 Â&#x2030; V3 Â&#x2030; V4) be a special set linear 4 algebra over the set Z+ Â&#x2030; {0}. Take V1 = V11 , V21, V31, V41 ,

V2 = V12 , V22 ,

V3 = V13 , V23 , V33

V4 = V14 , V24 .

and

In V1 = V11 , V21, V31, V41 ,

½° ­°§ a b ¡

V11 = Ž¨ ¸ a, b,c,d Â? Z Â&#x2030; {0} ž , ¯°Š c d š ¿° V21 = {S u S u S | S = Z+ Â&#x2030; {0}}, ­§ a1 °¨ V = Ž¨ a 3 °¨ a ¯Š 5 1 3

½ a2 ¡ ° ¸ a 4 ¸ a i � S;1 d i d 6 ž ° a 6 ¸š ¿

and ½ ­§ a1 a 2 a 3 ¡ ° °¨ ¸ V = Ž¨ 0 a 4 a 5 ¸ a i � S;1 d i d 6 ž . °¨ 0 0 a ¸ ° 6š ¯Š ¿ Now we see V1 is a special set linear algebra over the set S. Here we write V1 as a special set direct summand as follows.

1 4

151

°­ § 1 0 ·§ 0 0 · °½ °­ § 0 1 · °½ °­§ 0 0 · °½ V11 = ® ¨ ¸¨ ¸ ¾ ® ¨ ¸ ¾ ®¨ ¸¾ °¯ © 0 0 ¹© 1 0 ¹ °¿ °¯ © 0 0 ¹ °¿ ¯°© 0 1 ¹ ¿° 1 = W111 W21 W311

is a special set direct summand of V11 .

V12 = {¢(1, 00)²} {¢(0, 11)²} = W122 W222

is a special set direct summand of V21 . ­ ° V = ® ° ¯

§1 0· ½ ­ § 0 1· ½ ­ § 0 0· ½ ¨ ¸ ° ° ¨ ¸ ° ° ¨ ¸ ° ¨ 0 0¸ ¾ ® ¨ 0 0¸ ¾ ® ¨1 0¸ ¾ ¨ 0 0¸ ° ° ¨ 0 0¸ ° ° ¨ 0 0¸ ° © ¹ ¿ ¯ © ¹ ¿ ¯ © ¹ ¿

1 3

­ ° ® ° ¯

§0 0· ½ ­ ¨ ¸ ° ° ¨0 1¸ ¾ ® ¨0 0¸ ° ° © ¹ ¿ ¯

§0 0· ½ ­ ¨ ¸ ° ° ¨0 0¸ ¾ ® ¨1 0¸ ° ° © ¹ ¿ ¯

§ 0 0· ½ ¨ ¸ ° ¨ 0 0¸ ¾ ¨0 1¸ ° © ¹ ¿

= W133 W233 W333 W433 W533 W633

is a special set direct summand of V31 .

Finally ­ ª1 0 0 º ½ ­ ª 0 1 0 º ½ ° ° ° ° 1 V4 = ® «« 0 0 0 »» ¾ ® ««0 0 0 »» ¾ ° «0 0 0» ° ° «0 0 0 » ° ¼ ¿ ¯ ¼ ¿ ¯ ­ ° ® ° ¯

ª0 0 0º ½ «0 0 0» ° « » ¾ «¬ 0 0 1 »¼ °¿

­ ° ® ° ¯

152

ª0 0 0º ½ «0 1 0» ° « » ¾ «¬ 0 0 0 »¼ ¿°

­ ° Ž ° ¯

ÂŞ0 0 0Âş ½ ­ ÂŤ0 1 0Âť ° Â&#x2020; ° ÂŤ Âť ž ÂŽ ÂŤÂŹ 0 0 0 Ÿ ¿° ¯°

ª0 0 0º ½ 0 0 1  °   ž 0 0 0 Ÿ ¿°

= W144 Â&#x2020; W244 Â&#x2020; W344 Â&#x2020; W444 Â&#x2020; W544 Â&#x2020; W644

is a special set direct summand of V41 .

Thus V1 = V11 , V21, V31, V41

W

1 11

=

W

3 13

W

4 14

1 Â&#x2020; W21 Â&#x2020; W311 , W122 Â&#x2020; W222 ,

Â&#x2020; W233 Â&#x2020; W333 Â&#x2020; W433 Â&#x2020; W533 Â&#x2020; W633 ,

Â&#x2020; W244 Â&#x2020; W344 Â&#x2020; W444 Â&#x2020; W544 Â&#x2020; W644

Consider V2 = V12 , V22 where ­°§ a a1 ¡ ½°

V12 = Ž¨ ¸ a1 ,a Â? S Z Â&#x2030; {0}ž °¯Š a a1 š ¿°

and ­§ a a a 2 °¨ V = Ž¨ a a a 2 °¨ a a a 2 ¯Š 2 2

½ a1 ¡ ° ¸

a1 ¸ a1 ,a,a 2 Â? Z Â&#x2030; {0} Sž ° a1 ¸š Âż

be the special set vector space over S. Now ­° §1 0 ¡ ½° ­° § 0 1¡ ½° V12 = ÂŽ ¨ ¸ žÂ&#x2020;ÂŽ ¨ ¸ ž ¯° Š1 0 š ¿° °¯ Š 0 1š °¿ 1 = W121 Â&#x2020; W22

and

153

â&#x20AC;Ś I.

­ ° V = ® ° ¯ 2 2

ª1 1 0 0 º « » «1 1 0 0 » «¬1 1 0 0 »¼ ­ ° ® ° ¯

½ ­ ° ° ¾ ® ° ° ¿ ¯

½ ° ¾ ° ¿

ª0 0 1 0º « » «0 0 1 0» «¬0 0 1 0 »¼

ª 0 0 0 1º « 0 0 0 1» « » «¬ 0 0 0 1»¼

½ ° ¾ ° ¿

= W132 W232 W332 .

Thus 1 V2 = V12 , V22 = W121 W22

, W132 W232 W332

^

`

…

II

is a special set direct summand of the special set linear algebra V2. Now V3 = V13 , V23 , V33 where ­°§ a a a a1 · ½° V13 = ®¨ ¸ a,a1 S¾ , °¯© a a a a1 ¹ ¿° ­ ªa °« ° a 3 V2 = ® « ° «a ° «¬ a ¯

½ a a1 º ° » a a1 » ° a,a1 S¾ a a1 » ° » ° a a1 ¼ ¿

and V33 = {S u S u S uS} be the special set vector space over S. ­° ª1 1 1 0 º ½° ­° ª0 0 0 1º ½° V13 = ® « » ¾ ® « » ¾ °¯ ¬1 1 1 0 ¼ °¿ °¯ ¬0 0 0 1¼ °¿ 1 = W131 W23

154

­ ° ° V23 = ® ° ° ¯

ª1 «1 « «1 « ¬1

1 0º 1 0 »» 1 0» » 1 0¼

½ ­ ° ° ° ° ¾ ® ° ° ° ° ¿ ¯

= W232 W332

ª0 «0 « «0 « ¬0

½ ° ° ¾ ° ° ¿

0 1º 0 1»» 0 1» » 0 1¼

and V33 = {S u S u {0} u {0}} {{0} u {0} u S u S}

= W133 W233 .

Thus =

^ W

1 13

V3 = V13 , V23 , V33

`

1 W23

, W232 W332 , W133 W233

… III

is a special set direct summand of V3. Hence using I, II and III we get V = (V1 V2 V3) 1 1 W311 , W122 W222 , = { W11 W21

W

3 13

W233 ... W633 , W144 W244 W344 ... W644 } 1 { W121 W22

, W132 W232 W332 }

1 { W131 W23

, W232 W332 , W133 W233 }

to be special set 3-direct summand of V over S. Now it is pertinent here to mention the following: 1. When we use special set vector n-space the problem of representation in terms of generating sets or a direct union of subspaces are no doubt tedious when compared with special set linear n-algebras. Special set linear n-algebras yield easy direct summand and a small or manageable ngenerating subsets.

2. But yet we cannot rule out the use of special set vector nspaces or completely replace special set n-spaces for we need them when the component subspaces of V = (V1, …,

155

Vn) takes the form as Vtii = {(1 1 1 1 1), (1 1 1), (0 0 0), (0 0 0 0 0), (1 0 1), (1 0), (0 1), (0 0)} or of the form only a few polynomials; i.e., a finite set in such cases we see special set vector n-spaces are preferable to special set linear n-algebras.

2.4 Special Set Fuzzy Vector Spaces

Now in the following section. We proceed onto define the notion of fuzzy analogue of these new types of special set vector spaces.

DEFINITION 2.4.1: Let V= {S1, S2, â&#x20AC;Ś, Sn} be a special set vector space over the set p. If K = (K1,â&#x20AC;Ś,Kn) is a n-map from V into [0, 1] where each Ki: Si o [0,1], i = 1, 2, â&#x20AC;Ś, n; Ki(rai) t Ki(ai) for all ai Â? Si and rÂ? p then we call VK = V(K1 ,K 2 ,...,K n ) = (V1, V2,

â&#x20AC;Ś,Vn)K = (V1K1, V2K2 ,â&#x20AC;Ś, VnKK) to be a special set fuzzy vector space.

We illustrate this by an example.

Example 2.4.1: Let V = (V1, V2, V3, V4) where

­°§ a b ¡ ½° V1 = Ž¨ ¸ a, b,c,d Â? Z2 ž , ¯°Š c d š ¿° V2 = {(Zo u Zo u Zo); Zo = Z+ Â&#x2030; {0}}, ­°§ a a a a ¡ ½°

V3 = Ž¨ ¸ a Â? Z Â&#x2030; {0}ž °¯Š a a a a š ¿° and V4 = {all polynomials of finite degree over Z2} is a special set vector space over the set S = {0, 1}.

Define K = (K1, K2, K3, K4) : V = (V1, V2, V3, V4) o [0, 1] such that each Ki: Vi o [0, 1]; i = 1, 2, 3, 4 with

156

K1: V1 o [0, 1] is defined by 1 ­ if ad bc z 0 §a b¡ ° K1 ¨ ¸ Ž (ad bc) Šc dš ° 1 if ad bc 0 ¯ K2 : V2 o [0,1]

­ 1 if a b c z 0 ° K2 (a, b, c) = Ž a b c °¯ 1 if a b c 0 K3 : V3 o [0,1] ­1 ° if a z 0 Ža °¯1 if a 0

ÂŞa a a a Âş K3 ÂŤ Âť ÂŹa a a a Âź K4: V4 o [0,1]

1 ­ if deg p(x) z 0 ° K4 (p(x)) = Ž deg p(x) °1 if p(x) = 0 or a constant polynomial ¯ Clearly VK = V1 , V2 , V3 , V4 ( K , K 1

= (V1K1, V2K2 , V3K3, V4K4)

2 ,K3 , K4 )

is a special set fuzzy vector space.

We define the notion of special set fuzzy vector subspace.

DEFINITION 2.4.2 Let V = {V1, â&#x20AC;Ś, Vn} be a special set vector space over the set S. Let W = (W1, W2, â&#x20AC;Ś,Wn) Â&#x17D; (V1, V2, â&#x20AC;Ś, Vn) = V, that is each Wi Â&#x17D; Vi; i = 1, 2, â&#x20AC;Ś, n be a special set vector subspace of V. Define K: W o [0, 1], i.e., K = (K1, K2,â&#x20AC;Ś,Kn): (W1, W2,â&#x20AC;Ś,Wn) o [0,1]; that is Ki: Wi o [0,1], for i = 1, 2, â&#x20AC;Ś,n such that Ki (sai) t Ki(ai) for all ai Â? Wi for all s Â? S; 1d i d n .

Then WK = W1 ,W2 ,...,Wn (K ,K ,...K ) = (W1K1, W2K2 ,â&#x20AC;Ś, WKKK) is 1

2

n

a special set fuzzy vector subspace.

We shall illustrate this by a simple example.

157

Example 2.4.2: Let V = (V1, V2, â&#x20AC;Ś, V5) where

­§ a b c ¡ ½ °¨ ° ¸ V1 = Ž¨ 0 d e ¸ a, b,c,d,e,f Â? Z Â&#x2030; {0}ž , °¨ 0 0 f ¸ ° š ¯Š Âż V2 = {(a a a a a) | a Â? Z+ Â&#x2030; {0}}, V3 = {S[x], all polynomials of degree less than or equal to if with coefficients from S}, ­ ÂŞ a1 Âş ° Âť ° ÂŤa 2 Âť ° V4 = ÂŽ ÂŤ a 3 Âť > a1 a 2 ° ÂŤa Âť ° 4 Âť ÂŤa Âť ¯° ÂŹ 5 Âź

a3

a4 @

½ ° ° ° a i Â? Z Â&#x2030; {0};1 d i d 5ž ° ° °¿

and V5 = {SuSuSuSuSuS} is a special set vector space over the set P = {0, 1}.

Let W = (W1, W2,W3,W4, W5) Â&#x17D; (V1, V2,V3,V4, V5) = V be a proper subset of V where ­§ a a a ¡ ½ °¨ ° ¸ W1 = Ž¨ 0 a a ¸ a Â? Sž Â&#x17D; V1, °¨ 0 0 a ¸ ° š ¯Š Âż W2 = {(a a a a a) | a Â? 7Z+ Â&#x2030; {0}} Â&#x17D; V2, W3 = {All polynomials of degree less than or equal to 26 with coefficients from 9Z+ Â&#x2030; {0}} Â&#x17D; V3, ­ ÂŞ a1 Âş ½ ° Âť ° ° ÂŤa 2 Âť ° ° ° ÂŤ Âť W4 = ÂŽ a 3 a i Â? S; 1 d i d 5 ž Â&#x17D; V4 ° ÂŤa Âť ° ° 4 Âť ° °¯ ÂŤÂŹ a 5 Ÿ ¿° 158

and W5 = {S u S u {0} u S u S u {0}} Â&#x17D; V5. W is clearly a special set vector subspace of V over the set P. Define K : W o [0,1] as follows.

K = (K1, K2, K3, K4, K5) : W = (W1, W2, W3, W4, W5) o [0,1] such that each Ki : Wi o [0,1] ; i = 1, 2, 3, 4, 5.

K1 : W1 o [0,1] such that § a a a ¡ ­1 ¨ ¸ ° if a z 0 K1 ¨ 0 a a ¸ Ž a ¨ 0 0 a ¸ °¯1 if a 0 Š š K2 : W2 o [0,1] such that K2 [a a a a a]

­1 if a z 0 ° Ž 5a °¯1 if a 0

K3 : W3 o [0, 1]

1 ­ if deg p(x) z 0 ° K3 (p(x)) = Ž deg p(x) °1 if deg p(x) = 0 or a constant polynomial ¯ K4 : W4 o [0,1]

§ ª a1 º ¡ ¨ ¸ ¨ a 2  ¸ K4 ¨  a 3  ¸ ¨ ¸ ¨ a 4  ¸ ¨ a  ¸ Š 5Ÿš

5 1 ­ ° a a a a a if Œ a i z 0 ° 1 i 1 2 3 4 5 Ž 5 ° 1 if Œ a i 0 °¯ i 1

159

K5: W5 o [0, 1] ­ 1 if b a z 0 ° K5(a b 0 c d 0) = Ž a b °¯ 1 if b a 0 Clearly

WK = W1 , W2 , W3 , W4 , W5 ( K , K 1

2 ,K3 , K4 , K5 )

= (W1K1, W2K2, W3K3, W4K4, W5K5) is a special set fuzzy vector subspace.

Now we proceed on to define the new notion of special set fuzzy linear algebra.

DEFINITION 2.4.3: Let V = (V1, V2, â&#x20AC;Ś,Vn) be a special set linear algebra over the set S. Take a n-map K = (K1, K2, â&#x20AC;Ś,Kn) : V = (V1, V2, â&#x20AC;Ś,Vn) o [0,1] such that Ki : Vi o [0,1] satisfying the condition Ki (aQi)t Ki(Qi) for all a Â? S and vi Â? V; 1 d i d n.

Then we call (V1K1, V2K2 ,â&#x20AC;Ś, VnKn) to be a special set fuzzy linear algebra.

It is important to note that both special set fuzzy vector space and special set fuzzy linear algebra are the same. Thus the concept of fuzziness makes them identical or no difference exist. We call such algebraic structures which are distinct but become identical due to fuzzyfying them are said to be fuzzy equivalent. Thus we see special set vector spaces and special set linear algebras are fuzzy equivalent.

We shall exhibit the above definition by an example.

Example 2.4.3: Let V = (V1, V2, V3, V4) be a special set linear algebra over the set {0, 1} = S, where

­§ a1 °¨ V1 = Ž¨ a 5 °¨ a ¯Š 9

a2 a6

a3 a7

a10

a11

½ a4 ¡ ° ¸ a 8 ¸ a i �{0,1};1 d i d 12 ž , ° a12 ¸š ¿

160

V2 = (S u S u S u S u S);

­ ª a1 ° ° a V3 = Ž  3 °a 5 °  a 7 ¯

½ a2 º °  a4  ° a i �{0, 1}; 1 d i d 8ž a6  °  ° a8 Ÿ ¿

and V4 = {all 4u4 matrices with entries from {0,1}}.

Define K = (K1, K2, K3, K4) : V = (V1, V2, V3, V4) o [0,1] such that each Ki : Vi o [0,1] ; i = 1, 2, 3, 4 as follows.

K1 : V1 o [0,1] § ª a1 ¨ K1 ¨  a 5 ¨ a Š 9

a2

a3

a6 a10

a7 a11

a4 º ¡ ¸ a 8  ¸ a12 Ÿ ¸š

­ 1 ° 12 °° Œ a i Ži 1 ° ° 1 °¯

12

ÂŚa

if

ÂŚa i 1

if e a z 0 if e a

K3 : V3 o [0, 1] is such that ÂŞ a1 ÂŤa K3 ÂŤ 3 ÂŤa 5 ÂŤ ÂŹa 7

a2 º a 4  a6   a8 Ÿ

­ 1 if ° 8 °° Œ a i Ži 1 ° ° 1 if °¯

161

ÂŚa

i

z0

i

ÂŚa i

z0

i

0

12

if

K2 : V2 o [0, 1] defined by ­ 1 ° K2 (a, b, c, d, e) = Ž a e °¯ 1

i

i 1

i

0

0

K4 : V4 o [0, 1]

­ 1 if | A | z 0 ° K4 (A) = ® | A | ° 1 if | A | 0 ¯ Here A is a 4u4 matrix. Thus VK = V1 , V2 , V3 , V4 ( K , K ,K ,K ) = (V1K1, V2K2 , V3K3, V4K4) 1

2

3

4

is a special set fuzzy linear algebra.

This is the same as a special set fuzzy vector space.

Now we proceed onto define the notion of special set fuzzy linear subalgebra.

DEFINITION 2.4.4: Let V = (V1, V2, …,Vn) be a special set linear algebra over the set S. Let W = (W1, W2, …, Wn) (V1, V2, …, Vn) where W is a special set linear subalgebra of V over the set S. Define K : W = (W1, W2, …,Wn) o [0,1] as follows: K = (K1, K2, …,Kn) : (W1, W2, …,Wn) o [0,1] such that Ki : Wi o [0,1], i=1,2,…,n. WK = W1 , W2 ,..., Wn K =

(W1K1, W2K2 ,…, WKKK) is a special set fuzzy linear subalgebra.

Let us now proceed onto define the notion of special set fuzzy bispaces.

DEFINITION 2.4.5: Let V = V11 , V21,...,Vn11 V12 , V22 ,..., Vn22 =

V1 V2 be a special set bivector space over the set S. Let the bifuzzy map K = K1 K2 = K11 , K 21 ,...,Kn11 K12 , K 22 ,...,K n22 : V1

V2 o [0,1] be defined as K : Vi o [0,1]; 1d id n1 and K : V j2 1 i

2 j

1

o [0, 1], j = 1, 2, …, n2 satisfy the condition Ki1 (rai1 ) t Ki1 (ai1 ) and K 2j (ra 2j ) t K 2j (a 2j ) for all r S and ai1 Vi1 , a 2j V j2 , i = 1, 2, …, n1 and j = 1, 2, …, n2. Thus VK = (V1 V2 )K1 K2 = (V11 ,V21 ,...,Vn11 ) K1 ,K1 ,...,K1 (V12 ,V22 ,...,Vn22 ) K 2 ,K 2 ,...,K 2

1

2

n1

162

1

2

n2

= (V11K1 ,V21K1 ,...,Vn1K1 ) Â&#x2030; (V1K2 2 ,...,Vn2K 2 ) 1

1 n1

2

1

2 n2

is a special set fuzzy vector bispace.

We illustrate this by the following example.

Example 2.4.4: Let V = (V1 Â&#x2030; V2) = V11 , V21, V31 Â&#x2030; V12 , V22 , V32 , V42

be a special set vector bispace over the set S = {0, 1}. Here V1 = V11 , V21, V31 is given by ­°§ a b ¡ ½ ° V11 = Ž¨ ¸ a, b,c,d � Z ž , °¯Š c d š ¿° o 1 V2 = {[a a a a a] | a � Z } and ­§ a1 °¨ ° a V31 = Ž¨ 2 °¨¨ a 3 °Š a 4 ¯

½ a5 ¡ ° ¸ a6 ¸ ° a i � Zo ;1 d i d 8ž , a7 ¸ ° ¸ ° a8 š ¿

V2 = V12 , V22 , V32 , V42 where V12 = {3 u 3 upper triangular matrices with entries from Zo},

V22 = {Zo u Zo u Zo}, V32 = {[a a a a a a ] | a � Zo} and ­§ a1 ¡ ½ °¨ ¸ ° °¨ a 2 ¸ ° °°¨ a 3 ¸ °° V42 = Ž¨ ¸ a i � Zo ;1 d i d 6 ž . °¨ a 4 ¸ ° °¨ a 5 ¸ ° °¨¨ ¸¸ ° ¯°Š a 6 š ¿°

163

Define the bifuzzy set K = K Â&#x2030; K = K11 , K12 , K13 Â&#x2030; K12 , K22 , K32 , K24

= =

(V1 Â&#x2030; V2) V11 , V21, V31 Â&#x2030; V12 , V22 , V32 , V42

as follows : K11 : V11 o [0, 1] defined by ­1 if ad z 0 §a b¡ ° K ¨ ¸ = Ž ad Š c d š ° 1 if ad 0 ¯ 1 1

K12 : V21 o [0, 1] is defined by ­1 if a z 0 ° K [a a a a a] = Ž 5a °¯ 1 if a 0 1 2

K13 : V31 o [0,1] by § a1 ¨ a K13 ¨ 2 ¨ a3 ¨ Š a4

a5 ¡ ¸ a6 ¸ = a7 ¸ ¸ a8 š

1 ­ if a1a 5 a 2 a 6 a 3a 7 a 4 a 8 z 0 ° Ž a1a 5 a 2 a 6 a 3a 7 a 4 a 8 ° 1 if a1a 5 a 2 a 6 a 3a 7 a 4 a 8 0 ¯ K12 : V12 o [0, 1] is given by ­ 1 if | A | z 0 ° K (A) = Ž | A | ° 1 if | A | 0 ¯ 2 1

164

where A is the 3u3 upper triangular matrix with entries from Zo. K22 : V22 o [0, 1] is defined by ­ 1 if abc z 0 ° .

K22 (a b c) = Ž abc °¯ 1 if abc 0 K32 : V32 o [0, 1] is such that

­1 if a z 0 ° K [a a a a a a] = Ž 6a °¯ 1 if a 0 2 3

K24 : V42 o [0,1] is given by § a1 ¡ ¨ ¸ ¨ a2 ¸ ­ 1 ¨ a3 ¸ ° if atleast one of a1a 2 a 3 z 0 2 K4 ¨ ¸ = ÂŽ a1a 2 a 3 a 4 a 5 a 6 ¨ a4 ¸ ° 1 if both a1a 2 a 3 = 0, a 4 a 5 a 6 = 0 ¨a ¸ ÂŻ 5 ¨¨ ¸¸ Š a6 š Thus the bifuzzy set K = K Â&#x2030; K = K11 , K12 , K13 Â&#x2030; K12 , K22 , K32 , K24 :

(V1 Â&#x2030; V2) = V11 , V21, V31 Â&#x2030; V12 , V22 , V32 , V42 o [0, 1]

is a special set fuzzy vector bispace and is denoted by V =

(V1 Â&#x2030; V2 )( K1 Â&#x2030;K2 )

=

(V1K1 Â&#x2030; V2K2)

=

V

1 1K11

, V21K1 , V31K1 Â&#x2030; V12K2 , V22K2 , V32K2 , V42K2 .

2

3

1

165

2

3

4

Now having defined the notion of special set fuzzy vector bispace now we proceed on to define the notion of special set fuzzy vector subbispace.

DEFINITION 2.4.6: Let V = (V1 V2) = V11 , V21,...,Vn11 V12 , V22 ,..., Vn22

be a special set vector bispace over a set S. Take W = (W1 W2) = W11 , W21,...,Wn11 W12 , W22 ,...,Wn22

(V1 V2) = V to be a special set vector subbispace of V over the set S. Let K = K K be a bimap from W = (W1 W2) into the set [0, 1]; i.e., K K = K11 , K 21 ,...,K n1 K12 , K 22 ,...,K n2 : 1

W= (W1 W2) = W , W ,...,W 1 1

1 2

1 n1

W

2 1

2

2 2

o [0, 1]

2 n2

, W ,...,W

such that Ki1 : Wi1 o [0,1] for each i = 1, 2, …, n, Ki2 : Wi 2 o [0, 1] for each i = 1, 2, …, n such that WiK11 and WiK22 are fuzzy i

i

2

1

set vector bispace of Vi and Vi respectively. Then we call WK = = = =

(W1 W2 )K1K2

W , W ,...,W

W , W ,...,W

W12 , W22 ,...,Wn22

1 1

1 2

1 n1 K 1

1 1

1 2

1 n1 (K1 ,K1 ...K1 ) n1 1 2

W

1 1K11

K2

W , W ,...,Wn22

2 1

2 2

, W21K 2 ,...,Wn1K1 W1K2 2 , W22K 2 ,...,Wn2K 2 1

1 n1

1

2

2 n2

(K12 ,K22 ...Kn22 )

the special set fuzzy vector subbispace or bisubspace.

Now having defined a special set fuzzy bisubspace we proceed onto illustrate it by some example.

Example 2.4.5: Let V = (V1 V2) = V11 , V21, V31 V12 , V22 , V32 , V42

be a special set vector bispace over the set S = Zo = Z+ {0} where V11 = S u S u S,

166

­°§ a b · ½ o° V21 = ®¨ ¸ a, b,c,d Z ¾ , °¯© c d ¹ ¿° ­°§ a V31 = ®¨ 1 ¯°© a 4

a2 a5

½ a3 · o° ¸ ai Z ¾ . a6 ¹ ¿°

V2 = V12 , V22 , V32 , V42 where V12 = {3 u 3 upper triangular matrices with entries from Zo},

V22 = {S u S u S u S u S}, ­§ a1 °¨ ° a 2 V3 = ®¨ 2 °¨¨ a 3 ° ¯© a 4

½ a5 · ° ¸ a6 ¸ ° o a i Z ;1 d i d 8 ¾ a7 ¸ ° ¸ ° a8 ¹ ¿

and V42 = {all 4 u lower triangular matrices with entries from Zo}.

Now consider the set vector subspaces of these spaces; take W11 = {S u S u{0}} V11 , ­°§ a b · ½ o° 1 W21 = ®¨ ¸ a, b,c,d 5Z ¾ V2 , c d ¹ ¯°© ¿° ½ °­§ a a a · o° 1 W31 = ®¨ ¸ a Z ¾ V3 , a a a ¹ ¯°© ¿° W12 = {all 3 u 3 upper triangular matrices with entries from Zo} V12 , W22 = {S u S u S u {0} u S} V22 ,

167

­§ a °¨ ° a W32 = Ž¨ °¨¨ a °Š a ¯

½ a¡ ° ¸ a¸ ° a i Â? Zo ž Â&#x17D; V32 ¸ a ° ¸ ° aš Âż

and W42 = {all 4 u lower triangular matrices with entries from 19Zo} Â&#x17D; V42 . Clearly = W , W ,W 1 1

1 2

1 3

W = (W1 Â&#x2030; W2) Â&#x2030; W12 , W22 , W32 , W42 Â&#x17D; (V1 Â&#x2030; V2)

is a special set vector subbispace of V over the set S = Zo. Define a fuzzy bimap K = K Â&#x2030; K = K , K , K13 Â&#x2030; K12 , K22 , K32 , K24 : 1 1

1 2

(W1 Â&#x2030; W2) = W11 , W21, W31 Â&#x2030; W12 , W22 , W32 o [0, 1] as K11 : W11 o [0, 1] is defined by

­ 1 if a b z 0 ° K11 (a b 0) = Ž a b °¯ 1 if a b 0 K12 : W21 o [0,1] is given by 1 ­ if a b c d z 0 §a b¡ ° K ¨ ¸ = Ža b c d Šc dš ° 1 if a b c d 0 ¯ 1 2

K13 : W31 o [0,1] is given by ­1 if a z 0 ªa a a º ° = Ž 6a K    a a a Ÿ ° 1 if a 0 ¯ 1 3

168

K12 : W12 o [0, 1] is given by ªa a a º ­ 1 if a z 0 ° K «« 0 a a »» = ® 6a «¬ 0 0 a »¼ °¯ 1 if a 0 2 1

K22 : W22 o [0, 1] is defined by ­ 1 if abcd z 0 ° K22 (a b c 0 d) = ® abcd °¯ 1 if abcd 0 K32 : W32 o [0,1] is such that

ªa «a K32 « «a « ¬a

aº ­1 a »» ° if a z 0 = ® 8a a» ° » ¯ 1 if a 0 a¼

K24 : W42 o [0,1] is such that ª a1 «a K24 « 2 «a 4 « ¬a 7

0 a3 a5 a8

0 0 a6 a9

0º 1 ­ if a1a 3a 6 a10 z 0 0 »» ° = ® a1a 3a 6 a10 0» ° 1 if a1a 3a 6 a10 0 » ¯ a10 ¼

Thus WK = (W1 W2 )K1 K2 = =

W , W ,W

1 1

W

1 1K11

1 2

1 3 ( K1 , K1 , K1 ) 1 2 3

W12 , W22 , W32 , W42

( K12 , K22 , K32 , K42 )

, W21K2 , W31K1 W12K2 , W22K2 , W32K2 , W42K2 1

3

1

2

is the special set fuzzy vector subbispace.

169

3

4

Now we proceed onto define the notion of special set fuzzy linear bialgebra.

DEFINITION 2.4.7: Let V = (V1 Â&#x2030; V2) = V11 , V21,...,Vn11 Â&#x2030; V12 , V22 ,..., Vn22

be a special set linear bialgebra over the set S. Let K K Â&#x2030; K = be a bimap from (V1 Â&#x2030; V2) into [0,1], i.e., K K Â&#x2030; K = K11 , K 21 ,...,Kn11 Â&#x2030; K12 , K 22 ,...,K n22 : V= (V1Â&#x2030; V2) = V11 , V21,...,Vn11

Â&#x2030; V12 , V22

,..., V o [0,1]. Such that K 2 n2

1 i

: Vi1 o [0,1] such that

ViK11 is a set fuzzy linear algebra for i = 1, 2, â&#x20AC;Ś, n. and Ki2 : Vi 2 i

o [0,1] such that V2 K is a set fuzzy linear algebra for i = 1, 2, 2 i

â&#x20AC;Ś, n2; then we call VK = (V1 Â&#x2030; V2 )K1K2 = V1K1 Â&#x2030; V2K2 = V11 , V21,...,Vn11

=

V

1 1K11

(K11 ,K12 ...K1n1 )

Â&#x2030; V12 , V22 ,...,Vn22

, V21K 2 ,...,Vn1K1 Â&#x2030; V1K2 2 , V22K 2 ,...,Vn2K 2 1

1 n1

1

2

2 n2

(K12 ,K22 ...Kn22 )

the special set fuzzy linear bialgebra.

We illustrate this by some simple examples.

Example 2.4.6: Let V = (V1 Â&#x2030; V2) = V11 , V21, V31 , V41 Â&#x2030; V12 , V22 , V32 , V42

be a special set vector bispace over the set S = Zo = Z+ Â&#x2030; {0} where ­°§ a a 2 a 3 ¡ ½° o

V11 = Ž¨ 1 ¸ a i Â? Z S {Z Â&#x2030; {0}ž , ¯°Š a 4 a 5 a 6 š ¿° V21 = S u S u S u S, V31 = {(a a a a a a) | a Â? S}, V41 = {all 5 u5 upper triangular matrices with entries from S} and

170

­°§ a b · ½ o° V12 = ®¨ ¸ a, b,c,d Z ¾ , °¯© c d ¹ ¿° ­§ a · ½ °¨ ¸ ° °¨ a ¸ ° ° ° V22 = ®¨ a ¸ a Zo ¾ , °¨ a ¸ ° °¨ ¸ ° °¯¨© a ¸¹ ¿° V32 = S u S u S u S u S u S and V42 = {4 u 4 lower triangular matrices with entries from S}. Clearly V = (V1 V2) is a special set linear bialgebra over the set S. Let us define the fuzzy bimap K

=

V = =

K K K11 , K12 , K13 , K14 K12 , K22 , K32 , K24 : (V1 V2) V11 , V21, V31 , V41 V12 , V22 , V32 , V42 o [0, 1]

by K11 : V11 o [0, 1] such that §a K ¨ 1 © a4 1 1

a2 a5

­ 1 if a1a 2 a 3 z 0 a3 · ° ¸ = ® a1a 2 a 3 a6 ¹ ° if a1a 2 a 3 0 ¯ 1

K12 : V21 o [0,1] is defined by

­ 1 if abcd z 0 ° K12 [a b c d] = ® abcd °¯ 1 if abcd 0

171

K13 : V31 o [0, 1] is defined by ­1 if a z 0 ° K (a a a a a a) = ® 6a °¯ 1 if a 0 1 3

K14 : V41 o [0, 1] is defined by

§ a1 a 2 ¨ ¨ 0 a6 1 K4 ¨ 0 0 ¨ ¨0 0 ¨0 0 ©

a3 a7 a10 0 0

a4 a8 a11 a13 0

a5 · ¸ a9 ¸ ­ 1 if a1a 6 a10 a13a15 z 0 ° a12 ¸ = ® a1a 6 a10 a13a15 ¸ 1 if a1a 6 a10 a13a15 0 a14 ¸ °¯ ¸ a15 ¹

K12 : V12 o [0,1] is defined by

­1 if bc z 0 §a b· ° K ¨ ¸ = ® bc © c d ¹ ° 1 if bc 0 ¯ 2 1

K22 : V22 o [0, 1] is such that §a· ¨ ¸ ­1 ¨a¸ if a z 0 ° 2 K2 ¨ a ¸ = ® 5a ¨ ¸ °¯ 1 if a 0 ¨a¸ ¨a¸ © ¹ K32 : V32 o [0, 1] is defined by ­ 1 if ace z 0 ° K [a b c d e f] = ® ace °¯ 1 if ace 0 2 3

172

K24 : V42 o [0, 1] is given by

§a ¨ b K24 ¨ ¨d ¨ ©g

0 0 0· ¸ ­ 1 if acbj z 0 c 0 0¸ ° = ® acbj e f 0¸ ° ¸ ¯ 1 if acbj 0 h i j¹

Thus K = K K : (V1 V2) o [0, 1] is a bimap such that VK = (V1 V2 )K1 K2

1 1

1 2

= V , V ,..., V

V12 , V22 ,..., Vn22

1 n1 ( K1 , K1 ...K1 ) 1 2 n1

= V11K1 , V21K2 ,..., Vn1 K1 1

1

1 n1

V

2 1K12

( K12 , K22 ...K2n 2 )

, V22K2 ,..., Vn2 K2 2

2 n2

is a special set fuzzy linear bialgebra, here n1 = n2 = 4.

Now we proceed on to define the notion of special set fuzzy linear subbialgebra.

DEFINITION 2.4.8: Let V = (V1 V2) = V11 , V21,...,Vn11 V12 , V22 ,..., Vn22

be a special set linear bialgebra over the set S. Take W = (W1 W2) = W11 , W21,...,Wn11 W12 , W22 ,...,Wn22 (V1 V2) be a

special set linear subbialgebra of V over the set S. If K = K K 1 1 = K1 , K 2 ,...,Kn11 K12 , K 22 ,...,K n22 : (W1

W ) = W , W ,...,W W 2

1 1

1 2

1 n1

2 1

, W22 ,...,Wn22

o [0,1]

be such that Ki1 : Wi1 o [0,1] such that WiK11 is a set fuzzy linear i

algebra for every i = 1, 2, …, n, and K : W j2 o [0,1] is such 2 j

that W j2K 2 is a set fuzzy linear algebra for each j = 1, 2, …, n2 j

then we call

173

=

W

1 1K11

WK = (W1 W2 )K1 K2

, W21K 2 ,...,Wn1K1 1

1 n1

W

2 1K12

, W22K 2 ,...,Wn2K 2 2

2 n2

is a special set fuzzy linear subbialgebra.

We illustrate this by a simple example.

Example 2.4.7: Let V = V11 , V21, V31 V12 , V22 , V32 , V42 , V52 be

a special set linear bialgebra of V over the set Zo = S = Z+ {0}. Here V11 = S u S u S,

½° °­§ a b · V21 = ®¨ ¸ a, b,c, S¾ ¯°© 0 c ¹ ¿° and V31 = {(a a a a a a a)| a =R} ­°§ a b · ½ o° V12 = ®¨ ¸ a, b,c,d Z ¾ , ¯°© c d ¹ ¿°

­ §a °¨ ° a 2 V2 = ® ¨ ° ¨¨ a ° ©a ¯

½ a· ° ¸ a¸ o° a Z ¾, a¸ ° ¸ ° a¹ ¿

V32 = {all 4u4 lower triangular matrices}, ­°§ a a a a a a · ½° V42 = ®¨ ¸ a S¾ ¯°© a a a a a a ¹ ¿° and V52 = {all 5 u upper triangular matrices}. Consider

W = (W1 W2) = W11 , W21, W31 W12 , W22 , W32 , W42 , W52

(V1 V2)

174

be a subbialgebra of V over the set S, where W11 = {SuSu^ `` V11 , ½ °­§ a b · o° 1 W21 = ®¨ ¸ a, b,c, 7Z ¾ V2 0 c ¹ ¯°© ¿° and W31 = {(a a a a a a a) | a 5=R} V31 , ­°§ a b · ½ 2 o° W12 = ®¨ ¸ a, b,c,d Z ¾ V1 , c d ¹ ¯°© ¿°

­§ a °¨ ° a 2 W2 = ®¨ °¨¨ a °© a ¯

½ a· ° ¸ a¸ o° a 10Z ¾ V22 a¸ ° ¸ ° a¹ ¿

W32 = {all 4u4 lower triangular matrices} V32 , ½ °­§ a a a a a a · o° 2 W42 = ®¨ ¸ a 2Z ¾ V4 a a a a a a ¹ ¯°© ¿° and W52 = {all 5 u upper triangular matrices} V52 .

Define K = K K : (W1 W2) o [0, 1] as follows.

K11 : W11 o [0,1] is such that

­1 if ab z 0 ° K (a b 0) = ® ab °¯ 1 if ab 0 1 1

K12 : W21 o [0,1] is defined by

175

­ 1 if abc z 0 §a b· ° = K12 ¨ ¸ ® abc © 0 c ¹ ° 1 if abc 0 ¯ K13 : W31 o [0, 1] is given by

­1 if a z 0 ° K (a a a a a a a) = ® 7a °¯ 1 if a 0 1 3

K12 : W12 o [0, 1] is given by

­ 1 if ab bc z 0 a b · °° a b 2 § K1 ¨ ¸ = ® ©c d¹ ° c d if ab bc 0 ¯° 1 K22 : W22 o [0,1] is given by

§a ¨ a K22 ¨ ¨a ¨ ©a

a· ¸ ­1 a¸ ° if a z 0 = ® 8a a¸ ° ¸ ¯ 1 if a 0 a¹

K32 : W32 o [0,1] is such that ªa «b K32 « «d « ¬g

0 0 0º ­ 1 if | A | z 0 c 0 0 »» ° = ®| A | e f 0» ° » ¯ 1 if | A | 0 h i j¼

where

176

ªa «b A= « «d « ¬g

0 0 0º c 0 0 »» . e f 0» » h i j¼

K24 : W42 o [0, 1] is given by ­ 1 if a z 0 ªa a a a a a º ° = ®12a K « » ¬ a a a a a a ¼ ° 1 if a 0 ¯ 2 4

K52 : W52 o [0, 1] is such that ªa «0 « K52 « 0 « «0 «¬ 0

c d eº g h i »» ­ 1 if afjmp z 0 ° j k l » = ® afjmp » 0 m n » °¯ 1 if afjmp 0 0 0 p »¼

b f 0 0 0

Thus

WK = (W1 W2 )K1 K2

= W11K1 , W21K2 ,..., Wn1 K1 1

1

1 n1

W

2 1K12

, W22K2 ,..., Wn2 K2 2

2 n2

is the special set fuzzy linear subbialgebra.

Now we are bound to make the following observations: 1.

2.

3.

The notion of special set fuzzy vector bispace and special set fuzzy linear bialgebra are fuzzy equivalent.

Likewise the notion of special set fuzzy vector subbispace and special set fuzzy linear subbialgebra are also fuzzy equivalent.

Now using the special set linear bialgebra and special set vector bispace we may define a infinite number of special set fuzzy linear bialgebra and special set fuzzy vector bispace.

177

The same holds good for special set fuzzy linear subbialgebra and special set fuzzy vector subbispaces.

We now proceed onto define the notion of special set fuzzy n vector spaces and special set fuzzy linear n- algebras; though we know both the concepts are fuzzy equivalent.

DEFINITION 2.4.9: Let V = (V1 V2 Vn)

= V11 , V21,...,Vn11 V12 , V22 ,..., Vn22

… V

n 1

, V2n ,..., Vnnn

be a special set vector n – space over the set S. Let K = K K KK

= K11 , K 21 ,...,K n11

K

2 1

, K 22 ,...,K n22 K1n , K 2n ,...,Knnn :

V = (V1 V2 Vn) o [0, 1] such that for each i1, Ki11 : Vi11 o [0, 1] so that Vi1K1 is a set fuzzy 1 i1

vector space and this is true for 1d i1 d n1; K : Vi22 o [0,1] such 2 i2

that that Vi 2K 2 is a set fuzzy vector space for each i2; 1d i2 d n2; 2 i2

and so on Kinn : Vinn o [0,1] such that that Vi nK n is a set fuzzy n in

vector space for each in; 1d in d nn.

Thus VK = (V1 V2 ... Vn )K1 ... Kn

= V1K1 VnKn

1 1K11

1 2K12

1 n1K1n1

= V , V ,...,V

V

2 1K12

, V22K 2 ,...,Vn2K 2 2

2 n2

... V

n nK1n

, ...,VnnK n

n nn

is a special set fuzzy n-vector space.

We illustrate this by the following example: Example 2.4.8: Let

V = V1 V2 V3 V4 = V11 , V21, V31 V12 , V22 V13 , V23 , V33 , V43 V14 , V24 , V34

be a special set 4 vector space over the set S = Z+ {0} where 178

V11 = S u S u S u S, ½° °­§ a b · V21 = ®¨ ¸ a, b,c,d S¾ , ¯°© c d ¹ ¿°

­ ½ ªa º ° ° « » ° ªa a a a º «a » ° 1 V3 = ® « a S¾ , » « » ° ¬a a a a ¼ a ° « » ° ° a ¼ ¯ ¿

V12 = {3 u matrices with entries from S}, V22 = S u S u S, V13 = S u S u S u S u S u S, V23 = {set of all 4 u 4 matrices with entries from the set S}, ­ ªa º ½ °« » ° ° a ° V33 = ® « » > a a a @ a S¾ « » a ° ° ° «¬ a »¼ ° ¯ ¿ ­° ª a a º ªa a a a a º ½° V43 = ® « ,« a S¾ , » » ¯° ¬ a a ¼ ¬a a a a a ¼ ¿° V14 = {set of 3u upper triangular matrices with entries from S},

V24 = {S u S u S u S}, V34 = {(a a a a a a a) | a S}.

Define K = K K K : V = V1 V2 V3 V4 o [0, 1] such that Ki: Vi o [0, 1] for i = 1, 2, 3, 4.

Now K K11 , K12 , K13 : V11 , V21, V31 o [0,1] is such that K11 : V11 o [0, 1] is defined as

179

­1 if ad z 0 ° K11 (a b c d) = ® ad °¯ 1 if ad 0 K12 : V21 o [0, 1] given by

­ 1 if ad bc z 0 §a b· ° K ¨ ¸ = ® ad bc ©c d¹ ° 1 if ad bc 0 ¯ 1 2

K13 : V31 o [0,1] is defined by ­1 if a z 0 §a a a a· ° K ¨ ¸ = ® 8a © a a a a ¹ ° 1 if a 0 ¯ 1 3

Thus V1K1 = V11K1 , V21K1 , V31K1 is a special set fuzzy vector space.

1

2

3

Now K2: V2 o [0,1], i.e., K2 = K12 , K22 : V12 , V22 o [0,1] where K12 : V12 o [0,1] is such that §a b c· ­ 1 if aei z 0 ¨ ¸ ° K ¨ d e f ¸ = ® aei ¨ g h i ¸ °¯ 1 if aei 0 © ¹ 2 1

K22 : V22 o [0, 1] is given by ­ 1 if abc z 0 ° K22 (a b c) = ® abc °¯ 1 if abc 0

Thus V2K2 = V12K2 , V22K2 1

2

is again special set fuzzy vector space.

180

K3:V3 o [0,1] is given by K3 = K13 , K32 , K33 , K34 : V3 = V13 , V23 , V33 , V43 o [0,1]

is such that K13 : V13 o [0,1] is given by K13 ([a b c d e f]) =

1 ­ if a b c d e f z 0 ° Ža b c d e f °¯ 1 if a b c d e f 0 K32 : V23 o [0,1] is given by

§ a11 a12 ¨ a a 22 K32 ¨ 21 ¨ a 31 a 32 ¨ Š a 41 a 42

a13 a 23 a 33 a 43

a14 ¡ 1 ¸ ­ if a11a 22 a 33a 44 z 0 a 24 ¸ ° = Ž a11a 22 a 33a 44 a 34 ¸ ° 1 if a11a 22 a 33a 44 0 ¸ ¯ a 44 š

K33 : V33 o [0,1] is defined by ­1 if a z 0 §a a¡ ° K ¨ ¸ = Ž 4a Š a a š ° 1 if a 0 ¯ 3 3

K34 : V43 o [0,1] is such that

­ 1 if a z 0 ªa a a a a º ° K  = Ž10a   a a a a a Ÿ ° 1 if a 0 ¯ 3 4

Thus V3K3 = V13K3 , V23K3 , V33K3 , V43K3 1

2

3

4

space.

181

is a special set fuzzy vector

Finally K4: V4 o [0, 1], i.e., K4 = K14 , K42 , K34 : V14 , V24 , V34 o [0,1] is such that K14 : V14 o [0,1] such that ­1 § a b ¡ ° if ac z 0 K ¨ ¸ = Ž ac Š d c š ° 1 if ac 0 ¯ 4 1

K42 : V24 o [0,1] is defined such that ­ 1 if ab cd z 0 ° K42 (a b c d) = Ž ab cd °¯ 1 if ab cd 0 K34 : V34 o [0,1] is given by

­1 if a z 0 ° K34 (a a a a a a a) = Ž 7a °¯ 1 if a 0

Thus V4K4 = V14K4 , V24K4 , V34K4 is a special set fuzzy vector space.

1

2

3

Thus V = = =

(V1 Â&#x2030; V2 Â&#x2030; V3 Â&#x2030; V4)K (V1K1 Â&#x2030; V2K2 Â&#x2030; V3K3 Â&#x2030; V4K4)

V V

1 1K11

, V21K1 , V31K1

3 1K13

, V23K3 , V33K3

2

2

3

3

Â&#x2030; V ,V Â&#x2030; ,V Â&#x2030; V ,V ,V

2 1K12

3 4 K34

2 2 K22

4 1K14

4 2 K24

4 3K34

is a special set fuzzy vector 4-space.

Now we proceed onto define the notion of special set fuzzy vector n-subspace.

182

DEFINITION 2.4.10: Let V = (V1 V2 Vn) 1 1 1 = V1 , V2 ,...,Vn1 V12 , V22 ,..., Vn22 V1n , V2n ,...,Vnnn

be a special set vector n-space over the set S. Suppose W = (W1 , W2 , … , Wn) 1 1 1 = W1 , W2 ,...,Wn1 W12 , W22 ,...,Wn22 … W1n , W2n ,...,Wnnn

(V1 V2 Vn) i.e., Wtii Vtii , 1d ti d ni and i = 1, 2, …, n, is a special set vector n-subspace of V over the set S. Now define a n-map K = (K1, K2, Kn) 1 1 1 = K1 , K 2 ,...,Kn1 K12 , K 22 ,...,Kn22 K1n , K 2n ,...,Knnn :

(W1 , W2 , … , Wn) o [0, 1] such that Ki: Wi o [0, 1]; i = 1, 2, …, n and Ktii : Wtii o [0,1] is so defined that Wt iK i is a set fuzzy vector subspace true for ti = i ti

1, 2, …, ni , 1d i d n.

WK = (W1 W2 … Wn)K WK = (W1 W2 … Wn) K1 K2 ... Kn

= W11 , W21,...,Wn11

(K11 ,K12 ...K1n1 )

W12 , W22 ,...,Wn2

2

… W1n , W2n ,...,Wnn

n

(K12 ,K 22 ...K n22 )

(K1n ,K2n ...K nnn )

= W11K1 , W21K 2 ,...,Wn1K1 W1K2 2 , W22K 2 ,...,Wn2K 2 1

1

1 n1

1

2

… W1Kn , W2nK ,...,WnnK n 1

n 1

n n nn

2 n2

is a special set fuzzy vector n-subspace.

We illustrate this by the following example: Example 2.4.9: Let V = (V1 V2 V3 V4 V5) be a special set 5-vector space over the set S = Zo = Z+ {0}. Here V1 = V11 , V21, V31 , V2 = V12 , V22 ,

183

V3 = V13 , V23 , V33 ,V4 = V14 , V24 and V5 = V15 , V25 , V35 , V45

V11 = S u S u S u S,

where

­°§ a b · ½° V21 = ®¨ ¸ a, b,c,d S¾ , ¯°© c d ¹ ¿° ½° °­§ a a a a a a · V31 = ®¨ ¸ a, b S¾ , ¯°© b b b b b b ¹ ¿°

V12 = {all upper triangular 4 u matrices of the form ­§ a °¨ °¨ 0 ®¨ °¨ 0 °© 0 ¯

a b 0 0

a b a 0

½ a· ° ¸ b¸ ° a, b S¾ , a¸ ° ¸ ° b¹ ¿

V22 = S u S u S, ­§ a a a · ½ °¨ ° ¸ V = ®¨ b b b ¸ a, b,c S¾ , °¨ ° ¸ ¯© c c c ¹ ¿ 3 1

V23 = SuSuSuSuS, ­§ a a a a a · ½ °¨ ° ¸ V = ®¨ b b b b b ¸ a, b,c S¾ , °¨ c c c c c ¸ ° ¹ ¯© ¿ 3 3

V14 = S u S u S u S u S, V24 = {all upper triangular 5 u matrices with entries from S},

184

V15 = {lower triangular 3u matrices of the form ­§ a 0 0 · ½ °¨ ° ¸ ®¨ a a 0 ¸ a ¾ S , °¨ ° ¸ ¯© a a a ¹ ¿ V25 = {SuSuSuSuS},

­§ a b c d · ½ ° ° ¨ ¸ V35 = ®¨ a b c d ¸ a, b,c,d S¾ °¨ ° ¸ ¯© a b c d ¹ ¿ and ­§ a °¨ ° b V45 = ®¨ °¨¨ c ° ¯© d

a b c d

a b c d

½ a· ° ¸ b¸ ° a, b,c,d S¾ ¸ c ° ¸ ° d¹ ¿

where Vjii is a special set vector space; 1d i d 5; 1 d ji d ni. Now we define the 5-map K K1 K2 K3 K4 K5) = K11 , K12 , K13 K12 , K22 K13 , K32 , K33 K14 , K42

K15 , K52 , K53 , K54 :

V = (V1 V2 V3 V4 V5) o [0,1] where Kiji : Vjii o [0, 1]; i = 1, 2, 3, 4, 5; 1 d ji d ni; i = 1, 2, 3, 4, 5.

K11 : V11 o [0, 1] is defined by ­ 1 if abcd z 0 ° K11 (a b c d) = ® abcd °¯ 1 if abcd 0

185

K12 : V21 o [0, 1] is such that ­ 1 if ad z bc §a b¡ ° K ¨ ¸ = Ž ad bc Šc dš ° 1 if ad bc ¯ 1 2

K13 : V31 o [0, 1] is given by

­ 1 if ab z 0 §a a a a a a¡ ° = K13 ¨ ¸ Ž 6ab Š b b b b b b š ° 1 if ab 0 ¯ K12 : V12 o [0, 1] is given by §a ¨ 0 K12 ¨ ¨0 ¨ Š0

a b 0 0

a b a a

a¡ ¸ ­1 b¸ ° if ab z 0 = Ž ab a¸ ° ¸ ¯ 1 if ab 0 bš

K22 : V22 o [0, 1] is such that

­ 1 if abc z 0 ° K (a b c) = Ž abc °¯ 1 if abc 0 2 2

K13 : V13 o [0, 1] is such that §a a a¡ ­ 1 if a b c z 0 ¨ ¸ ° K ¨ b b b¸ = Ža b c ¨ c c c ¸ ¯° 1 if a b c 0 Š š 3 1

K32 : V23 o [0, 1] is given by

186

­ 1 if abc de z 0 ° K32 (a b c d e) = Ž abc de °¯ 1 if abc de 0 K33 : V33 o [0, 1] is define by

§a a a a a¡ ­ 1 if abc z 0 ¨ ¸ ° K ¨ b b b b b ¸ = Ž abc ¨ c c c c c ¸ °¯ 1 if abc 0 Š š 3 3

K14 : V14 o [0,1] such that

1 ­ if a b c d e z 0 ° K (a b c d e) = Ž a b c d e °¯ 1 if a b c d e 0 4 1

K42 : V24 o [0, 1] is defined by §a ¨ ¨0 4 K2 ¨ 0 ¨ ¨0 ¨0 Š

b f 0 0 0

c d e¡ ¸ g h i¸ ­ 1 if afjmp z 0 ° j k l ¸ = Ž afjmp ¸ 0 m n ¸ °¯ 1 if afjmp 0 0 0 p ¸š

K15 : V15 o [0,1] is defined by § a 0 0¡ ­ 1 if a z 0 ¨ ¸ ° K ¨ a a 0 ¸ = Ž 6a ¨ a a a ¸ °¯ 1 if a 0 Š š 5 1

K52 : V25 o [0,1] is such that

187

­1 ° if a z 0 K52 (a b c d e) = ® a °¯ 1 if a 0 K53 : V35 o [0,1] is defined by

ªa b c d º ­ 1 if abcd z 0 ° K «« a b c d »» = ® abcd «¬ a b c d »¼ °¯ 1 if abcd 0 5 3

K54 : V45 o [0,1] is given by

§a ¨ 5 ¨b K4 ¨c ¨ ©d

a b c d

a b c d

a· ¸ ­ 1 b¸ ° if abcd z 0 = ® abcd ¸ c ¸ °¯ 1 if abcd 0 d¹

Thus VK V K1 K2 K3 K4 K5) = (V1 V2 V3 V4 V5) K1 K2 K3 K4 K5) = V1K1 V2K2 V3K3 V4K4 V5K5) =

V ,V , V V , V V ,V V , V V , V , V , V

1 1K11

1 2 K12

4 1K14

1 3 K13

4 2 K42

2 1K12

5 1K15

2 2 K22

5 2 K52

3 1K13

5 3K53

3 2 K32

, V33K3

3

5 4 K54

is a special set fuzzy 5-vector space.

Now for this we give a special set fuzzy 5-vector subspace in the following.

Now take W11 = (S u S u {0} u {0}) V11 , ­°§ a b · ½ o° 1 W21 = ®¨ ¸ a, b,c,d 7Z ¾ V2 , °¯© c d ¹ °¿

188

­°§ a a a a a a ¡ ½° 1 W31 = Ž¨ ¸ a Â? Sž Â&#x17D; V3 , °¯Š a a a a a a š ¿° W12 = {all 4u4 upper triangular matrices of the form

­§ a °¨ ° 0 Ž¨¨ °¨ 0 °Š 0 ¯

a b 0 0

a b a 0

½ a¡ ° ¸ b¸ ° a, b Â? 3Zo ž Â&#x17D; V12 , ¸ a ° ¸ ° bš Âż

W22 = S u {0} u {0} Â&#x17D; V22 , ­§ a a a ¡ ½ ° ° ¨ ¸ W13 = Ž¨ b b b ¸ a, b,c Â? 2Zo ž Â&#x17D; V13 °¨ c c c ¸ ° š ¯Š Âż

W23 = (S u {0} u S u {0}u S) Â&#x17D; V23 , ­§ a a a a a ¡ ½ °¨ ¸ o° W = Ž¨ b b b b b ¸ a, b,c Â?11Z ž Â&#x17D; V33 , °¨ c c c c c ¸ ° š ¯Š Âż 3 3

W14 = (S u S u {0} u {0} u S) Â&#x17D; V14 , W24 = {all 5u upper triangular matrices with entries from 13Zo} Â&#x17D; V24 , W15 = {low triangular matrices of the form

­§ a 0 0 ¡ ½ °¨ ¸ o° 5 Ž¨ a a 0 ¸ a Â?12Z ž Â&#x17D; V1 , °¨ a a a ¸ ° š ¯Š Âż W25 = {S u {0} u {0} u {0} u S} Â&#x17D; V25 ,

189

­§ a b a b ¡ ½ °¨ ° ¸ W = Ž¨ a b a b ¸ a, b Â? Sž Â&#x17D; V35 , °¨ a b a b ¸ ° š ¯Š Âż 5 3

­§ a °¨ ° b 5 W4 = Ž¨ °¨¨ c °Š d ¯

a b c d

a b c d

½ a¡ ° ¸ b¸ o° a, b,c,d Â? 23Z ž Â&#x17D; V45 , c¸ ° ¸ ° dš Âż

here Wjii Â&#x17D; Vjii is a special set vector subspace of Vjii , 1 d ji d ni, 1 d i d 5. Now define a 5-map K K1 Â&#x2030; K2 Â&#x2030; K3 Â&#x2030; K4 Â&#x2030; K5): W = (W1 Â&#x2030; W2 Â&#x2030; â&#x20AC;Ś Â&#x2030; W5) o [0, 1] by Ki: Wi o [0,1], i = 1, 2, 3, 4, 5 such that K1 = K11 , K12 , K13 : W11 , W21, W31 o [0, 1]

by K11 : W11 o [0, 1] is given by ­1 if ab z 0 ° K (a b 0 0) = Ž ab °¯ 1 if ab 0 1 1

K12 : W21 o [0, 1] is defined by ­ 1 if a d z 0 ªa b º ° = Ža d K    c d Ÿ ° 1 if a d 0 ¯ 1 2

K13 : W31 o [0, 1] is such that

­ 1 if a z 0 ªa a a a a a º ° = K13  Ž12a   a a a a a a Ÿ ° 1 if a 0 ¯

190

W1K1 = W11K1 , W21K1 , W31K1 1

2

3

is a special fuzzy vector subspace.

K2 = K12 , K22 : W2 = W12 , W22 o [0,1]

is given by K12 : W12 o [0,1] is such that ÂŞa ÂŤ0 K12 ÂŤ ÂŤ0 ÂŤ ÂŹ0

a b 0 0

aº ­1 b  ° if ab z 0 = Ž ab a °  ¯ 1 if ab 0 bŸ

a b a 0

K22 : W22 o [0,1] is defined by

­1 ° if a z 0 K (a 0 0) = Ž a °¯ 1 if a 0 2 2

K3 = K , K , K 3 1

3 2

3 3

: W3 o [0,1];

K13 : W13 o [0,1] is such that §a a a¡ ­ 1 if a b c z 0 ¨ ¸ ° K ¨ b b b¸ = Ža b c ¨ c c c ¸ °¯ 1 if a b c 0 Š š 3 1

K32 : W23 o [0,1] is given by ­1 if a z 0 ° K (a 0 a 0 a) = Ž 3a °¯ 1 if a 0 3 2

K33 : W33 o [0,1] is such that 191

§a a a a a¡ ­ 1 if a b c z 0 ¨ ¸ ° K ¨ b b b b b¸ = Ža b c ¨ c c c c c ¸ ¯° 1 if a b c 0 Š š 3 3

W

Thus W3K3 =

3 1K13

, W23K3 , W33K3 2

3

is a special set fuzzy

vector subspace.

K4 = K14 , K42 : W4 = W14 , W24 o [0,1] such that K14 : W14 o [0,1] is given by

­ 1 if a b e z 0 ° K (a b 0 0 e) = Ž a b e °¯ 1 if a b e 0 4 1

K42 : W24 o [0,1] is such that §a ¨ ¨0 4 K2 ¨ 0 ¨ ¨0 ¨0 Š

c d e¡ ¸ g h i¸ ­ 1 if a p z 0 ° j k l ¸ = Ža p ¸ 0 m n ¸ °¯ 1 if a p 0 0 0 p ¸š

5 5 K5 : W5 = W1 , W2 , W35 , W45 o [0,1] b f 0 0 0

K15 : W15 o [0,1] is given by ªa 0 0º ­ 1 ° if a z 0 K  a a 0  = Ž a  a a a Ÿ °¯ 1 if a 0 5 1

192

K52 : W25 o [0, 1] is such that ­ 1 if a b z 0 ° K (a 0 0 0 b) = Ž a b °¯ 1 if a b 0 5 2

K53 : W35 o [0, 1] so that

ªa b a b º ­ 1 if ab z 0 ° K  a b a b  = Ž ab  a b a b Ÿ °¯ 1 if ab 0 5 3

K54 : W45 o [0, 1] is such that

ÂŞa ÂŤb K54 ÂŤ ÂŤc ÂŤ ÂŹd

a b c d

aº 1 ­ b  ° if a b c d z 0 = Ža b c d c ° 1 if a b c d 0  ¯ dŸ

a b c d

Thus W5K5 = W15K5 , W25K5 , W35K5 , W45K5 1

2

3

4

is a special set fuzzy

vector subspace.

Thus WK (W1 Â&#x2030; W2 Â&#x2030; W3 Â&#x2030; W4 Â&#x2030; W5)K =

W ,W ,W Â&#x2030; W ,W Â&#x2030; W ,W Â&#x2030; W , W Â&#x2030; W , W , W , W

1 1K11

1 2 K12

4 1K14

1 3K13

4 2 K42

2 1K12

5 1K15

2 2 K22

5 2 K52

3 1K13

5 3K53

3 2 K32

, W33K3

3

5 4 K54

is a special set fuzzy vector 4-subspace.

We now proceed onto define the notion of special set fuzzy linear n-algebra. It is pertinent to mention here that infact the special set fuzzy vector n-space and special set linear n-algebra are fuzzy equivalent.

193

DEFINITION 2.4.11: Let V = (V1 V2 Vn) 1 1 1 = V1 , V2 ,...,Vn1 V12 , V22 ,..., Vn22 V1n , V2n ,...,Vnnn

be a special set n-linear algebra over the set S. Take K = (K1 K2 Kn) a n-map from V = (V1 V2 Vn) into [0,1], i.e.,

= K , K ,...,K 1 1

1 2

1 n1

(K1 K2 Kn) K12 , K 22 ,...,Kn22 K1n , K 2n ,...,Knnn :

V = (V1 V2 Vn)

V , V ,..., V V , V ,...,V o [0, 1]; K = K ,...,K :V o [0,1] is defined such that K : W

1 1

1 2

1 n1

2 1

= V , V ,...,V

i 1

i

i ni

2 2

n 1

2 n2

n 2

n nn

i ti

i

i ti

o [0,1] so that Vt iK is a set fuzzy linear algebra, 1 d i d n, 1 d ti i i ti

d ni; i = 1, 2, …, n.

Thus ViKi is a special set fuzzy linear algebra.

VK = (V1 V2 ... Vn )K1 ... K n =

V V

1 1K11

, V21K 2 ,...,Vn1K1

n 1K1n

,V2nK n ,...,VnnK n

1

2

1 n1

n nn

V

2 1K12

, V22K 2 ,...,Vn2K 2 2

2 n2

…

is a special set fuzzy n linear algebra.

We shall illustrate this by the following example.

Example 2.4.10: Let V = V1 V2 V3 V4 1 1 2 = V1 , V2 V1 , V22 , V32 V13 , V23 , V33 , V43 V14 , V24 , V34

be a special set 4 linear algebra over the set S = {0, 1}.

Here ­°§ a b · ½ o° V11 = ®¨ ¸ a, b,c,d Z ¾ °¯© c d ¹ ¿°

194

­°§ a a a a · ½ o° V21 = ®¨ ¸ a Z ¾, °¯© a a a a ¹ ¿° ­§ a b c · ½ °¨ ¸ o° V = ®¨ d e f ¸ a, b,c,d,e,f ,g, h,i Z ¾ , °¨ g h i ¸ ° ¹ ¯© ¿ 2 1

­§ a °¨ ° a 2 V2 = ®¨ °¨¨ a °© a ¯

½ a· ° ¸ a¸ o° a Z ¾, a¸ ° ¸ ° a¹ ¿

V32 = Zo u Zo u Zo u Zo, V13 = Zo u Zo u Zo

­°§ a o · ½ o° V23 = ®¨ ¸ a, b,d Z ¾ , °¯© b d ¹ ¿°

½ °­§ a a a a a · o° V33 = ®¨ ¸ a, b Z ¾ , ¯°© b b b b b ¹ ¿° ­§ a °¨ °¨ b ° 3 V4 = ®¨ c °¨ d °¨ °¯¨© e

a b c d e

½ a· ° ¸ b¸ ° o° ¸ c a, b,c,d,e Z ¾ , ¸ ° d¸ ° e ¸¹ ¿°

­§ a a a a a · ½ °¨ ¸ o° V = ®¨ b b b b b ¸ a, b,c Z ¾ , °¨ c c c c c ¸ ° ¹ ¯© ¿ 4 1

195

­§ a °¨ ° a V24 = ®¨ °¨¨ b °© b ¯

0 a b b

0 0 b b

0· ¸ 0¸ s.t a, b Zo ¸ 0 ¸ b¹

½ ° ° ¾ ° ° ¿

and V34 = {Zo u Zo u Zo u Zo u Zo u Zo}.

K , K

1 1

1 2

K = K K K K : K , K22 , K32 K13 , K32 , K33 , K34 K14 , K42 , K34 : 2 1

V = (V1 V2 V3 V4 ) o [0,1] where Kiji : Vjii o [0, 1]; i = 1, 2, 3, 4; 1 d ti d ni ; 1 d i d 4 such that each Vti Ki is a set fuzzy vector space, so that ViKi is a special i ti

set fuzzy vector space; we define for each 1 d ti d ni, 1 d i d 4 as follows: K11 : V11 o [0,1] is such that ­ 1 if ad z bc §a b· ° K ¨ ¸ = ® ad bc ©c d¹ ° 1 if ad bc ¯ 1 1

K12 : V21 o [0, 1] is given by ­1 if a z 0 §a a a a· ° = K12 ¨ ¸ ® 8a © a a a a ¹ ° 1 if a 0 ¯ K12 : V12 o [0, 1] is defined by

§a b c· ­ 1 if abc z 0 ¨ ¸ ° K ¨ d e f ¸ = ® abc ¨ g h i ¸ °¯ 1 if abc 0 © ¹ 2 1

196

K22 : V22 o [0, 1] is given by ÂŞa ÂŤ 2 ÂŤa K2 ÂŤa ÂŤ ÂŹa

aº ­1 a  ° if a z 0 = Ž 2a a °  ¯ 1 if a 0 aŸ

K32 : V32 o [0,1] is defined by ­ 1 if abc d z 0 ° K32 (a b c d) = Ž abc d °¯ 1 if abc d 0 K13 : V13 o [0,1] is such that ­ 1 if a b c z 0 ° K (a b c) = Ž a b c if a b c 0 ¯° 1 3 1

K32 : V23 o [0,1] is defined to be

­1 if ad z 0 §a 0¡ ° K ¨ ¸ = Ž ad Š b d š ° 1 if ad 0 ¯ 3 2

K33 : V33 o [0,1] is given by ­ 1 if 3a 2b z 0 §a a a a a¡ ° K33 ¨ = ¸ Ž 3a 2b Šb b b b bš ° 1 if 3a 2b 0 ¯ K34 : V43 o [0,1] is given by

197

ÂŞa ÂŤb ÂŤ K34 ÂŤ c ÂŤ ÂŤd ÂŤÂŹ e

a b c d e

aº b  ­ 1 if a b c d e z 0 ° c = Ža b c d e  1 if a b c d e 0 d  ¯° e Ÿ

K14 : V14 o [0, 1] is such that §a a a a a¡ ­ 1 if 5a 5b 5c z 0 ¨ ¸ ° K ¨ b b b b b ¸ = Ž 5a 5b 5c ¨ c c c c c ¸ °¯ 1 if 5a 5b 5c 0 Š š 4 1

K42 : V24 o [0, 1] is given by ÂŞa ÂŤa K42 ÂŤ ÂŤb ÂŤ ÂŹb

0 a b b

0º 0  = 0  bŸ

0 0 b b

­ 1 if 3a 7b z 0 ° Ž 3a 7b °¯ 1 if 3a 7b 0

K34 : V34 o [0,1] is such that

­ 1 if a f z 0 ° K (a b c d e f) = Ž a f °¯ 1 if a f 0 4 3

Thus

VK = (V1 Â&#x2030; V2 Â&#x2030; V3 Â&#x2030; V4)K Â&#x2030; K Â&#x2030; K Â&#x2030; K

= V , V21K1 Â&#x2030; V12K2 , V22K2 , V32K2 Â&#x2030; V13K3 , V23K3 , V33K3 , V43K3 1 1K11

2

1

2

3

Â&#x2030; V14K4 , V24K4 , V34K4 1

2

is a special set fuzzy linear 4-algebra.

198

3

1

2

3

4

Next we proceed onto define the notion of special set fuzzy linear n-subalgebra.

DEFINITION 2.4.12: Let V = (V1 V2 Vn)

= V11 , V21,...,Vn11 V12 , V22 ,..., Vn22

V

n 1

, V2n ,...,Vnnn

be a special set linear n – algebra defined over the set S. Suppose W = (W1, W2, … , Wn) 1 1 1 = W1 , W2 ,...,Wn1 W12 , W22 ,...,Wn22 W1n , W2n ,...,Wnnn

(V1 V2 Vn) be a proper special set linear n-subalgebra of V over S. Define K = (K1, K2, Kn) : : (W1 , W2 , … , Wn) o [0,1] such that WK = (W1 W2 ... Wn )K1 K 2 ... K n

= W11K1 , W21K 2 ,...,Wn1K1 W1K2 2 , W22K 2 ,...,Wn2K 2 1

1

1 n1

1

2

W1Kn , W2nK ,...,WnnK n 1

n 1

n n nn

2 n2

be a special set fuzzy linear n-algebra then we call WK to be the special set fuzzy linear n-subalgebra where K = (K1, K2, Kn) 1 1 1 = K1 , K 2 ,...,Kn1 K12 , K 22 ,...,Kn22 K1n , K 2n ,...,Knnn

is a map such that WK = (W1K1 , W2K2 , … , WnKn) where WiKi is a special set fuzzy linear subalgebra, i = 1, 2, …, n.

Now we illustrate this by a simple example.

Example 2.4.11: Let V = (V1 V2 V3 V4) where V1 = V11 , V21, V31 , V41 , V2 = V12 , V22 , V32 , V42 , V3 = V13 , V23 and V4

= V14 , V24 , V34 be a special set linear 4-algebra over the set S =

Zo, where V11 = S u S u S, V21 = {all polynomials of degree less than or equal to four with coefficients from Zo}, V31 = {all 4u4 upper triangular matrices with entries from Zo} and

199

­°§ a a a a a · ½ o° V41 = ®¨ ¸ a, b Z ¾ . °¯© b b b b b ¹ °¿ V12 = {3 u matrices with entries from Zo},

­§ a · ½ °¨ ¸ ° °¨ a ¸ ° °¨ ¸ o° 2 V2 = ® a a Z ¾ °¨ a ¸ ° °¨ ¸ ° °¯¨© a ¸¹ ¿° V32 = S u S u S u S and V42 = {5 u 5 lower triangular matrices

with entries from S}. V13 = S u S u S u S u S and ­°§ a a a · ½ o° V23 = ®¨ ¸ a, b Z ¾ . ¯°© b b b ¹ ¿° V14 = S u S u S u S, V24 = {4 u lower triangular matrices with entries from Zo} and ­§ a °¨ °¨ a ° 4 V3 = ®¨ a °¨ a °¨ °¯¨© a

½ b· ° ¸ b¸ ° ° b ¸ a, b Zo ¾ . ¸ ° b¸ ° b ¸¹ ¿°

Consider W = (W1 W2 W3 W4) V where W1 = W11 , W21, W31, W41 V1 is such that W11 = S u {0} u S V11 , W21 = {all polynomials of degree less than or equal to four with

200

coefficients from 3Zo} V21 , W31 = {all 4 u 4 upper triangular matrices with entries from 5Zo} V31 , and ­°§ a a a a a · ½ o° 1 W41 = ®¨ ¸ a, b 7Z ¾ V4 , b b b b b ¹ ¯°© ¿° W12 = {3 u matrices with entries from 11Zo} V12 , ­§ a · ½ °¨ ¸ ° °¨ a ¸ ° ° ° V22 = ®¨ a ¸ a 2Zo ¾ V22 , °¨ a ¸ ° °¨ ¸ ° °¯¨© a ¸¹ ¿° W32 = {S u S u {0} u {0}} V32 and W42 = {5 u 5 lower triangular matrices with entries from S} V42 , W13 = S u S u {0} u {0} u ^ ` V13 , ½ °­§ a a a · o° 3 W23 = ®¨ ¸ a Z ¾ V2 , a a a ¹ ¯°© ¿° W14 = {S u {0} u {0} u {0}} V14 , W24 = {4 u lower triangular matrices with entries from Zo} V24 and ­§ a °¨ °¨ a ° 4 W3 = ®¨ a °¨ a °¨ °¯¨© a

½ a· ° ¸ a¸ ° ° a ¸ a Zo ¾ V34 . ¸ ° a¸ ° a ¸¹ ¿°

201

Define K K1 Â&#x2030; K2 Â&#x2030; K3 Â&#x2030; K4): W = (W1 Â&#x2030; W2 Â&#x2030; W3 Â&#x2030; W4) o [0, 1] by the following rule,

K1 : W1 o [0,1] is such that K1 = K11 , K12 , K13 , K14 : W11 , W21, W31 , W41 o [0, 1] given by K11 : W11 o [0, 1] is defined by ­1 if ab z 0 ° K11 (a 0 b) = Ž ab °¯ 1 if ab 0 K12 : W21 o [0, 1] is defined by 1 ­ if p(x) is not a constant ° K (p (x)) = Ž deg p(x) °1 if p(x) is a constant ¯ 1 2

K13 : W31 o [0, 1] is given by ÂŞa ÂŤ0 K13 ÂŤ ÂŤ0 ÂŤ ÂŹ0

b e 0 0

c dº 1 ­ if abcd efg z 0 f g  ° = Ž abcd efg h i ° 1 if abcd efg 0  ¯ 0 jŸ

K14 : W41 o [0,1] is defined by

­1 if ab z 0 ªa a a a a º ° = Ž ab K    b b b b b Ÿ ° 1 if ab 0 ¯ 1 4

202

Thus

W1K1 = W11K1 , W21K1 , W31K1 , W41K1 1

2

3

4

is a special set fuzzy linear subalgebra.

K2 = K12 , K22 , K32 , K24 : W2 = W12 , W22 , W32 , W42 o [0, 1] is such that K12 : W12 o [0, 1] is defined by ªa b c º ­ 1 if abcdg z 0 ° K «« d e f »» = ® abcdg ° ¬« g h i »¼ ¯ 1 if abcdg 0 2 1

K22 : W22 o [0,1] such that ªa º «a » « » °­ 1 if a z 0 K22 « a » = ® 5a « » ° 1 if a 0 «a » ¯ «¬ a »¼ K32 : W32 o [0,1] is such that ­ 1 if a b z 0 ° K (a b 0 0) = ® a b °¯ 1 if a b 0 2 3

K24 : W42 o [0,1] is given by

203

§a ¨ ¨d 2 K4 ¨ g ¨ ¨j ¨o Š

0 0 0 0¡ ¸ e 0 0 0¸ h i 0 0¸ = ¸ k l m 0¸ p q r s ¸š

1 ­ if a e i m s z 0 ° Ža e i m s °¯ 1 if a e i m s 0 Thus W2K2 =

W

2 1K12

, W22K2 , W32K2 , W42K2 2

3

4

is a special set fuzzy

linear subalgebra.

K3: W3 o [0, 1]; K3 = K13 , K32 : W13 , W23 o [0, 1] is given by; K13 : W13 o [0, 1] is such that ­1 if ab z 0 ° K (a b 0 0 0) = Ž ab °¯ 1 if ab 0 3 1

K32 : W23 o [0,1] is defined by ­1 if a z 0 ªa a a º ° = Ž 6a K    a a a Ÿ ° 1 if a 0 ¯ 3 2

Thus W3K3 =

W

3 1K13

, W23K3

2

is a special set fuzzy linear sub

algebra.

Define K4 : W4 o [0,1] as follows.

204

K4 = K14 , K42 , K34 : W14 , W24 , W34 o [0, 1]; K14 : W14 o [0, 1] is defined by ­1 ° if a z 0 K (a 0 0 0) = ® a °¯ 1 if a 0 4 1

K42 : W24 o [0, 1] is given by

§a ¨ b K42 ¨ ¨d ¨ ©g

0 0 0· ¸ ­ 1 if acfj z 0 c 0 0¸ ° = ® acfj e f 0¸ ° ¸ ¯ 1 if acfj 0 h i j¹

K34 : W34 o [0, 1] §a ¨ ¨a 4 K3 ¨ a ¨ ¨a ¨a ©

a· ¸ a¸ ­ 1 if a z 0 ° a ¸ = ®10a ¸ a ¸ °¯ 1 if a 0 a ¸¹

Thus W4K4 = W14K4 , W24K4 , W34K4 1

2

3

is a special set fuzzy linear

subalgebra of V4.

Now WK

= =

(W1 W2 W3 W4 W5)K (W1K W2K W3K W4K )

W , W , W , W W , W W , W W , W , W

1 1K11

1 2 K12

3 1K13

1 3K13

1 4 K14

3 2 K32

4 1K14

205

2 1K12

4 2 K42

2 2 K22

4 3 K34

, W32K2 , W42K2 3

4

is a special set fuzzy linear 4-subalgebra.

It is important to mention that the notion of special set fuzzy linear n-subalgebra and special set vector n-subspace are equivalent. However we can associate with a given special set linear n-subalgebra infinite number of special set fuzzy linear nsubalgebras. Secondly the notion of special set fuzzy linear nsubalgebras and special set fuzzy vector spaces are fuzzy equivalent.

So even if the models are different in reality by fuzzifying they can be made identical or equivalent.

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Chapter Three

SPECIAL SEMIGROUP SET VECTOR SPACES AND THEIR GENERALIZATION

This chapter has two sections. First section just recalls some of the basic definitions from [60]. Section two defines the notion of special semigroup set vector semigroup set vector spaces and generalizes them.

3.1 Introduction to Semigroup Vector Spaces In this section we just recall the basic definitions essential to make this chapter a self contained one.

DEFINITION 3.1.1: Let V be a set, S any additive semigroup with 0. We call V to be a semigroup vector space over S if the following conditions hold good.

1. sv V for all s S and v V. 2. 0. v = 0 V for all v V and 0 S; 0 is the zero vector.

3. (s1 + s2) v = s1 v + s2 v for all s1, s2 S and v V.

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We illustrate this by the following examples.

Examples 3.1.1: Let V = (Z+ Â&#x2030; {0}) u 2Z+ Â&#x2030; {0} u (3Z+ Â&#x2030; {0}) be a set and S = Z+ Â&#x2030; {0} be a semigroup under addition. V is a semigroup vector space over S. Example 3.1.2: Let

­°§ a V = Ž¨ 1 °¯Š a 5

a2 a6

a3 a7

½° a4 ¡

¸ a i Â? Z Â&#x2030; {0};1 d i d 8ž a8 š °¿

be a set. Suppose S = 2Z+ Â&#x2030; {0} be a semigroup under addition.

V is a semigroup vector space over S. Example 3.1.3: Let V = 3Z+ Â&#x2030; {0} be a set and S = Z+ Â&#x2030; {0} be a semigroup under addition. V is a semigroup vector space over S. DEFINITION 3.1.2: Let V be semigroup vector space over the semigroup S. A set of vectors {v1, â&#x20AC;Ś, vn} in V is said to be a semigroup linearly independent set if (i) vi z svj for any s Â? S for i z j; 1 < i, j < n.

DEFINITION 3.1.3: Let V be a semigroup vector space over the semigroup S under addition. Let T = {v1, â&#x20AC;Ś, vn} Â&#x17D; V be a subset of V we say T generates the semigroup vector space V over S if every element v of V can be got as v = svi, vi Â? T; s Â? S. DEFINITION 3.1.4: Let V be a semigroup vector space over the semigroup S. Suppose P is a proper subset of V and P is also a semigroup vector space over the semigroup S, then we call P to be semigroup subvector space of V. Example 3.1.4: Let V = {Q+ Â&#x2030; {0}} be a semigroup vector space over the semigroup S = Z+ Â&#x2030; {0} Take W = 2Z+ Â&#x2030; {0}, W is a semigroup subvector space of V over S. 208

Example 3.1.5: Take V = {(1 1 0 1 0), (0 0 0 0 0), (1 1 1 1 1), (1 0 1 0 1), (0 1 1 1 0), (0 0 0), (0 1 1), (0 1 0), (1 0 1)} be a set. V is a semigroup vector space over the semigroup S = {0, 1} under addition 1 + 1 = 1.

Take W = {(1 0 1), (0 1 1), (0 0 0), (0 1 0)} V; W is a semigroup subvector space over the semigroup S = {0, 1}. In fact every subset of V with the two elements (0 0 0) and or (0 0 0 0 0) is a semigroup subvector space over the semigroup S = {0, 1}.

DEFINITION 3.1.5: Let V be a semigroup vector space over the semigroup S. If V is itself a semigroup under ‘+’ then we call V to be a semigroup linear algebra over the semigroup S, if s (v1 + v2) = sv1+ sv2 ; v1, v2 V and s S. DEFINITION 3.1.6: Let V be a semigroup linear algebra over the semigroup S. Suppose P is a proper subset of V and P is a subsemigroup of V. Further if P is a semigroup linear algebra over the same semigroup S then we call P a semigroup linear subalgebra of V over S. DEFINITION 3.1.7: Let V be a semigroup vector space over the semigroup S. Let P V be a proper subset of V and T a subsemigroup of S. If P is a semigroup vector space over T then we call P to be a subsemigroup subvector space over T. DEFINITION 3.1.8: Let V be a semigroup linear algebra over the semigroup S. Let P V be a proper subset of V which is a subsemigroup under ‘+’. Let T be a subsemigroup of S. If P is a semigroup linear algebra over the semigroup T then we call P to be a subsemigroup linear subalgebra over the subsemigroup T. DEFINITION 3.1.9: Let V be a semigroup linear algebra over the semigroup S. If V has no subsemigroup linear subalgebras over any subsemigroup of S then we call V to be a pseudo simple semigroup linear algebra.

209

DEFINITION 3.1.10: Let V be a semigroup linear algebra over a semigroup S. Suppose V has a proper subset P which is only a semigroup vector space over the semigroup S and not a semigroup linear algebra then we call P to be the pseudo semigroup subvector space over S. DEFINITION 3.1.11: Let V be a semigroup linear algebra over the semigroup S. Let P be a proper subset of V and P is not a semigroup under the operations of V. Suppose T S, a proper subset of S and T is also a semigroup under the same operations of S; i.e., T a subsemigroup of S, then we call P to be a pseudo subsemigroup subvector space over T if P is a semigroup vector space over T. DEFINITION 3.1.12: Let V be a semigroup linear algebra over the semigroup S. If V has no subsemigroup linear algebras over subsemigroups of S then we call V to be a simple semigroup linear algebra.

DEFINITION 3.1.13: Let V be a semigroup under addition and S a semigroup such that V is a semigroup linear algebra over the semigroup S. If V has no proper subset P ( V) such that V is a pseudo subsemigroup vector subspace over a subsemigroup, T of S then we call V to be a pseudo simple semigroup linear algebra.

DEFINITION 3.1.14: Let V and W be any two semigroup linear algebras defined over the same semigroup, S we say T from V to W is a semigroup linear transformation if T(cD + E) = cT (D) + T (E) for all c S and D, E V. DEFINITIONS 3.1.15: Let V be a semigroup linear algebra over the semigroup S. A map T from V to V is said to be a semigroup linear operator on V if T (cu + v) = cT (u) + T (v) for every c S and u, v V. DEFINITION 3.1.16: Let V be a semigroup vector space over the semigroup S. Let W1, …, Wn be a semigroup subvector spaces of

210

n

V over the semigroup S. If V = * Wi but Wi Wj z I or {0} if i i 1

z j then we call V to be the pseudo direct union of semigroup vector spaces of V over the semigroup S. DEFINITION 3.1.17: Let V be a semigroup linear algebra over the semigroup S. We say V is a direct sum of semigroup linear subalgebras W1, …, Wn of V if 1. V = W1 + … + Wn 2. Wi Wj = {0} or I if i z j (1 d i, j d n).

DEFINITION 3.1.18: Let V be a set with zero, which is non empty. Let G be a group under addition. We call V to be a group vector space over G if the following condition are true.

1. For every v V and g G gv and vg V. 2. 0.v = 0 for every v V, 0 the additive identify of G. We illustrate this by the following examples.

Example 3.1.6: Let V = {0, 1, 2, …, 15} integers modulo 15. G = {0, 5, 10} group under addition modulo 15. Clearly V is a group vector space over G, for gv { v1 (mod 15), for g G and v, v1 V. Example 3.1.7: Let V = {0, 2, 4, …, 10} integers 12. Take G = {0, 6}, G is a group under addition modulo 12. V is a group vector space over G, for gv { v1 (mod 12) for g G and v, v1 V. Example 3.1.8: Let

­°§ a M2 u 3 = ®¨ 1 ¯°© a 4

a2 a5

½° a3 · ¸ a i { f ,..., 4, 2,0, 2, 4,..., f}¾ . a6 ¹ ¿°

Take G = Z be the group under addition. M2 u 3 is a group vector space over G = Z.

211

DEFINITION 3.1.19: Let V be the set which is a group vector space over the group G. Let P V be a proper subset of V. We say P is a group vector subspace of V if P is itself a group vector space over G. DEFINITION 3.1.20: Let V be a group vector space over the group G. We say a proper subset P of V to be a linearly dependent subset of V if for any p1, p2 P, (p1 z p2) p1 = ap2 or p2 = a'p1 for some a, a' G. If for no distinct pair of elements p1, p2 P we have a, a1 G such that p1 = ap2 or p2 = a1p1 then we say the set P is a linearly independent set.

DEFINITION 3.1.21: Let V be a group vector space over the group G. Suppose T is a subset of V which is linearly independent and if T generates V i.e., using t T and g V we get every v V as v = gt for some g G then we call T to be the generating subset of V over G. The number of elements in V gives the dimension of V. If T is of finite cardinality V is said to be finite dimensional otherwise V is said to be of infinite dimension.

DEFINITION 3.1.22: Let V be a group vector space over the group G. Let W V be a proper subset of V. H G be a proper subgroup of G. If W is a group vector space over H and not over G then we call W to be a subgroup vector subspace of V. DEFINITION 3.1.23: Let V be a group vector space over the group G. Suppose W V is a subset of V. Let S be a subset of G. If W is a set vector space over S then we call W to be a pseudo set vector subspace of the group vector space.

DEFINITION 3.1.24: Let V be a group linear algebra over the group G. Suppose W1, W2, …, Wn are distinct group linear subalgebras of V. We say V is a pseudo direct sum if

212

1. W1 + …+ Wn = V 2. Wi Wj z {0}, even if i z j 3. We need Wi’s to be distinct i.e., Wi Wj z Wi or Wj if i z j.

For more please refer [60].

3.2 Special Semigroup Set Vector Spaces and Special Group Set Vector Spaces

Now in this section we proceed on to define yet another new type of vector spaces called special semigroup set vector space over sets.

DEFINITION 3.2.1: Let V = (S1, S2, …, Sn) be a set of collection of semigroups. Suppose P is any nonempty set such that for every p P and si Si, psi Si true for each i = 1, 2, …, n.

Then we call V to be a special semigroup set vector space over the set P.

We now illustrate this by the following example.

Example 3.2.1: Let V = {S1, S2, S3, S4}, where

­°§ a b · S1 = ®¨ ¸ a, b,c,d Z2 ¯°© c d ¹

½° {0,1}¾ ¿°

is a semigroup under addition modulo 2, S2 = Z2 u Z2 u Z2 u Z2 is a semigroup under component wise addition, S3 = {Z2[x]; all polynomials of degree less than or equal to 3} is a semigroup under polynomial addition and ­§ a °¨ ° c S4 = ®¨ °¨¨ e °© g ¯

b· ¸ d¸ a, b,c,d,e,f ,g, h Z2 f¸ ¸ h¹

213

½ ° ° {0,1}¾ ° ° ¿

is a semigroups under matrix addition modulo 2.

Then V = {S1, S2, S3, S4} is a special semigroup set vector space over the set P = {0, 1}.

Example 3.2.2: Let V = {S1, S2, S3} where

­§ a1 °¨ S1 = Z u Z u Z , S2 = Ž¨ a 4 °¨ a ¯Š 7 +

+

+

a2 a5 a8

½ a3 ¡ ° ¸

a 6 ¸ a i � Z ;1 d i d 9 ž ° a 9 ¸š ¿

and S3 = {Z+[x]; all polynomials of degree less than or equal to 5 with coefficients from Z+}; clearly S1, S2 and S3 are semigroups under addition. Thus V = {S1, S2, S3} is a special semigroup set vector space over the set P Â&#x17D; Z+.

Example 3.2.3: Let V = {S1, S2, S3, S4, S5} where S1 = {Z6 u Z6} is a semigroup under addition modulo 6,

­°§ a b ¡ ½° S2 = Ž¨ ¸ a, b,c,d � Z6 ž °¯Š c d š ¿° and ­§ x °¨ ° y S3 = Ž¨ °¨¨ z °Š w ¯

½ x¡ ° ¸ y¸ ° x, y, z, w � Z6 ž z¸ ° ¸ ° wš ¿

are semigroups under matrix addition modulo 6. ­°§ a a a a ¡ ½° S4 = Ž¨ ¸ a � Z6 ž ¯°Š a a a a š ¿° and

214

­§ a °¨ °¨ 0 ° S5 = ®¨ 0 °¨ 0 °¨ ¨ ¯°© 0

½ a a a a· ° ¸ a a a a¸ ° ° ¸ 0 a a a a Z6 ¾ ¸ ° 0 0 a a¸ ° 0 0 0 a ¸¹ ¿°

are semigroups under addition modulo 6. V = {S1, S2, S3, S4, S5} is a special semigroup set vector space over any subset of Z6.

Now we proceed to define the notion of special semigroup set vector subspace of a special semigroup set vector space over a set P. DEFINITION 3.2.2: Let V = {S1, S2, …,Sn} be a special semigroup set vector space over the set P, that is each Si is a semigroup and for each si Si and p P, psi Si; 1 d i d n. Let W = {T1, …, Tn} where each Ti is a proper subsemigroup of Si for i = 1, 2, …, n. If W is a special semigroup set vector space over the set P, then we call W to be the special semigroup set vector subspace of V over P.

Now we illustrate this by a few examples.

Example 3.2.4: Let V = {S1, S2, S3, S4, S5} where S1 = {Z5 u Z5 u Z5 u Z5}, a semigroup under component wise addition modulo 5, ­°§ a a · ½° S2 = ®¨ ¸ a, b Z5 ¾ ¯°© a b ¹ ¿° a semigroup under matrix addition modulo 5, S3 = {Z5[x]; all polynomials of degree less than or equal to six}, is a semigroup under polynomial addition,

­§ a a a1 a1 · ½ °¨ ° ¸ S4 = ®¨ a a a1 a1 ¸ a1 ,a Z5 ¾ °¨ a a a a ¸ ° 1 1¹ ¯© ¿

215

is a semigroup under matrix addition modulo 5 and ­§ a °¨ ° b S5 = ®¨ °¨¨ c °© d ¯

½ 0 0 0· ° ¸ e 0 0¸ ° a, b,c,d,e,f ,g, h,i, j, k Z5 ¾ f i 0¸ ° ¸ ° g j k¹ ¿

is again a semigroup under matrix addition modulo 5. Thus V is a special semigroup set vector space over the set P = {0, 1, 2, 3, 4} = Z5. Take W = (W1, W2, W3, W4, W5) where W1 = {(a a a a) / a Z5} V1, ­°§ a a · ½° W2 = ®¨ ¸ a Z5 ¾ V2 °¯© a a ¹ ¿° a subsemigroup of V2 ; W3 = {Z5[x]; all polynomials of even degree with coefficients from Z5} is a subsemigroup of V3, ­°§ a a a a · ½° W4 = ®¨ ¸ a Z5 ¾ V4; °¯© a a a a ¹ °¿

a subsemigroup of V4 and ­§ a °¨ ° a W5 = ®¨ °¨¨ a °© a ¯

½ 0 0 0· ° ¸ a 0 0¸ ° a Z5 ¾ V5; a a 0¸ ° ¸ ° a a a¹ ¿

a subsemigroup of V5. Clearly W = (W1, W2, W3, W4, W5) is a special semigroup set subvector space over the set P = {0, 1, 2, 3, 4} Z5.

Now we proceed on to give yet another example.

Example 3.2.5: Let V = (V1, V2, V3, V4) where

216

­§ a b c ¡ ½ °¨ ¸ ° V1 = Ž¨ d e f ¸ a, b,c,d,e,f ,g, h,i � Z ž °¨ g h i ¸ ° š ¯Š ¿ is a semigroup under matrix addition; V2 = {Z+ u Z+ u Z+} a semigroup under component wise addition, V3 = {Z+[x]; all polynomials in the variable x with coefficients from Z+} is a semigroup under polynomial addition and ­§ a1 0 °¨ ° 0 a2 V4 = Ž¨ °¨¨ 0 0 °Š 0 0 ¯

0 0 a3 0

½ 0¡ ° ¸ 0¸ ° a i � Z ;1 d i d 4 ž 0¸ ° ¸ ° a4 š ¿

is again a semigroup under matrix addition. Clearly V = (V1, V2, V3, V4) is a special semigroup set vector space over the set Z+.

Take W = {W1, W2, W3, W4} where ­§ a a a ¡ ½ °¨ ¸ ° W1 = Ž¨ a a a ¸ a � Z ž °¨ a a a ¸ ° š ¯Š ¿ is a subsemigroup of V1 under matrix addition, W2 = (3Z+ u 3Z+ u5Z+) is a subsemigroup of Z+ u Z+ u Z+, W3 = {all polynomials of even degree in x with coefficients from Z+}is a subsemigroup under polynomial addition of V3 and ­§ a °¨ ° 0 W4 = Ž¨ °¨¨ 0 °Š 0 ¯

½ 0 0 0¡ ° ¸ a 0 0¸ ° a, b � 2Z ž 0 b 0¸ ° ¸ ° 0 0 bš ¿

217

is again a subsemigroup of V4. Thus W = {W1, W2, W3, W4} Â&#x17D; V is a special semigroup set vector subspace of V. Now we define the notion of special semigroup set linear algebra over a semigroup P. DEFINITION 3.2.3: Let V = (V1, V2, â&#x20AC;Ś, Vn) be a special semigroup set vector space over the set P. If P is an additive semigroup then we call V to be a special semigroup set linear algebra over the semigroup P.

It is important to note that all special semigroup set linear algebras are special semigroup set vector spaces but special semigroup set vector spaces in general are not special semigroup set linear algebras.

We see by the very definitions of special semigroup set vector spaces and special semigroup set linear algebras all special semigroup set linear algebras are special semigroup set vector spaces; as every semigroup can be realized also as a set.

On the contrary a special semigroup set vector space in general is not a special semigroup set linear algebra. This is proved by an example.

Take V = (V1, V2, V3) where V1 = {Z+ u Z+}, V2 = {Z+ [x]} and ½ °­§ a b ¡ ° V3 = Ž¨ ¸ a, b,c,d � Z ž ; ¯°Š c d š ¿° , V and V are semigroups under addition.

Suppose P clearly V1 2 3 = {1, 2, 5, 7, 3, 8, 12, 15} then V is a special semigroup set vector space over the set P. Clearly P is not a semigroup so V cannot be a special semigroup set linear algebra.

Now we give some examples of special semigroup set linear algebras.

Example 3.2.6: Let V = {V1, V2, V3, V4} be such that V1 = {Z10 u Z10}, V2 = {Z10[x] where x an indeterminate, and Z10[x] contains all polynomials in x with coefficients from Z10},

218

­§ a °¨ ° a V3 = Ž¨ °¨¨ a °Š a ¯ ­§ a °¨ ° b V4 = Ž¨ °¨¨ c °Š d ¯

½ a¡ ° ¸ a¸ ° a � Z10 ž , ¸ a ° ¸ ° aš ¿

½ 0 0 0¡ ° ¸ e 0 0¸ ° a, b,e,c,f ,g,d, h,f ,i � Z10 ž ¸ f g 0 ° ¸ ° g h iš ¿

and ­°§ a b c d e ¡ ½° V5 = Ž¨ ¸ a, b,c,d,e,f ,g, h,i, j � Z10 ž °¯Š f g h i j š °¿

are semigroups under addition. Clearly if we take P = Z10, P is also a semigroup under addition modulo 10. V is a special semigroup set linear algebra over the semigroup Z10.

Example 3.2.7: Let V = (V1, V2, V3, V4) where

V1 = {Z+ u Z+ u Z+}, ­°§ a b ¡ ½ ° V2 = Ž¨ ¸ a,c,d � Z ž , °¯Š 0 d š °¿

­§ 0 ½ a b c ¡ °¨ ° ¸ d e ¸ ° a 0 ° V3 = Ž¨ a, b,c,d,e,f � Z ž ¨ ¸

b d 0 f °¨ ° °Š c e f 0 ¸š ° ¯ ¿ and

219

­§ a °¨ ° b V4 = Ž¨ °¨¨ c °Š d ¯

½ b c d¡ ° ¸ e f g¸ ° a, b,c,d,e,f ,g, h,i and j are in 2Z ž . ¸ f h i ° ¸ ° g i jš ¿

Clearly V1, V2, V3 and V4 are semigroup under addition and V is a special semigroup set linear algebra over the semigroup Z+.

Example 3.2.8: Let V = (V1, V2, V3, V4, V5) where V1 = Z5, V2 = Z7, V3 = Z3, V4 = Z12 and V5 = Z13 are semigroups under addition modulo the appropriate n, n Â? {5, 7, 3, 12, 13}. V is a special semigroup set vector space over the set S = {0, 1}.

Remark: When we say V = (V1 ,â&#x20AC;Ś, Vn) is a special semigroup set linear algebra over the semigroup T, we unlike in a linear algebra demand only two things

1.

2.

3.

4.

For every pi Â?Vi and ti Â? T, tipi Â? Vi. 1 d i d n Vi is a semigroup T is a semigroup We do not have (a + b)pi = api+ bpi in general; 1 d i d n;

that is this sort of distribution laws are never assumed in case of special semigroup set linear algebras over the semigroup T. Now we proceed on to define the notion of special semigroup linear subalgebra and special subsemigroup linear subalgebras.

Before we go for these two definitions we proceed onto show how this new structure varies from other structures.

Example 3.2.9: Suppose V = (S1, S2) = (Z12, Z7) be the semigroup special linear algebra over the semigroup P = Z6.

Then P = {0, 1, 2, â&#x20AC;Ś, 5}, semigroup under addition modulo 6.

For 7 Â? Z12 and 5 Â?Z6 5.7 { 11 (mod 12). For 4 Â? Z6 and 6 Â? Z7 4.6 = 24 { 3(mod 7) 4.6 { 0 (mod 12) if 6 Â? Z12 and 4 Â?Z6. So as an element of one semigroup its impact on the same element from Z6 is distinctly different.

220

Thus when we have two different sets (semigroups) they act very differently on the same element from the semigroup over which they are defined, or in truth even the elements may represent the same property but its acting on the same element from the semigroup yields different values.

This is very evident from the example we give yet another example before we proceed to work with other properties.

Example 3.2.10: Let V = (V1, V2, V3) where V1 = Z12, V2 = Z9 and V3 = Z8. Suppose V is a special semigroup set linear algebra over the semigroup P = Z6. Take 6 Z12, 6 Z9 and 6 Z8.

Take 4 P = Z6. Now 4.6 { 0 (mod 12) 4.6 { 6 (mod 9) and 4.6 { 0 (mod 8). So for these two semigroups V1 and V3, 4 P acts as an annulling element were as for V2 it gives back the same element.

This special type of properties are satisfied by many real models, it may be useful in industries or experiments in such cases they can use these special algebraic structures.

DEFINITION 3.2.4: Let V = (V1, V2, …, Vn) be a special semigroup set linear algebra over the semigroup S. Let W= (W1, W2, …, Wn) (V1, V2, …, Vn) (Wi Vi; Wi subsemigroup of Vi, 1d id n). If W is itself a special semigroup set linear algebra over the semigroup S then we call W to be a special semigroup set linear subalgebra of V over the semigroup S.

We illustrate this by some examples.

Example 3.2.11: Let V = (V1, V2, V3, V4) where V1 = Z8, V2 = Z6, V3 = Z9 and V3 = Z12. V is a semigroup set linear algebra over the semigroup P = Z4. Take W = (W1, W2, W3, W4) where W1 = {0, 2, 4, 6} Z8, W2 = {0, 2, 4} Z6, W3 = {0, 3, 6} Z9 and W4 = {0, 3, 6, 9} Z12. It is easily verified W = (W1, W2, W3, W4) is a special semigroup set linear subalgebra over the semigroup P = Z4.

221

Example 3.2.12: Let V = (V1, V2, V3, V4, V5) where V1 = Z7, V2 = Z5, V3 = Z6, V4 = Z8 and V5 = Z4. V is a special semigroup set linear algebra over the semigroup P = Z2 = {0, 1}. We see V has no proper subset W which can be a special semigroup set linear algebra over the semigroup P. For V1 and V2 have no proper subsemigroups.

In view of this we define the notion of special semigroup set simple linear algebra.

DEFINITION 3.2.5: Let V = (V1, V2, …, Vn) be a special semigroup set linear algebra over the semigroup P. If V has no proper subset W = (W1, W2, …, Wn) such that Wi is a proper subsemigroup of Vi for atleast for one i, 1d i d n then we call V to be a special semigroup set simple linear algebra. In a special set simple linear algebra V we do not have proper special semigroup set linear subalgebras.

We illustrate this situation by some examples.

Example 3.2.13: Let V = (V1, V2, V3, V4, V5) where V1 = Z3, V2 = Z7, V3 = Z5, V4 = Z6 and V5 = Z11 be a special semigroup set linear algebra over the semigroup P = Z8. We see V1, V2, V3 and V5 has no proper subsemigroups. So V is only a special semigroup set simple linear algebra.

Example 3.2.14: Let V = (V1, V2, V3, V4) where V1 = Z7, V2 = Z13, V3 = Z19 and V4 = Z11; V is a special semigroup set linear algebra over the semigroup P = Z6. We see V1, V2, V3 and V4 do not have any proper subsemigroups, i.e., none of the semigroups V1, V2, V3 and V4 have subsemigroups. Thus V = (V1, V2, V3, V4) is a special semigroup set simple linear algebra over Z6.

In view of this we have the following definition.

DEFINITION 3.2.6: Let V = (V1, V2, …, Vn) be a special semigroup set linear algebra over the semigroup P. In none of the semigroups Vi, 1 d i d n has proper subsemigroups then we

222

call V to be a special semigroup set strong simple linear algebra.

Example 3.2.15: Let V = (V1, V2, V3, V4) where V1 = Z5, V2 = Z23, V3 = Z11 and V4 = Z13 be the special semigroup set linear algebra over the semigroup P = Z4. It is easily verified V is a special semigroup set strong simple linear algebra.

We have the following interesting theorem.

THEOREM 3.2.1: Let V = (V1, V2, …, Vn) be a special semigroup set strong simple linear algebra then V is a special semigroup set simple linear algebra. But a special semigroup set simple linear algebra need not in general be a special semigroup set strong simple linear algebra.

Proof: Let V = (V1, V2, …, Vn) be a special semigroup set strong simple linear algebra over the semigroup P. This implies every semigroup Vi of V has no proper subsemigroup, 1 d i d n.

So V is also a special semigroup set simple linear algebra.

We prove the converse by a simple example.

Let V = (V1, V2, V3, V4, V5) where V1 = Z7, V2 = Z8, V3 = Z9, V4 = Z11 and V5 = Z13. V is a special semigroup set linear algebra over the semigroup Z6. Clearly the semigroups V1, V4 and V5 have no subsemigroups but V2 and V3 have subsemigroups. Thus V is only a special semigroup set simple linear algebra over the semigroup Z6 and V is not a special semigroup set strongly simple linear algebra over the semigroup Z6. Hence the theorem.

Now in case of special semigroup set vector spaces also we have these concepts which is defined briefly.

DEFINITION 3.2.7: Let V = (V1, V2, …, Vn) be a special semigroup set vector space over the set P, where V1, V2, …, Vn are semigroups under addition. If V does not contain a W = (W1, …, Wn) such that Wi’s are proper subsemigroups of Vi for atleast some i, 1 d i d n, then we define V to be a special

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semigroup set simple vector space. If none of the semigroups Vi in V has subsemigroups, 1 d i d n then we say the special semigroup set vector space V is a special set semigroup strong simple vector space over P. Now we illustrate this by some examples.

Example 3.2.16: Let V = (V1, V2, V3, V4, V5) where V1 = Z8, V2 = Z7, V3 = Z5, V4 = Z6 and V5 = Z11 be a special semigroup set vector space over the set P = {0, 1, 2, 3, 4}. V is only a special semigroup set simple vector space over P, for V2, V3 and V5 have no subsemigroups.

Example 3.2.17: Let V = (V1, V2, V3, V4) where V1 = Z5, V2 = Z7, V3 = Z13 and V4 = Z23 be a special semigroup set vector space over the set P = {0, 1, 2, 3, 4, 5, 6}. Clearly V is a special semigroup set strong simple vector space over the set P = {0, 1, 2, 3, 4, 5, 6}.

DEFINITION 3.2.8: Let V = (V1, V2, â&#x20AC;Ś, Vn) be a special semigroup set linear algebra over the semigroup S. If W = (W1, W2,â&#x20AC;Ś,Wn) where Wi Â&#x17D; Vi is such that Wi is a proper subsemigroup of the semigroup Vi, 1 d i d n and P be a proper subsemigroup of S. Suppose W is a special semigroup set linear algebra over P then we call W to be a special subsemigroup set linear subalgebra of V over P.

We illustrate this by some simple examples.

Example 3.2.18: Let V = (V1, V2, V3, V4, V5) where V1 = {Z+ u Z+ u Z+}, V2 = Z+[x], ­°§ a b ¡ ½ ° V3 = Ž¨ ¸ a, b,c,d � Z ž , °¯Š c d š ¿° ­°§ a V4 = Ž¨ 1 ¯°Š a 4

a2 a5

½ a3 ¡ ° ¸ a1 ,a 2 ,a 3 ,a 4 ,a 5 ,a 6 � Z ž a6 š ¿°

and

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­§ a b c d ¡ ½ °¨ ° ¸ ° b t e f¸ ° a, b,c,d,e,f ,g, h, k, t � Z ž V5 = Ž¨ ¨ ¸ °¨ c e g h ¸ ° °Š d f h k š ° ¯ ¿ is a special semigroup linear algebra over the semigroup S = Z+.

Take W = (W1, W2, W3, W4, W5) where W1 = (2Z+u 2Z+ u 2Z+), W2 = {all polynomials of only degree with coefficients from Z+}, ­°§ a a ¡ ½ ° W3 = Ž¨ ¸ a�Z ž , ¯°Š a a š ¿° ½ °­§ a a a ¡ ° W4 = Ž¨ ¸ a�Z ž ¯°Š a a a š ¿° and ­§ a °¨ ° a W5 = Ž¨ °¨¨ a °Š a ¯

½ a a a¡ ° ¸ a a a¸ ° a�Z ž . a a a¸ ° ¸ ° a a aš ¿

Clearly W = (W1, W2, W3, W4, W5) is a special semigroup linear algebra over the semigroup 2Z+.

Thus W = (W1, W2, W3, W4, W5) is a special subsemigroup set linear subalgebra over the subsemigroup P = 2Z+ Â&#x17D; S = Z+.

We give yet another example.

Example 3.2.19: Let V = (V1, V2, V3, V4) where V1 = Z5, V2 = Z7, V3 = Z11 and V4 = Z12, V1, V2, V3 and V4 are semigroups under addition modulo appropriate n, n Â? {5, 7, 11, 12}. V is a special semigroup set linear algebra over the semigroup S = Z3, semigroup under addition modulo 3. Now V has no proper subset W = (W1, W2, W3, W4) such that each Wi is a subsemigroup of Vi, 1d i d 4 and S has no proper subsemigroup.

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So we see V has no special subsemigroup set linear subalgebra.

This leads to the concept of a new algebra structure.

DEFINITION 3.2.9: Let V = (V1, V2, …, Vn) be a special semigroup set linear algebra over the semigroup S. If V has no proper subset W = (W1, …, Wn) such that each Wi, is a subsemigroup of the semigroup Vi; i = 1, 2, …,n and S has no proper subsemigroup then we call V to be a doubly simple special semigroup set linear algebra.

We have the following interesting result.

THEOREM 3.2.2: Let V = (V1, V2, …, Vn) be a special semigroup set linear algebra over the semigroup. If V is a doubly simple special semigroup set linear algebra then V is a special semigroup set strong simple linear algebra.

Proof: Given V = (V1, V2, …, Vn) is a doubly simple special semigroup set linear algebra, so V does not contain a proper nsubset W = (W1, W2, …, Wn) such that each Wi is a proper subsemigroup of the semigroup Vi; (1 d i d n). So V has no special semigroup set linear subalgebra and each semigroup Vi has no proper subsemigroup Wi, 1 d i d n. Hence V is a special semigroup strong simple set linear algebra.

Now it is pertinent to mention here that we cannot say if V is a special semigroup strong simple set linear algebra then V is a doubly simple special semigroup linear algebra. For we may have a special semigroup strongly simple set linear algebra but it may not be a doubly simple special semigroup linear algebra.

For take V = (V1, V2, V3, V4) where V1 = Z5, V2 = Z7, V3 = Z11 and V4 = Z19. V is clearly a special semigroup set strongly simple linear algebra over the semigroup Z4, as V has no proper subset W = (W1, W2, W3, W4) such that Wi is a proper subsemigroup of Vi, 1 d i d 4. For Z5 has no subsemigroup, Z7 has no subsemigroup, Z11 cannot have a proper subsemigroup and Z19 has no proper subsemigroup. But the semigroup V is defined over Z4 and Z4 has proper subset P = {0, 2} which is a proper subsemigroup of Z4 hence we cannot say V = (V1, V2, V3, V4) is a doubly simple special semigroup set linear algebra.

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Thus every doubly simple special semigroup set linear algebra is a special semigroup set linear algebra is a special semigroup set strong simple linear algebra.

Next we proceed on to define the generating n set of a special semigroup set vector space over a set S. DEFINITION 3.2.10 Let V = (V1, â&#x20AC;Ś, Vn) be a special semigroup set vector space over the set S. Let X = (X1, â&#x20AC;Ś, Xn) be a proper subset of V i.e., each Xi Â&#x17D; Vi, 1 d i d n. If each Xi generates Vi over S then we say X is the generating n set of the special semigroup set vector space over the set S.

We illustrate this by the following example.

Example 3.2.20 Let V = (V1, V2, V3, V4) where V1 = {(a a a) / a � Z+}, ­°§ a a ¡ ½ ° V2 = Ž¨ ¸ a�Z ž , °¯Š a a š ¿°

­°§ a a a ¡ ½ ° V3 = Ž¨ ¸ a�Z ž °¯Š a a a š ¿° and V4 = {a + ax + ax2 + ax3 | a� Z+}. Clearly V1, V2, V3 and V4 are semigroups under addition. V = (V1, V2, V3, V4) is a special semigroup set vector space over the set Z+. Take X = {X1, X2, X3, X4} where X1 = (1 1 1),

§1 1¡ X2 = ¨ ¸ , X3 = Š1 1š

§ 1 1 1¡ ¨ ¸, Š 1 1 1š

X4 = {1 + x + x2 + x3} Â&#x17D; V = (V1, V2, V3, V4), i.e., Xi Â&#x17D;Vi, i = 1, 2, 3, 4. We see X is a 4-generating subset of V over the set Z+.

Example 3.2.21: Let V = (V1, V2, V3, V4, V5) where V1 = {Z12 u Z12 u Z12},

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­°§ a b e · ½° V2 = ®¨ ¸ a, b,c,d,e,f Z12 ¾ , °¯© c d f ¹ °¿ ­§ a °¨ ° a V3 = ®¨ °¨¨ a °© a ¯

½ a· ° ¸ a¸ ° a Z12 ¾ , a¸ ° ¸ ° a¹ ¿

V4 = {Z12[x], all polynomials of degree less than or equal to 4} and ­§ 0 a b c · ½ °¨ ° ¸ ° a 0 d e¸ ° V5 = ®¨ a b c d f x Z12 ¾ ¨ ¸ b d 0 f °¨ ° °© c e f 0 ¸¹ ° ¯ ¿ be a special semigroup set linear algebra over the semigroup S = Z12. X = (X1, X2, X3, X4, X5) = {(1 0 0 0), (0 1 0 0), (0 0 1 0), (0 0 0 1)}, °­§ 1 0 0 ·§ 0 1 0 ·§ 0 0 1 · ®¨ ¸¨ ¸¨ ¸ °¯© 0 0 0 ¹© 0 0 0 ¹© 0 0 0 ¹

§ 0 0 0 ·§ 0 0 0 ·§ 0 0 0 · °½ ¨ ¸¨ ¸¨ ¸¾ , © 1 0 0 ¹© 0 1 0 ¹© 0 0 1 ¹ °¿

­§ 0 °¨ °¨ 1 ®¨ °¨ 1 °© 1 ¯

1 0 1 1

1 1 0 1

­ ª1 °« ° «1 ® ° «1 ° «¬1 ¯

1º ½ ° 1»» ° 2 3 4 ¾ , {1, x, x , x , x }, 1» ° » 1¼ ¿°

1 ·½ ¸° 1 ¸° ¾ (V1, V2, V3, V4, V5) 1 ¸° ¸ 0 ¹ °¿

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is the 5-generating 5-subset of V over the semigroup S = Z12.

The dimension of V is (4, 6, 1, 5, 1).

Now we define special semigroup set linear transformation for special semigroup set vector spaces defined only on the same set S. DEFINITION 3.2.11: Let V = (V1, â&#x20AC;Ś, Vn) and W = (W1,â&#x20AC;Ś,Wn) be two special semigroup set vector spaces defined over the same set S. Let T = (T1, T2, â&#x20AC;Ś, Tn) be a n-map from V into W such that Ti: Vi o Wj, 1 d i, j d n. Clearly no two Viâ&#x20AC;&#x2122;s can be mapped into the same Wj that is each Vi has a unique Wj associated with it.

If Ti(P + Q) = Ti(P) + T(Q); P, Q Â? Vi, this is true for each i, i = 1, 2; T = (T1, T2, T3, T4) then we call T to be special semigroup set linear transformation of V into W.

We illustrate this by a simple example.

Example 3.2.22: Let V = (V1, V2, V3, V4) and W = (W1, W2, W3, W4) where ­°§ a b ¡ ½ ° V1 = Ž¨ ¸ a, b,c,d � Z ž , ¯°Š c d š ¿°

V2 = {Z+ [x]; all polynomials of degree less than or equal to 4}, ­§ a °¨ ° a V3 = Ž¨ °¨¨ a ° ¯Š a

½ a¡ ° ¸ a¸ ° a�Z ž a¸ ° ¸ ° aš ¿

and V4 = Z+ u Z+ u Z+ u Z+ u Z+ u Z+ be a special semigroup set vector space over the set S = Z+. W1 = {Z+ u Z+ u Z+ u Z+ u Z+}, ½ °­§ a a ¡ ° W2 = Ž¨ ¸ a�Z ž, ¯°Š a a š ¿°

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­°§ a b f ¡ ½ ° W3 = Ž¨ ¸ a, b,c,d � Z ž °¯Š c d g š ¿° and ­°§ a a a a ¡ ½ ° W4 = Ž¨ ¸ a�Z ž ¯°Š a a a a š ¿°

be a special semigroup vector space over the same set S = Z+.

Define T = (T1, T2, T3, T4): V o W as T1: V1 o W2 defined by §a b¡ §a a ¡ T1 ¨ ¸ =¨ ¸ Šc dš Ša aš

T2 : V2 o W1 defined by T2 (a0 + a1x + a2x2 + a3x3 + a4x4) = (a0, a1, a2, a3, a4).

T3 : V3 o W4 defined by §a ¨ a T3 ¨ ¨a ¨ Ša

a¡ ¸ a¸ §a a a a¡ =¨ ¸ a¸ Ša a a aš ¸ aš

and T4 : V4 o W3 defined by §a T4 (a1 a2 a3 a4 a5 a6) = ¨ 1 Š a4

a2 a5

a3 ¡ ¸. a6 š

Clearly T = (T1, T2, T3, T4) is a special semigroup set linear transformation from V to W. Thus if one wishes to study using a special semigroup set vector space another special semigroup set vector space provided both of them are defined on the same set one by using the special semigroup set linear transformations study them.

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Now we proceed on to define the notion of special semigroup set linear operator on a special semigroup set vector space V over a set S. DEFINITION 3.2.12: Let V = (V1, V2, …, Vn) denote the special semigroup set vector space V over the set S. Let T = (T1, T2, …, Tn) be a special semigroup set linear transformation from V to V, then we call T to be a special semigroup set linear operator on V. Thus if in a special semigroup set linear transformation W is replaced by V itself then we call that linear transformation as special semigroup set linear operator on V.

We now illustrate it by some examples.

Example 3.2.23: Let V = (V1, …, V5) be a special semigroup set vector space over the set S = Z4. Here V1 = Z4 u Z4 u Z4 u Z4, V2 = {Z4[x] all polynomials of degree less than or equal to three}. °­§ a b · °½ V3 = ®¨ ¸ a, b,c,d Z4 ¾ , ¯°© c d ¹ ¿°

­§ a 0 0 · ½ °¨ ° ¸ V4 = ®¨ b 0 0 ¸ a, b,c,d Z4 ¾ °¨ c 0 d ¸ ° ¹ ¯© ¿ and ­°§ a 0 b 0 c · ½° V5 = ®¨ ¸ a, b,c,d Z4 ¾ ¯°© 0 d 0 0 0 ¹ ¿°

are semigroups under appropriate addition modulo 4.

Let T = (T1, T2, T3, T4, T5) be a 5-map from V into V defined by T1 : V1 o V2 T2 : V2 o V4 T3 : V3 o V1 T4 : V4 o V5 231

T5 : V5 o V3 where T1(a b c d) = a + bx + cx2 + dx3, T2:V2 oV4 is defined by §a 0 0· ¨ ¸ T2 (a+bx+cx +dx ) = ¨ b 0 0 ¸ , ¨c 0 d¸ © ¹ 2

3

§a b· T3 ¨ ¸ = (a b c d), ©c d¹ §a 0 0· ¨ ¸ §a 0 b 0 c· T4 ¨ b 0 0 ¸ = ¨ ¸ ¨ c 0 d¸ ©0 d 0 0 0¹ © ¹ and §a 0 b 0 c· §a b· T5 ¨ ¸ =¨ ¸. © 0 d 0 0 0¹ © c d ¹ Thus T is a special semigroup set linear operator on V. How many such T’s can be defined on V? For we have one set SH Z4 (V, V) given by ( Hom Z4 (V1, V2), Hom Z4 (V2, V4), Hom Z4 (V3, V1), Hom Z4 (V4, V5), Hom Z4 (V5, V3)) for T = (T1, T2, T3, T4, T5).

Thus we have many such T’s yielding different sets of SH Z4 (V, V).

DEFINITION 3.2.13: Let V = (V1, V2, …, Vn) be a special semigroup set linear algebra over the semigroup S. W = (W1, W2, …, Wn) be another special semigroup set linear algebra over the same semigroup S. If T = (T1, T2, …, Tn) : V o W where Ti : Vi o Wj such that no two Vi’s are mapped into the same Wj and Ti(P + Q) = Ti(P) + Ti(Q) for all D S and P,Q Vi, true for i= 1, 2, …, n and 1d j d n, then we call T to be a special semigroup set linear transformation of V to W.

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Note: If SHoms(V, W) = {Homs(V1, Wi1 ), Homs(V2, Wi2 ), …, Homs(Vn, Win )}, what is the algebraic structure of SHoms(V, W)}? Is SHoms(V, W) again a special semigroup set linear algebra over the same semigroup S? If in the definition we replace W by V then we call T = (T1, T2, …, Tn) as the special semigroup linear operator of V over the semigroup S. If SHoms (V, V) = {Homs(V1, Vi1 ), Homs (V2, Vi2 )… Homs(Vn, Vin )}; is SHoms (V,V) a special semigroup set linear algebra over S? This work is left as an exercise for the reader.

Now we illustrate these definitions by some examples.

Example 3.2.24: Let V = (V1, V2, V3, V4) where V1 = {Z10 u Z10 u Z10 u Z10}, V2 = {Z10[x], all polynomials of degree less than or equal to 3}, °­§ a b c · °½ V3 = ®¨ ¸ a, b,c,d,e,f Z10 ¾ ¯°© d e f ¹ ¿° and ­§ a a a · ½ °¨ ° ¸ °¨ a a a ¸ ° °¨ ° ¸ V4 = ® a a a a Z10 ¾ . °¨ a a a ¸ ° ¸ °¨ ° °¯¨© a a a ¸¹ ¿°

Clearly V1, V2, V3 and V4 are semigroups under addition. V is a special semigroup set linear algebra over the semigroup Z10.

Let W = (W1, W2, W3, W4) where ­°§ a a a · ½° W1 = ®¨ ¸ a Z10 ¾ , ¯°© a a a ¹ ¿°

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­§ a a a a a · ½ °¨ ° ¸ W2 = ®¨ a a a a a ¸ a Z10 ¾ °¨ a a a a a ¸ ° ¹ ¯© ¿ ­°§ a b · ½° W3 = ®¨ ¸ a, b,c,d Z10 ¾ ¯°© c d ¹ ¿° and ­§ a 0 b · ½ °¨ ° ¸ W4 = ®¨ 0 c 0 ¸ a, b,c,d Z10 ¾ . °¨ 0 0 d ¸ ° ¹ ¯© ¿ Clearly W1, W2, W3 and W4 are semigroups under addition.

Thus W = (W1, W2, W3, W4) is a special semigroup set linear algebra over the semigroup Z10. Now let us define T = (T1, T2, T3, T4) from V into W by T1 : V1 o W3 given by §a b· T1 (a b c d) = ¨ ¸. ©c d¹ T2 : V2 o W4 defined by § a0 ¨ T2(a0 + a1x + a2x + a3x ) = ¨ 0 ¨0 © 2

3

0 a2 0

T3 : V3 o W1 defined by §a b c· §a a a· T3 ¨ ¸ =¨ ¸. ©d e f ¹ ©a a a¹ T4 : V4 o W2 defined by

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a1 · ¸ 0 ¸. a 3 ¸¹

§a ¨ ¨a T4 ¨ a ¨ ¨a ¨a ©

a a· ¸ a a¸ a a¸ ¸ a a¸ a a ¸¹

ªa a a a a º «a a a a a » . « » «¬ a a a a a »¼

Clearly T = (T1, T2, T3, T4) is a special semigroup linear transformation V to W. Suppose SHom Z10 (V,W) = { Hom Z10 (V1,W3), Hom Z10 (V2,W4), Hom Z10 (V3,W1), Hom Z10 (V4,W2)}.

Show Hom Z10 (V,W) is a special semigroup set vector space over Z10. We give an example of a special semigroup set linear operator on V. Example 3.2.25: Let V = (V1, V2, V3, V4, V5) where V1 = Z20 a semigroup under addition modulo 20, V2 = Z12 a semigroup under addition modulo 12, V3 = {Z10 a semigroup under addition modulo 10}, V4 = Z23 a semigroup under addition modulo 23 and V5 = Z8 a semigroup under addition modulo 8.

Now V = (V1, V2, …, V5) is a special semigroup set linear algebra over the semigroup S = Z2 under addition modulo 2.

Now find SHom Z2 (V,V) = { Hom Z2 (V1, V3), Hom Z2 (V2,

V4), Hom Z2 (V3, V1), Hom Z2 (V4, V5), Hom Z2 (V5, V2)}.

Now we proceed onto define yet another new notion called special group set vector space over the set S. DEFINITION 3.2.14: Let V = (V1, …,Vn) where V1, …, Vn are distinct additive abelian groups. Let S be any non empty set. If for every x Vi and a S, a x Vi, 1 d i d n then we call V to be a special group set vector space over the set S.

We illustrate this definition by some examples.

Example 3.2.26: Let V = (V1, V2, V3, V4, V5) where V1 = Z10 group under addition modulo 10, V2 = Z26 group under addition 235

modulo 26, V3 = Z15 group under a addition modulo 15, V4 = Z12 group under addition modulo 12 and V5 = Z14 group under addition modulo 14.

Take S = {0, 1, 2, 3, 4} it is easily verified V is a special group set vector space over the set S. For we see if 4 S and 10 Z15 = V3, 4.10 = 40 { 10 (mod 15) so 4.10 V3; likewise for 14 Z26 and 3 S; 3.14 = 42 { 16 (mod 26) so 3.14 Z26 = V2 and so on.

Now we can give yet another example of a special group set vector space over a set.

Example 3.2.27: Let V = {V1, V2, V3, V4, V5, V6} where V1 = 2Z, V2 = 5Z, V3 = 7Z, V4 = 3Z, V5 = 11Z and V6 = 11Z. Clearly V1, V2, …, V6 are groups under addition. Take S = {0 1 2 3 4 5 6 7 8 9 10}. Clearly V = (V1, …, V6) is a special group set vector space over the set S.

Now we can define their substructures.

DEFINITION 3.2.15: Let V = (V1, V2, V3, …, Vn) be n distinct abelian groups under addition. Suppose V be a special group set vector over the set S. Let W = (W1, W2, …, Wn) where each Wi is a subgroup of the group Vi, 1 d i d n. Suppose W is a special group set vector space over the same set S then we call W to be a special group set vector subspace of V over the set S.

We now illustrate this situation by some simple examples.

Example 3.2.28: Let V = (V1, V2, V3, V4, V5) where V1 = Z10, V2 = Z12, V3 = Z25, V4 = Z9 and V5 = Z21 all groups under modulo addition for appropriate n. (n {10, 12, 25, 9 and 21}).

V is a special group set vector space over the set S = {0, 1, 2, 3, 4}. Let W = (W1, W2, …, W5) where W1 = {0, 2, 4, 6, 8} Z10, W2 = {0, 6} Z12, W3 = {0, 5, 10, 15, 20} Z5, W4 = {0, 3, 6} Z9 and W5 = {0, 7, 14} Z21. W = (W1, W2, W3, W4, W5) is a special group set vector subspace of V over the same set S. Suppose T = (T1, T2, …, T5) where T1 = {0, 5} Z10 = V1, T2 = {0, 2, 4, 6, 8, 10} Z12 = V2 ,T3 = {0, 5, 10, 15, 20} Z25 236

= V3, T4 = {0, 3, 6} Z9 = V4 and T5 = {0, 3, 6, 9, 12, 15, 18} Z21 = V5. Clearly T1, T2, …, T5 are subgroups of V1, …, V5 respectively. Thus T = (T1, T2, …, T5) (V1, V2, …, V5) is a special group set vector subspace of V over the same set S. Example 3.2.29: Let V = (V1, V2, V3, V4) where

­°§ a b · ½° V1 = ®¨ ¸ a, b,c,d Z ¾ , ¯°© c d ¹ ¿° ½° °­§ a a a · V2 = ®¨ ¸ a Z¾ , ¯°© a a a ¹ ¿° V3 = (Z u Z u Z u Z) and V4 = (3Z u 5Z u 7Z u 11Z u 15Z).

Clearly V = (V1, V2, V3, V4) is a special group set vector space over the set S = {0, 1, 2, …, 20}. Now take W = (W1, W2, W3, W4) where ­°§ a a · ½° W1 = ®¨ ¸ a Z¾ °¯© a a ¹ ¿° a proper additive subgroup of V1, ­°§ a a a · ½° W2 = ®¨ ¸ a 3Z ¾ V2 °¯© a a a ¹ ¿°

is a proper subgroup of V2, W3 = {Z u {0} u Z u {0}} V3 is also a subgroup of V3 and W4 = (3Z u 5Z u {0} u {0} u 15Z) V4 is a subgroup V4. Thus W = (W1, W2, W3, W4) (V1, V2, V3, V4) = V is a special group set vector subspace of V over the set S. Example 3.2.30: Let V = (V1, V2, V3, V4, V5) where V1 = Z5, V2 = Z7, V3 = Z11, V4 = Z2 and V5 = Z13 all of them are additive groups modulo n where n {5, 7, 11, 2, 13}. Clearly V is a special group set vector space over the set S = {0, 1}.

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Now we see V does not contain a proper subset W = (W1, W2, W3, W4, W5) such that W is a special group vector subspace of V that is each of the groups Vi in V have no proper subgroup, the only subgroups of each of these Vi is just {0} and Vi, this is true for i =1, 2, 3, 4, 5. Thus W does not exists for this V. In view of this example we are interested to define yet a new type of special group set vector spaces over the set S. DEFINITION 3.2.16: Let V = (V1, V2, …, Vn) be a special group set vector space over the set S. If V does not contain a proper nsubset W = (W1, W2, …, Wn) (V1, …, Vn) = V such that Vi’s cannot have Wi’s which are different from Vi’s and {0} i.e., if V has no proper special group set vector subspace over S then, we call V to be a special group set simple vector space over the set S.

We have just now given an example of a special group set simple vector space. Now we give yet another example.

Example 3.2.31: Let V = (V1, V2, V3, V4) where V1 = Z11, V2 = Z19, V3 = Z23 and V4 = Z29. V is a special group set vector space over the set {0, 1, 2, 3, 4, 5, 6}. Clearly each Vi is a simple group for they do not have proper subgroups, hence V = (V1, V2, V3, V4) is a special group set simple vector space over the set S.

Now we give yet another example.

Example 3.2.32: Let V = (V1, V2, V3, V4, V5) where V1 = Z5, V2 = Z6, V3 = Z7, V4 = Z9 and V5 = Z11. V is a special group set vector space over the set {0, 1, 3}. Take W = (W1, W2, W3, W4, W5) where W1 = Z5, W2 = {0, 2, 4} Z6 = V2, W3 = V3, W4 = {0, 3, 6} Z9 = V4 and W5 = {0}. Now W = (W1, W2, W3, W4, W5) is a special group set vector space over the set S. We call this W by a different name for only some groups Vi in V are simple and other groups Vj in V are not simple.

In view of this we give yet another new definition.

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DEFINITION 3.2.17: Let V = (V1, V2, ..., Vn) be a special group set vector space such that some Vi’s do not have proper subgroups and other Vj’s have proper subgroups i z j (1 d i, j d n). Let W = (W1, W2, …, Wn) (V1, V2, …, Vn) = V such that some of the subgroups Wi in W are trivial subgroups of Vi in V and some of the subgroups Wj in W are nontrivial subgroups of Vj(i z j); 1 d i, j d n. We call this W to be a special group set semisimple vector subspace over S provided W is a special group set vector space over the set S.

We now illustrate this by some simple examples.

Example 3.2.33: Let V = (V1, V2, V3, V4) where V1 = Z u Z, V2 = Z7, V3 = Z5 and ­°§ a b · ½° V4 = ®¨ ¸ a, b,c,d Z ¾ . °¯© c d ¹ °¿

V is clearly a special group set vector space over the set S = {0, 1}. Take W = {W1, W2, W3, W4} where W1 = 2Z u 3Z V1, W2 = Z7 = V2, W3 = Z5 = V3 and ­°§ a b · ½° W4 = ®¨ ¸ a, b,c,d 3Z ¾ V4. ¯°© c d ¹ ¿°

Clearly W = (W1, W2, W3, W4) is a special group set vector space over the set S = {0, 1}. Thus W = (W1, W2, W3, W4) is a special group set semisimple vector subspace of V over the set S = {0, 1}.

Now we define yet another substructure of these special group set vector spaces over a set S. DEFINITION 3.2.18: Let V = (V1, …, Vn) be a special group set vector space over the set S. Let W = (W1, W2, …, Wn) (V1, V2, …, Vn) such that each Wi Vi is a subsemigroup of Vi under the same operations of Wi and if W = (W1,…, Wn) happens to be a special semigroup set vector space over the set S then we call W to be a pseudo special semigroup set vector subspace of V.

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The following observations are pertinent at this juncture.

1. All special group set vector subspaces of V are trivially pseudo special semigroup set vector subspaces of V. 2. Pseudo special semigroup set vector subspaces of V is never a special group set vector subspace of V for atleast one of the Wiâ&#x20AC;&#x2122;s in W will not be a group only semigroup.

We illustrate this situation by some simple examples.

Example 3.2.34: Let V = (V1, V2, V3, V4, V5) where V1 = Z u Z u Z, °­§ a b ¡ °½ V2 = Ž¨ ¸ a, b,c,d � Z ž , ¯°Š c d š ¿°

­°§ a a a ¡ ½° V3 = Ž¨ ¸ a � Zž , °¯Š a a a š ¿° V4 = {Z[x] / all polynomials of degree less than or equal to 5 in the variable x with coefficients from Z} and ­§ a °¨ ° b V5 = Ž¨ °¨¨ c °Š g ¯

½ 0 0 0¡ ° ¸ d 0 0¸ ° a, b,c,d,e,f ,g, h,i, j � Z ž , e f 0¸ ° ¸ ° h i jš ¿

is a special group set vector space over the set S = {0, 1, 3, 5, 4, 9}. Take W = {(W1, W2, W3, W4, W5} Â&#x17D; (V1, V2, V3, V4, V5) = V where W1 = Z+ Â&#x2030; {0} u Z+ Â&#x2030; {0} u Z+ Â&#x2030; {0} Â&#x17D; Z u Z u Z = V1, °­§ a b ¡ °½

W2 = Ž¨ ¸ a, b,c,d Â? Z Â&#x2030; {0}ž Â&#x17D; V2, ¯°Š c d š ¿°

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­°§ a a a ¡ ½°

W3 = Ž¨ ¸ a Â? Z Â&#x2030; {0}ž Â&#x17D; V3, °¯Š a a a š ¿° W4 = {Z+ Â&#x2030;{0}[x] / all polynomials of degree less than or equal to 5 in the variable x with coefficients from Z+ Â&#x2030; {0}} Â&#x17D; V4 and ­§ a °¨ ° a W5 = Ž¨ °¨¨ a °Š a ÂŻ

½ 0 0 0¡ ° ¸ a 0 0¸ °

a Â? Z Â&#x2030; {0}ž Â&#x17D; V5. a a 0¸ ° ¸ ° a a aš Âż

Clearly W = (W1, â&#x20AC;Ś, W5) Â&#x17D; (V1, V2, â&#x20AC;Ś, V5) and W is only a pseudo special semigroup set vector subspace of V over S. We see in this example all Wi in W are only semigroups and not subgroups of Vi in V. Example 3.2.35: Let V = (V1, V2, V3, V4) where V1 = Z u Z,

­°§ a b ¡ ½° V2 = Ž¨ ¸ a, b,c,d Â? 2Z ž , °¯Š c d š ¿° ­°§ a a a a ¡ ½° V3 = Z12 and V4 = Ž¨ ¸ a Â? 3Z ž , °¯Š a a a a š ¿° V is a special group set vector space over the set {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}. Take W = {W1, W2, W3, W4} Â? (V1, V2, V3, V4) = V where W1 = Z+Â&#x2030;{0}uZ+Â&#x2030;{0}, W3 = {0, 2, 4, 6, 8, 10} Â&#x17D; V3, ­°§ a b ¡ ½°

W2 = Ž¨ ¸ a, b,c,d Â? 2Z Â&#x2030; {0}ž Â&#x17D; V2 ¯°Š c d š ¿° and

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­°§ a a a a ¡ ½°

W4 = Ž¨ ¸ a Â? 3Z Â&#x2030; {0 ž Â&#x17D; V4. °¯Š a a a a š °¿ W = (W1, W2, W3, W4) Â&#x17D; V is a pseudo special semigroup set vector subspace of V but W3 is only a subgroup.

In view of this we define yet another substructure for V. DEFINITION 3.2.19: Let V = (V1, â&#x20AC;Ś, Vn) be a special group set vector space over the set S. Suppose W = (W1, W2, â&#x20AC;Ś, Wn) Â&#x17D; (V1, â&#x20AC;Ś, Vn) = V such that only some of the Wi Â&#x17D; Vi are semigroups and other Wj Â&#x17D; Vj are only groups (so trivially a semigroup) i z j, (1 d i, j d n), then we call W = (W1, W2, â&#x20AC;Ś, Wn) Â&#x17D; V to be a pseudo special quasi semigroup set vector subspace of V over the set S.

We illustrate this by some simple examples.

Example 3.2.36: Let V = (V1, V2, V3, V4, V5) where V1 = Z u Z u Z, V2 = Z29, V3 = Z13, ­°§ a b ¡ ½° V4 = Ž¨ ¸ a, b,c,d � Z ž °¯Š c d š ¿° and ­§ a a ¡ ½ °¨ ° ¸ °¨ a a ¸ ° ° ° V5 = Ž¨ a a ¸ a � Z ž ; °¨ a a ¸ ° ¸ °¨ ° °¯¨Š a a ¸š ¿°

V is a special group set vector space over the set S = {0, 1, 2, 3, 4, 5, 6}. Take W = (W1, W2, W3, W4, W5) where W1 = Zo u Zo u Zo (where Zo = Z+ Â&#x2030; {0}) Â&#x17D; V1, W2 = V2 = Z29, W3 = Z13 = V3, °­§ a b ¡ o W4 = Ž¨ ¸ a, b,c,d Â? Z c d š ¯°Š

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°½ Z Â&#x2030; {0}ž Â&#x17D; V4 ¿°

and

­§ a °¨ °¨ a ° W5 = Ž¨ a °¨ a °¨ °¯¨Š a

a¡ ¸ a¸ a ¸ a � Zo ¸ a¸ a ¸š

½ ° ° ° Z Â&#x2030; {0}ž Â&#x17D; V5. ° ° ¿°

Clearly W = (W1, W2, W3, W4, W5) Â&#x17D; (V1, V2, V3, V4, V5) = V is only a pseudo special quasi semigroup set vector subspace of V over S. It is important to note that all pseudo special semigroup set vector subspaces of V are pseudo special quasi semigroup vector subspaces of V, however every pseudo quasi semigroup set vector subspaces of V need not in general be a pseudo special semigroup set vector subspaces of V over S in general.

The above example is an illustration of the above statement.

Now we proceed into define the new notion of special group set linear algebras over the group G. DEFINITION 3.2.20: Let V = (V1, V2, â&#x20AC;Ś, Vn) be a special group set vector space over the set S. If the set S is closed under addition and is an additive abelian group then we call V to be a special group set linear algebra over the group S, we only demand for every Q Â? Vi and s Â? S, s Q Â? Vi; 1 d i d n.

We illustrate this by some simple examples.

Example 3.2.37: V = (V1, V2, V3, V4) is a special group set linear algebra over the group G = {Z} where V1 = Z u Z u Z, ­°§ a b ¡ ½° V2 = Ž¨ ¸ a, b,c,d � Z ž , °¯Š c d š ¿°

V3 = {Z [x] all polynomials of degree less than or equal to 4 in the variable x with coefficients from Z} and

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­°§ a a a a a · ½° V4 = ®¨ ¸ a Z¾ . °¯© a a a a a ¹ ¿° Example 3.2.38: Let V = (V1, V2, V3, V4, V5) where

V1 = Z10 u Z10 u Z10, ­°§ a b c · ½° V2 = ®¨ ¸ a, b,c,d,e,f Z10 ¾ , °¯© d e f ¹ °¿

­§ x 0 0 · ½ °¨ ° ¸ V3 = ®¨ y z 0 ¸ x, y, z,g, h,i Z10 ¾ , °¨ g h i ¸ ° ¹ ¯© ¿ ­§ a b · ½ °¨ ° ¸ V4 = ®¨ c d ¸ a, b,c,d,e,f Z10 ¾ °¨ e f ¸ ° ¹ ¯© ¿ and V5 = {Z10 [x]; all polynomials of degree less than or equal to three. Then V = (V1, V2, …, V5) is a special group set linear algebra over the group Z10 under addition modulo 10.

Now we proceed onto illustrate various substructure of a special group set linear algebra over a group G. DEFINITION 3.2.21: Let V = (V1, V2, …, Vn) be a special group set linear algebra over the group G. If W = (W1, …, Wn) (V1, V2, …, Vn) = V be a proper subset of V such that for each i, Wi Vi; is a subgroup of Vi, 1 d i d n then we say W = (W1, …, Wn) is a special n-subgroup of V. If W = (W1, …, Wn) V is a special group set linear algebra over the same group G, then we call W to be a special group set linear subalgebra of V over G.

We illustrate this by some simple examples.

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Example 3.2.39: Let V = (V1, V2, …, V5) where

½° °­§ a b · V1 = ®¨ ¸ a, b,c,d Z ¾ , ¯°© c d ¹ ¿° V2 = {Z[x]; all polynomials of degree less than or equal to five in the variable x with coefficients from Z}, ­§ a °¨ °¨ a ° V3 = ®¨ a °¨ a °¨ °¯¨© a

½ a· ° ¸ a¸ ° ° a ¸ a Z ¾ , V4 = {Z u Z u Z u Z u Z} ¸ ° a¸ ° a ¸¹ °¿

and ½° °­§ a a a a · V5 = ®¨ ¸ a Z¾ . ¯°© a a a a ¹ ¿°

V = (V1, V2, V3, V4, V5) is a special group set linear algebra over the group Z. Let W = (W1, W2, …, W5) (V1, V2, …, V5) where ­°§ a b · ½° W1 = ®¨ ¸ a, b,c,d 2Z ¾ V1, ¯°© c d ¹ ¿° is a proper subgroup of V1, W2 = {All polynomials in the variable x of degree less than or equal to five with coefficients from 3Z}, W3 V3, where ­§ a °¨ °¨ a ° W3 = ®¨ a °¨ a °¨ °¯¨© a

½ a· ° ¸ a¸ ° ° a ¸ a 5Z ¾ , ¸ ° a¸ ° a ¸¹ ¿°

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W4 = {3Z u 3Z u 3Z u 3Z u 3Z} V4 and ½° °­§ a a a a · W5 = ®¨ ¸ a 5Z ¾ V5, ¯°© a a a a ¹ ¿° W = (W1, W2, W3, W4, W5) (V1, V2, …, V5) is a special group set linear subalgebra over the group Z. Example 3.2.40: Let V = (V1, V2, V3, V4) where V1 = {Z12 u Z12 u Z12}, V2 = {Z12[x] / all polynomials of degree less than or equal to 5 in the variable x with coefficients from Z12},

­°§ a b · ½° V3 = ®¨ ¸ a, b,c,d Z12 ¾ °¯© c d ¹ ¿° and ­°§ a a a a a · ½° V4 = ®¨ ¸ a Z12 ¾ . ¯°© a a a a a ¹ ¿° V = (V1, V2, V3, V4) is a special group set linear algebra over the group Z12. Let W = (W1, W2, W3, W4) where W1 = Z12 u {0} u Z12 V1, W2 = {All polynomials in the variable x of degree less than or equal to 5 with coefficients from {0, 2, 4, 6, 8, 10}} V2, ­°§ a a · ½° W3 = ®¨ ¸ a Z12 ¾ V3 ¯°© a a ¹ ¿° and °­§ a a a a a · °½ W4 = ®¨ ¸ a {0, 2, 4,6,8,10}¾ V4, ¯°© a a a a a ¹ ¿° W = (W1, W2, W3, W4) V is a special group set linear subalgebra of V over Z12.

We define yet another new substructure.

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DEFINITION 3.2.22: Let V = (V1, V2, …, Vn) be a special group set linear algebra over the group G. Let W = (W1, …, Wn) (V1, V2, …, Vn) be a n-subgroup of V, i.e., each Wi is a subgroup of Vi, 1d i d n. If for some proper subgroup H of G we have W to be a special group set linear algebra over H then we call W = (W1, W2,…, Wn) to be a special subgroup set linear subalgebra of V over the subgroup H of G.

We illustrate this by some examples.

Example 3.2.41: Let V = (V1, V2, V3, V4) be a special group set linear algebra over the group Z, where V1 = Z u Z u Z u Z,

­°§ a b · ½° V2 = ®¨ ¸ a, b,c,d Z ¾ , ¯°© c d ¹ ¿° ½° °­§ a a a a · V3 = ®¨ ¸ a Z¾ ¯°© a a a a ¹ ¿° and ­§ a °¨ ° c V4 = ®¨ °¨¨ a °© c ¯

½ b· ° ¸ d¸ ° a, b,c,d Z ¾ . ¸ b ° ¸ ° d¹ ¿

Take W = (W1, W2, W3, W4) (V1, V2, V3, V4) = V where W1 = 2Z u 2Z u 2Z u 2Z V1, ­°§ a b · ½° W2 = ®¨ ¸ a, b,c,d 2Z ¾ V2, °¯© c d ¹ ¿° ½° °­§ a a a a · W3 = ®¨ ¸ a 2Z ¾ V3 ¯°© a a a a ¹ ¿°

and

247

­§ a °¨ ° c W4 = ®¨ °¨¨ a °© c ¯

½ b· ° ¸ d¸ ° a, b,c,d 2Z ¾ V4. ¸ b ° ¸ ° d¹ ¿

Clearly W = (W1,W2,W3,W4) (V1,V2, V3, V4) = V is a special subgroup linear subalgebra of V over the subgroup 2Z Z. Example 3.2.42: Let V = (V1, V2, …, V5) where V1 = {Z6 u Z6 u Z6 u Z6 u Z6}, °­§ a a · °½ V2 = ®¨ ¸ a Z6 ¾ , ¯°© a a ¹ ¿° ­°§ a a a a a · ½° V3 = ®¨ ¸ a Z6 ¾ , °¯© a a a a a ¹ °¿

­§ a °¨ ° a V4 = ®¨ °¨¨ a °© a ¯

½ a· ° ¸ a¸ ° a Z6 ¾ a¸ ° ¸ ° a¹ ¿

and V5 = {Z6[x] all polynomials in the variable x with coefficients from Z6 of degree less than or equal to 6}. V = (V1, V2, V3, V4, V5) is a special group set linear algebra over the group Z6. Take W = (W1, W2, …, W5) (V1, V2, …, V5) = V where W1 = {S u S u S u S u S where S = {0, 2, 4}} V1, ­°§ a a · ½° W2 = ®¨ ¸ a {0, 2, 4}¾ V2, °¯© a a ¹ °¿

­°§ a a a a a · W3 = ®¨ ¸ °¯© a a a a a ¹

248

½° a {0, 2, 4}¾ V3, °¿

­§ a °¨ ° a W4 = ®¨ °¨¨ a °© a ¯

½ a· ° ¸ a¸ ° a {0, 2, 4}¾ ¸ a ° ¸ ° a¹ ¿

V4 and W5 = {all polynomials in the variable x with coefficients from the set S = {0, 2, 4} of degree less than or equal to 6} V5. W = (W1, W2, W3, W4, W5) (V1, V2, …, V5) = V is a special subgroup set linear subalgebra of V over the subgroup H = {0, 2, 4} Z6.

Example 3.2.43: Let V = (V1, V2, V3, V4) where V1 = Z7, V2 = Z5, V3 = Z11, and V4 = Z13 be group such that V is a special group set linear algebra over the group Z2 = {0, 1} = G addition modulo 2.

We see G has no proper subgroups. Further each Vi is such that, they do not contain proper subgroups; 1 d i d 4. Thus V has no special group set linear subalgebra as well as V does not contain any special subgroup linear subalgebra.

In view of this example we define a special type of linear algebra.

DEFINITION 3.2.23: Let V = (V1, V2, …, Vn) be a special group set linear algebra over the group G. Let W = (W1, W2, …, Wn) (V1, V2, …, Vn) = V such that for each i, Wi Vi is either Wi = {0} or Wi=Vi, i.e., none of the Vi’s have proper subgroup; 1 d i d n. Suppose G has no proper subgroup H G, i.e., either H = {0} or H = G. Then we call V = (V1, V2, …, Vn) to be a doubly simple special group linear algebra over G. Example 3.2.44: Let V = (V1, V2, V3, V4, V5, V6) where V1 = Z3, V2 = Z5, V3 = Z7, V4 = Z11, V5 = Z13 and V6 = Z17. V is a special group set linear algebra over the group G = Z2 = {0, 1} addition modulo 2. Clearly V is a doubly simple special group set linear algebra over G.

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Now we proceed onto define yet another new algebraic substructure of V. DEFINITION 3.2.24: Let V = (V1, V2, â&#x20AC;Ś, Vn) be a special group linear algebra over the group G. Let W = (W1, W2, â&#x20AC;Ś, Wn) Â&#x17D; (V1, V2, â&#x20AC;Ś, Vn) = V be a proper n-subset of V such that each Wi Â&#x17D; Vi is a subsemigroup of Vi, under the same operations of Vi, i=1,2,â&#x20AC;Ś, n. If W = (W1, â&#x20AC;Ś, Wn) is a special set semigroup linear algebra over the semigroup H of G, then we call W to be a pseudo special set subsemigroup linear subalgebra of V over H Â&#x17D; G.

We illustrate this situation by an example.

Example 3.2.45: Let V = (V1, V2, V3, V4) where V1 = Z u Z u Z u Z, ­°§ a b ¡ ½° V2 = Ž¨ ¸ a, b,c,d � Z ž , ¯°Š c d š ¿°

V3 = {Z[x], all polynomials in the variable x with coefficients from Z of degree less than or equal to 5} and ­§ a °¨ °¨ a °°¨ a V4 = Ž¨ °¨ a °¨ a °¨¨ °¯Š a

½ a¡ ° ¸ a¸ ° °° ¸ a ¸ a � Zž . a¸ ° ° a¸ ¸¸ ° aš ¿°

V = (V1, V2, V3, V4) is a special group set linear algebra over the group Z. Let W = (W1, W2, W3, W4) Â&#x17D; (V1, V2, V3, V4) where W1 = Zo u Zo u Zo where Zo = Z+ Â&#x2030; {0}} Â&#x17D; V1, ­°§ a b ¡ o W2 = Ž¨ ¸ a, b,c,d Â? Z °¯Š c d š

250

½° Z Â&#x2030; {0}ž Â&#x17D; V2, °¿

W3 = {Zo[x] all polynomials in x of degree less than or equal to 5 with coefficients from Zo} Â&#x17D; V3 and

­§ a °¨ °¨ a °°¨ a W4 = Ž¨ °¨ a °¨ a °¨¨ °¯Š a

a¡ ¸ a¸ a¸ o ¸ a�Z a¸ a¸ ¸ a ¸š

½ ° ° °° Z Â&#x2030; {0}ž Â&#x17D; V4. ° ° ° °¿

W = (W1, W2, W3, W4) is a special semigroup set linear algebra over the semigroup Zo Â&#x17D; Z. Thus W is a pseudo special subsemigroup set linear subalgebra of V over the subsemigroup Zo Â&#x17D; Z. Example 3.2.46: Let V = (V1, V2, V3, V4, V5) where V1 = Z16 u Z16 u Z16, ­°§ a b ¡ ½° V2 = Ž¨ ¸ a, b,c,d Â? Z16 ž , °¯Š c d š ¿°

V3 = {Z16[x] all polynomials of degree less than or equal to 2}, ­°§ a a a ¡ ½° V4 = Ž¨ ¸ a � Z16 ž °¯Š a a a š ¿° and ­§ a °¨ °¨ a °°¨ a V5 = Ž¨ °¨ a °¨ a °¨¨ °¯Š a

½ a a¡ ° ¸ a a¸ ° °° a a¸ ¸ a � Z16 ž a a¸ ° ¸ ° a a ¸¸ ° a aš ¿°

251

be a special group set linear algebra over the group Z16. W = (W1, W2, W3, W4, W5) where W1 = {S u S u S | S = {0, 2, 4, 6, 8, 10, 12, 14}} V1, ­°§ a a · ½° W2 = ®¨ ¸ a Z16 ¾ V2, °¯© a a ¹ ¿° W3 = {all polynomials in x of degree less than to 2 with coefficients from S = {0, 2, 4, …, 14}, ­°§ a a a · ½° W4 = ®¨ ¸ a S {0, 2, 4,6,8,10,12,14}¾ V4 °¯© a a a ¹ °¿ and ­§ a °¨ °¨ a °°¨ a W5 = ®¨ °¨ a °¨ a °¨¨ °¯© a

½ a a· ° ¸ a a¸ ° °° a a¸ ¸ a S {0, 2, 4,6,8,10,12,14}¾ V5. a a¸ ° ° a a¸ ¸¸ ° a a¹ °¿

W = (W1, W2, W3, W4, W5) (V1, V2, …, V5) is a special subgroup set linear subalgebra over the subgroup S = {0, 2, 4, …, 14} Z16.

Now we proceed on to define another new substructure in V = (V1, V2, …, Vn).

DEFINITION 3.2.25: Let V = (V1, V2, …, Vn) be a special group set linear algebra over the group G. Suppose H = (H1 ,…, Hn) (V1,V2,…,Vn) such that each Hi Vi, 1 d i d n is a subsemigroup of the group Vi then we all H = (H1, …, Hn) the pseudo special set subsemigroup linear subalgebra over the subsemigroup P of the group G if H = (H1, …, Hn) is a special semigroup set linear algebra over the semigroup P of the group G.

We illustrate this situation by a simple example.

252

Example 3.2.47: Let V = (V1, V2, V3, V4, V5) where V1 = Z u Z,

­°§ a b ¡ ½° V2 = Ž¨ ¸ a, b,c,d � Z ž , ¯°Š c d š ¿° ½° °­§ a a a a ¡ V3 = Ž¨ ¸ a � Zž , ¯°Š a a a a š ¿° ­ §a °¨ ° ¨a ° V4 = Ž ¨ a ° ¨a °¨ °¯ ¨Š a

½ a¡ ° ¸ a¸ ° ° ¸ a a � Zž ¸ ° a¸ ° a ¸š ¿°

and V5 = {Z[x] all polynomials of degree less than or equal to 3 with coefficients from Z}. V is a special group set linear algebra over the group Z. Let H = (H1, H2, â&#x20AC;Ś, H5) Â&#x17D; (V1, â&#x20AC;Ś, V5) where H1 = {Z+Â&#x2030;{0} u Z+Â&#x2030;{0}} Â&#x17D; V1, ­°§ a a ¡ ½°

H2 = Ž¨ ¸ a Â? Z Â&#x2030; {0}ž Â&#x17D; V2, °¯Š a a š ¿° ­°§ a a a a ¡ ½°

H3 = Ž¨ ¸ a Â? Z Â&#x2030; {0}ž Â&#x17D; V3, ¯°Š a a a a š ¿° ­§ a °¨ °¨ a ° H4 = Ž¨ a °¨ a °¨ °¯¨Š a

½ a¡ ° ¸ a¸ ° ° a ¸ a Â? Z Â&#x2030; {0}ž ¸ ° a¸ ° a ¸š ¿°

253

Â&#x17D; V4 and H5 = {(Z+ Â&#x2030; {0})[x] / this consists of all polynomials of degree less than or equal to 3 with coefficients from Z+ Â&#x2030; {0}} Â&#x17D; V5. H = (H1, H2, â&#x20AC;Ś, H5) Â&#x17D; (V1, V2, â&#x20AC;Ś, V5) = V is a pseudo special subsemigroup linear subalgebra over the subsemigroup Z+ Â&#x2030; {0} Â&#x17D; Z. Example 3.2.48: Let V = (V1, V2, V3, V4) where V1 = Q u Q u Q, V2 = {Q[x] / all polynomials of degree less than or equal to 5 with coefficients from Q in the variable x},

­°§ a b ¡ ½° V3 = Ž¨ ¸ a, b,c,d � Q ž ¯°Š c d š ¿° and ­§ a °¨ ° a V4 = Ž¨ °¨¨ a °Š a ¯

½ a¡ ° ¸ a¸ ° a � Qž . a¸ ° ¸ ° aš ¿

V is a special group set linear algebra over the group Z. Take W = (W1, W2, W3, W4) where W1 = {Z u Z u Z+ Â&#x2030; {0}} Â&#x17D; V1, W2 = {S[x], where S = Z+ Â&#x2030; {0}, all polynomials in the variable x with coefficients from S of degree less than or equal to 5} Â&#x17D; V2, ­°§ a b ¡ ½°

W3 = Ž¨ ¸ a, b,c,d Â? S Z Â&#x2030; {0}ž Â&#x17D; V3 ¯°Š c d š ¿° and ­§ a °¨ ° a W4 = Ž¨ °¨¨ a °Š a ÂŻ

½ a¡ ° ¸ a¸ °

a Â? Z Â&#x2030; {0} Sž Â&#x17D; V4. a¸ ° ¸ ° aš Âż

Clearly each Wi is a only semigroup under addition; 1 d i d 4; and W = (W1, W2, W3, W4) Â&#x17D; (V1, V2, V3, V4) is a pseudo

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special subsemigroup set linear subalgebra of V over the subsemigroup S = Z+ {0} contained in the group Q. Now we having seen some substructures in V we now proceed onto define the notion of linear transformations and linear operators.

DEFINITION 3.2.26: Let V = (V1, …, Vn) and W = (W1, …, Wn) be two special group set vector spaces over the same group G. Let T = (T1, …, Tn): V o W be defined by Ti : Vi o Wi, i = 1, 2, …, n, such that Ti (xP +X) = xTi(P) + Ti(X) for x G and P, X in Vi; i = 1, 2, …, n. We call T to be a special group set linear transformation on V. Suppose SHomG(V, W) = {HomG (V1,W1), HomG (V2, W2), …, HomG (Vn, Wn)} where for each i, HomG (Vi, Wi) = {all maps Ti from Vi to Wi such that Ti’s are group special linear transformations}. Clearly T : Vi o Wi; T (Xi) = 0 for all vi Vi , for every Ti, Pi from Vi to Wi we have (Ti + Pi)(xP+X) = Ti (xP+X) + Pi (xP+X) = xTi(P) + Ti(X)) + x(Pi(P) + Pi(X)) = x[(Ti + Pi)(P)] + (Ti + Pi)(X) for all P,X Vi and x G. Now for every Ti HomG (Vi, Wi) there exists, –Ti HomG (Vi, Wi) by Ti (xP+X) = x Ti(P) + Ti(X) then –Ti (xP+X) = – xTi (P) + (–Ti(X)) so that Ti + (– Ti)(xP+X) = (Ti – Ti) (xP) + (Ti – Ti)(X) = T (xP) + T (X) = 0+0 = 0 (xP+X) = 0 + 0.

Thus if for a G and Ti HomG (Vi, Wi), aTi : Vi o Wi. Hence each HomG (Vi, Wi) is a special group vector space over the group G. Thus SHom (V,W) = {HomG (V1, W1) , HomG (V2, W2), …, HomG (Vn, Wn)} is also a special group set vector space over the group G. Suppose T = (T1, T2, …, Tn): V o W be defined by Ti :

255

Vi o Wj such that no two Vi’s are mapped onto same Wj then also T is a special group set linear transformation from V to W and it is also denoted by SHomG (V, W) = {HomG (V1, Wi1 ) , HomG (V2, Wi2 ), …, HomG (Vn, Win )}, i.e., HomG (Vm, Wim ) = {Set of all group linear transformation from Vm to Wim }; clearly HomG (Vm, Wim ) is an additive group i.e., each HomG (Vm, Vim ) is a special group vector space over the group G. Thus SHomG (V, W) is also a special group set vector space over the group G.

We first illustrate this situation by some simple examples.

Example 3.2.49: Let V = (V1, V2, V3, V4) and W = (W1, W2, W3, W4) where V1 = Z10 u Z10 u Z10,

­°§ a b · ½° V2 = ®¨ ¸ a, b,c,d Z10 ¾ , °¯© c d ¹ ¿° ­°§ a a a a · ½° V3 = ®¨ ¸ a Z10 ¾ ¯°© a a a a ¹ ¿° and V4 = {Z10[x] all polynomials of degree less than or equal to 5} and W1 = Z10 u Z10 u Z10 u Z10 u Z10 u Z10, W2 = {Z10[x] all polynomials of degree less than or equal to 2}, ­§ a °¨ ° a W3 = ®¨ °¨¨ a °© a ¯

½ a· ° ¸ a¸ ° a Z10 ¾ a¸ ° ¸ ° a¹ ¿

and ­§ a b 0 · ½ °¨ ° ¸ W4 = ®¨ 0 0 0 ¸ a, b,c,d Z10 ¾ °¨ c d 0 ¸ ° ¹ ¯© ¿

256

are special group set vector space over the group Z10. Define T = (T1, T2, T3, T4): V o W defined by T1 : V1 o W2 where T1(a b c) = (a + bx+cx2), T2 : V2 o W4 defined by

§ a b 0· §a b· ¨ ¸ T2 ¨ ¸ = ¨ 0 0 0¸ , c d © ¹ ¨ c d 0¸ © ¹ T3 : V3 o W3 defined by §a a· ¨ ¸ §a a a a· ¨a a¸ = T3 ¨ ¸ ©a a a a¹ ¨a a¸ ¨ ¸ ©a a¹ and T4 : V4 o W1 defined by T4(a0 + a1x + a2x2 + a3x3 + a4x4 + a5x5) = (a0, a1, a2, a3, a4, a5).

Clearly T = (T1, T2, T3, T4) is a special group set linear transformation of V into W. It is left as an exercise for the reader to find SH Z10 (V,W) = { Hom Z10 (V1,W2), Hom Z10 (V2,W4), Hom Z10 (V3,W3), Hom Z10 (V4, W1)}. Is SH Z10 (V, W) is a special group set vector space over Z10?

Example 3.2.50: Let V = (V1, V2, V3, V4) and W = (W1, W2, W3, W4) where ­°§ a b · ½° V1 = ®¨ ¸ a, b,c,d Z ¾ , ¯°© c d ¹ ¿°

°­§ a a a a · °½ V2 = ®¨ ¸ a Z¾ , ¯°© a a a a ¹ ¿° V3 = {Z[x] all polynomials of degree less than or equal to 5}

257

and ­§ a 0 0 · ½ °¨ ° ¸ V4 = ®¨ b d 0 ¸ a, b,c,d,e,f Z ¾ °¨ c e f ¸ ° ¹ ¯© ¿ is a special group set vector space over Z. W1 = {Z u Z u Z uZ}, ­§ a °¨ ° a W2 = ®¨ °¨¨ a °© a ¯

½ a· ° ¸ a¸ ° a Z¾ , a¸ ° ¸ ° a¹ ¿

­§ a b c · ½ °¨ ° ¸ W3 = ®¨ 0 d e ¸ a, b,c,d,e,f Z ¾ °¨ 0 0 f ¸ ° ¹ ¯© ¿ and ­§ a a a · ½ °¨ ° ¸ W4 = ®¨ a a a ¸ a Z ¾ °¨ ° ¸ ¯© a a a ¹ ¿ be a special group set vector space over Z. Define T = (T1, T2, T3, T4) where T1: V1 o W1 is defined by §a b· T1 ¨ ¸ = (a b c d), ©c d¹ T2 : V2 o W2 is given by §a ¨ §a a a a· ¨a T2 ¨ = ¸ ©a a a a¹ ¨a ¨ ©a

258

a· ¸ a¸ a¸ ¸ a¹

T3: V3 o W3 is such that § a0 ¨ T3 (a0 + a1x + a2x + a3x +a4x +a5x ) = ¨ 0 ¨0 © and T4 : V4 o W4 is given by 2

3

4

5

a1 a3 0

a2 · ¸ a4 ¸ a 5 ¸¹

§ a 0 0· §a a a · ¨ ¸ ¨ ¸ T4 ¨ b d 0 ¸ ¨ a a a ¸ . ¨ c e f ¸ ¨a a a¸ © ¹ © ¹ T = (T1, T2, T3, T4) is a special group set linear transformation from V to W. Now we proceed on to define special group set linear operator on the special group set vector space over a group G. DEFINITION 3.2.27: Let V = (V1, V2, …, Vn) be a special group set vector space over a group G. Let T = (T1,…, Tn) such that Ti: Vi o Vi, i = 1, 2, …, n; if each Ti is a special group vector space over G, then we call T to be a special group set linear operator of V over G.

We illustrate this by some examples.

Example 3.2.51: Let V = (V1, V2, V3, V4, V5) be a special group set vector space over the group Z where V1 = Z u Z u Z u Z,

½° °­§ a b · V2 = ®¨ ¸ a, b,c,d Z ¾ , ¯°© c d ¹ ¿° V3 = {Z[x] all polynomials of degree less than or equal to 6} and ­§ a a · ½ °¨ ° ¸ °¨ a a ¸ ° a Z¾ .

V4 = ® °¨¨ a a ¸¸ ° °© a a ¹ ° ¯ ¿

259

Define T = (T1, T2, T3, T4) from V to V by T1: V1 o V1 defined by T1(x y z Z) = (x + y, y + z, z + Z, Z + x), T2 : V2 o V2 is given by §a b· §a a · T2 ¨ ¸ =¨ ¸, ©c d¹ ©a a¹ T3 : V3 o V3 is defined by T3(a0 + a1x + … + a6x6) = a0 + a2x2+a4x4+a6x6 and T4 : V4 o V4 is given by §a ¨ a T4 ¨ ¨a ¨ ©a

a · § 2a ¸ ¨ a ¸ ¨ 2a = a ¸ ¨ 2a ¸ ¨ a ¹ © 2a

2a · ¸ 2a ¸ .

2a ¸ ¸ 2a ¹

It is easily verified that T = (T1, T2, T3, T4) is a special group set linear operator on V. Example 3.2.52: Let V = (V1, V2, V3, V4 , V5) where

­§ a °¨ ° a V1 = ®¨ °¨¨ a °© a ¯

½ a· ° ¸ a¸ ° a Z5 ¾ , ¸ a ° ¸ ° a¹ ¿

V2 = Z5 u Z5 u Z5 u Z5, ­§ a b c · ½ °¨ ° ¸ V3 = ®¨ d e f ¸ a, b,c,d,e,f ,g, h,i Z5 ¾ , °¨ g h i ¸ ° ¹ ¯© ¿

260

­°§ a a a a · ½° V4 = ®¨ ¸ a Z5 ¾ °¯© a a a a ¹ °¿ and ­§ a °¨ ° b V5 = ®¨ °¨¨ d °© g ¯

½ 0 0 0· ° ¸ c 0 0¸ ° a, b,c,d,e,f ,g, h,i, j Z5 ¾ . e f 0¸ ° ¸ ° h i j¹ ¿

V is a special group set vector space over the group Z5. Let T = (T1, T2, T3, T4, T5) where T1:V1 o V1 is defined by §a ¨ a T1 ¨ ¨a ¨ ©a

a · § 2a 2a · ¸ ¨ ¸ a ¸ ¨ 2a 2a ¸ = , a ¸ ¨ 2a 2a ¸ ¸ ¨ ¸ a ¹ © 2a 2a ¹

T2 : V2 o V2 is given by T2(x y z w) = (x + Z, y, z, x – Z), T3 : V3 o V3 is such that §a b c· ¨ ¸ T3 ¨ d e f ¸ ¨g h i ¸ © ¹

ªa a a º «a a a » , « » «¬a a a ¼»

T4:V4 o V4 is given by § a a a a · § 2a 2a 2a 2a · T4 ¨ ¸ =¨ ¸ © a a a a ¹ © 2a 2a 2a 2a ¹ and T5 : V5 o V5 is defined by

261

§a ¨ b T5 ¨ ¨d ¨ ©g

0 0 0· §a ¸ ¨ c 0 0¸ ¨a e f 0¸ ¨a ¸ ¨ h i j¹ ©a

0 a a a

0 0 a a

0· ¸ 0¸ .

0¸ ¸ a¹

T = (T1, T2, T3, T4, T5) is a special group set linear operator on V. Suppose for V = (V1, …, Vn) we define T = (T1 … Tn) by Ti: Vi o Vj, (i z j); 1 d i, j d n i.e., no two Vi’s are mapped on to the same Vj and Vi is mapped onto Vj with j z i then we define such T to be a quasi special group set linear operator on V. We illustrate this by a few examples.

Example 3.2.53: Let V = (V1, V2, V3, V4) where ­°§ a b · ½° V1 = ®¨ ¸ a, b,c,d Z ¾ , °¯© c d ¹ ¿°

V2 = {Z[x] all polynomials of degree less than or equal to 3}, ­§ a °¨ °¨ a ° V3 = ®¨ a °¨ a °¨ °¯¨© a

½ a· ° ¸ a¸ ° ° ¸ a a Z¾ ¸ ° a¸ ° a ¸¹ ¿°

and ­°§ a a a a a · ½° V4 = ®¨ ¸ a Z¾ °¯© a a a a a ¹ ¿° be the special group set linear operator on the group Z. Take T = (T1, T2, T3, T4) where T1: V1 o V2, T2: V2 o V1, T3: V3 o V4 and T4: V4 o V3 defined by

262

§a b· 2 3 T1 ¨ ¸ = {a + bx + cx + dx }, c d © ¹ §a b· T2 (a + bx + cx2 + dx3) = ¨ ¸, ©c d¹ §a ¨ ¨a T3 ¨ a ¨ ¨a ¨a ©

a· ¸ a¸ §a a a a a· a¸ ¨ ¸ ¸ ©a a a a a¹ a¸ a ¸¹

and §a ¨ a §a a a a a· ¨ ¨ T4 ¨ ¸ a ©a a a a a¹ ¨ ¨a ¨a ©

a· ¸ a¸ a¸ . ¸ a¸ a ¸¹

It is easily verified that T is a quasi special group set linear operator on V. Example 3.2.54: Let V = (V1, V2, V3, V4, V5) where

½° °­§ a b · V1 = ®¨ ¸ a, b,c,d Z7 ¾ , ¯°© c d ¹ ¿° V2 = {Z7[x] all polynomials of degree less than or equal to 5 with coefficients from Z7 in the indeterminate x}; ­°§ a V3 = ®¨ 0 ¯°© a 3

a1 a4

½° a2 · ¸ a i Z7 ;0 d i d 5¾ , a5 ¹ ¿°

263

­§ a b g · ½ °¨ ° ¸ V4 = ®¨ b p d ¸ p,a, b,c,d,g Z7 ¾ °¨ g d c ¸ ° ¹ ¯© ¿ and V5 = Z7 u Z7 u Z7 u Z7 be a special group set vector space over the group Z7.

Define T = (T1, T2, T3, T4, T5) : V o V by T1 : V1 o V5, T2 : V2 o V3, T3 : V3 o V4, T4 : V4 o V2 T5 : V5 o V1

and where

§a b· T1 ¨ ¸ = (a b c d). ©c d¹ §a T2 (a0 + a1x + a2x2 + a3x3 + a4x4 + a5x5) = ¨ 0 © a3

§a T3 ¨ 0 © a3

a1 a4

§a a2 · ¨ 0 ¸ = a a 5 ¹ ¨¨ 1 © a2

a1 a3 a4

a1 a4

a2 · ¸. a5 ¹

a2 · ¸ a4 ¸ . a 5 ¸¹

§ a` b g · ¨ ¸ T4 ¨ b p d ¸ = (a + bx + gx2 + px3 + dx4 + cx5) ¨ g d c¸ © ¹ and §a b· T5(a b c d) = ¨ ¸. ©c d¹ Clearly T = (T1, T2, T3,T4, T5) is a quasi special group linear operator on V.

264

Now we proceed onto define the notion of inverse of a special group linear operator on V. DEFINITION 3.2.28: Let V = (V1, V2, …, Vn) be a special group set vector space over the group G. Let T = (T1, …, Tn) be a special group set linear operator on V for each i, i.e., Ti: Vi o Vi if their exists for each Ti, a group linear operator Ti 1 :Vi o Vi such that Ti 1 o Ti = Ti o Ti 1 = Ii, Ii is the group identity map

on Vi. This is true for i=1, 2, …, n. If T–1 = T1 1 , T2 1 ,..., Tn 1

then we call T –1 the special group set inverse linear operator on V; and T o T –1 = T –1 o T = (I1, I2, …, In).

We illustrate this by a simple example.

Example 3.2.55: Let V = (V1, V2, V3, V4) where

­°§ a b · ½° V1 = ®¨ ¸ a, b,c,d Z ¾ , °¯© c d ¹ °¿ V2 = Z u Z u Z u Z u Z, ½° °­§ a a a a a · V3 = ®¨ ¸ a, b Z ¾ ¯°© b b b b b ¹ ¿° and V4 = {all polynomials in the variable x with coefficients from Z of degree less than or equal to 6}.

V is a special group set vector space over Z. Take T = (T1, T2, T3, T4) defined by T1 : V1 o V1 is defined by §a b· §d c· T1 ¨ ¸. ¸ =¨ ©c d¹ ©b a¹

265

T2 : V2 o V2 is given by T2 (a b c d e) = (e d c b a).

T3 : V3 o V3 is defined by §a a a a a · § b b b b b· T3 ¨ ¸ ¨ ¸ ©b b b b b¹ ©a a a a a ¹ and T4 : V4 o V4 is defined by T4{a0 + a1x + a2x2 + a3x3 + a4x4+a5x5+a6x6} = {a0x6 + a1x5 + a2x3 + a3x3 + a4x2 + a5x + a6}.

It is easily verified that T = (T1, T2, T3, T4) is a special group linear operator on V. Define T–1 = T1 1 ,T2 1 ,T3 1 ,T4 1 on V by T1 1 : V1 o V1 defined by

§a b· §d c· T1 1 ¨ ¸ =¨ ¸. ©c d¹ ©b a¹ T2 1 : V2 o V2 is given by T2 1 (a b c d e) = (e d c b a).

T3 1 : V3 o V3 is defined by

§a a a a a · § b b b b b· T3 1 ¨ ¸ ¨ ¸ ©b b b b b¹ ©a a a a a ¹ and T4 1 : V4 o V4 is given by T4 1 (a0 + a1x + a2x2 + a3x3 + a4x4+a5x5+a6x6) = (a0x6+a1x5+a2x3+a3x3+ a4x2+a5x+a6).

Now

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§a b· T1 o T1 1 ¨ ¸ = T1 ©c d¹

§d c· §a b· ¨ ¸ =¨ ¸. ©b a¹ ©c d¹

d c· §a b· §a b· 1 § T1 1 o T1 ¨ ¸ = T1 ¨ ¸ =¨ ¸. ©c d¹ ©b a¹ ©c d¹ Thus T1 o T1 1 = T1 1 o T1 = I1 (identity on V1) T2 o T2 1 (a b c d e) = T2(e d c b a) = (a b c d e).

T2 1 o T2 (a b c d e) = T2 1 (e d c b a) = (a b c d e).

Thus T2 1 o T2 = T2 o T2 1 = I2 (identity on V2).

§a a a T3 1 o T3 ¨ ©b b b §a =¨ ©b

a· b b b b· 1 § b ¸ = T3 ¨ ¸ b b¹ ©a a a a a¹ a a a a· ¸. b b b b¹

§a a a T3 o T3 1 ¨ ©b b b §a =¨ ©b

a· ¸ = T3 b b¹ a a a

a

a

§b b b b b· ¨ ¸ ©a a a a a¹ a· ¸. b b b b¹

Thus T3 o T3 1 = T3 1 o T3 = I3 (identity on V3).

T4 o T4 1 (a0 + a1x + a2x2 + a3x3 + a4x4+a5x5+a6x6) = T4 (a0x6+a1x5+a2x3+a3x3+ a4x2+a5x+a6) = (a0 + a1x + a2x2 + a3x3 + a4x4+a5x5+a6x6).

Now T4 1 o T4 (a0 + a1x + a2x2 + a3x3 + a4x4+a5x5+a6x6)

= T4 1 (a0x6+a1x5+a2x3+a3x3+ a4x2+a5x+a6) = (a0 + a1x + a2x2 + a3x3 + a4x4+a5x5+a6x6).

267

Thus T4 1 o T4 = T4 o T4 1 = I4 (identity on V4).

Thus T–1 =

T

1 1

,T2 1 ,T3 1 ,T4 1 is the special group set

inverse linear operator on V. Now we proceed onto define the quasi special group set inverse linear operator on V. DEFINITION 3.2.29: Let V = (V1, V2, …, Vn) be a special group set vector space over a set G. Let T = (T1, …, Tn) be a quasi special set inverse linear operator on V. We call T–1 = T1 1 ,T2 1 ,...,Tn 1 to be a quasi special set inverse linear

operator of T on V if T o T–1 = T–1 o T = (I1, …, In); i.e., each Ti 1 o Ti = Ti o Ti 1 = Ii for i = 1, 2,…,n.

We illustrate this situation by an example.

Example 3.2.56: Let V = (V1, V2, V3, V4) where

V1 = Z u Z u Z u Z,

½° °­§ a b · V2 = ®¨ ¸ a, b,c,d Z ¾ , ¯°© c d ¹ ¿° ­§ a1 a 2 a 3 · ½ °¨ ° ¸ V3 = ®¨ a 4 a 5 a 6 ¸ a i Z ¾ °¨ a a a ¸ ° 8 9¹ ¯© 7 ¿ and V4 = {Z[x] | all polynomials of degree less than or equal to 8 with coefficients from Z in the variable x}. Clearly V is a special group set vector space over the set Z. Now define T = (T1, T2, T3, T4) on V by T1:V1 o V2, is such that

§a b· T1 (a b c d) = ¨ ¸. ©c d¹

268

T2 : V2 o V1 is defined by §a b· T¨ ¸ = (a b c d). ©c d¹ T3 : V3 o V4 is defined by

§ a1 ¨ T3 ¨ a 4 ¨a © 7

a2 a5 a8

a3 · ¸ a 6 ¸ = (a1 + a2x + a3x2 + a4x3 + a5x4 + a6x5 + a7x6 + a 9 ¸¹ a8x7 + a9x8)

and T4 : V4 o V3 is defined by

§ a1 ¨ T4 (a1 + a2x + … + a9x ) = ¨ a 4 ¨a © 7 8

a2 a5 a8

a3 · ¸ a6 ¸ . a 9 ¸¹

It is easily verified that T is a special group set linear operator on V. Define T-1 = T1 1 ,T2 1 ,T3 1 ,T4 1 from V to V as follows: T1 1 : V2 o V1 is defined by

§a b· T1 1 ¨ ¸ = (a b c d). ©c d¹ T2 1 : V1 o V2 defined by

§a b· T2 1 (a b c d) = ¨ ¸. ©c d¹ Now

§ 1 § a b · · §a b· T1 o T1 1 ¨ ¸ = T1 ¨ T ¨ ¸¸ . © c d¹¹ ©c d¹ ©

269

§a b· = T1 ( a b c d) = ¨ ¸. ©c d¹ That is T1 o T1 1 = I2 : V2 o V2

§a b· T1 1 o T1 = T1 1 (T1 (a b c d)) = T1 1 ¨ ¸ = (a b c d). ©c d¹ So T1 1 o T1 = I1 : V1 o V1. Consider

§a b· T2 o T2 1 (a b c d) = T2 ¨ ¸ = (a b c d) ©c d¹ That is T2 o T2 1 = I1: V1 o V1. Now

§a b· §a b·· 1 § T2 1 o T2 ¨ ¸ = T2 ¨ T2 ¨ ¸¸ ©c d¹ © © c d¹¹

So T2 1

§a b· T2 1 (a b c d) = ¨ ¸ ©c d¹ o T2 = I2: V2 o V2. We see

T3 1 : V4 o V3 defined by 1 3

T

§ a1 ¨ (a1 + a2x + … + a8x ) = ¨ a 4 ¨a © 7 9

a2 a5 a8

a3 · ¸ a6 ¸ a 9 ¸¹

and T4 1 : V3 o V4 is defined by

§ a1 ¨ T ¨ a4 ¨a © 7 Clearly 1 4

a2 a5 a8

a3 · ¸ a 6 ¸ = (a1 + a2x+ a3x2 + … + a8x7 + a9x8). a 9 ¸¹

270

T3 o T3 1 (a1 + a2x + … + a8x9)

§ a1 ¨ = T3 ¨ a 4 ¨a © 7

a2 a5 a8

a3 · ¸ a 6 ¸ = a1 + a2x + … + a8x9. a 9 ¸¹

Thus T3 o T3 1 : V4 o V4 and T3 o T3 1 = I4 on V4

§ a1 ¨ T o T3 ¨ a 4 ¨a © 7 1 3

1 3

= T

a2 a5 a8

§ § a1 a3 · ¸ ¨ 1 ¨ a 6 ¸ = T3 ¨ T3 ¨ a 4 ¨ ¨a a 9 ¸¹ © © 7

§ a1 ¨ (a1 + a2x + … + a8x + a9x ) ¨ a 4 ¨a © 7 7

8

a2 a5 a8 a2 a5 a8

a3 · · ¸¸ a6 ¸ ¸ a 9 ¸¹ ¸¹ a3 · ¸ a6 ¸ . a 9 ¸¹

Thus T3 1 o T3: V3 o V3 and T3 1 o T3 = I3. Finally T4 1 o T4 (a1 + a2x + … + a8x7 + a9x8)

§ a1 ¨ T ¨ a4 ¨a © 7 1 4

a2 a5 a8

a3 · ¸ a 6 ¸ = a1 + a2x + … + a8x7 + a9x8. a 9 ¸¹

Thus T4 1 o T4 : V4 o V4 i.e., T4 1 o T4 = I4 : V4 o V4. But

1 4

T4 o T

§ a1 ¨ ¨ a4 ¨a © 7

a2 a5 a8

a3 · ¸ a 6 ¸ = T4 a 9 ¸¹

271

§ § a1 ¨ 1 ¨ ¨ T4 ¨ a 4 ¨a ¨ © 7 ©

a2 a5 a8

a3 · · ¸¸ a6 ¸ ¸ a 9 ¸¹ ¸¹

§ a1 ¨ = T4 (a1 + a2x+ … + a8x + a9x ) = ¨ a 4 ¨a © 7 7

8

a2 a5 a8

a3 · ¸ a6 ¸ . a 9 ¸¹

Thus T4 o T4 1 :V3 o V3 is such that T4 o T4 1 = I3 : V3oV3. Thus

we have verified T–1 = T1 1 ,T2 1 ,T3 1 ,T4 1 is the special group

set quasi inverse linear operator of T. Thus T o T–1 = T–1 o T = (I1, I2, I3, I4) = I. On similar lines one can always define the notion of inverse for any special group set linear operator on a special group set vector space as well as inverse of special group set linear transformation on a special group set vector spaces V and W defined on the same set S. We define the notion of direct sum in case of special group set vector spaces defined over a set S. DEFINITION 3.2.30: Let V = (V1, …, Vn) be a special group set vector space over the set S. We say V is a direct sum if each Vi can be represented as a direct sum of subspaces i = 1, 2,…, n. that is V = (W11 ... Wt11 ,W12 ... Wt22 ,..., W1n ... Wtnn )

where each Vi = W1i W2i ... Wtii is a direct sum, i.e., each

vi Vi can be represented uniquely as a sum of elements from W1i ,W2i ,...,Wtii and W pi Wqi = (0) if p z q. This is true for

each i, i = 1, 2, …, n.

We represent this by some examples.

Example 3.2.57: Let V = (V1, V2, V3, V4) where

­°§ a b · ½° V1 = ®¨ ¸ a, b,c,d Z ¾ , °¯© c d ¹ ¿°

272

V2 = {Z[x]; all polynomials of degree less than or equal to 4}, V3 = Z u Z uZ and

­§ a1 °¨ °¨ a 3 ° V4 = ®¨ a 5 °¨ a °¨ 7 °¯¨© a 9

½ a2 · ° ¸ a4 ¸ ° ° a 6 ¸ a i Z :1 d i d 10 ¾ ¸ ° a8 ¸ ° a10 ¸¹ ¿°

be a special group set vector space over the group Z. Let ­°§ a 0 · ½° W11 = ®¨ ¸ a,d Z ¾ °¯© 0 d ¹ °¿ and ­°§ 0 e · ½° W21 = ®¨ ¸ e,g Z ¾ ¯°© g 0 ¹ ¿° be a special group vector subspaces of V1. We see §0 0· V1 = W11 W21 and W11 W21 = ¨ ¸. ©0 0¹ Consider W12 = {all polynomials of degree one or three with

coefficients from Z i.e., ax + bx3; a, b Z} V2. W12 is a special group vector subspace of V2. W22 = {all polynomials of the form a0 + a1x2 + a2x4 | a0, a1, a2 Z} V2 is also a special group vector subspace of V2. We see V2 = W12 W22 and W12 W22 = (0). W13 = {Z u {0} u Z} V3 is a special group vector subspace of V3 and W23 = {{0} u Z u {0}} V3 is a special group vector subspace of V3.

Further V3 = W13 W23 and W13 W23 = (0).

Take

273

­§ a1 °¨ °¨ a 3 ° 4 W1 = ®¨ 0 °¨ 0 °¨ °¯¨© 0

½ a2 · ° ¸ a4 ¸ ° ° ¸ 0 a i Z;1 d i d 4 ¾ V4 ¸ ° 0¸ ° 0 ¸¹ ¿°

is a special group set vector space over Z. Take ­§ 0 °¨ °¨ 0 ° 4 W2 = ®¨ a1 °¨ a °¨ 3 °¯¨© a 5

0· ¸ 0¸ a2 ¸ ¸ a4 ¸ a 6 ¸¹

½ ° ° ° a i Z; 1 d i d 6 ¾ V4 ° ° °¿

is a special group set vector space over Z. For V4 = W14 W24 and ª0 0º «0 0» « » W14 W24 = « 0 0 » . « » «0 0» «¬ 0 0 »¼ Now we show this representation is not unique. For take §a 0· W11 = ¨ ¸ V1, ©0 0¹ §0 b· W21 = ¨ ¸ V1, ©0 0¹ §0 0· W31 = ¨ ¸ V1 © c 0¹ 274

and §0 0· W41 = ¨ ¸ V1. ©0 d¹ W11 , W21 , W31 and W41 are special group subspaces of V1 and

V1 = W11 W21 W31 W41 with §0 0· Wj1 Wi1 = ¨ ¸ if i z j. ©0 0¹ Take the space V2, let W12 = {a + bx | a, b Z} V2,

W22 = {ax2 + cx3 | a, c Z} V2 W32 = {dx4 | d Z} V2.

and

It is easily verified W12 , W22 and W32 are special group vector subspaces of V2.

Further V2 = W12 W22 W32 and Wi2 Wj2 = {0} if i z j. 1 d j, i d 3. So V2 is a direct sum of W12 , W22 and W32 .

Now consider V3, W13 = Z u {0} u {0} V3, W23 = {0} u Z u {0} V3 and

W33 = {0}u{0}uZ V3 are special group vector subspaces of V3 such that V3 = W13 W23 W33 with Wi3 Wj3 = {0}u{0}u{0}, if i z j; 1 d i, j d 3.

275

Consider V4, take ­§ a1 °¨ °¨ 0 ° 4 W1 = ®¨ a 3 °¨ 0 °¨ °¯¨© 0

a2 · ¸ 0¸ a4 ¸ ¸ 0¸ 0 ¸¹

½ ° a i Z °° ¾ V4, 1 d i d 4° ° ¿°

­§ 0 0 · ½ °¨ ° ¸ °¨ a1 a 2 ¸ ° ° ° W24 = ®¨ 0 0 ¸ a1 ,a 2 Z ¾ V4 °¨ 0 0 ¸ ° ¸ °¨ ° °¯¨© 0 0 ¸¹ ¿° ­§ 0 °¨ °¨ 0 ° 4 and W3 = ®¨ 0 °¨ a °¨ 1 °¯¨© a 3

½ 0· ° ¸ 0¸ a i Z °° ¸ 0 ¾. ¸ 1d i d 4 ° a2 ¸ ° a 4 ¸¹ ¿°

We see V4 = W14 W24 W34 with ª0 «0 « Wi4 Wj4 = « 0 « «0 «¬ 0

0º 0 »» 0 » if i z j; 1 d i, j d 3. » 0» 0 »¼

Thus we see V = (V1, V2, V3, V4) is the direct sum of V =

( W11 W21 W31 W41 , W12 W22 W32 ,

276

W13 W23 W33 , W14 W24 W34 )

…

I

Thus the representation of the special group set vector spaces as a direct sum of special group set vector subspaces is not unique.

Further one of the uses of finding the direct sum, is we can get many sets of special group set vector subspaces of V. For instance in the representation of V given in I. We have 108 proper special group set vector subspaces of V. W1 = ( W11 , W21 , W31 , W41 ), W2 = ( W12 , W22 , W32 ) W3 = ( W13 , W23 , W33 ) and W4 = ( W14 , W24 , W34 ).

Now having seen the direct sum and special group set vector subspaces we can define special group set projection operators and special group set idempotent operator.

Let V = (V1, V2, …, Vn) be a special group set vector space over a set S. If T = (T1, …, Tn) is a special group set linear operator on V we call T to be a special group set linear idempotent operator on V if T o T = T, i.e., Ti o Ti = Ti for every i = 1, 2, …, n.

Now for us to define the notion of special group set projection linear operator on V we in the first place need, some proper special group set vector subspace of V. Let W = (W1, W2, …, Wn) be a special group set vector subspace of V, i.e., each Wi Vi is a special group vector subspace of V; i = 1, 2, …, n; i.e., W = (W1, W2,…,Wn) (V1, V2, …, Vn). Let P = (P1, P2, …, Pn) be a special group set linear operator on V such that P: V o V, that is Pi:Vi oVi is such that Pi: Vi o Wi Vi; i = 1, 2, …, n; that is Pi is a special group projection operator on Vi for each i and Pi o Pi = Pi for each i, 1 d i d n. We call P = (P1, P2, …, Pn) to be the special group set projection operator of V. Clearly P o P = P. Now we illustrate this by an example.

277

Example 3.2.58: Let V = (V1, V2, V3, V4, V5) be a special group set vector space over the set Z where V1 = ZuZ,

­°§ a · ½° V2 = ®¨ ¸ a, b Z ¾ , ¯°© b ¹ ¿° ½° °­§ a b · V3 = ®¨ ¸ a, b,d Z ¾ , ¯°© 0 d ¹ ¿° ­°§ a b c · ½° V4 = ®¨ ¸ a, b,c,d,e,f Z ¾ °¯© d e f ¹ °¿

and V5 = {Z[x] all polynomials of degree less than or equal to three with coefficients from Z}. Take W = (W1, W2, W3, W4, W5) (V1, V2, V3, V4, V5) where W1 = {(a, 0) | a Z} V1, ­°§ 0 · ½° W2 = ®¨ ¸ b Z ¾ V2, °¯© b ¹ ¿° ­°§ a b · ½° W3 = ®¨ ¸ a, b Z ¾ V3, ¯°© 0 0 ¹ ¿° ½° °­§ a b c · W4 = ®¨ ¸ a, b,c Z ¾ V4 ¯°© 0 0 0 ¹ ¿°

and W5 = {a0 + a1x3 | a0, a1 Z} V5. Clearly W = (W1, W2, W3, W4, W5) is a special group set vector subspace of V. Define P = (P1, P2, …, P5) from V to V by P1: V1 o V1 where P1(a , b) = (a, 0). P2 : V2 o V2 is defined by

278

­° ª a º ½° ª 0 º P2 ® « » ¾ « » ¯° ¬ b ¼ ¿° ¬ b ¼

P3: V3 o V3 is given by °­ ª a b º °½ ªa b º P3 ® « »¾ « » °¯ ¬ 0 d ¼ °¿ ¬0 0 ¼

P4 : V4 o V4 is such that ­° ª a b c º ½° ªa b c º P4 ® « »¾ « » ¯° ¬ d e f ¼ ¿° ¬0 0 0 ¼

and P5: V5 o V5 is defined by P5 (a0 +a1x + a2x2 + a3x3) = a0 + a3x3.

Now it is easily verified that P = (P1, P2, P3, P4, P5) is a special group set linear operator on V which is a projection of V onto W. It is further evident P o P = P. For P1 o P1 (a, b) = P1 (a, 0) = (a, 0); i.e., P1 o P1 = P1. ­° ª a º ½° P2 o P2 ® « » ¾ = P2 ¯° ¬ b ¼ ¿° Hence P2 o P2 = P2.

­° ª 0 º ½° ª 0 º ®« » ¾ « » ¯° ¬ b ¼ ¿° ¬ b ¼

­° ª a b º ½° ªa b º P3 o P3 ® « ¾ P3 « » » ¬0 0 ¼ ¯° ¬ 0 d ¼ ¿° So P3 o P3 = P3. ªa b c º °­ ª a b c º °½ P4 o P4 ® « ¾ P4 « » » °¯ ¬ d e f ¼ °¿ ¬0 0 0¼

ªa b º «0 0 » ¼

ªa b c º «0 0 0» , ¼

thus P4 o P4 = P4 and P5 o P5 (a0 +a1x + a2x2 + a3x3) = P5(a0 + a3x3) = (a0 + a3x3); hence P5 o P5 = P5.

279

Thus P o P = P. Hence P is an idempotent special group set linear operator on V. It may so happen that we have V = (V1, V2, â&#x20AC;Ś, Vn) to be a special group set vector space over a set S. Suppose

V = W11 Â&#x2020; ... Â&#x2020; Wt11 , W12 Â&#x2020; ... Â&#x2020; Wt22 ,..., W1n Â&#x2020; ... Â&#x2020; Wtnn

is the direct sum of special group set vector subspaces of V over the set S. Suppose in addition to these conditions we assume t1 = t2 = â&#x20AC;Ś = tn = t. Let W1 = W11 , W12 , ! , W1n Â&#x17D; V, W2 = W21 , W22 , ..., W2n Â&#x17D; V, and so on;

Wt = Wt1 , Wt2 , ..., Wtn Â&#x17D; V.

If P1, P2, â&#x20AC;Ś, Pn be projection operators of V to W1, V to W2, â&#x20AC;Ś, V to Wt respectively. We have Pi o Pi = Pi and Pi o Pj = 0 if i z j.

Further if P1 = P11 , P12 , ..., P1n , P2 = P21 , P22 , ..., P2n

and so on â&#x20AC;Ś

Pt = Pt1 , Pt2 , ..., Ptn

then P1 + â&#x20AC;Ś + Pt = P11 P21 ... Pt1 , P12 P22 ... Pt2 ,..., P1n P2n ... Ptn

= (I1, I2, â&#x20AC;Ś, In).

We see and

Pir oPjr

0 ; if i z j, 1 d r d n.

Pir oPjr

Pir if i = j, 1 d r d n.

As in case of vector spaces in the case of special group set vector spaces we would not be in a position to define precisely the notion of eigen values eigen vectors etc. We to over come this difficulty make use of subspaces and their direct sum concept. We also wish to state that we need to write each and every special group vector space as a sum of the same number of subspace that is each Vi is expressed as

280

Vi = W1i ... Wti

for i = 1, 2, …, n then also we can have the above mentioned results to be true.

If they (Vi’s) are of different sums we cannot get the above mentioned results.

We shall illustrate this by some examples.

Example 3.2.59: Let V = (V1, V2, V3, V4) where

V1 = Z u Z u Z, ½° °­§ a · V2= ®¨ ¸ a, b Z ¾ , ¯°© b ¹ ¿°

­°§ a b · ½° V3 = ®¨ ¸ a, b,c,d Z ¾ °¯© c d ¹ ¿°

and V4 = {Z[x]; all polynomials of degree less than or equal to three in the variable x with coefficients from Z} be a special group set vector space over Z. Take the substructures in V1 as W11 = Z u Z u {0} V1 and W21 = {0} u {0} u Z V1, V1 = W11 W21 , where W11 and W21 are special group set vector subspaces of V1.

Consider V2, take °­§ a · °½ W12 = ®¨ ¸ a Z ¾ V2 ¯°© 0 ¹ ¿° and ­°§ 0 · ½° W22 = ®¨ ¸ b Z ¾ °¯© b ¹ ¿° as special group set vector subspaces of V2.

281

V2 = W12 W22 i.e., §0· W12 W22 = ¨ ¸ . ©0¹

In V3 define ­°§ a b · ½° W13 = ®¨ ¸ a, b Z ¾ V3 °¯© 0 0 ¹ ¿°

and ­°§ 0 0 · ½° W23 = ®¨ ¸ c,d Z ¾ V3. ¯°© c d ¹ ¿° W13 and W23 are special group set vector subspaces of V3 and

V3 = W13 W23 . In the special group set vector space V4 take W14 = {a0 + a2x2 | a0, a2 Z} V4 and W24 = {a1x + a3x3 | a1, a3 Z} V4.

Clearly V4 = W14 W24 and W14 W24 = {0}.

Thus V = (V1, V2, V3, V4) 1 1 = ( W1 W2 , W12 W22 , W13 W23 , W14 W24 ).

Take W1 = ( W11 , W12 , W13 , W14 ) and W2 = ( W21 , W22 , W23 , W24 ).

Let P1 : V into W1, a projection; i.e., a special group set linear operator on V. P = P11 , P12 , P13 , P14 defined by P11 : V1 o W1 is such that

P11 (a b c ) = (a, b, 0) P11 o P11 (a b c) P11 (a b 0) = (a b 0)

that is P11 o P11 = P11 .

282

Consider P12 : V2 o W2 is defined by ªa º P12 « » ¬b¼

ªa º «0» ¬ ¼

we see ªa º P12 o P12 « » ¬b¼

ªa º P12 « » ¬0 ¼

ªa º «0» ¬ ¼

P12 o P12 = P12 .

Now P13 : V3 o W3 is defined by ªa b º P13 « » ¬c d¼

ªa b º «0 0 » ¼

also ªa b º P13 o P13 « » ¬c d¼

ªa b º P13 « » ¬0 0 ¼

ªa b º «0 0 » ¼

P13 o P13 = P13 .

Finally P14 : V4 o W4 is defined by P14 (a0 + a1x + a2x2 + a3x3) = (a0 + a2x2).

Thus P14 o P14 (a0 + a1x + a2x2 + a3x3) = P14 (a0 + a2x2) = a0 + a2x2.

Thus P14 o P14 = P14 . So we see P1 o P1 = P1, P1 : V o( W11 , W12 , W13 , W14 ).

Now take P2 : V = ( W21 , W22 , W23 , W24 ) where P2 = ( P21 , P22 , P23 , P24 ) : (V1, V2, V3, V4) o ( W21 , W22 , W23 , W24 );

283

defined by P21 : V1 o W21 ; P21 (a b c) = ( 0 0 c) so P21 o P21 (a b c) = P21 ( 0 0 c) = (0 0 c).

Thus P21 o P21 = P21 .

Consider P11 o P21 (a b c) = P11 ( 0 0 c) = (0 0 0).

Also P21 o P11 (a b c) = P21 (a b 0) = (0 0 0).

Thus we get P21 o P11 = P11 o P21 = T1 : V1 o W1.

Now consider P22 : V2 o W22 ; defined by ªa º P22 « » ¬b¼

ª0º «b» ¬ ¼

so ªa º P22 o P22 « » ¬b¼

ª0º P22 « » ¬b¼

ª0º «b» ¬ ¼

which implies P22 o P22 = P22 .

Now ªa º P22 o P12 « » ¬b¼

ªa º P22 « » ¬0 ¼

ª0º «0» ¬ ¼

ªa º P12 o P22 « » ¬b¼

ª0º P12 « » ¬b¼

ª0º «0» . ¬ ¼

and

Thus P12 o P22 = P22 o P12 = T2 : V2 o V2 that is

284

ªa º T2 « » ¬b¼ 3 3 P2 : V3 o W2 is defined by ªa b º P23 « » ¬c d¼ ªa b º P23 o P23 « » ¬c d¼

ª0º «0» . ¬ ¼

ª0 0 º «c d» ; ¼

ª0 0 º P23 « » ¬c d ¼

ª0 0 º «c d» . ¼

Thus P23 o P23 = P23 .

Now ªa b º P23 o P13 « » ¬c d¼

ªa b º P23 « » ¬0 0 ¼

ª0 0º «0 0» ¼

ªa b º P13 o P23 « » ¬c d¼

ª0 0 º P13 « » ¬c d ¼

ª0 0º «0 0» . ¼

P13 o P23 = P23 o P13 = T3 : V3 o V3; such that ªa b º T3 « » ¬c d¼

ª0 0º «0 0» . ¼

Finally P24 : V4 o W24 is defined by P24 (a0 + a1x + a2x2 + a3x3) = a1x + a3x3.

P14 o P24 (a0 + a1x + a2x2 + a3x3)= P14 (a1x + a3x3) = 0

P24 o P14 (a0 + a1x + a2x2 + a3x3) = P24 (a0 + a2x2) = 0.

Thus P24 o P14 = P14 o P24 = T4 : V4 o V4 defined by T4 (a0 + a1x + a2x2 + a3x3) = 0.

285

Thus we see P1 + P2 = P11 P21 , P12 P22 , P13 P23 , P14 P24 : V o V P11 P21 (a b c) = = =

P11 (a b c) + P21 (a b c) (a b 0) + (0 0 c) (a b c).

Thus P11 + P21 = I1 : V o V.

P

2 1

§ ªa º ¡ P22 ¨   ¸ Š bŸ š

ÂŞa Âş ÂŞ a Âş ÂŞa Âş ÂŞ 0 Âş ÂŞ a Âş P12 ÂŤ Âť + P22 ÂŤ Âť = ÂŤ Âť ÂŤ Âť ÂŤ Âť . ÂŹ b Âź ÂŹ0Âź ÂŹ b Âź ÂŹ b Âź ÂŹb Âź 2 2 Thus P1 + P2 = I2 : V2 o V2 (identity special group operator on V2).

Now § a b ¡ a b a b P13 P23 ¨ ª c d º ¸ = P13 ª c d º P23 ª c d º Ÿš  Ÿ  Ÿ Š ªa b º ª0 0 º =     0 0 Ÿ  c d Ÿ

Hence

P

3 1

ÂŞa b Âş ÂŤc dÂť . ÂŹ Âź

P23 I3 . I3 : V3 o V3 (identity special group

operator on V3).

Finally P14 + P24 (a0 + a1x + a2x2 + a3x3)

= P14 (a0 + a1x + a2x2 + a3x3) + P24 (a0 + a1x + a2x2 + a3x3) = a0 + a2x2 + a1x + a3x3 = a0 + a1x + a2x2 + a3x3. that is P14 + P24 = I4 : V4 o V4. Thus P1 + P2 = (I1, I2, I3, I4).

Now we give yet another example.

286

Example 3.2.60: Let V = (V1, V2, V3) where V1 = Z12 u Z12 u Z12 u Z12, V2 = {Z12[x] all polynomials in the variable x with coefficients from Z12 of degree less than or equal to 6} and

­§ a b c ¡ ½ °¨ ° ¸ V3 = Ž¨ d e f ¸ a, b,c,d,e,f ,g, h,i � Z12 ž . °¨ g h i ¸ ° š ¯Š ¿

V = (V1, V2, V3) is a special group set vector space over the set Z10. Now let W1 = W11 , W12 , W13 Â&#x17D; V W2 = W21 , W22 , W23 Â&#x17D; V

W3 = W31 , W32 , W33 Â&#x17D; V

and Such that

V = (W1 + W2 + W3) = W11 W21 W31 , W12 W22 W32 , W13 W23 W33

= (V1, V2, V3) that is Vi = W1i W2i W3i , 1 d i d 3. For the special group vector space V1, W11 = Z12 u Z12 u {0} u {0}, W21 = {0} u Z12 u {0}

and

W31 = {0}u{0}u{0}uZ12;

clearly W11 , W21 and W31 are special group vector subspaces of V1. Here V1 = W11 Â&#x2020; W21 Â&#x2020; W31 , with Wi1 Â&#x2C6; Wj1 = (0 0 0 0) if i z j, 1 d i, j d 3. Now consider the special group vector space V2, take W12 = {a0 +a1x | a0, a1 Â? Z12} Â&#x17D; V2, W22 = {a2x2 + a3x3 | a2, a3 Â? Z12} Â&#x17D; V2 and W32 = {a4x4 + a5x5 + a6x6 | a4, a5, a6 Â? Z12} Â&#x17D; V2.

287

Clearly W12 , W22 and W32 are special group vector subspaces of V2 with V2 = W12 W22 W32 where Wi2 Wj2 = 0 if i z j; 1 d i, j d 3.

Finally take ­ªa b c º ½ °« ° » V3 = ® « d e f » a, b,c,d,e,f ,g, h,i Z12 ¾ ° «g h i » ° ¼ ¯¿

with ­ ª0 b c º ½ °« ° » W = ® « 0 0 f » b,c,f Z12 ¾ , ° ° ¯ «¬ 0 0 0 »¼ ¿ 3 1

­ ªa 0 0º ° W = ® «« 0 e 0 »» a,e,i Z12 ° «0 0 i » ¼ ¯3 2

½ ° ¾ ° ¿

and ­ªa 0 0º ½ °« ° » W = ® « d 0 0 » d,g, h Z12 ¾ ° «g h 0» ° ¼ ¯¿ 3 3

as special group vector subspaces of V3.

Clearly V3 = W13 W23 W33 with

W W 3 i

3 j

ª0 0 0º « 0 0 0 » ; i z j. 1 d i, j d 3. « » «¬ 0 0 0 »¼

­ªa 0 0º ½ °« ° » ® « d 0 0 » d,g, h Z12 ¾ ° «g h 0» ° ¼ ¯¿

288

V = W11 W21 W31 , W12 W22 W32 , W13 W23 W33 .

V1 = W11 W21 W31 V2 = W12 W22 W32 and V3 = W13 W23 W33 .

Define P1 = P11 , P12 , P13 , P2 = P21 , P22 , P23 and P3 = P31 , P32 , P33

on V by

P1 : V o W11 , W12 , W13

where P11 : V1 o W11 , P12 : V2 o W12 and

P13 : V3 o W13 defined by P11 [( x y z Z) = (x y 0 0), P12 [a0 + a1x + … + a6x6] = (a0 + a1 x)

and § ªa b c º · ¨ ¸ P ¨ ««d e f »» ¸ ¨ «g h i » ¸ ¼¹ ©3 1

ª0 b c º «0 0 f » . « » «¬0 0 0 »¼

Thus P1 = P11 , P12 , P13 is a special group set projection operator from V into W11 , W12 , W13 .

Now consider P2 = P21 , P22 , P23 where

P2 : V o W21 , W22 , W23 defined by P21 : V1 o W21 as

P21 (x y z Z) = (0 0 z 0).

P22 : V2 o W22 defined by

P22 (a0 + a1x + … + a6x6) = a2x2 + a3x3 and

289

P23 : V3 o W23 defined by ªa b c º 3  P2  d e f   g h i Ÿ

ªa 0 0º 0 e 0 .    0 0 i Ÿ

Thus P2 = P21 , P22 , P23 is a special group set projection operator of V = (V1,V2,V3) into the special group set vector subspace W2 = W21 , W22 , W23 .

Now P3 = P13 , P23 , P33 : V o W3 = W31 , W32 , W33 where P13 : V1 o W31 is defined by P13 (x y z Z) = (0 0 0 Z)

P23 : V2 o W32 is defined by P23 (a0 + a1x + â&#x20AC;Ś + a6x6) = a4x4 + a5x5 + a6x6) and P33 : V3 o W33 by ÂŞa b c Âş P ÂŤÂŤ d e f  ÂŹÂŤ g h i Ÿ 3 3

ª 0 0 0º d 0 0 .   g h 0 Ÿ

It is easily verified P3 = P13 , P23 , P33 : V o W3 = W31 , W32 , W33

is a special group set projection operator of V into W3.

Now consider P1 + P2 + P3 = P11 P21 P31 , P12 P22 P32 , P13 P23 P33 ) : V o V .

P

1 1

P21 P31 (x y z Z)

= =

P11 (x y z Z) + P21 (x y z Z) + P31 (x y z Z) (x y 0 0) + (0 0 z 0) + (0 0 0 Z) = (x y z Z);

290

Thus P11 P21 P31 = I1, that is identity map on V1, i.e., I1: V1 o V1 is the special group set linear operator which is an identity operator on V1.

Now consider P12 P22 P32 : V2 o V2 defined by

P

2 1

P22 P32 (a0 + a1x + â&#x20AC;Ś + a6x6)

= P12 (a0 + a1x + â&#x20AC;Ś + a6x6) + P22 (a0 + a1x + â&#x20AC;Ś + a6x6) + P32 (a0 + a1x + â&#x20AC;Ś + a6x6) = (a0 + a1x) + (a2x2 + a3x3) + (a4x4 + a5x5 + a6x6) = (a0 + a1x + â&#x20AC;Ś + a6x6),

that is

P

2 1

P22 P32 = I2 : V2 o V2.

Now consider P13 P23 P33 : V3 o V3; §a b c¡ P P P ¨¨ d e f ¸¸ ¨g h i ¸ Š š 3 1

3 2

3 3

§a b c¡ §a b c¡ §a b c¡ ¨ ¸ ¸ ¸ 3¨ 3¨ = P ¨ d e f ¸ P2 ¨ d e f ¸ P3 ¨ d e f ¸ ¨g h i ¸ ¨g h i ¸ ¨g h i ¸ Š š Š š Š š 3 1

§ 0 b c ¡ § a 0 0¡ § 0 0 0¡ ¨ ¸ ¨ ¸ ¨ ¸ = ¨ 0 0 f ¸ ¨ 0 e 0¸ ¨ d 0 0¸ ¨ 0 0 0¸ ¨ 0 0 i ¸ ¨ g h 0¸ Š š Š š Š š §a b c¡ ¨ ¸ = ¨d e f ¸ ; ¨g h i ¸ Š š

291

thus

P

3 1

Thus P1 + P2 + P3

P23 P33 = I3 : V3 o V3.

=

P

=

(I1, I2, I3): V o V.

1 1

P21 P31 , P12 P22 P32 , P13 P23 P33

Thus we see the sum of the projection operators from a direct sum of subspaces is a special group set linear identity operator on V = (V1, V2, V3).

Now consider the composition of Pji oPti , j z t; 1 < i < 3.

P21 o P11 (a b c d) = P21 (a b 0 0) = (0 0 0 0)

and P11 o P21 (a b c d) = P11 (0 0 c d) = (0 0 0 0).

Thus P11 o P21 = P21 o P11 = T1

Also P21 o P31 (a b c d) = P21 ( 0 0 0 d) = (0 0 0 0) and P31 o P21 (a b c d) = P31 (0 0 c 0) = (0 0 0 0).

So P31 o P21 = P21 o P31 = T1.

Likewise P12 o P32 (a0 + a1x + â&#x20AC;Ś + a6x6) = P12 (a4x4 + a5x5 + a6x6) = T2.

Now P32 o P12 (a0 + a1x + â&#x20AC;Ś + a6x6) = P32 (a0 + a1x) = 0.

Hence P32 o P12 = P12 o P32 = T2.

292

ª§ a b c ¡ º § 0 0 0¡ ¨ ¸ ¸ 3 ¨ P o P ¨ d e f ¸  = P1 ¨ d 0 0 ¸ ¨g h 0¸ ¨Š g h i ¸š Ÿ Š š 3 1

3 3

§ 0 0 0¡ ¨ ¸ = ¨ 0 0 0¸ . ¨ 0 0 0¸ Š š

Also §a b c¡ ¨ ¸ P o P ¨d e f ¸ ¨g h i ¸ Š š 3 3

3 1

§ a b c ¡ § 0 0 0¡ ¨ ¸ ¨ ¸ P ¨ 0 0 f ¸ = ¨ 0 0 0¸ . ¨ 0 0 0¸ ¨ 0 0 0¸ Š š Š š 3 3

Thus we have P33 o P13

P13 o P33 = T3. T3: V3 o V3

such that T3(x) = 0 for all x Â? V3 so Pj o Pk = (T1 T2, T3). where j z k, 1 d k, j d 3.

These concepts can be extended to special group set linear algebras defined over the group G. As mentioned earlier when we use special group set linear algebra in the place of special group set vector spaces we see the cardinality of the generating set becomes considerably small in case of special group set linear algebras apart from that all properties defined for special group set vector spaces can be easily extended to special group set linear algebras.

293

Infact while using the set vector spaces or group vector spaces or semigroup vector spaces or special set vector spaces and so on we do not have the means to transform these algebraic structures to matrix equivalent. But when we want to use them in problems in which the notion of matrix is not used we find these structures can be easily replaced by the conventional ones.

Now we proceed onto describe the fuzzy analogue of these notions.

294

Chapter Four

SPECIAL FUZZY SEMIGROUP SET VECTOR SPACE AND THEIR GENERALIZATIONS

This chapter has two sections. Section one defines the notion of special fuzzy semigroup set vector spaces and gives some important properties related to them. Section two defines the new concept of special semigroup fuzzy set n-vector spaces and special group fuzzy set n-vector spaces and describe some properties about them.

4.1 Special Fuzzy Semigroup Set Vector Spaces and their Properties In this section we introduce the fuzzy structures defined in the earlier chapters.

More specifically we define the new notion of special fuzzy semigroup set vector spaces and describe some of its properties.

DEFINITION 4.1.1: Let V = (S1 S2,â&#x20AC;Ś, Sn) be a special semigroup set vector space over the set P.P: V o [0, 1] is said to be a

295

special fuzzy semigroup set vector space (special semigroup set fuzzy vector space) if the following conditions hold good.

P = (P1, P2,…, Pn):V = (S1, S2, … , Sn) o [0, 1] is such that for each i, Pi: Si o [0, 1]; 1 d i d n satisfies the following conditions: 1. Pi(xi + yi) t min {Pi(xi), Pi(yi)} 2. Pi(s xi) t Pi(xi) for all s P and for all xi, yi in Si for every i, 1 d i d n. We denote the special semigroup set fuzzy vector space by VP = (S1, …, Sn)P S1 , S 2 ,..., S n P ,..., P

1

n

= S1P1 , S2 P2 ,..., S nPn .

We illustrate this by a simple example.

Example 4.1.1: Let V = {S1, S2, S3, S4} be a special semigroup set vector space over the set S = {0, 1} where ­°§ a b · ½° S1 = ®¨ ¸ a, b,c,d Z2 {0,1}¾ , °¯© c d ¹ °¿ S2 = {Z2 u Z2 u Z2 u Z2}, S3 = {Z2[x] all polynomials of degree less than or equal to 5} and ­ªa b º °« » ° c d» S4 = ® « a, b, c, d, e, f, g, h Z2 = {0, 1}}. °« e f » ° «¬ g h »¼ ¯ Define K = (K1, K2, K3, K4): V = (S1 S2 S3 S4) o [0,1] by K1 : S1 o [0,1] K : S2 o [0,1] K3 : S3 o [0,1] 296

K4 : S4 o [0,1].

and where

­1 ª a b º ° if a z 0 = K1   Ž2  c d Ÿ °¯ 1 if a 0 ­1 ° if a b c d z 0 K2 [a b c d] = Ž 5 °¯ 1 if a b c d 0 1 ­ if p(x) is not a constant ° deg p(x) [p(x)] = K3 Ž ° 1 if p(x) = constant ¯

ÂŞa ÂŤc K4 ÂŤ ÂŤe ÂŤ ÂŹg

bº ­1 d  ° if a b z 0 = Ž6 f  ° 1 if a b 0  ¯ hŸ

Thus VK = S1K1 ,S2 K2 ,S3K3 ,S4 K4 is a special semigroup set fuzzy vector space.

Now we proceed onto define the notion of special semigroup set fuzzy vector subspace.

DEFINITION 4.1.2: Let V = (S1, S2, â&#x20AC;Ś, Sn) be a special semigroup set vector space over the set P. Take W = (W1, W2, W3, â&#x20AC;Ś, Wn) Â&#x17D; (V1, â&#x20AC;Ś, Vn) be a special semigroup set vector subspace of V over the same set P. Define K = (K1, K2, â&#x20AC;Ś, Kn): W o [0,1] as Ki: Wi o [0,1] for every i such that W1K1 ,W2K2 ,W3K3 ,...,WnKn is a special semigroup

set fuzzy vector space then we call WK = W1K1 ,W2K2 ,W3K3 ,...,WnKn

297

to be a special semigroup set fuzzy vector subspace.

We shall illustrate this by an example.

Example 4.1.2: Let V = (S1, S2, S3, S4, S5) where S1 = {S u S u S | S = Zo = Z+ {0}}, S2 = {[a a a a a a] | a S}, ½ °­§ a b · o° S3 = ®¨ ¸ a, b, c, d 2Z ¾ , °¯© c d ¹ °¿

S4 = {all polynomials of degree less than or equal to 5 with coefficients from Zo} and

­ ªa °« ° a S5 = ® « ° «a ° «¬ a ¯

½ aº ° » a» o° a Z ¾ a» ° » ° a¼ ¿

be a special semigroup set vector space over the set S. Choose W = (W1, W2, …, W5) such that W1 = (S u S u {0}) S1, W2 = {[a a a a a a] | a 3Zo} S2, ½ ­°§ a a · o° W3 = ®¨ ¸ a 6Z ¾ S3, °¿ ¯°© a a ¹

W4 = {all polynomials of degree less than or equal to 5 with coefficients from 3Zo} S4 and ­ ªa °« ° a W5 = ® « ° «a ° «¬ a ¯

½ aº ° » a» o° a 7Z ¾ a» ° » ° a¼ ¿

be a special semigroup set vector subspace of V over the set

298

S = Zo. Define K = (K1, K2, â&#x20AC;Ś, K5) : W = (W1, W2, â&#x20AC;Ś, W5) o [0,1] such that Ki : Wi o [0,1] for i = 1, 2, â&#x20AC;Ś, 5 is defined as follows: K1 : W1 o [0,1] is defined by

­1 ° 2 if a b is even ° K1(a b 0) = Ž 1 ° 3 if a b is odd ° ¯ 1 if a b 0

K2 : W2 o [0,1] is such that

­1 ° if a is odd, a z 0 K2(a a a a a a) = Ž 6 °¯ 1 if a is even or 0 K3: W3 o [0, 1] is given by

­1 § a a ¡ ° if a z 0 K3 ¨ ¸ = Ž5 Š a a š ° 1 if a 0 ¯

K4: W4 o [0, 1] is such that 1 ­ if p(x) z a constant ° K4(p(x)) = Ž deg p(x) ° 1 if p(x) = constant polynomial ¯ K5 : W5 o [0,1] is defined by

299

§a ¨ a K5 ¨ ¨a ¨ ©a

a· ¸ a¸ a¸ ¸ a¹

­1 ° 3 if a is even a z 0 ° ® 1 if a is odd a z 0 °6 ° ¯ 1 if a 0

WK = W1 , W2 , W3 , W4 , W5 K ,K

2 ,..., K5

1

= W1K1 , W2 K2 ,..., W5K5

is a special semigroup set fuzzy vector subspace.

DEFINITION 4.1.3: Let V = {V1, V2, …, Vn} be a special semigroup set linear algebra over the semigroup S, S a semigroup under addition. If K = (K1, K2, …, Kn): V = (V1, …, Vn) o [0, 1] is such that Ki: Vi o [0, 1]; Ki(ai + bi) t min(Ki(ai), Ki(bi)) and Ki(sai) t Ki(ai) for every ai, bi, Vi and for all s S and this is true for every i = 1, 2, …, n; then we call VK = V1 ,V2 ,...,Vn K ,,...,K = V1K1 ,V2K2 ,...,VnKn

1

n

be a special semigroup set fuzzy linear algebra. It is pertinent to mention here that if VK is a special semigroup set fuzzy linear algebra is the same as the special semigroup set fuzzy vector space. Thus the notion of special semigroup set fuzzy vector space and special semigroup set fuzzy linear algebra are fuzzy equivalent.

However we illustrate this by a simple example.

Example 4.1.3: Let V = (V1, V2, V3, V4) where V1 = {S u S u S u S such that S = Zo = Z+ {0}}, V2 = {all polynomials of degree less than or equal to 5 with coefficients from S}, ­° ª a a a a a º ½° V3 = ® «

a S ¾ » °¯ ¬ a a a a a ¼ ¿°

and V4 = {All 3 u 3 matrices with entries from S} be a special semigroup set linear algebra over the semigroup S = Zo = Z+

300

Â&#x2030; {0}. Define K = (K1, K2, K3, K4): V = (V1, V2, V3, V4) o [0,1] such that Ki: Vi o [0,1] for every i = 1, 2, 3, 4. Kiâ&#x20AC;&#x2122;s are defined in the following way K1: V1 o [0,1] is such that

­1 ° if ab cd is even K1(a b c d) = Ž 2 °¯ 1 if ab cd is odd or zero K2: V2 o [0,1] is given by

­1 ° 5 if deg p(x) is even ° K2 (p (x)) = Ž 1 ° 2 if deg p(x) is odd ° ¯ 1 if p(x) is constant K3: V3 o [0,1] is defined by ­1 ° 5 if a is even §a a a a a¡ ° K3 ¨ ¸ = Ž1 Š a a a a a š ° 2 if a is odd ° ¯ 1 if a 0 K4 : V4 o [0,1] is such that ­1 if trace sum is even a b c § ¡ °2 ¨ ¸ ° K4 ¨ d e f ¸ = Ž 1 ¨ g h i ¸ ° 3 if trace sum is odd Š š ° ¯ 1 if a e i 0 Thus

301

VK = V1 , V2 , V3 , V4 K ,K 1

2 , K3 , K4

= V1K1 , V2 K2 , V3K3 , V4 K4

is a special semigroup set fuzzy linear algebra. Clearly VK is also a special semigroup set fuzzy vector space.

Next we proceed on to define the notion of special semigroup set fuzzy linear subalgebra.

DEFINITION 4.1.4: Let V = (V1, V2, …, Vn) be a special semigroup set linear algebra over the additive semigroup S. Let W = (W1, W2, …, Wn) (V1, V2, …, Vn) be such that W is a special semigroup set linear subalgebra of V over S. Now define K = (K1, K2, …, Kn) : W = (W1, W2, …, Wn) o [0,1] by Ki: Wi o [0,1] for each i such that WK = W1K1 ,..., WnKn is also a

special semigroup set fuzzy linear algebra then we call WK to be a special semigroup set fuzzy linear subalgebra.

We shall illustrate this by an example.

Example 4.1.4: Let V = (V1, V2, V3, V4, V5) where

½° °­ ª a a a a º V1 = ® « a Z6 ¾ , » ¯° ¬ a a a a ¼ ¿° ­ ªa °« ° «a ° V2 = ® « a ° «a °« « ¯° ¬ a

½ a aº ° » a a» ° ° a a » a Z6 ¾ , » ° a a» ° a a »¼ ¿°

V3 = {all polynomials in the variable x with coefficients from Z6 of degree less than or equal to 4}, V4 = {3 u 3 matrices with entries from Z6} and V5 = {Z6 u Z6 u Z6 u Z6} be a special semigroup set linear algebra over the semigroup S = Z6.

Consider W = (W1, W2, W3, W4, W5) (V1, V2, V3, V4, V5) = V where 302

­° ª a a a a º W1 = ® « » a {0, 2, 4}; °¯ ¬ a a a a ¼ addition of matrices modulo 6} V1, ­ ªa a a º °« » ° «a a a » ° W2 = ® « a a a » a {0, 2, 4}; ° «a a a » » °« °¯ «¬ a a a »¼ addition of matrices modulo 6} V2, W3 = {all polynomials in x of degree less than or equal to three with coefficients from Z6} V3, W4 = {upper triangular 3 u 3 matrices with entries from Z6} V4 and W5 = {Z6 u Z6 u {0} u {0}} V5; W = (W1, W2, W3, W4, W5) is a special semigroup set linear subalgebra of V over the semigroup Z6.

Take K = (K1, K2, K3, K4, K5): W = (W1, W2, …, W5) o [0,1] where Ki : Wi o [0,1] for i = 1, 2, …, 5 such that

K1: W1 o [0,1] is defined by

­1 ª a a a a º ° if a z 0 K1 « » = ®2 ¬ a a a a ¼ ° 1 if a 0 ¯

303

K2: W2 o [0,1] is such that ÂŞa ÂŤa ÂŤ K2 ÂŤ a ÂŤ ÂŤa ÂŤÂŹ a

a aº a a  a a  a a a a Ÿ

­1 ° if a z 0 Ž9 °¯ 1 if a 0

K3: W3 o [0,1] is given by 1 ­ if p(x) z a constant ° K3(p(x)) = Ž deg p(x) ° 1 if p(x) = constant polynomial ¯

K4 : W4 o [0,1] is defined by

ªa a a º ­ 1 ° if a d e z 0 K4  0 d e  = Ž 2 °  0 0 f Ÿ ¯ 1 if a d e 0

K5: W5 o [0,1] is given by ­1 ° if a b z 0 K5 (a b 0 0) = Ž 2 °¯ 1 if a b 0 Thus WK= W1 , W2 , W3 , W4 , W5 K ,K 1

2 ,..., K5

= W1K1 , W2 K2 ,..., W5K5

is a special semigroup set fuzzy linear subalgebra.

However it is pertinent to mention here that the notion of special semigroup set fuzzy vector subspace and the special semigroup set fuzzy linear subalgebra are fuzzy equivalent.

304

Now we proceed on to define the notion of special group set fuzzy vector space.

DEFINITION 4.1.5: Let V = (V1, V2, …, Vn) be a special group set vector space over the set S. Let K = (K1, K2, …, Kn) : V = (V1, … Vn) o [0,1] be such that Ki : Vi o [0,1] for each i, 1 d i d n satisfying the following conditions

1.

2.

3.

4.

Ki(ai + bi) t min{Ki(ai), Ki(bi)} Ki(– ai) = K(ai) Ki (0) = 1 Ki (rai) t Ki(ai) for all r S and for all ai, bi Vi, true for each i = 1, 2, …, n.

Thus ViKi is a group set fuzzy vector space, 1 d i d n. Now VK = V1 ,V2 ,...,Vn K ,K 1

2 ,...,K n

= V1K1 ,V2K2 ,...,VnKn

is a special set group fuzzy vector space.

We illustrate this by a simple example.

Example 4.1.5: Let V = (V1, V2, V3, V4, V5) where V1 = Z10 additive abelian group modulo 10. V2 = Z15 u Z15 u Z15 is again a group under component wise addition modulo 15, V3 = Z12, V4 = Z14 u Z14 and V5 = Z7 u Z7 u Z7 u Z7 is a special set group vector space over the set S = {0, 1}. Define K = (K1, K2, K3, K4, K5): V = (V1, V2, V3, V4, V5) o [0, 1]

by Ki : Vi o [0,1] , 1 d i d 5 as

K1: V1 o [0,1] is defined by

­1 ° 2 if a {2, 4, 6, 8} ° K1 (a) = ® 1 if a 0 °1 ° if a {1, 3, 5, 7, 9} ¯3

305

K2: V2 o [0,1] is given by

­1 °6 ° °1 °5 ° K2 (a b c) = Ž 1 °4 ° °1 °3 °1 ¯

if a b c Â? {1, 2, 8} if a b c Â? {4, 6, 10, 12} if a b c Â? {3, 7, 11} if a b c Â? {5, 9, 13, 14} if a

0 or a b c 0

K3: V3 o [0, 1] is such that ­1 ° 9 if a � {2, 4, 6, 8, 10} ° K3(a) = Ž 1 if a 0 °1 ° if a � {1, 3, 5, 7, 9, 11} ¯8 K4: V4 o [0,1] is given by ­1 ° 3 if a b � {2, 4, 6, 8, 10, 12} ° K4 (a, b) = Ž 1 if a b 0 °1 ° if a b � {1, 3, 5, 7, 9, 11, 13} ¯5

K5: V5 o [0,1] ­1 ° 3 if a b c d � {2, 4, 6} ° K5 (a b c d) = Ž 1 if a b 0 °1 ° if a b c d � {1, 3, 5} ¯2

306

Thus VK = V1 , V2 , V3 , V4 , V5 K ,K 1

2 , K3 , K4 , K5

= V1K1 , V2 K2 ,..., VnKn

is a special set group fuzzy vector space.

Now we proceed on to define the notion of special group set fuzzy vector subspace.

DEFINITION 4.1.6: Let V = (V1, V2, …, Vn) be a special group set vector over the set S. Let W = (W1, W2, …, Wn) (V1, V2, …, Vn) = V; (Wi Vi, 1 d i d n) be a special set group vector subspace of V over the set S. Let K = (K1, K2, …, Kn): W = (W1, …, Wn) o [0,1] be such that Ki :Wi o [0,1] gives WiKi to be a

group set vector subspace for each i = 1, 2, …, n. Then WK = W1K1 ,W2K2 ,...,WnKn

is defined as the special set group fuzzy vector subspace.

We illustrate this by an example.

Example 4.1.6: Let V = (V1, V2, V3, V4, V5) be a special set group vector space over the set S = {0, 1, 2, …, 10} where V1 = 2Z, V2 = 3Z, V3 = 4Z, V4 = 5Z and V5 = 7Z. Now take W = (W1, W2, W3, W4, W5) (V1, V2, V3, V4, V5), a special group set vector subspace of V where W1 = 14Z V1, W2 = 6Z V2, W3 = 12Z V3, W4 = 10Z V4 and W5 = 21Z V5. Define K = (K1, K2, K3, K4, K5): W = (W1, W2, W3, W4, W5) o [0, 1] by

K1 : W1 o [0,1] such that ­1 ° if a z 0 K1(a) = ® 5 °¯ 1 if a 0

K2 : W2 o [0,1] defined by ­1 ° if a z 0 K2 (a) = ® 6 °¯ 1 if a 0

307

K3 : W3 o [0,1] is such that ­1 ° if a z 0 K3 (a) = ® 2 °¯ 1 if a 0 K4 : W4 o [0,1] is such that ­1 ° if a z 0 K4 (a) = ® 9 °¯ 1 if a 0 and K5 : W5 o [0,1] is defined by ­1 ° if a z 0 K5 (a) = ® 7 °¯ 1 if a 0 Thus WK =

W

1K1

, W2 K2 ,..., W5K5 is a special group set fuzzy

vector subspace.

We will now proceed on to define special group set fuzzy linear algebra.

DEFINITION 4.1.7: Let V = (V1, V2, …, Vn) be a special group set linear algebra defined over the set S. Let K = (K1, …, Kn) be a map from V into [0,1], that is K (K1, K2, … Kn): V = (V1, …, Vn) o [0, 1]; such that Ki: Vi o [0,1] for each i; ViKi is a group

set fuzzy linear algebra, 1 d i d n. Then VK = V1K1 ,V2K2 ,...,VnKn

is a special group set fuzzy linear algebra.

We illustrate this by an example, we also observe that the notion of special group set fuzzy linear algebra and special group set fuzzy vector space are fuzzy equivalent.

308

Example 4.1.7: Let V = (V1, V2, V3, V4) be a special group set linear algebra over the set Z+ Â&#x2030; {0} = S. Here V1 = {set of all 2 u 2 matrices with entries from Z}, V2 = Z u Z u Z u Z,

½ ­ ªa º ° °  ° ° a  ° ° V3 = Ž  a  a � Z ž ° a  ° °  ° °¯  a Ÿ °¿ and V4 = {Z7[x] all polynomials of degree less than or equal to 7 with coefficients from Z}. Now define a map K = (K1, K2, K3, K4) : V = (V1, V2, V3, V4) o [0,1]

by Ki : Vi o [0, 1] such that ViKi is a group fuzzy linear algebra for i = 1, 2, 3, 4 as follows: K1: V1 o [0, 1] is defined by ­1 ° 2 if a is even ° §a b¡ K1 ¨ ¸ = Ž 1 if a is odd Šc dš °3 ° ¯ 1 if a 0

K2: V2 o [0, 1] is such that

­1 ° 5 if a b c d is even ° K2(a b c d) = Ž 1 ° 7 if a b c d is odd ° ¯ 1 if a b c d 0 K3: V3 o [0, 1] is defined by

309

ªa º ­1 «a » ° if a is even « » 1 °12 K3 « a » ®1 « » 12 ° if a is odd 13 «a » ° «¬ a »¼ ¯ 1 if a 0 Finally K4 : V4 o [0,1] is given by ­1 ° 23 if deg p(x) is even ° K4(p(x)) = ® 1 ° 2 if deg p(x) is odd ° ¯ 1 if p(x) is constant Thus VK= V1K1 ,V2 K2 , V3K3 , V4 K4 is a special group set fuzzy linear algebra.

Now we proceed on to define the notion of special group set fuzzy linear subalgebra.

DEFINITION 4.1.8: Let V = (V1, V2, …, Vn) be a special group set linear algebra over the set S. Let W = (W1, W2, …,Wn) (V1, V2, …,Vn) = V be a special set linear subalgebra of V over the set S. Let K = (K1, …, Kn) : W o [0,1] be such that Ki : Wi o [0,1] for each i; i = 1, 2, …, n. WiKi is a group set fuzzy linear

subalgebra for every 1 d i d n. So that WK = W1K1 ,W2K2 ,...,WnKn

is a special group set fuzzy linear subalgebra.

It is interesting to note that the notion of special group set fuzzy vector subspaces and special group set fuzzy linear subalgebras are fuzzy equivalent.

We how ever illustrate special group set fuzzy linear subalgebra.

310

Example 4.1.8: Let V = (V1, V2, V3, V4, V5) be a special group set linear algebra over the set S = 2Z+ {0} where V1 = Z u Z u Z, V2 = {All 5 u 5 matrices with entries from Z}, V3 = [a a a a a a a] | a Z}, ­ ªa º ½ °« » ° ° «a » ° V4 = ® a Z ¾ ° «a » ° ° «¬ a »¼ ° ¯ ¿

and V5 = {Z[x] polynomials of degree less than or equal to 9 with coefficients from Z}. Take W = (W1, W2, W3, W4, W5) (V1, V2, … ,V5) that is Wi Vi, 1 < i < 5, where W1 = Z u {0}u {0} V1, W2 = {all 5u5 matrices with entries from 3Z} contain as a group set linear subalgebra of V2, W3 = {[a a a a a a a] | a 5Z} V3, ­ ªa º ½ °« » ° ° «a » ° W4 = ® a 7Z ¾ V4 ° «a » ° ° «¬ a »¼ ° ¯ ¿ and W5 = {All polynomials in x of degree less than or equal to 5 with coefficients from Z} V5. Thus W = (W1, W2, …, W5) V is a special group set linear subalgebra of V over the set 2Z+ {0}. Now define K = (K1, K2, K3, K4, K5): W = (W1, W2, W3, W4, W5) o [0, 1] by Ki: Wi o [0, 1], 1 d i d 5 such that WiKi is the group set fuzzy linear subalgebra for i = 1, 2, …, 5.

Define K1: W1 o [0, 1] by

­1 ° 7 if a is even ° K1 (a 0 0 ) = ® 1 ° 5 if a is odd ° ¯ 1 if a 0

311

K2 : W2 o [0,1] by § a1 a 2 ¨ ¨ a6 a7 K2 ¨ a11 a12 ¨ ¨ a16 a17 ¨a Š 21 a 22

a3

a4

a8 a13

a9 a14

a18 a 23

a19 a 24

a5 ¡ ¸ a10 ¸ a15 ¸ = ¸ a 20 ¸ a 25 ¸š

­1 °11 if a1 a 7 a13 a19 a 25 is even ° Ž 1 if a a a a a is odd 1 7 13 19 25 °12 ° ¯ 1 if a1 a 7 a13 a19 a 25 0 Define K3: W3 o [0,1] by ­1 °19 if a is even ° K3 (a a a a a a a) = Ž 1 ° 5 if a is odd ° ¯ 1 if a 0 K4: W4 o [0, 1] is defined by

§a¡ ¨ ¸ ­ 1 if a is even or 0 a ° K4 ¨ ¸ = Ž 1 ¨ a ¸ ° if a is odd ¨ ¸ ¯4 Šaš K5 : W5 o [0,1] is given by

312

­1 ° 5 if p(x) is odd degree ° K5(p(x)) = ® 1 ° 7 if p(x) is even degree ° ¯ 1 if p(x) is constant

Thus WK = W1 , W2 , W3 , W4 , W5 K ,K 1

2 , K3 , K4 , K5

= W1K1 , W2 K2 ,..., W5K5

is a special group set fuzzy linear subalgebra.

It is important mention at this juncture when we define various special group set substructures most of them happen to be fuzzy equivalent.

4.2 Special Semigroup Set n-vector Spaces

In this section we proceed on to define the new notion of special semigroup set n-vector spaces, special group set n-vector spaces and their fuzzy analogue.

Now we proceed on to define special semigroup set n-vector spaces, special group set n-vector spaces and their fuzzy analogue.

DEFINITION 4.2.1: Let V = (V1 V2) = V11 , V21,...,Vn11 V12 , V22 ,..., Vn22

be a special semigroup set vector bispace over the set S if both V1 and V2 are two distinct special semigroup set vector spaces over the set S, V1 z V2, V1 V2 or V2 V1.

We illustrate this by a simple example.

Example 4.2.1: Let V = (V1 V2) = V11 , V21, V31 , V41 V12 , V22 , V32 , V42 , V52

313

where ½° °­§ a b ¡

V11 = Ž¨ ¸ a, b,c,d Â? Z Â&#x2030; {0}ž , ¯°Š c d š ¿° V21 = S u S u S, V31 = {[a a a a a] | a Â? S},

­ ªa º ½ °  ° V = Ž  a  a � Sž . V12 = SuSuSuS, ° a  ° ¯ Ÿ ¿ 1 4

­ ÂŞa Âş ½ ° Âť ° ° ÂŤa Âť ° 2 V2 = ÂŽ a Â? Sž , ÂŤ Âť ° a ° ° ÂŤÂŹ a Ÿ ° ÂŻ Âż V32 = {set of all 3 u 3 matrices with entries from S}, V42 = {All polynomials of degree less than or equal to 3 with coefficients from S} and V52 = {all 4 u 4 lower triangular matrices with entries from S}. Thus V = (V1 Â&#x2030; V2) is a special semigroup set vector bispace over the set S = Z+ Â&#x2030; {0}.

Now we proceed to define the notion of special semigroup set vector subbispace of V over the set S. DEFINITION 4.2.2: Let V = (V1 Â&#x2030; V2) = V11 , V21,...,Vn11 Â&#x2030; V12 , V22 ,..., Vn22

be a special semigroup set vector bispace over the set S. Take W = (W1 Â&#x2030; W2) = W11 , W21,...,Wn11 Â&#x2030; W12 , W22 ,...,Wn22

Â&#x17D; (V1

Â&#x2030; V ) = V , V ,...,V Â&#x2030; V , V 2

1 1

1 2

1 n1

314

2 1

2 2

, ..., Vn22

that is Wtii Vtii ; i = 1,2 and 1 d ti d ni and 1 d ti d n2 is such

that Wtii is a semigroup set vector subspace of Vt ii . Then we call W = W1 W2 to be a special semigroup set vector subbispace of V over the set S. We illustrate this situation by a simple example.

Example 4.2.2: Let V = (V1 V2) = V11 , V21, V31 V12 , V22 , V32 , V42 , V52

where V11 = {All 5u5 matrices with entries from S = Z+ {0}}, V21 = {[a a a a a a a] | a S},

­ ªa °« ° «a ° 1 V3 = ® « a ° «a °« °¯ «¬ a

½ aº ° » a» ° ° a » a S¾ . » ° a» ° a »¼ ¿°

­° ª a a a a a º ½° V12 = ® « a, b S¾ , » ¯° ¬ b b b b b ¼ ¿° V22 = {set of all 4u4 lower triangular matrices with entries from S}, V32 = {set of all polynomials of degree less than or equal to 4 with coefficients form S}, V42 = {S u S u S u S u S} and

­ ªa º ½ °« » ° ° «a » ° ° ° V52 = ® « a » a S¾ ° «a » ° °« » ° °¯ «¬ a »¼ ¿°

315

be a special semigroup set vector bispace over the set S = Z+ {0} = Z0. Take W = (W1 W2) = W11 , W21, W31 W12 , W22 , W32 , W42 , W52

V11 , V21, V31 V12 , V22 , V32 , V42 , V52

where W11 = {all 5u5 matrices with entries from 5S} V11 , W21 = {(a a a a a a a) | a 7S} V21 ,

­ ªa °« ° «a ° 1 W3 = ® « a ° «a °« °¯ «¬ a

½ aº ° » a» ° ° » a a 7S¾ V31 ; » ° a» ° a »¼ ¿°

­° ª a a a a a º ½° W12 = ® « a, b 11S¾ V12 , » °¯ ¬ b b b b b ¼ °¿ W22 = {set of all 4u4 lower triangular matrices with entries from

3Zo {0} V22 , W32 = {set of all polynomials in the variable x with coefficients from 12S} V32 , W42 = {S u S u S u {0} u {0}} V42 and

­ ªa º ½ °« » ° ° «a » ° ° ° W52 = ® « a » a 11S¾ V52 . ° «a » ° °« » ° °¯ «¬ a »¼ ¿° Clearly W = (W1 W2) = W11 , W21, W31 W12 , W22 , W32 , W42 , W52

(V1 V2) V

316

is a special semigroup set vector bisubspace of V over the set S. Now having seen an example of a special semigroup set vector subbispace we now proceed onto define the notion of special semigroup set linear algebra.

DEFINITION 4.2.3: Let V = (V1 V2) = V11 , V21,...,Vn11 V12 , V22 ,..., Vn22

be a special semigroup set vector bispace over the set S if each Vi is a special semigroup set linear algebra over S then we call V =(V1 V2) to be a special set semigroup linear bialgebra.

We now illustrate this definition by a simple example.

Example 4.2.3: Let V = (V1 V2) = V11 , V21, V31 , V41 V12 , V22 , V32 , V42 , V52

where V11 = S u S where S = Z+ {0} = Zo, V21 = {set of all 3 u 3 matrices with entries from Zo} ­ªa º ½ °« » ° °«b » 1 o° V3 = ® a, b,c,d Z ¾ , °« c » ° ° «¬ d »¼ ° ¯ ¿

½° °­ ª a a a a a º V41 = ® « a Zo ¾ » ¯° ¬ a a a a a ¼ ¿° is such that V1 =

V , V ,V ,V

1 1

1 2

1 3

1 4

is a special semigroup set

linear algebra over Zo. Now let V2 = (V12 , V22 , V32 , V42 , V52 ) where V12 = S u S u S u S, V22 = {all 4 u 4 lower triangular matrices

with entries from Zo}, V32 = {set of all polynomials of degree less than or equal to 7 with coefficients from S},

317

­ ªa °« ° a V42 = ® « ° «a °« ¯ ¬a

½ bº ° » b» ° a, b S¾ » b ° » ° b¼ ¿

and

­ªa a a a a º ½ °« ° » V = ® « b b b b b » a, b,c S¾ . °« c c c c c » ° ¼ ¯¿ 2 5

Clearly V = V1 V2 is a special semigroup set linear bialgebra over S. Now we proceed onto define the notion of special semigroup set linear subbialgebra of a special semigroup set linear bialgebra.

DEFINITION 4.2.4: Let V = (V1 V2) = V11 , V21,...,Vn11 V12 , V22 ,..., Vn22

be a special semigroup set linear bialgebra defined over the additive semigroup S. Suppose W = (W1 W2) = W11 , W21,...,Wn11 W12 ,W22 ,...,Wn22

(V1 V2) be such that Wi is a special semigroup set linear algebra over the semigroup S of the special semigroup set linear algebra Vi, i=1, 2. Then we call W = (W1 W2) to be a special semigroup set linear subbialgebra of V over the semigroup S. We illustrate this by an example.

Example 4.2.4: Let V = (V1 V2) = V11 , V21, V31 V12 , V22 , V32 , V42

where V11 = {set of all 2 u matrices with entries from Z5}, V21 = Z5 u Z5 u Z5,

318

­° ª a V31 = ® « 1 °¯ ¬ a 4

a2 a5

½° a3 º a i Z5 ,1 d i d 6 ¾ , » a6 ¼ ¿°

V12 = Z5 u Z5 u Z5 u Z5 u Z5,

­ ª a1 °« ° a 2 V2 = ® « 4 °« a 7 ° «¬ a10 ¯

½ a2 a3 º ° » a5 a6 » ° a i Z5 ,i 1, 2,3,...,12 ¾ , » a8 a9 ° » ° a11 a12 ¼ ¿

V32 = {All polynomials in the variable x with coefficients from Z5 of degree less than or equal to 6} and

­ªa a a a a º ½ °« ° » V = ® « b b b b b » a, b,c Z5 ¾ ° ° ¯ «¬ c c c c c »¼ ¿ 2 4

be a special semigroup set linear bialgebra over the semigroup Z5 .

Choose W = (W1 W2) = W11 , W21, W31 W12 , W22 , W32 , W42 , W52

(V1 V2) where W11 = {set of all 2 u 2 upper triangular matrices with entries from Z5} V11 , W21 = {Z5uZ5u{0}} V21 ,

½° °­ ª a a a º W31 = ® « a Z5 ¾ V31 ; » ¯° ¬ a a a ¼ ¿° W12 = {Z5u^ ` u Z5u{0}u Z5} V12 ,

319

­ ªa °« ° a W22 = ® « ° «a ° «¬ a ¯

½ a aº ° » a a» ° a Z5 ¾ V22 , » a a ° » ° a a¼ ¿

W32 = {all polynomials of degree less than or equal to

four in the variable x with coefficients from Z5} V32 ,

­ ªa a a a a º ½ °« ° » W = ® « a a a a a » a Z5 ¾ . ° ° ¯ «¬ a a a a a »¼ ¿ 2 4

W = (W1 W2) (V1 V2) V is a special semigroup set linear subbialgebra of V over the semigroup S. Now we proceed on to define the notion of special semigroup set linear bitransformation of a special semigroup set vector bispace V = V1 V2.

DEFINITION 4.2.5: Let V = (V1 V2) = V11 , V21,...,Vn11 V12 , V22 ,..., Vn22

be a special semigroup set vector bispace over the set S. Suppose W = (W1 W2) = W11 , W21,...,Wn11 W12 ,W22 ,...,Wn22

be another special semigroup set vector bispace over the same set S. Let T = T1 T2: V = V1 V2 o W = (W1 W2) be a bimap such that each Ti: Vi o Wi; i = 1, 2 is a special semigroup set linear transformation of the special semigroup set vector space Vi into the special semigroup set vector space Wi, i = 1, 2 then we define T = T1 T2 to be a special semigroup set linear bitransformation from V into W. If in the above definition V = W then we call T = T1 T2 : V = V1 V2 o V = V1 V2 to be a special semigroup set linear bioperator on V.

320

As in case of special set vector spaces we can define pseudo special set linear bioperators on V. Also if the special semigroup set vector bispaces are replaced by special semigroup set linear bialgebras we get the special semigroup set linear bioperators or special semigroup set linear bioperators and so on.

We illustrate this definition by some examples.

Example 4.2.5: Let V = (V1 Â&#x2030; V2) = V11 , V21, V31 Â&#x2030; V12 , V22 , V32 , V42

and W = (W1 Â&#x2030; W2) = W11 , W21, W31 Â&#x2030; W12 , W22 , W32 , W42 be two special semigroup set vector bispaces over the same set S = Zo. Let T = T1 Â&#x2030; T2 o W1 Â&#x2030; W2 where T1 : V1 o W1 with T = T11 , T21,T31 : V1 = V11 , V21, V31 o W1 = W11 , W21, W31

and

T2 = T12 ,T22 ,T32 ,T42 :V2= V12 , V22 , V32 , V42

oW2 = W12 , W22 , W32 , W42 .

We have

­°§ a b ¡ o V11 = Ž¨ ¸ a, b,c,d � Z °¯Š c d š

½° Z Â&#x2030; {0}ž , ¿°

V21 = {S u S u S u S u S u S}, V31 = {All polynomials in the variable x of degree less than or equal to 5 of with coefficients from Zo}.

W11 = {SuSuSuS},

321

­§ a b c · ½ °¨ ¸ o° W = ®¨ 0 d e ¸ a, b,c,d,e,f Z ¾ , °¨ 0 0 f ¸ ° ¹ ¯© ¿ 1 2

­°§ a W31 = ®¨ 1 ¯°© a 4

a2 a5

½° a3 · o ¸ a i Z ;1 d i d 6 ¾ . a6 ¹ ¿°

V12 = {S u S u S u S u S}

­ªa °« ° c 2 V2 = ® « °« e ° «¬g ¯

½ bº ° » d» ° a, b,c,d,e,f ,g, h S¾ , f» ° » ° h¼ ¿

V32 = {S(x) is the set all polynomials of degree less than or equal to 8 with coefficients from S} and ­° ª a a 2 a 3 a 4 º ½° V42 = ® « 1 a i Zo ¾ . » ¯° ¬ a 5 a 6 a 7 a 8 ¼ ¿°

Now in W2, W12 = {all polynomials of degree less than or equal to 4 with coefficients from S},

½° °­ ª a b c d º W22 = ® « a, b,c,d,e,f ,g, h S¾ , » ¯° ¬ e f g h ¼ ¿° ­ ª a1 ° W = ® «« a 4 ° «a ¯¬ 7 2 3

a2 a5 a8

½ a3 º ° » a 6 » a i S;1 d i d 9 ¾ ° a 9 ¼» ¿

and

322

­ ª a1 °« ° a W42 = ® « 2 °«a 3 ° «¬ a 4 ¯

½ a5 º ° » a6 » ° a i S;1 d i d 8 ¾ . » a7 ° » ° a8 ¼ ¿

Now T = T1 T2 = T11 , T21,T31 T12 , T22 ,T32 ,T42 : V = V1 V2 into W = (W1 W2) is defined as follows T1 = T11 , T21,T31 : V1 = V11 , V21, V31 o W11 , W21, W31

and

T2 = T12 , T22 ,T32 ,T42 :

V2 = V12 , V22 , V32 , V42 o W12 , W22 , W32 , W42 = W2; such that T11 : V11 o W11 is defined as

§a b· T11 ¨ ¸ = (a, b, c, d). ©c d¹ T21 : V21 o W21 is given by §a b c· ¨ ¸ T (a, b, c, d, e, f) = ¨ 0 d e ¸ ¨0 0 f ¸ © ¹ 1 2

and T31 : V31 o W31 is such that §a T21 (a0 + a1x + a2x2 + a3x3 + a4x5) = ¨ 0 © a3

323

a1 a4

a2 · ¸. a5 ¹

T2 = T12 , T22 ,T32 ,T42 : V2 = V12 , V22 , V32 , V42 o W2 = W12 , W22 , W32 , W42

is defined as T12 : V12 o W12 is such that T12 (a b c d e) = (a + bx + cx2 + dx3 + ex4) T22 : V22 o W22 is given by ªa «c T22 « «e « ¬g

bº d »» § a c e g · ¨ ¸ . f » ©b d f h¹ » h¼

T32 : V32 o W32 is defined by ªa 0 T (a0 + a1x + … + a8x ) = «« a 3 «¬ a 6 and T42 : V42 o W42 is defined by 8

2 3

ªa T42 « 1 ¬a 5

Thus

a2

a3

a6

a7

ª a1 a 4 º «« a 2 = a 8 »¼ « a 3 « ¬a 4

a1 a4 a7

a2 º a 5 »» a 8 »¼

a5 º a 6 »» . a7 » » a8 ¼

T = T1 T2 = T11 , T21,T31 T12 , T22 ,T32 ,T42

V = V1 V2 o W = W1 W2 is a special semigroup set linear bitransformation.

Now if T = T1 T2 : V = V1 V2

324

= V11 , V21,..., Vn11 V12 , V22 ,..., Vn22

W = (W1 W2) = W11 , W21,..., Wn11

o

W , W ,..., W

2 1

2 2

2 n2

is such that Ti : Vi o Wi; T2 : V2 o W2; Ti = T , T ,...,Tni1 : V1i , V2i ,..., Vni1 o W1i , W2i ,..., Wni1 ;

i 1

i 2

i = 1, 2, T : V o W ; i = 1, 2, 1 < ki < ni. i ki

i ki

i ki

Here =

SHom (V,W) = SHom (V1, W1) SHom (V2, W2) {Hom V11 , W11 , Hom V21 , W21 ,…, Vn11 , Wn11 } {Hom

V , W , V , W 2 1

2 1

2 2

2 2

, …, Hom V

2 n2

,W } 2 n2

is again a special semigroup set vector bispace over S. Now we will illustrate by an example the special semigroup set linear bioperator on a special semigroup set vector bispace V. Example 4.2.6: Let V = (V1 V2) = V11 , V21, V31 , V41 V12 , V22 , V32 , V42

be a special semigroup set vector bispace over the set S = Z+ {0} = Zo. Here ­°§ a b · ½ o° V11 = ®¨ ¸ a, b,c,d Z ¾ , °¯© c d ¹ ¿° V21 = {SuSuSuS},

­§ a · ½ °¨ ¸ ° °¨ b ¸ ° °¨ ¸ ° 1 V3 = ® c a, b,c,d,e S¾ , °¨ d ¸ ° °¨ ¸ ° °¯¨© e ¸¹ °¿

325

V41 = {Upper triangular 3u3 matrices}. V12 = {SuSuSuSuSuS}, °­ ª a V22 = ® « 1 ¯° ¬ a 4

a2 a5

½° a3 º a i S1 d i d 6 ¾ , » a6 ¼ ¿°

V32 = {all polynomials of degree less than or equal to 4},

­ªa °« °« c ° 2 V4 = ® « e ° «g °« °¯ «¬ i

½ bº ° » d» ° ° f » a, b,c,d,e,f ,g, h,i, j Zo ¾ » ° h» ° j »¼ °¿

and V52 = {All 4u4 lower triangular matrices with entries from Zo}.

Define the special semigroup set linear bioperator on V by T = T1 T2 = T , T ,T ,T41 T12 , T22 ,T32 ,T42 ,T52 : 1 1

1 2

1 3

V = V1 V2 = V11 , V21, V31 , V41 V12 , V22 , V32 , V42 , V52 o V = V1 V2 = V11 , V21, V31 , V41 V12 , V22 , V32 , V42 , V52

as follows: T11 : V11 o V11 is defined by §a b· §b a · T11 ¨ ¸ =¨ ¸, ©c d¹ ©d c¹

T21 : V21 o V21 is given by T21 (a, b, c, d) = (b c d a), T31 : V31 o V31 is such that

326

§ ªa º · ¨« »¸ ¨ «b » ¸ 1¨ T3 « c » ¸ ¨« »¸ ¨ «d » ¸ ¨ «e» ¸ ©¬ ¼¹

§a· ¨ ¸ ¨a ¸ ¨a ¸ ¨ ¸ ¨a ¸ ¨ ¸ ©a ¹

and T41 : V41 o V41 is defined as §a b c· §a d f · ¨ ¸ ¨ ¸ T ¨0 d e¸ ¨0 b e¸ ¨0 0 f ¸ ¨0 0 c¸ © ¹ © ¹ 1 4

and T12 : V12 o V12 is such that T12 (a b c d e f) = ( a a a d d f), T22 : V22 o V22 is defined by ªa T22 « 1 ¬a 4

a2 a5

a3 º § a4 ¨ a 6 »¼ © a1

a5 a2

a6 · ¸, a3 ¹

T32 : V32 o V32 is such that T32 (a0 + a1x + a2x2 + a3x3 + a4x4) = (a0 + a2x2 + a4x4),

T42 : V42 o V42 is given by ªa «c « T42 « e « «g «¬ i

bº d »» f» » h» j »¼ 327

ªa «a « «a « «a «¬a

bº b »» b» » b» b »¼

and T52 : V52 o V52 is defined by §a ¨ b T52 ¨ ¨d ¨ ©g

0 0 0· §a 0 ¸ ¨ c 0 0¸ ¨a b e f 0¸ ¨a b ¸ ¨ h i j¹ ©a b

0 0 e e

0· ¸ 0¸ .

0¸ ¸ f¹

Thus we see T = T1 T2 = T11 , T21,T31 ,T41 T12 , T22 ,T32 ,T42 ,T52

V = V1 V2 o V1 V2 = V is a special semigroup set linear bioperator on V. Further it is still important to note SHom(V,V) = {SHom(V1,V1) SHom (V2,V2)} = {Hom V11 , V11 , Hom V21 , V21 , …, Hom Vn11 , Vn11 } ^Hom V , V 2 1

, Hom V , V

2 1

2 2

2 2

, …, Hom V

,V }

2 n2

2 n2

is only a special semigroup set vector bispace over S as even if each Hom Vtii , Vtii is a semigroup under the composition of

maps yet S over which they are defined is not a semigroup.

Thus it is pertinent at this juncture to mention the following.

Suppose V = V1 V2 = V11 , V21,..., Vn11 V12 , V22 ,..., Vn22 be

a special semigroup set linear bialgebra over the semigroup S and if SHoms (V,V) = SHoms(V1,V1) SHoms(V2,V2) denotes the collection of all special semigroup set linear bioperators on V = V1 V2 then SHoms(V,V) = SHoms(V1V1) SHoms(V2,V2) = {Homs V11 , V11 , Homs V21 , V21 , …, Homs Vn11 , Vn11 } ^Homs V , V 2 1

2 1

, Homs V , V , …, Homs V 2 2

2 2

2 n2

2 n2

,V

}

is a special semigroup set linear bialgebra over the semigroup S. From the notion of special semigroup set linear bialgebra we can have pseudo special semigroup linear bioperator which may not in general be a special semigroup set linear bialgebra. As in

328

case of special semigroup set linear algebras we can define the new substructure in special semigroup set linear bialgebras also.

Further as in case of special semigroup set linear algebras (vector space) we can also in the case of special semigroup set linear bialgebras (vector bispaces) define the notion of special semigroup set projections and the notion of special semigroup set direct union / direct summand. These tasks are left as simple exercises for the reader.

Now we proceed onto generalize these notions to special semigroup set linear n-algebras (vector n-spaces), n>3.

DEFINITION 4.2.6: Let V = V1 V2 … Vn 1 1 1 = V1 , V2 ,...,Vn1 V12 , V22 ,..., Vn22 V1n , V2n ,..., Vnnn

be such that each Vi is a special semigroup set vector space over the set S for i = 1, 2, …, n. Then we call V to be the special semigroup set vector n-space over S, n t 3. For if n = 2 we get the special semigroup set vector bispace, when n = 3 we also call the special semigroup set 3-vector space as special semigroup set trivector space or special semigroup set vector trispace.

We illustrate this by a simple example.

Example 4.2.7: Let V = (V1 V2 V3 V4) 1 1 1 = V1 , V2 , V3 V12 , V22 V13 , V23 , V33 , V43 V14 , V24

be a special semigroup set 4-vector space over the set S = Z6 where V11 = Z6 u Z6 u Z6, V21 = {Z6[x] all polynomials of degree less than or equal to 5 with coefficients from Z0}, ½° °­§ a b · V31 = ®¨ ¸ a, b,c,d Z6 ¾ , ¯°© c d ¹ ¿° V12 = {Z6 u Z6 u Z6 u Z6},

329

V22 = {all 5u5 lower triangular matrices with entries from Z6}, V13 = {Z6 u Z6}, V23 = {all 7u2 matrices with entries from Z6},

­§ a · ½ °¨ ¸ ° V = ®¨ b ¸ a, b,c Z6 ¾ , °¨ c ¸ ° ¯© ¹ ¿ 3 3

V43 = {all 4u4 upper triangular matrices with entries from Z6},

V14 = {Z6 u Z6 u Z6 u Z6 u Z6} and ­ ªa a a º ½ °« ° » V = ® « a a a » a Z6 ¾ . ° «a a a » ° ¼ ¯¿ 4 2

It is easily verified that V = (V1 V2 V3 V4) is a special semigroup set vector 4-space over the set Z6.

Now we proceed on to define the notion of special semigroup set vector n-subspace of V. DEFINITION 4.2.7: Let V = V1 V2 … Vn 1 1 1 = V1 , V2 ,...,Vn1 V12 , V22 ,..., Vn22 V1n , V2n ,..., Vnnn

be a special semigroup set vector n-space over the set S. Take W = W1 W2 … Wn 1 1 1 = W1 , W2 ,...,Wn1 W12 ,W22 ,...,Wn22 W1n , W2n ,...,Wnnn

V = (V1 V2 … Vn) if each Wi is a special semigroup set vector subspace of Vi for i = 1, 2, …, n, then we call W to be the special semigroup set vector n-subspace of V over the set S. We shall illustrate this situation by an example.

330

Example 4.2.8: Let V = (V1 V2 V3 V4) 1 1 2 = V1 , V2 V1 , V22 , V32 , V42 V13 , V23 , V33 , V43 V14 , V24

be a special semigroup set vector space over the set Z12; where V11 = Z12 u Z12 u Z12, V21 = {all 3u3 matrices with entries from Z12}, V12 = (all 4u4 lower triangular matrices with entries from Z12}, V22 = Z12 u Z12 u Z12u Z12, V23 = {[a a a a a a a] | a Z12} and ­ªa a º ½ °« ° » ° b b» ° a, b,c,d Z12 ¾ .

V42 = ® « « » c c ° ° ° «¬ d d »¼ ° ¯ ¿ ­° ª a V13 = ® « 1 ¯° ¬ a 5

a2 a6

a3 a7

½° a4 º a i Z12 ;1 d i d 8¾ , » a8 ¼ ¿°

V23 = {Z12uZ12 uZ12uZ12uZ12uZ12}, ­§ a · ½ °¨ ¸ ° °¨ a ¸ ° ° ° V33 = ®¨ a ¸ a Z12 ¾ °¨ a ¸ ° °¨ ¸ ° °¯¨© a ¸¹ ¿° 3 and V4 = {all 5 u upper triangular matrices with entries from

Z12}. V14 = {Z12 u Z12} and V24 = {all 7 u 7 upper triangular matrices with entries from Z12}. Take W = (W1 W2 W3 W4) (V1 V2 V3 V4) where W1 = ^ W11 , W21` V1,

^ W , W , W ` = W2 V2, 2 1

2 2

2 3

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W3 = ^ W13 , W23 , W33 , W43 ` V3 and

W4 = ^ W14 , W24 ` V4

with W11 = {Z12 u Z12 u {0}} V11 , W21 = {3 u 3 matrices with entries from {0, 2, 4, 6, 8, 10}} V21 , W12 = {all 4 u 4 lower triangular matrices with entries from {0, 2, 4, 6, 8, 10}} V12 , W22 = {Z12 u Z12 u Z12 u {0}} V22 , W32 = {[a a a a a a a] | a {0, 2, 4, 6, 8, 10}} V32 , ­ ªa °« ° a 2 W4 = ® « ° «a ° «¬ a ¯

½ aº ° » a» ° a Z12 ¾ V42 , a» ° » ° a¼ ¿

­° ª a a a a º ½° W13 = ® « a Z12 ¾ V13 , » °¯ ¬ a a a a ¼ °¿

W23 = {Z12 u {0} u Z12 u{0} u Z12 u {0}} V23 , ­ ªa º ½ °« » ° ° «a » ° ° ° W33 = ® « a » a {0, 2, 4,6,8,10}¾ V33 ° «a » ° °« » ° °¯ «¬ a »¼ °¿

and W43 = {all 5 u 5 upper triangular matrices with entries from {0, 2, 4, 6, 8, 10}} V43 , W14 = {SuS | s = {0, 3, 6, 9}} V14 and W24 = {all 7 u 7 upper triangular matrices with entries from S =

{0, 3, 6, 9}} V24 . Thus

332

= ^W , W 1 1

1 2

`

W = (W1 W2 W3 W4) ^ W12 , W22 , W32 , W42 ` ^ W13 , W23 , W33 , W43 `

^ W , W ` ^V , V ` ^V , V , V , V ` ^V , V , V , V ` ^V , V ` 4 1

4 2

1 1

3 1

3 2

1 2

3 3

2 1

3 4

2 2

4 1

2 3

2 4

4 2

= (V1 V2 V3 V4) = V is a special semigroup set vector 4-subspace of V over the set Z12.

Now we proceed onto define the new notion of special semigroup set n-linear algebras or special semigroup set linear n-algebras n t 3. For when n = 2 we have the special semigroup set linear bialgebra.

DEFINITION 4.2.8: Let V = (V1 V2 … Vn) be a special semigroup set n-vector space over the semigroup S instead of a set S, n-algebra then we call V to be the special semigroup set linear n-algebra.

Thus if in the definition of a special semigroup set n-vector space over the set S, we replace the set by a semigroup we get the new notion of special semigroup set n-linear algebra over the semigroup S. It is still important to mention that all special semigroup set n-linear algebras are special semigroup set vector n-spaces, however in general all special semigroup set vector n-spaces are not special semigroup set linear n-algebras.

We illustrate this by an example.

Example 4.2.9: Let V = (V1 V2 V3 V4 V5) 1 = ^V1 , V21, V31` ^V12 , V22 ` ^V13 , V23 , V33 , V43 `

^V14 , V24 ` ^V15 , V25 , V35 `

be a special semigroup set n-linear algebra over the semigroup S = Zo = Z+ {0}, where V11 = {SuSuS},

333

­°§ a b · ½° V21 = ®¨ ¸ a, b,c,d S¾ , °¯© c d ¹ ¿°

V31 = {(a a a a a a) | a S}, ­§ a · ½ °¨ ¸ ° °¨ a ¸ ° 2 V1 = ® a S¾ , °¨¨ a ¸¸ ° °© a ¹ ° ¯ ¿ V22 = SuSuSuS,

­§ a °¨ °¨ a ° 3 V1 = ®¨ a °¨ a °¨ °¯¨© a

½ a· ° ¸ a¸ ° ° a ¸ a S¾ , ¸ ° a¸ ° a ¸¹ ¿°

½° °­§ a a a a a a · V23 = ®¨ ¸ a S¾ , ¯°© a a a a a a ¹ ¿°

V33 = S u S u S u S u S, V43 = {all 4 u 4 lower triangular matrices with entries from S}, V14 = {S u S u S}, V24 = {all 7 u 2 matrices with entries from S}, V15 = {all 3 u 6 matrices with entries from S}, V25 = S u S u S u S and V35 = {6 u 3 matrices with entries from S}.

Clearly V is a special semigroup set linear n-algebra over the semigroup S.

334

DEFINITION 4.2.9: Let V = (V1 V2 … Vn) be a special semigroup set linear n-algebra over the semigroup S. Take

^

W = (W1 W2 … Wn)

1 1

1 2

1 n1

= W ,W ,...,W

^V11,V21 ,...,Vn1

1

` ^W ` ^V

` ^ ` … ^V

2 1

,W22 ,...,Wn22 … W1n ,W2n ,...,Wnnn

2 1

,V22 ,...,Vn22

n 1

,V2n ,...,Vnnn

`

`

be a proper subset of V. If W = (W1 W2 … Wn) is itself a special semigroup set n-linear algebra over the semigroup S then we call W = (W1 W2 … Wn) 1 1 1 = W1 ,W2 ,...,Wn1 W12 ,W22 ,...,Wn22 … W1n ,W2n ,...,Wnnn

^

` ^

`

^

`

to be the special semigroup set linear n-subalgebra of V over the semigroup S. We illustrate this by a simple example.

Example 4.2.10: Let V = (V1 V2 V3 V4 V5) be a special semigroup set linear 5-algebra over the semigroup Z15 where V1 = ^V11, V21, V31` with V11 = {Z15u Z15}, V21 = {(a1, a2, a3, a4, a5) | ai Z15}, 1 d i d 5},

­ ª a1 º ½ °« » ° ° «a 2 » ° ° ° V31 = ® « a 3 » a Z15 ¾ , ° «a » ° °« 4 » ° °¯ «¬ a 5 »¼ ¿°

V2 = ^V12 , V22 ` where

­§ a b c · ½ °¨ ° ¸ V = ®¨ d e f ¸ a, b,c,d,e,f ,g, h,i Z15 ¾ °¨ g h i ¸ ° ¹ ¯© ¿ 2 3 3 3 3 and V2 = Z15 u Z15 u Z15 u Z15, V3 = ^V1 , V2 , V3 , V4 ` 2 1

335

where V13 = Z15uZ15uZ15,

­§ a a a a a · ½ °¨ ° ¸ V = ®¨ b b b b b ¸ a, b,c, Z15 ¾ , °¨ c c c c c ¸ ° ¹ ¯© ¿ 3 2

V33 = {4u4 lower triangular matrices with entries for Z15} and

­ ª a1 ° V = ® «« a 2 °«a ¯¬ 3 3 4

V4 = ^V14 , V24 ` such that

½ a4 º ° » a 5 » a i Z15 ;1 d i d 6 ¾ . ° a 6 »¼ ¿

­ ª a1 º ½ °« » ° °« a2 » ° ° ° V14 = ® « a3 » a i Z15 ;1 d i d 15 ¾ °« a » ° °« 4 » ° °¯ «¬ a5 »¼ ¿° and V24 = Z15 u Z15 u Z15 u Z15 u Z15.

V5 = ^V15 , V25 , V35 ` where V15 ={Z15uZ15uZ15uZ15},

­§ a1 °¨ V = ®¨ a 4 °¨ a ¯© 9 5 2

a2 a5

a3 a6

a10

a11

½ a7 · ° ¸ a 8 ¸ a i Z15 ;1 d i d 12 ¾ ° a12 ¸¹ ¿

and V35 = {5 u 5 lower triangular matrices with entries from Z15}.

Take W1 = ^ W11, W21 , W31` V1

336

where W11 = Z15 u {0} V11 , W21 = {(a a a a a) | a Z15} V21 and ­ ªa º ½ °« » ° ° «a » ° ° ° W31 = ® « a » a Z15 ¾ V31 . ° «a » ° °« » ° °¯ «¬ a »¼ ¿° W1 a special semigroup set linear subalgebra of V1 over Z15.

W2 = ^ W12 , W22 ` where

­§ a a a · ½ °¨ ° ¸ W = ®¨ a a a ¸ a Z15 ¾ V12 °¨ a a a ¸ ° ¹ ¯© ¿ 2 1

and W22 = {Z15 u Z15 u {0} u{0}} V22 . Thus W2 is also a special semigroup set linear subalgebra of V2 over Z15.

W3 = ^ W13 , W23 , W33 , W43 ` V3 with W13 = Z15 u Z15 u {0} V13 ,

­§ a a a a a · ½ °¨ ° ¸ W = ®¨ a a a a a ¸ a Z15 ¾ V23 , °¨ a a a a a ¸ ° ¹ ¯© ¿ 3 2

­§ a °¨ ° a 3 W3 = ®¨ °¨¨ a °© a ¯

½ 0 0 0· ° ¸ a 0 0¸ ° a Z15 ¾ V33 a a 0¸ ° ¸ ° a a a¹ ¿

and

337

­§ a a · ½ °¨ ° ¸ W = ®¨ a a ¸ a Z15 ¾ V43 . °¨ ° ¸ ¯© a a ¹ ¿ 3 4

W3 is again a special semigroup set linear subalgebra of V3 over the semigroup Z15. W4 = ^ W14 , W24 ` with ­§ a · ½ °¨ ¸ ° °¨ a ¸ ° ° ° W14 = ®¨ a ¸ a Z15 ¾ V14 °¨ a ¸ ° °¨ ¸ ° °¯¨© a ¸¹ ¿° and W24 = {Z15 u {0} u {0} u {0} u Z15} V24

^W , W ` 4 1

so that W4 =

4 2

is a special semigroup set linear

subalgebra of V4 over the semigroup Z15.

Finally W5 = ^ W15 , W25 , W35 ` where W15 = {0} u S u S (where S = {0, 3, 6, 9, 12} Z15) V15 , ­§ a a a a · ½ °¨ ° ¸ W = ®¨ a a a a ¸ a Z15 ¾ V25 °¨ a a a a ¸ ° ¹ ¯© ¿ 5 2

and ­§ a °¨ °¨ a ° 5 W3 = ®¨ a °¨ a °¨ °¯¨© a

½ 0 0 0 0· ° ¸ b 0 0 0¸ ° ° b c 0 0 ¸ a, b,c,d Z15 ¾ V35 ¸ ° b c d 0¸ ° b c d a ¸¹ °¿

338

and W5 is a special semigroup set linear subalgebra of V5.

Hence W = W1 W2 W3 W4 W5 is a special semigroup set linear 5-subalgebra of V over the semigroup S = Z15.

Now as in case of special semigroup set linear algebra we can also define in case of special semigroup set linear n-algebra the notion of pseudo special semigroup set linear sub n-algebra. We shall define special semigroup set linear n-transformations both in case of special semigroup set vector n-spaces and special semigroup set linear n-algebra.

DEFINITION 4.2.10: Let V = (V1 V2 … Vn) 1 1 1 = V1 ,V2 ,...,Vn1 V12 ,V22 ,...,Vn22 … V1n ,V2n ,...,Vnnn

^

` ^

`

^

`

be a special semigroup set vector n space over the set S and W = (W1 W2 … Wn) 1 1 1 = W1 ,W2 ,...,Wn1 W12 ,W22 ,...,Wn22 … W1n ,W2n ,...,Wnnn

^

` ^

`

^

`

be another special semigroup set vector n-space over the set S. A n-map T = (T1 T2 … Tn) 1 1 1 = T1 , T2 ,..., Tn1 T12 , T22 ,..., Tn22 … T1n , T2n ,..., Tnnn

^

` ^

`

^

`

is said to be a special semigroup set linear n-transformation of V to W if each Ti = T1i , T2i ,..., Tnii is a special semigroup set

linear transformation of Vi into Wi for i= 1, 2, …, n; i.e., T jii : V jii o W jii ; 1 d i d n; 1 d ji d ni is a set linear transformation

of the vector space V jii into W jii , 1d jid ni; i =1, 2, …, n. Now if we replace the special semigroup set n-vector spaces V and W by special semigroup set n-linear algebras then we also we have the special semigroup set linear n-transformation of special semigroup set n-linear algebras. We denote the collection of all special semigroup set linear n-transformations from V into W

by SHom (V, W) = {SHom (V1, W1) SHom (V2, W2) …

SHom (Vn, Wn)}.

339

= {(Hom V11 , W11 , Hom V21 , W21 , …, Hom Vn11 , Wn11 }

, Hom V , W , …, Hom V , W } … {Hom V , W , Hom V , W , …, Hom V W }.

{Hom V , W 2 1

2 1

n 1

2 2

2 2

n 1

2 n2

n 2

n 2

2 n2

n nn

n nn

We will illustrate this situation by a simple example.

Example 4.2.11: Let V = (V1 V2 V3 V4) 1 1 = ^V1 , V2 ` ^V12 , V22 ` ^V13 , V23 , V33` ^V14 , V24 `

and = ^W , W 1 1

`

1 2

W = (W1 W2 W3 W4) ^W12 , W22 ` ^W13 , W23 , W33` ^W14 , W24 `

be special semigroup set vector 4-space over the set S = Z+ {0}.

V11 = {S u S u S}, ­°§ a b · ½° V21 = ®¨ ¸ a, b,c,d S¾ , °¯© c d ¹ ¿° ­°§ a V12 = ®¨ 1 °¯© a 5

a2 a6

a3 a7

½° a4 · ¸ a i S; 1 d i d 8¾ a8 ¹ °¿

and V22 = S u S u S u S. V13 = {3 u 3 matrices with entries from S}, V23 = S u S u S u S u S, V33 = {4 u 4 diagonal matrices with entries from S}, V14 = S u S u S u S u S u S and V24 = {S[x] all polynomials of degree less than or equal to 5}. ½° °­§ a b · 1 W11 = ®¨ ¸ a, b,c S¾ , W2 = S u S u S u S, 0 c ¹ ¯°© ¿°

340

W12 = {all polynomials of degree less than or equal to 7 with coefficients from S},

­§ a1 · ½ °¨ ¸ ° °¨ a 2 ¸ ° 2 a i S;1 d i d 4 ¾ , W2 = ® °¨¨ a 3 ¸¸ ° °© a 4 ¹ ° ¯ ¿ W13 = {3u3 upper triangular matrices with entries from S}, W23 = {all polynomials of degree less than or equal to 4 with coefficients from S}, W33 = {4 u 4 lower triangular matrices with entries from S}. °­§ a a 2 a 3 · °½ W14 = ®¨ 1 ¸ a i S; 1 d i d 6 ¾ ¯°© a 4 a 5 a 6 ¹ ¿°

and W24 = {6 u 6 diagonal matrices with entries from the set S}.

Let T = T1 T2 T3 T4 1 1 = ^T1 ,T2 ` ^T12 ,T22 ` ^T13 ,T23 ,T33` ^T14 ,T24 ` : V = V1 V2 V3 V4 o W1 W2 W3 W4.

T11 : V11 o W11 is such that

§a b· T11 (a b c) = ¨ ¸. ©0 c¹ T21 : V21 o W21 is given by §a b· T21 ¨ ¸ = (a b c d). ©c d¹ T12 : V12 o W12 is given by § a a2 a3 a4 · T12 ¨ 1 ¸ = © a5 a 6 a 7 a8 ¹ (a1 + a2x + a3x2 + a4x3 + a5x4 + a6x5+ a7x6 + a8x7)

341

and T22 : V22 o W22 is defined by ªa º «b» T22 (a b c d) = « » . «c » « » ¬d ¼ T13 : V13 o W13 is such that ª a b c º ªa b c º T «« d e f »» = «« 0 e f »» , ¬« g h i »¼ «¬ 0 0 i ¼» 3 1

T23 : V23 o W23 is given by T23 (a b c d e) = a + bx + cx2 + dx3 + ex4 and T33 : V33 o W33 is such that ªa «0 T33 « «0 « ¬0

0 b 0 0

0 0 c 0

0 º ªa 0 »» «« a = 0 » «a » « d ¼ ¬a

0 b b b

0 0 c c

0º 0 »» .

0» » d¼

T14 : V14 o W14 is such that ªa b c º T14 (a b c d e f) = « » ¬d e f ¼ and T24 : V24 o W24 is defined by T24 (p0 + p1x + p2x2 + p3x3 + p4x4 + p5x5) =

342

ª p0 «0 « «0 « «0 «0 « ¬« 0

0 p1 0 0 0 0

0 0 p2 0 0 0

0 0 0 p3 0 0

0 0 0 0 p4 0

0º 0 »» 0» ».

0» 0» » p5 ¼»

Thus T = (T1 T2 T3 T4): V o W is a special semigroup set linear 4 – transformation of V into W. Note if in the special semigroup set linear n-transformation; V into W we replace the range space W by V itself then that specific special semigroup set linear n-transformation from V into V will be known as the special semigroup set linear n-operator on V. We shall denote the special semigroup set linear operator of a special semigroup set linear n-algebra V into itself by SHom (V, V) = {S Hom (V1, V1) S Hom (V2, V2) … S Hom (Vn, Vn)} 1 1 = {(Hom V1 , V1 , Hom V21, V21 , …, Hom Vn11 , Vn11 )

(Hom V12 , V12 , Hom V22 , V22 , …, Hom Vn22 , Vn22 ) …

(Hom V1n , V1n , …, Hom Vnn1 , Vnn1 )} .

Clearly SHom(V, V) is again a special semigroup set linear nalgebra over the semigroup. However if we replace the special semigroup set linear n-algebra V by a special semigroup set vector n-space then we see SHom(V,V) is not a special semigroup set linear n-algebra it is only a special semigroup set vector n-space.

We shall give one example of a special semigroup set linear noperator of V. Example 4.2.12: Let V = (V1 V2 V3) where V1 = ^V11 , V21, V31, V41` ,

343

V2 = ^V12 , V22 , V32 ` and

V3 = ^V13 , V23 `

defined by V11 = {S u S u S u S | S = Zo = Z+ {0}}, V21 = {set of all 3 u 3 matrices with entries from S}, ­ ª a1 a 2 º ½ °« ° » °«a 3 a 4 » ° ° ° V31 = ® « a 5 a 6 » a i Zo ;1 d i d 10 ¾ , ° «a a » ° °« 7 8 » ° °¯ «¬ a 9 a10 »¼ °¿ V41 = {set of all polynomials in the variable x with coefficients from S of degree less than or equal to 9}, V12 = S u S u S, V22 = {all 4 u 4 low triangular matrices with entries from S} and °­ ª a V32 = ® « 1 ¯° ¬ a 6

a2 a7

a3 a8

a4 a9

½° a5 º a i S;1 d i d 10 ¾ . » a10 ¼ ¿°

V13 = S u S u S u S u S u S and V23 = {all 5 u 5 upper triangular matrices with entries from S}, be a special semigroup set vector 3-space over the set S. Define T = T1 T2 T3 = ^T11, T21,T31,T41` ^T12 , T22 ,T32 ` ^T13 ,T23 ` : V = V1 V2 V3 = ^V , V , V , V ` ^V12 , V22 , V32 ` ^V13 , V23 ` o 1 1

1 2

1 3

1 4

V = V1 V2 V3 = ^V11 , V21, V31, V41` ^V12 , V22 , V32 ` ^V13 , V23 ` as follows: Ti : Vi o Vi where

344

Tji : Vji o Vji ; 1 d j d ni, i = 1, 2, 3 T11 : V11 o V11 is such that T11 (a b c d) = (a a a a), T21 : V21 o V21 is defined by

§a b c· §a a a · ¸ ¨ ¸ ¨ T ¨d e f ¸ = ¨a a a ¸, ¨g h i ¸ ¨b b b¸ © ¹ © ¹ 1 2

T31 : V31 o V31 is given by

§ a1 ¨ ¨ a3 1 T3 ¨ a 5 ¨ ¨ a7 ¨a © 9

a2 · § a a · ¸ ¨ ¸ a4 ¸ ¨ a a ¸ a6 ¸ = ¨ a a ¸ ¸ ¨ ¸ a8 ¸ ¨ a a ¸ a10 ¸¹ ¨© a a ¸¹

and T41 : V41 o V41 is defined by T41 (a0 + a1 x + … a9 x9) = a0 + a3x3 + a6x6 + a9x9.

T12 : V12 o V12 is given by T12 (a b c) = (b c a) and T22 : V22 o V22 is defined by §a ¨ b T22 ¨ ¨d ¨ ©g

0 0 0· § a ¸ ¨ c 0 0¸ ¨ a = e f 0¸ ¨ a ¸ ¨ h i j¹ ©a

345

0 a a a

0 0 a a

0· ¸ 0¸ .

0¸ ¸ a¹

T32 : V32 o V32 is defined by ªa T32 « 1 ¬a 6

a2 a7

a3 a8

a4 a9

a 5 º ªa 6 = a10 »¼ «¬ a1

a7 a2

a8 a3

a9 a4

a10 º . a 5 »¼

T13 : V13 o V13 is given by

T13 (a b c d e f) = (a a a a a a).

T23 : V23 o V23 is defined by ªa «0 « T23 « 0 « «0 «¬ 0

b f 0 0 0

c d e º ªa a g h i »» «« 0 b j k l » = «0 0 » « 0 m n » «0 0 0 0 p »¼ «¬ 0 0

a b c 0 0

a b c d 0

aº b »» c» . » d» e »¼

Clearly T = T1 T2 T3: V o V is a special semigroup set linear 3-operator of V. We can also define special semigroup set pseudo linear noperators on V. Now in case of special semigroup set n-vector spaces we can define the notion of direct sum of special semigroup set vector n-spaces. Also we can define the notion of special semigroup set linear n-idempotent operator and special semigroup set linear n-projections on V. Now we proceed onto show by an example how a special semigroup set vector n-space is represented as a direct sum of special semigroup set vector n-subspaces.

Example 4.2.13: Let V = (V1 V2 V3 V4) 1 1 1 = ^V1 , V2 , V3 ` ^V12 , V22 ` ^V13 , V23 ` ^V14 , V24 , V34 `

346

be a special semigroup set linear 4-algebra over the set Zo = S = Z+ {0}. Here V11 = S u S u S u S, ½° °­§ a b · V21 = ®¨ ¸ a, b,c,d S¾ , ¯°© c d ¹ ¿° V31 = {all polynomials in x of degree less than or equal 5 with

coefficients from S}, V12 = S u S u S and ­° ª a V22 = ® « 1 °¯ ¬ a 4

a2 a5

­ ª a1 ° V = ® «« a 3 ° «a ¯¬ 5 3 1

½° a3 º a i S; 1 d i d 6 ¾ , » a6 ¼ ¿° ½ a2 º ° » a 4 » a i S;1 d i d 6 ¾ ° a 6 »¼ ¿

and V23 = S u S u S u S u S. ­°§ a b · ½° V14 = ®¨ ¸ a, b,c,d S¾ , ¯°© c d ¹ ¿° V24 = S u S u S u S and ­ ª a1 º ½ °« » ° V = ® « a 2 » a i S;1 d i d 3 ¾ . °«a » ° ¯¬ 3 ¼ ¿ 4 3

We now represent each semigroup set linear algebra as a direct sum of semigroup set linear subalgebras.

V11 = S u {0} u S u {0} {0} u S u {0} u {0} {0} u {0} u S u{0} 347

1 1 = W111 W21 W31 .

½° °­§ a 0 · °­§ 0 0 · °½ °­§ 0 b · °½ V21 = ®¨ ¸ a,c S¾ ®¨ ¸ d S¾ ®¨ ¸ b S¾ ¯°© c 0 ¹ ¿° ¯°© 0 d ¹ ¿° ¯°© 0 0 ¹ ¿° 1 = W121 W22 W321 .

V13 = {all polynomials of degree less than or equal to 3 with coefficients from S} {all polynomials of degree strictly greater than 3 but is of degree less than or equal to 5} 1 = W131 W23 .

Now ­°§ a a 2 a 3 · ½° V22 = ®¨ 1 ¸ a1 ,a 2 ,a 3 S¾ ¯°© 0 0 0 ¹ ¿° °­§ 0 ®¨ ¯°© a 4

3 1

V =

0 a5

½° 0· 2 2 ¸ a 4 ,a 5 ,a 6 S¾ = W12 W22 . a6 ¹ ¿°

­§ a1 a 2 · ½ ­§ 0 °¨ ° °¨ ¸ ®¨ 0 0 ¸ a1 ,a 2 S¾ ®¨ a 3 °¨ 0 0 ¸ ° °¨ 0 ¹ ¯© ¿ ¯© ­§ 0 °¨ ®¨ 0 °¨ a ¯© 5

½ 0· ° ¸ a 4 ¸ a 3 ,a 4 S¾ ° 0 ¸¹ ¿

½ 0· ° ¸ 0 ¸ a 5 ,a 6 S¾ = W113 W213 W313 . ° a 6 ¸¹ ¿ V23 = {S u {0} u {0} u{0} u S} {{0} u S u {0} u {S} u {0}} {0} u {0} u S u {0} u {0} = W123 W223 W323 .

348

­°§ a V14 = ®¨ °¯© 0 ­°§ 0 ®¨ ¯°© c

½° ­°§ 0 b · ½° 0· ¸ b S¾ ¸ a S¾ ®¨ 0¹ °¯© 0 0 ¹ °¿ ¿° 0· °½ °­§ 0 0 · °½ ¸ c S¾ ®¨ ¸ d S¾ 0¹ ¿° ¿° ¯°© 0 d ¹

= W114 W214 W314 W414 .

V24 = S u {0} u {0} u S {0} u S u {0} u {0} {0} u {0} u S u {0} = W124 W224 W324 .

4 3

V =

­ ­ a1 ½ ½ ­­ 0 ½ ½ °° ° ° °° ° ° ®®a 2 ¾ a1 ,a 2 S¾ ®® 0 ¾ a 3 S¾ °° 0 ° ° °°a ° ° ¯¯ ¿ ¿ ¯¯ 3 ¿ ¿ 4 4 = W13 W23 .

Thus V = (V1 V2 V3 V4) 1 1 1 , W121 W22 W321 , W131 W23 ) = ( W W W31 1 11

1 21

( W112 W212 W122 W222 W113 W213 W313 , W123 W223 W323 ) ( W114 W214 W314 W414 ,

W124 W224 W324 , W134 W234 ) is a special semigroup set direct sum representation of the special set semigroup linear 4-algebra over the semigroup S = Zo = Z+ {0}.

However we can represent in the same way the special semigroup set vector n-space as direct sum. When we get a representation of special semigroup set vector n-space as direct sum we can define special semigroup set linear operator on V which are projections P and these projection are defined as follows: P: V o (W1 W2 … Wn) are such that P o P = P.

349

The interested reader is requested to construct examples.

Now we proceed onto introduce special group set vector nspaces and special group set linear n-algebras.

DEFINITION 4.2.11: Let V = (V1 V2 … Vn) 1 1 1 = V1 ,V2 ,...,Vn1 V12 ,V22 ,...,Vn22 … V1n ,V2n ,...,Vnnn

^

` ^

`

^

`

be such that each Vi is a special group set vector space over a set S and each Vtii is a group set vector space over the same set S, i d ti d ni; i = 1, 2, …, n.

Then we call V to be a special group set n-vector space (special group set vector n-space) over the set S. We shall illustrate this by an example.

Example 4.2.14 : Let V = (V1 V2 V3 V4) 1 1 = ^V1 , V2 ` ^V12 , V22 , V32 ` ^V13 , V23 ` ^V14 , V24 , V34 , V44 `

where each Vjii is defined as ­°§ a b · ½° V11 = ®¨ ¸ a, b,c,d S Z ¾ , °¯© c d ¹ ¿°

V21 = S u S u S, V12 = S u S u S u S, ­° ª a V22 = ® « 1 °¯ ¬ a 4

a2 a5

½° a3 º a i S; 1 d i d 6 ¾ , » a6 ¼ °¿

V32 = {all 3 u 3 upper triangular matrices with entries from Z}, V13 = S u S u S,

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­ªa º ½ °« » ° ° b ° V23 = ® « » a, b,c,d S¾ , « » ° c ° ° «¬ d »¼ ° ¯ ¿ V14 = S u S u S u S u S, V24 = {all 4 u 4 lower triangular matrices with entries from Z},

­° ª a a a a a º ½° V34 = ® « a Z¾ » ¯° ¬ a a a a a ¼ ¿° and ­ ª a1 ° V = ® «« a 3 ° «a ¯¬ 5 4 4

a2 º a 4 »» a i Z; a 6 »¼

½ ° 1 d i d 6¾ . ° ¿

Thus V = (V1 V2 V3 V4) is a special group set vector 4 space over the set S. It is interesting to observe in the definition when n = 2 we get the special group set vector bispace and when n = 3 we get the special group set vector trispace.

Now we proceed onto define the notion of special group set vector n-subspace W of a special group set vector n-space V defined over the set S. DEFINITION 4.2.12: Let V = (V1 V2 … Vn) 1 1 1 = V1 ,V2 ,...,Vn1 V12 ,V22 ,...,Vn22 … V1n ,V2n ,...,Vnnn

^

` ^

`

^

`

be a special group set vector n-space defined over the set S. Let W = (W1 W2 … Wn) 1 1 1 = W1 ,W2 ,...,Wn1 W12 ,W22 ,...,Wn22 … W1n ,W2n ,...,Wnnn

^

` ^

`

(V1 … Vn)

351

^

`

i.e., Wi Vi for every i, 1 d i d n and Wtii Vtii is such that Wtii is a proper subgroup of Vtii , 1 d tid ni, i =1, 2, …, n. Then we call W to be a special group set vector n-subspace of V over S if W = (W1 … Wn) is itself a special group set vector n-space over the set S. We shall represent this by a simple example.

Example 4.2.15: Let V = (V1 V2 V3 V4) = ^V11 , V21, V31` ^V12 , V22 , V32 , V42 ` ^V13 , V23 ` ^V14 , V24 , V34 `

be a special group set vector 4-space over the set Z12 where ­°§ a b · ½° V11 = ®¨ ¸ a, b,c,d Z12 ¾ , ¯°© c d ¹ ¿°

V21 = Z12 u Z12 u Z12, V31 = {all 7u2 matrices with entries from

Z12}, V12 = Z12 u Z12 u Z12 u Z12, ­°§ a b · ½° V22 = ®¨ ¸ a, b,d Z12 ¾ , °¯© 0 d ¹ ¿° V32 = {all 2 u 9 matrices with entries from Z12}, V42 = {(a a a a a) | a Z12}, V13 = Z12uZ12, V23 = {all 3u3 matrices with entries from Z12}, V14 = {all 4 u 4 lower triangular matrices with entries from Z12}, V24 = Z12 u Z12 u Z12 u Z12 and V34 = {6 u 3 matrices with entries from Z12}.

Consider a proper subset W = (W1 W2 W3 W4)

352

= ^W11, W21, W31` ^W12 , W22 , W32 , W42 `

^W , W ` ^W , W , W ` 3 1

3 2

4 1

4 2

4 3

V1 V2 V3 V4 where ­°§ a b · ½° 1 W11 = ®¨ ¸ a, b,c,d {0, 2, 4,6,8,10} T Z12 ¾ V1 °¯© c d ¹ °¿ is a subgroup of V11 , W21 = Z12 u Z12 u {0} V21 is a subgroup of V21 , W31 = {all 7 u 2 matrices with entries from T Z12} V31 is again a subgroup of V31 , W12 = {Z12 u Z12 u {0} u {0}} V12 is a subgroup of V12 , ­°§ a a · ½° 2 W22 = ®¨ ¸ a Z12 ¾ V2 °¯© 0 a ¹ ¿° is a subgroup of V22 , W32 = {2 u 9 matrices with entries from the set P = {0, 6, 9} Z12} V32 is a subgroup of V32 , W42 V42 is such that W42 = {(a a a a a) | a {0, 6, 9} Z12} V42 is a subgroup of V42 , W13 = Z12 u P | P = {0, 3, 6, 9} Z12} V13 is a subgroup of V13 , W23 = {all 3 u 3 matrices with entries from the set B = {0, 6} Z12} V23 , W14 = {all 4 u 4 lower triangular matrices with entries from T = {0, 2, 4, 6, 8, 10} Z12} V14 is a subgroup of V14 , W24 = Z12 u Z12 u {0} u {0} V24 is a subgroup of V24 , W34 = {all 6 u 3 matrices with entries from T = {0, 2, 4, 6, 8, 10} Z12} V34 is a subgroup of V34 .

Thus W = W1 W2 W3 W4 is a special group set vector 4subspace of V over Z12.

Now we proceed onto define the new notion of special group semi n-vector spaces which are simple.

353

DEFINITION 4.2.13: Let V = (V1 V2 … Vn) 1 1 1 = V1 ,V2 ,...,Vn1 V12 ,V22 ,...,Vn22 … V1n ,V2n ,...,Vnnn

^

` ^

`

^

`

be a special group set vector n-space over the set S. If each of the groups V jii are simple for 1 d ji d ni, i = 1, 2, …, n, then we call V to be a special group set simple vector n-space. If in the special group set vector n-space each V jii (1d jid ni, i = 1, 2, …, n) does not have proper subgroups then we call V to be a special group set strong simple vector n-space.

We denote these concepts by an example.

Example 4.2.16: Let

V = (V1 V2 V3) = ^V , V ` ^V12 , V22 ` ^V13 , V23 , V33 ` 1 1

1 2

where V11 = Z11, V21 = Z13, ½° °­§ a 0 · V12 = ®¨ ¸ a Z5 ¾ , ¯°© 0 0 ¹ ¿° ­°§ a 0 0 0 · ½° V22 = ®¨ ¸ a Z2 ¾ , °¯© 0 0 0 a ¹ ¿°

V13 = Z5, V23 = Z17 and V33 = Z19 be a special group set vector n space over the set S = {0, 1}, n = 3. We see each Vjii has no subgroup so V is a special group set strong simple vector 3space over the set S = {0, 1}.

Now we proceed onto define the notion of special group set linear n-algebra over a group G.

354

DEFINITION 4.2.14: Let V = V1 V2 … Vn be a special group set vector n-space over a group S under addition (instead of a set S) then we call V to be a special group set linear nalgebra over the group S. If n = 2 we call it the special group set linear bialgebra when n = 3 we call V as the special group set linear trialgebra.

We shall illustrate this definition by an example.

Example 4.2.17: Let V = (V1 V2 V3 V4 V5) = ^V11, V21` ^V12 , V22 , V32 ` ^V13 , V23 , V33 ` ^V14 , V24 `

^V , V , V , V ` 5 1

5 2

5 3

5 4

be a special group set linear 5-algebra over the group G = Z (the integers under additions); where V11 = {Z u Z u Z}, V21 = {all 3 u 3 matrices with entries from Z}, V12 = {all 2 u 5 matrices with entries from Z}, V22 = {all 4 u 4 upper triangular matrices with entries from Z}, V32 = Z u Z u Z u Z u Z, V13 = Z u Z, V23 = {7 u 2 matrices with entries from Z}, V33 = {all polynomials in Z[x] of degree less than or equal to 5}, V14 = {Z u Z u Z u Z}, V24 = {7 u 7 lower triangular matrices with entries from Z}, V15 = Z u Z u Z, V25 = {(a a a a a a a) | a Z}, V35 = {all 9 u 9 diagonal matrices with entries from Z} and V45 = {all 3 u 5 matrices with entries from Z}. It can be easily verified that each Vjii is a group under addition 1 d ji d ni, i = 1, 2, 3, 4, 5. Thus V = (V1 V2 V3 V4 V5) is a special group set linear 5algebra over the group Z. Now we proceed onto define their substructures.

DEFINITION 4.2.15: Let V = (V1 V2 … Vn) 1 1 1 = V1 ,V2 ,...,Vn1 V12 ,V22 ,...,Vn22 … V1n ,V2n ,...,Vnnn

^

` ^

`

355

^

`

be a special group set linear n-algebra over a group G. Let W = (W1 W2 … Wn) 1 1 1 = W1 ,W2 ,...,Wn1 W12 ,W22 ,...,Wn22 … W1n ,W2n ,...,Wnnn

^

` ^

^

`

`

(V1 V2 … Vn) where Wi Vi for i = 1, 2, …, n such that each Wi is a special set group linear subalgebra of Vi i.e., each component W jii o V jii is a group set linear subalgebra of V jii ; 1 d ji d ni, i = 1, 2, …, n. Then we call W = (W1 W2 … Wn) (V1 V2 …

Vn) = V to be the special group set linear n-subalgebra of V over the group G. We will illustrate this by a simple example.

Example 4.2.18: Let V = (V1 V2 V3 V4) = ^V11 , V21, V31` ^V12 , V22 , V32 , V42 ` ^V13 , V23 `

^V , V , V , V , V ` 4 1

4 2

4 3

4 4

4 5

be a special group set linear 4-algebra over the group Z where V11 = Z uZu Z u Z, V21 = {all 2 u 2 matrices with entries from Z}, V31 = {Z(x), all polynomials of degree less than or equal to 8}, V12 = Z u Z u Z, V22 = {3 u 7 matrices with entries from Z}, V32 = {(a a a a a a) | a Z}, V42 = {4 u 3 matrices with entries from Z}, V13 = Z u Z, V23 = {4 u 4 upper triangular matrices with entries from Z}, V14 = {Z u Z u Z u Z u Z}, V24 = 3 u 3 lower triangular matrices with entries from Z}, V34 = 3Z u 5Z u 11Z, ­ ªa º ½ °« » ° ° «a » ° °° « a » °° V44 = ® « » a Z ¾ ° «a » ° ° «a » ° °« » ° °¯ ¬« a ¼» ¿°

356

and V54 = {5 u 2 matrices with entries from Z}. Take W = (W1 W2 W3 W4) = ^W11, W21, W31`

^W , W , W , W ` ^W , W ` ^W , W , W , W , W ` 2 1

2 2

2 3

2 4

3 1

3 2

4 1

4 2

4 3

4 4

4 5

(V1 V2 V3 V4) be such that W11 = Z u Z u {0} u Z V11 is a subgroup of the group V11 , W21 = {2 u 2 matrices with entries from 3Z} is again a subgroup of the group V21 , W31 = {all polynomials in x of degree less than or equal to 4 with coefficients from Z} is again a subgroup of the group V31 . Thus W1 = ^W11, W21, W31` V1 is

a special group set linear subalgebra of V1 = ^V11, V21, V31` . Now

W12 = Z u {0} u {0} is a subgroup of V12 , W22 = {4 u 3 matrices with entries from 7Z} is a subgroup of V22 , W32 = {(a a a a a a) | a 19Z} is a subgroup of the group V32 , W42 = {set of all 4 u 3 matrices with entries from 11Z} V42 is a subgroup of the

group V42 . Thus W2 = ^W12 , W22 , W32 , W42 ` is a special group set linear subalgebra of the special group set linear algebra V2. W13

= 11Z u 11Z is a subgroup of the group Z u Z, W23 = {4 u 4 upper triangular matrices with entries from 2Z} is a subgroup of the group V23 . Thus W3 = ^W13 , W23 ` V3 is again a special group linear subalgebra of V3 over Z. Consider W14 = {7Z u 2Z u 3Z u 5Z u 7Z} V14 ,

W14 is a subgroup of the group V14 .

W24 = {3 u 3 lower triangular matrices with entries from 11Z} is a subgroup of the group V24 .

W34 = 6Z u 10Z u 33Z V34 is a subgroup of V34 ,

357

­ ªa º ½ °« » ° ° «a » ° °° « a » °° W44 = ® « » a 12Z ¾ ° «a » ° ° «a » ° °« » ° ¯° «¬ a »¼ ¿° is a subgroup of V44 and W54 = {5u2 matrices with entries from 6Z} is a subgroup of V54 . Thus

W4 = ^W14 , W24 , W34 , W44 , W54 ` V4 = ^V14 , V24 , V34 , V44 , V54 `

is a special group set linear subalgebra of V4. Thus W = (W1 W2 W3 W4) (V1 V2 V3 V4) = V is a special group set linear 4-subalgebra of V over the group G = Z. Now we proceed onto define the notion of pseudo special group set vector n-subspace of V. DEFINITION 4.2.16: Let V = (V1 V2 … Vn) 1 1 1 = V1 ,V2 ,...,Vn1 V12 ,V22 ,...,Vn22 … V1n ,V2n ,...,Vnnn

^

` ^

^

`

`

be a special group set linear n-algebra over a group G. If W = (W1 W2 … Wn) 1 1 1 = W1 ,W2 ,...,Wn1 W12 ,W22 ,...,Wn22 … W1n ,W2n ,...,Wnnn

^

` ^

`

^

`

(V1 V2 … Vn) such that each W jii V jii is a subgroup of V jii , 1d ji d ni, i = 1, 2, …, n and if S G be only a proper subset G and if W = W1 W2 … Wn V1 V2 … Vn is a special group set vector n-space over the set S then we call W = W1 … Wn to be a pseudo special group set vector n-subspace of V over the subset S G.

358

We shall illustrate this by a simple example.

Example 4.2.19: Let V = (V1 V2 V3 V4 V5) 1 1 = ^V1 , V2 ` ^V12 , V22 , V32 ` ^V13 , V23 ` ^V14 , V24 , V34 `

^V , V , V , V ` 5 1

5 2

5 3

5 4

be a special group set linear 5-algebra over the group Z where V11 = {Z u Z u Z}, V21 = {all 3 u 6 matrices with entries from Z}, V12 = 3Z u 5Z u 7Z u Z, V22 = {Z[x] all polynomials of degree less than or equal to 5},

V32 = {6 u 2 matrices with entries from Z}, V13 = {11Z u 2Z u 3Z u 11Z u 13Z}, V23 = {all 3 u 3 upper triangular matrices with entries from Z},

V14 = {Z u Z u Z u Z}, V24 = {all 4 u 4 lower triangular matrices with entries from Z}, V34 = {all polynomials in x of degree less than or equal to 7 with coefficients from Z}, V15 = Z u Z u Z u Z u 11Z u 13Z,

V25 = {7 u 7 upper triangular matrices with entries from Z}, V35 = {2 u 3 matrices with entries from Z} and

V45 = {5 u 2 matrices with entries from Z}.

Take = ^W , W 1 1

`

1 2

W = (W1 W2 W3 W4 W5) ^W12 , W22 , W32 ` ^W13 , W23 ` ^W14 , W24 , W34` ^W15 , W25 , W35 , W45 `

(V1 V2 V3 V4 V5) = V with W = Z u Z u {0} V11 is a subgroup of V11 , W21 = {all 1 1

3u6 matrices with entries from 2Z} V21 is a subgroup of V21 , W12 = {30Z u {0} u {0} u 10Z} is a subgroup of V12 , W22 = {all

359

polynomials of degree less than or equal to two with coefficients from Z} is a subgroup of V22 , W32 = {6 u 2 matrices with entries from 3Z} is a subgroup of V32 , W13 = {{0} u 4Z u {0} u 22Z} is a subgroup of V13 , W23 = {all 3 u 3 upper triangular matrices with entries from 5Z} is a subgroup of V23 , W14 = {Z u {0} u 3Z u {0}} is a subgroup of V14 , W24 = {all 4 u 4 lower triangular matrices with entries from 7Z} is a subgroup of V24 , W34 = {all polynomials in x of degree less than or equal 3 with coefficients from Z} is a subgroup of V34 , W15 = Z u {0} u {0} u {0} u {0} u 13Z is a subgroup of V15 , W25 = {7 u 7 upper triangular matrices with entries from 9Z} is a subgroup of V25 , W35 = {2 u 3 matrices with entries from 13Z} is a subgroup of V35 and W45 = {5 u 2 matrices with entries from 14Z} is a subgroup of V45 . Thus W = (W1 W2 W3 W4 W5) (V1 V2 V3 V4 V5) is a pseudo special group set vector 5-subspace of V over the set S = {0, +1} Z. Now we proceed onto define yet another new substructure.

DEFINITION 4.2.17: Let V = (V1 V2 … Vn) be a special group set linear n-algebra over the group G. Suppose W = (W1 W2 … Wn) (V1 V2 … Vn) where each Wi Vi is a special group set linear n-subalgebra over a proper subgroup H of G then we call W = (W1 … Wn) (V1 V2 … Vn) to be a pseudo special subgroup set linear n-subalgebra over of V over the subgroup H G.

We illustrate first this definition by an example.

Example 4.2.20: Let V = (V1 V2 V3 V4 V5) be a special group set linear algebra over the group Z, where V1 = ^V11, V21, V31` with V11 = Z u Z u Z,

V21 = {all 3 u 3 matrices with entries from Z} and

360

V31 = {3 u 5 matrices with entries from Z}.

V2 = ^V12 , V22 ` where

V12 = 3Z u 7Z u Z and

V22 = {2 u 6 matrices with entries from Z}.

V3 = ^V13 , V23 , V33 , V43 , V53 ` with

V13 = {2 u 5 matrices with entries from Z}, V23 = Z u Z u Z u 3Z,

V33 = {6 u 2 matrices with entries from Z}, V43 = {all polynomials in the variable x with coefficients from Z of degree less than or equal to 5} and ­ ªa º ½ °« » ° ° «a » ° ° ° V53 = ® « a » a Z ¾ . ° «a » ° °« » ° « » ¯° ¬ a ¼ ¿° V4 = ^V14 , V24 ` with V14 = {(a a a a a a) | a Z} and V24 = Zu3Zu2Zu5ZuZ

and V5 = ^V15 , V25 , V35 , V45 ` with V15 = Z u Z u Z u Z u 3Z,

V25 = {all 5 u 5 upper triangular matrices with entries from Z}, V35 = {(a a a a a) | a Z} and V45 = {2 u 9 matrices with entries from Z}.

Take W11 = 3Z u 3Z u 3Z V11 is a subgroup of V11 ,

W21 = {3 u 3 matrices with entries from 5Z} V21

361

is a subgroup of V21 and W31 = {3 u 5 matrices with entries from 7Z} V31

is a subgroup of V31 .

W12 = {6Z u {0} u 5Z} V12 is a subgroup of V12 and W22 = {2 u 6 matrices with entries from 7Z} V22 , W13 = {2 u 5 matrices with entries from 8Z} V13 is a subgroup of V13 , W23 = 3Z u {0} u {0} u 3Z} V23 is a subgroup of V23 , W33 = {6 u 2 matrices with entries from 10Z} V33

is a subgroup of V33 , W43 = {all polynomials in the variable x with coefficients from Z of degree less than or equal to 3} V43 is a subgroup of V43 , ­ ªa º ½ °« » ° ° «a » ° ° ° W53 = ® « a » a 10Z ¾ V53 ° «a » ° °« » ° °¯ «¬ a »¼ ¿° 3 is a subgroup of V5 , W14 = {(a a a a a a) | a 2Z} V14

is a subgroup of V14 and W24 = {3Z u {0} u {0} u {0} u 5Z} is a subgroup of V24 .

W15 = {0}u3Zu{0}u{0}u3Z is a subgroup of V15 , W25 = {all 5 u 5 upper triangular matrices with entries from 7Z}

362

is a subgroup of V25 , W35 = {(a a a a a) / a 2Z} V35

is a subgroup of V35 and W45 = {2 u 9 matrices with entries from 10Z} V45 is a subgroup of V45 .

Thus W = V = (W1 W2 W3 W4 W5) 1 = ^W1 , W21, W31` ^W12 , W22 ` ^W13 , W23 , W33 , W43 , W53 ` ^W14 , W24 ` ^W15 , W25 , W35 , W45 ` V = (V1 V2 V3 V4) is a pseudo special subgroup set linear 5-subalgebra over the subgroup 2Z Z. It is important to observe the following: If the group over which the special group set linear n algebra V defined does not contain any proper subgroups then we define those special group set linear n-algebras as pseudo simple special group set linear n-algebras.

We in this context prove the following result.

THEOREM 4.2.1: Let V = (V1 V2 … Vn) 1 1 1 = V1 ,V2 ,...,Vn1 V12 ,V22 ,...,Vn22 … V1n ,V2n ,...,Vnnn

^

` ^

`

^

`

be a special group set linear n-algebra over the group Zp. (The group of integers under addition modulo p, p a prime}. Then V is a pseudo simple special group set linear n-algebra over Zp. Proof: Given V = V = (V1 V2 … Vn) is a special group set linear n-algebra defined over the additive group Zp. Thus Zp being simple Zp cannot have non trivial subgroup so even if W is proper subset of V such that if W = (W1 W2 … Wn)

363

^

` ^

= W11, W21 ,..., Wn11 W12 , W22 ,..., Wn22

^W , W n 1

n 2

,..., Wnnn

`

` …

such that each W , 1 d ji d ni; i = 1, 2, …, n happens to be a i ji

subgroup of Vjii still the absence of subgroups in Zp makes V a pseudo simple special group set linear n-algebra over Zp. Thus from this theorem we have an infinite class of pseudo simple special group set linear n-algebras as the number of primes is infinite.

We shall illustrate this by a simple example.

Example 4.2.21: Let V = (V1 V2 V3 V4) 1 1 = ^V1 , V2 ` ^V12 , V22 , V32 ` ^V13 , V23 ` ^V14 , V24 `

be a special group set linear 4-algebra over the group Z7. Here V11 = Z7 u Z7, ­ªa º ½ ° ° V21 = ® «« b »» a, b,c Z7 ¾ ° ° ¯ «¬ c »¼ ¿ V12 = Z7 u Z7 u Z7, V22 = {2 u 2 matrices with entries from Z7}, V32 = [2 u 5 matrices with entries from Z7, V13 = {Z7 u Z7 u Z7 u

Z7}, V23 = {4 u 4 upper triangular matrices with entries from Z7}, V14 = {5 u 5 lower triangular matrices with entries from Z7} and V24 = Z7 u Z7 u Z7 u Z7 u Z7.

Take W = (W1 W2 W3 W4) 1 1 = ^W1 , W2 ` ^W12 , W22 , W32 ` ^W13 , W23 ` ^W14 , W24 ` where W11 = Z7u{0} V11 is a subgroup of the group V11 ,

364

­ ªa º ½ °« » ° W = ® « a » a Z7 ¾ V21 ° ° ¯ «¬ a »¼ ¿ 1 2

is a subgroup of the group V21 , W12 = Z7 u {0} u Z7 V12 is a subgroup of the group V12 , ½° °­§ a a · 2 W22 = ®¨ ¸ a Z7 ¾ V2 a a ¹ ¯°© ¿°

is a subgroup of the group V22 , ½° °­§ a a a a a · 2 W32 = ®¨ ¸ a Z7 ¾ V3 a a a a a ¹ ¯°© ¿° is a subgroup of the group V32 . W13 = {Z7 u {0} u Z7 u {0}} V13 is a subgroup of the group V13 ,

­§ a °¨ ° 0 3 W2 = ®¨ °¨¨ 0 ° ¯© 0

½ a a a· ° ¸ a a a¸ ° a Z7 ¾ V23 0 a a¸ ° ¸ ° 0 0 a¹ ¿

is a subgroup of the group V23 ­§ a °¨ °¨ a ° 4 W1 = ®¨ a °¨ a °¨ ¨ ¯°© a

½ 0 0 0 0· ° ¸ a 0 0 0¸ ° ° ¸ a a 0 0 a Z7 ¾ V14 ¸ ° a a a 0¸ ° a a a a ¸¹ ¿°

365

is a subgroup of the group V14 and W24 = Z7 u {0} u Z7 u {0} u {0} V24 is a subgroup of the group.

Though W = (W1 W2 W3 W4) (V1 V2 V3 V4) = V still as Z7 has no subgroup W cannot be a pseudo special subgroup set linear 4-subalgebra of V so V is itself a pseudo simple special group set linear 4-algebra.

Now we proceed onto define the notion of special group set linear n-transformation.

DEFINITION 4.2.18: Let V = (V1 V2 … Vn) 1 1 1 = V1 ,V2 ,...,Vn1 V12 ,V22 ,...,Vn22 … V1n ,V2n ,...,Vnnn

^

` ^

`

^

`

and W = (W1 W2 … Wn) = W12 ,W22 ,...,Wn22 … W1n ,W2n ,...,Wnnn

^W ,W ,...,W ` ^ 1 1

1 2

1 n1

`

^

be two special group set vector n-spaces over the set S. T: (T1 T2 … Tn) 1 1 1 = T1 , T2 ,..., Tn1 T12 , T22 ,..., Tn22 … T1n , T2n ,..., Tnnn

^

` ^

`

^

`

`:

V = (V1 V2 … Vn) o W = (W1 W2 … Wn) where Ti : Vi o Wi i.e., Ti : V1i ,V2i ,...,Vnii o W1i ,W2i ,...,Wnii

such that Ti is a special group set linear transformation of Vi into Wi over the group S for every i, i = 1, 2, …, n.

That is T jii : V jii o W jii ;

1 d ji d ni, i = 1, 2, …, n where Ti= T1i , T2i ,..., Tnii ; then we call T = (T1 T2 … Tn) to be a special group set linear ntransformation of V into W. We shall denote the collection of all such special group set linear n-transformation V into W by SHom(V, W) = {SHom (V1, W1)} {SHom (V2, W2)} …

SHom(Vn, Wn)}

366

= {Hom V11 ,W11 , Hom V21 ,W21 ,…, Hom Vn11 ,Wn11 }

, …, Hom V ,W } …

, Hom V ,W , …, Hom V ,W }.

{Hom V ,W 2 2

{Hom V1n ,W1n

2 2

2 n2

n 2

2 n2

n 2

n nn

n nn

Clearly S Hom (V, W) need not in general be a special group set vector n-space over S. Infact SHom (V, W) can in general be a special set vector n space over S. Example 4.2.22: Let V = (V1 V2 V3 V4) and W = (W1 W2 W3 W4) be two special group set vector spaces over the group S = {0, 1}. Here V1 = ^V11 , V21` , V2 = ^V12 , V22 , V32 ` ,

V3 = ^V13 , V23 , V33 ` , V4 = ^V14 , V24 `

with V11 = Z u Z u Z u Z, V21 = {all 2 u 3 matrices with entries from Z}, V12 = Z u Z u Z, V22 = {all polynomials in the variable x with coefficients from Z of degree less than or equal to 4}, V32 = {3 u 3 upper triangular matrices with entries from Z},

V13 = {3 u 3 matrices with entries from Z}, V23 = Z u Z, V33 = {9 u 1 column vector with entries from Z},

V14 = Z u Z u Z u Z u Z u Z, V24 = {3 u 2 matrices with entries from Z}, ­°§ a b · ½° W11 = ®¨ ¸ a, b,c,d Z ¾ , ¯°© c d ¹ ¿° ­ ª a1 ° W = ® «« a 3 ° ¯ «¬ a 5 1 2

½ a2 º ° » a 4 » a i Z; 1 d i d 6 ¾ , ° a 6 »¼ ¿

367

­°§ a b · ½° W12 = ®¨ ¸ a, b,c Z ¾ , °¯© 0 c ¹ ¿° ­ ª a1 º ½ °« » ° ° «a 2 » ° ° ° W22 = ® « a 3 » a i Z; 1 d i d 5¾ , ° «a » ° °« 4 » ° °¯ «¬ a 5 »¼ ¿° W32 = {{3 u 2 matrices with entries from Z}, W13 = {all polynomials in x degree less than or equal to 8 with coefficients from Z}, ½° °­§ a · W23 = ®¨ ¸ a, b Z ¾ , ¯°© b ¹ ¿°

W14 = {3 u 3 lower triangular matrices with entries from Z} and °­§ a b c · °½ W24 = ®¨ ¸ a, b,c,d,e,f Z ¾ . ¯°© d e f ¹ ¿° Now define the map T = T1 T2 T3 T4 : V = V1 V2 V3 V4 o W = W1 W2 W3 W4 as follows; = ^T ,T 1 1

`

1 2

T = (T1 T2 T3 T4) ^T12 ,T22 ,T32 ` ^T13 ,T23 ,T33 ` ^T14 ,T24 ` : V o W by

T11 : V11 o W11 given by §a b· T11 (a b c d) = ¨ ¸, ©c d¹

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T21 : V21 o W21 is defined by

§a d· §a b c· ¨ ¸ T ¨ ¸ = ¨b e¸, d e f © ¹ ¨c f ¸ © ¹ 2 2 2 T1 : V1 o W1 is given by 1 2

§a b· T12 (a b c) = ¨ ¸, ©0 c¹ T22 : V22 o W22 is such that § a0 · ¨ ¸ ¨ a1 ¸ 2 3 4 2 T2 (a0 + a1x + a2x + a3x + a4x ) = ¨ a 2 ¸ ¨ ¸ ¨ a3 ¸ ¨a ¸ © 4¹ and T32 : V32 o W32 is defined by § a b c · ªa d º ¨ ¸ T ¨ 0 d e ¸ = «« b e »» . ¨ 0 0 f ¸ «c f » © ¹ ¼ 2 3

T13 : V13 o W13 is given by §a b c· ¨ ¸ T ¨d e f ¸ ¨g h i ¸ © ¹ 3 1

= {a + bx + cx2 + dx3 + ex4 + fx5 + gx6 + hx7 + ix8}, T23 : V23 o W23 is defined by

ªa º T23 (a, b) = « » , ¬b¼

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T33 : V33 o W33 is such that ª a1 º «a » « 2» «a » T33 « 3 » = a1 + a2x + a3x2 + a4x3+a5x4+a6x5+a7x6+a8x7+a9x8, «a 4 » «#» « » «¬ a 9 »¼ T14 : V14 o W14 is given by § a 0 0· ¨ ¸ T (a b c d e f) = ¨ b d 0 ¸ ¨c e f ¸ © ¹ 4 1

and T24 : V24 o W24 is such that § a1 ¨ T ¨ a2 ¨a © 3 4 2

a4 · ¸ §a a5 ¸ = ¨ 1 a a 6 ¸¹ © 4

a2 a5

a3 · ¸. a6 ¹

Thus T = T1 T2 T3 T4 is a special group set linear 4 transformation of V into V. It is important at this stage to make a note that if W in the definition is replaced by V itself i.e., The domain and the range space are one and the same then we call the map T: V o V which is a special group set linear transformation from V into V is defined as the special group set linear n-operator on V. Now if SHom (V,V) = {S Hom (V1, V1) S Hom (V2, V2) … S Hom (Vn, Vn)} 1 1 = (Hom V1 , V1 , Hom V21, V21 , …, Hom Vn11 , Vn11 )

(Hom V12 , V12 , Hom V22 , V22 , …, Hom Vn22 , Vn22 ) …

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(Hom V1n , V1n , â&#x20AC;Ś, Hom Vnn1 , Vnn1 ) then we see SHom (V, V) is a special group set vector n-space over the sets over which V is defined.

We illustrate this by the following example.

Example 4.2.23: Let V = (V1 Â&#x2030; V2 Â&#x2030; V3 Â&#x2030; V4) 1 1 1 = ^V1 , V2 , V3 ` Â&#x2030; ^V12 , V22 ` Â&#x2030; ^V13 , V23 , V33 , V43 ` Â&#x2030; ^V14 , V24 , V34 `

where V11 = Z u Z u Z u Z, ½° °­ ª a b º V21 = Ž  a, b,c,d � Z ž ,  ¯°  c d Ÿ ¿°

V31 = {set of all polynomials in the variable x of degree less than or equal to 6}, V12 = 3Z u Z u Z, V22 = {3 u 3 upper triangular matrices with entries from Z},

V13 = Z u Z u Z u Z u Z, V23 = {all 4 u 4 upper triangular matrices with entries from Z}, V33 = {all polynomials in the variable x with coefficients from Z of degree less than or equal to 5},

­§ a a a a ¡ ½ °¨ ° ¸ V = Ž¨ b b b b ¸ a, b,c � Z ž , °¨ c c c c ¸ ° š ¯Š ¿ 3 4

­§ a ¡ ½ °¨ ¸ ° °¨ b ¸ ° 4 a, b,c,d � Z ž , V1 = Ž °¨¨ c ¸¸ ° °Š d š ° ¯ ¿ V24 = {Z u Z u Z u Z u Z u Z}

371

and V34 = {all 6 u 6 lower triangular matrices with entries from Z}, be the special group set 4-vector space over Z. Now define = T , T ,T 1 1

1 2

= V , V ,V 1 1

1 2

T = (T1 T2 T3) T ,T22 T13 , T23 ,T33 ,T43 T14 , T24 ,T34 :

V = (V1 V2 V3 V4

V12 , V22 V13 , V23 , V33 , V43 V14 , V24 , V34

1 3

1 3

2 1

o V = (V1 V2 V3 V4

as follows: T11 : V11 o V11 is defined by T11 (a b c d) = (a a d d);

T21 : V21 o V21 is defined by §a b· §d c· T21 ¨ ¸ =¨ ¸, ©c d¹ ©b a¹ T31 : V31 o V31 is given by T31 (ao + a1x + a2 x2 + a3 x3 + a4 x4 + a5 x5 + a6 x6) = ao x6 + a1 x5 + a2 x4 + a3 x3 + a4 x2 + a5 x + a6), T12 : V12 o V12 is defined by

T12 (x y z) = (x x + y + z z), T22 : V22 o V22 is given by § a b c · § a 0 0· ¨ ¸ ¨ ¸ T ¨ 0 d e ¸ ¨ 0 d 0¸ , ¨0 0 f ¸ ¨0 0 f ¸ © ¹ © ¹ 2 2

372

T13 : V13 o V13 is such that T13 (x y z Z t) = (x + y + z, Z, 3y, 2y, z),

T23 : V23 o V23 is given by

§a ¨ 0 T23 ¨ ¨0 ¨ ©0

b e 0 0

c d· §a b ¸ ¨ f g¸ ¨0 e h i ¸ ¨0 0 ¸ ¨ 0 j¹ ©0 0

c d· ¸ 0 0¸ , h 0¸ ¸ 0 j¹

T33 : V33 o V33 is defined as T33 (ao + a1x + a2 x2 + a3 x3 + a4 x4 + a5 x5) = a0 + a3x3 + a5x5,

T43 : V43 o V43 is given by §a a a a· §c c c c· ¨ ¸ ¨ ¸ T ¨b b b b¸ ¨a a a a ¸ , ¨ c c c c ¸ ¨ b b b b¸ © ¹ © ¹ 3 4

T14 : V14 o V14 is given by §a· ¨ ¸ 4 ¨b¸ T1 ¨c¸ ¨ ¸ ©d¹

§a· ¨ ¸ ¨d¸ , ¨ b¸ ¨ ¸ ©c¹

T24 : V24 o V24 is such that

T24 (a b c d e f) = (a a a f f f) and T34 : V34 o V34 is given by

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§a 0 0 ¨ ¨b c 0 ¨d e f T34 ¨ ¨g h i ¨k l m ¨¨ ©q r s

0 0 0 j n t

0 0 0 0 p u

0· ¸ 0¸ 0¸ ¸ 0¸ 0¸ ¸ v ¸¹

§a ¨ ¨b ¨d ¨ ¨g ¨k ¨¨ ©q

0 b d g k q

0 0 d g k q

0 0 0 g k q

0 0 0 0 k q

0· ¸ 0¸ 0¸ ¸.

0¸ 0¸ ¸ q ¸¹

Thus we see T = (T1 T2 T3 T4) : VoV is a special group set linear 4 operator on V. Now we can proceed onto define the notion of pseudo special group set linear n-operator on V. DEFINITION 4.2.19: Let V = (V1 V2 Vn) 1 1 1 = V1 , V2 ,...,Vn1 V12 , V22 ,..., Vn22 V1n , V2n ,..., Vnnn

be a special group set vector n-space over the set S. Let T = (T1 T2 Tn) 1 1 = T1 , T2 ,..., Tn11 T1n , T2n ,..., Tnnn .

V = (V1 V2 Vn) = V11 , V21,...,Vn11 V12 , V22 ,..., Vn22 V1n , V2n ,..., Vnnn

o V = (V1 V2 Vn) is such that Ti: Vi o Vi is given by T jii : V jii o Vki ; ji z k for atleast one ji; 1 d ji, k d ni, i = 1, 2, … , n be a pseudo special group set linear operator on Vi, true for each i. Then we define T = (T1 T2 Tn) to be the pseudo special group set linear n-operator on V.

We illustrate this by a simple example Example 4.2.24: Let V = (V1 V2 V3 V4 be a special group set vector 4-space over the set Z+ {0} = S where

374

V1 = V11 , V21, V31 , V41 , V2 = V12 , V22 , V32 ,

V3 = V13 , V23 and V4 = V14 , V24 , V34 , V44 , V54

with V11 = S u S u S u S,

­°§ a b · ½° V21 = ®¨ ¸ a, b,c,d S¾ , °¯© c a ¹ ¿° ­§ a1 · ½ °¨ ¸ ° °¨ a 2 ¸ ° 1 a i S; 1 d i d 4 ¾ , V3 = ® ¨ ¸ a °¨ 3 ¸ ° °© a 4 ¹ ° ¯ ¿ V41 = {all polynomials in the variable x with coefficients from S

of degree less than or equal to 5}, V12 = S u S u S u S u S u S, V22 = {all 3 u 3 upper triangular matrices with entries from S}, V32 = {all 6 u 2 matrices with entries from S}, V13 = S u S u S

u S u S u S, V23 = {all 4 u 4 lower triangular matrices with entries from S}, V14 = {S u S u S u S u S u S u S}, V24 = {5 u 2 matrices with entries from Z + {0}} V34 = {2 u 3 matrices with entries from Z + {0}}, V44 = {7 u 1 column matrix with entries from S} and V54 = {set of all polynomials in the variable x with coefficients from S of degree less than or equal to 9}. V is clearly a special group set vector 4-space over the set S. Let T = (T1 T2 T3 T4) = T11 , T21,T31 ,T41 T12 ,T22 ,T32

T13 , T23 T14 , T24 ,T34 ,T44 ,T54

= V = (V1 V2 V3 V4)

375

= V11 , V21, V31 , V41 V12 , V22 , V32 V13 , V23

V , V , V , V , V o V = (V1 V2 V3 V4) 4 1

4 2

4 3

4 4

4 5

be defined as follows.

Ti : Vi o Vi, 1 d i d 4; T1 : V1 o V1, i.e., T1 = T11 , T21,T31 ,T41 : V11 , V21, V31 , V41 o V11 , V21, V31 , V41

such that T11 : V11 o V21 ,

T21 : V21 o V31 ,

T31 : V31 o V41 and T41 : V41 o V11

are given by; T11 (a b c d)

=

§a b· ¨ ¸, ©c d¹

ªa º « » § a b · «b » 1 T2 : ¨ , ¸ = © c d ¹ «c» « » ¬d ¼

ªa º «b » 1 T3 : « » = a + bx + cx3 + dx5 «c» « » ¬d ¼ and T41 : (ao + a1x + a2x2 + a3x3 + a4 x4 + a5 x5) = (ao, a1 + a2, a3 + a4, a5).

Now T2 T12 , T22 ,T32 : V2 = V12 , V22 , V32 o V2 = V12 , V22 , V32

is defined to be from

376

T12 : V12 o V22 ,

T22 : V22 o V32 and

T31 : V31 o V41 ; are given in the following: T32 : V32 o V12

2 1

T (a b c d e f ) =

§a b c· ¨ ¸ ¨0 d e¸, ¨0 0 f ¸ © ¹

§a b c· T ¨ ¸ § a 0 c 0 d 0· T ¨0 d e¸ = ¨ ¸ , ¨0 0 f ¸ ©0 e 0 f 0 e¹ © ¹ 2 2

and §a b· ¨ ¸ ¨c d¸ ¨e f ¸ T32 ¨ ¸ = (a + b, c + d, e + f, g + h, i + j, k + l) ¨g h¸ ¨ i j¸ ¨¨ ¸¸ ©k l ¹

T3 = T13 , T23 : V3 = V13 , V23 o V13 , V23

are such that T13 : V13 o V23

and T23 : V23 o V13 are defined by

§a ¨ b T13 (a b c d e f) = ¨ ¨c ¨ ©d and

377

0 e f a

0 0 0 c

0· ¸ 0¸ 0¸ ¸ d¹

§a ¨ b T23 ¨ ¨d ¨ ©g Now

0 0 0· ¸ c 0 0¸ = (a, b + c, d, e, f + g, h + i). e f 0¸ ¸ h i j¹ T4 = T14 , T24 ,T34 ,T44 ,T54 :

V4 = V14 , V24 , V34 , V44 , V54 o V4 = V14 , V24 , V34 , V44 , V54

is defined by; T14 : V14 o V44 is such that ªa º «b » « » «c» « » 4 T1 (a b c d e f g) = « d » ; «e» « » «f » «g » ¬ ¼ T24 : V24 o V54 is defined by

§a b· ¨ ¸ ¨c d¸ T24 ¨ e f ¸ = a + bx + cx2 + dx3 + ex4 + fx5 + gx6 + hx7 +ix8+ jx9 ¨ ¸ ¨g h¸ ¨ i j¸ © ¹ T34 : V34 o V14 is such that §a b c· T34 ¨ ¸ = (a, b, c, d, e, f, a + f); ©d e f ¹ T34 : V34 o V54 is given by

378

ªa 0 º «a » « 1» «a 2 » » 4 « T4 « a 3 » = ao + a1x + a2x2 + a3x3 + a4x4 + a5x5 + a6x6 «a 4 » « » «a 5 » «a » ¬ 6¼ and T54 : V54 o V24 is such that T54 = ao + a1x + a2x2 + a3x3 + a4x4 ªa 0 «a « 2 + a5x5 + a6x6 + a7x7 + a8x8 + a9x9 = « a 4 « «a 6 «¬ a 8

a1 º a 3 »» a5 » . » a7 » a 9 »¼

Clearly T = (T1 T2 T3 T4) = T , T ,T31 ,T41 T12 , T22 ,T32 T13 , T23

1 1

1 2

T , T , T ,T ,T : 4 1

4 2

4 3

4 4

4 5

V = (V1 V2 V3 V4 o V is a pseudo special group set linear 4-operator on V. The following observations are important: If V = (V1 V2 Vn) 1 1 1 = V1 , V2 ,..., Vn1 V12 , V22 ,..., Vn22 V1n , V2n ,..., Vnnn

is a special group set vector n-space over the set S. The collection of all pseudo operators from V into V denoted by SHom(V, V) = SHom(V1, V1) SHom(V2, V2) SHom

Hom V , V … Hom V , V }

Hom V , V … Hom V , V } …

(Vn, Vn) = {Hom V11 , Vi11

{Hom V12 , Vi22

1 1

2 2

2 j2

379

1 i1

1 n1

2 n2

2 in2

1 i n1

{Hom V1n , Vpn2 Hom V2n , Vpn2 … Hom Vnnn , Vpnn

n

} is again a

special group set vector n-space over the set S . Here i1 ,...,i n1

is a permutation of (1, 2, …, n1), j1 ,..., jn 2

(1,2, …, n2) so on p1 ,..., p n n

is a permutation of

is a permutation of (1, 2, …, n ). n

As in case of special group set vector n-spaces we can in case of special group set linear n-algebras define special group set linear n-transformations, special group set linear n-operators and so on. Further we can define special projections of these nspaces.

We now define the fuzzy analogue of these notions.

DEFINITION 4.2.20: Let V = (S1, S2, … , Sn) be a special semigroup set vector space defined over the set P. Let = (K1, …, Kn): V o [0, 1] be such that Ki: Si o [0, 1]; 1 d i d n, Ki(ai + bi) t min{Ki(ai), Ki(bi)} Ki(cai) t Ki (ai) for all c P and for all ai, bi Si, true for i =1, 2, …, n. Then we call VK = V1 ,V2 ,...,Vn K ,,...,K = V1K1 ,V2K2 ,...,VnKn

1

n

to be a special semigroup set fuzzy vector space.

We illustrate this by some examples.

Example 4.2.25: Let V = (V1, V2, V3, V4, V5) be a special semigroup set vector space over the set {0, 1, 2, …, f} = S where V1 = {Zo u Zo u Zo | Zo S = Z+ {0}},

­§ a · ½ °¨ ¸ ° °¨ b ¸ ° V2 = ® a, b,c,d S¾ , °¨¨ c ¸¸ ° °© d ¹ ° ¯ ¿ V3 = {all 3u3 matrices with entries from S}, 380

­°§ a a a a ¡ ½° V4 = Ž¨ ¸ such that a, b � Sž °¯Š b b b b š ¿° and ­§ a °¨ °¨ c ° V5 = Ž¨ a °¨ c °¨ ¨ ¯°Š a

½ b¡ ° ¸ d¸ ° ° b ¸ such that a, b,c,d � Sž . ¸ ° d¸ ° b ¸š ¿°

Define K (K1, K2, K3, K4, K5):V = (V1,V2,V3,V4,V5) o [0, 1] by Ki: Vi o [0, 1]; 1 d i d 5 as follows: K1 : V1 o [0,1] is given by 1 ­ if x y z z 0 ° K1(x, y, z) = Ž x y z ° 1 if x y z 0 ¯ K2: V2 o [0, 1] is defined by ªa º 1 b ­ if a b c d z 0 ° K2   = Ž a b c d c  ° 1 if a b c d 0   ¯ d Ÿ K3: V3 o [0, 1] is such that §a b c¡ ­ 1 if a e i z 0 ¨ ¸ ° K3 ¨ d e f ¸ = Ž a e i ¨ g h i ¸ °¯ 1 if a e i 0 Š š K4 : V4 o [0,1] is defined by

381

­ 1 if 4b 2a z 0 ªa a a a º ° K4  = Ž 4b 2a  b b b bŸ ° 1 if 4b 2a 0 ¯ K5 : V5 o [0,1] is given by ªa c  K5  a  c  a Clearly

bº d  ­ 1 if 3a 2c z 0 ° b  = Ž 3a 2c  if 3a 2c 0 d  °¯ 1 b Ÿ

VK = (V1, V2, V3, V4, V5)K = V1K1 , V2 K2 ,..., V5K5

is a special semigroup set fuzzy vector space.

Example 4.2.26: Let V = (V1, V2, V3, V4) where

V1 = Z5uZ5uZ5uZ5uZ5, ­§ a ¡ ½ °¨ ¸ ° °¨ b ¸ ° a, b,c,d � Z5 ž , V2 = Ž ¨ ¸ °¨ c ¸ ° °Š d š ° ¯ ¿ V3 = {Z5[x] all polynomials of finite degree with coefficients from Z5} and V4 = {all 4 u 4 upper triangular matrices with entries from Z5} is a special semigroup set vector space over the set S = {0, 1}.

Define K (K1, K2, K3, K4): V = (V1 V2 V3 V4) o [0,1] by Ki: Vi o [0,1], i = 1, 2, 3, 4 such that K1 : V1 o [0,1] defined by

382

­1 ° if a z 0 K1(a, b, c, d, e) = Ž a °¯ 1 if a 0 K2: V2 o [0, 1] is given by ªa º b  ­ 1 if a d z 0 ° K2   = Ž a d c °   ¯ 1 if a d 0 d Ÿ K3: V3 o [0, 1] is such that 1 ­ if p(x) z constant ° K3(p(x)) = Ž deg p(x) ° 1 if p(x) is a constant ¯ K4: V4 o [0,1] is defined by ªa 0 K4  0  0

b c dº 1 ­ if a e h j z 0 e f g  ° = Ža e h j 0 h i ° 1 if a e h j 0  ¯ 0 0 jŸ

Clearly VK = V1 , V2 , V3 , V4 K ,K 1

2 , K3 , K4

= V1K1 , V2 K2 ,..., V4 K4

is a special semigroup set fuzzy vector space.

Now we proceed on to define the notion of special semigroup set fuzzy vector subspace.

DEFINITION 4.2.21: Let V = (V1, V2, â&#x20AC;Ś, Vn) be a special semigroup set vector space over the set S. Suppose W = (W1, W2, â&#x20AC;Ś, Wn) Â&#x17D; (V1, V2, â&#x20AC;Ś, Vn); Wi Â&#x17D; Vi; (1 d i d n) is a special

383

semigroup set vector subspace of V over the set S. Now define K (K1, K2, …, Kn) : W o [0, 1]; W = (W1, W2, …, Wn) o [0, 1] such that Ki : Wi o [0, 1] where WK = WK1 ,WK2 ,...,WKn is a special semigroup set fuzzy vector space then we call WK to be a special semigroup set fuzzy vector subspace of V.

We illustrate this by a simple example.

Example 4.2.27: Let V = (V1, V2, V3, V4, V5) be a special semigroup set vector space over the set S = {0, 1} where V1 = {Zo u Zo u Zo u Zo u Zo | Zo = Z + {0}}, V2 = {all 4 u 5 matrices with entries from Zo},

­§ a · ½ °¨ ¸ ° °¨ b ¸ o° a, b,c,d,e Z ¾ V3 = ® °¨¨ c ¸¸ ° °© d ¹ ° ¯ ¿ V4 = {all 4 u 4 lower triangular matrices with entries from Zo} and V5 = {Zo[x] all polynomials in the variable x with coefficients from Zo}. Take W = (W1, W2, W3, W4, W5) (V1, V2, V3, V4, V5) = V where W1 = {2Zo u 3Zo u Zo u 5Zo u 6Zo} V1, ­§ a1 a 2 °¨ ° b b2 W2 = ®¨ 1 °¨¨ c1 c 2 °© d1 d 2 ¯

a3 b3 c3 d3

a4 b4 c4 d4

½ a5 · ° ¸ b5 ¸ ° a i , b i ,c i ,d i , 3Zo ,1 d i d 5¾ V2, c5 ¸ ° ¸ ° d5 ¹ ¿

­§ a · ½ °¨ ¸ ° °¨ a ¸ ° ° ° W3 = ®¨ a ¸ a Zo ¾ V3, °¨ a ¸ ° °¨ ¸ ° °¯¨© a ¸¹ ¿°

384

­§ a °¨ ° a W4 = Ž¨ °¨¨ b °Š c ¯

½ 0 0 0¡ ° ¸ a 0 0¸ o° a, b,c Â? Z ž Â&#x17D; V4 b a 0¸ ° ¸ ° c b aš Âż

and W5 = {all polynomials in Zo[x] of degree less than or equal to 11 with coefficients from Zo} Â&#x17D; V5 . Clearly W = (W1, W2, W3, W4, W5) is special semigroup set vector subspace of V over the set {0, 1}.

Define K (K1, K2, K3, K4, K5) : W = (W1,W2,W3,W4,W5) o [0, 1] as follows: Ki : Wi o [0,1]; i = 1, 2, 3, 4, 5.

K1: W1 o [0, 1] is defined by 1 ­ if a b c d e z 0 ° K1(a b c d e) = Ž a b c d e °¯ 1 if a b c d e 0 K2 : W2 o [0,1] is such that ª a1 a 2 d d 2 K2  1  b1 b 2   p1 p 2

a3

a4

d3 b3

d4 b4

p3

p4

a5 º d 5  = b5   p5 Ÿ

1 ­ if a1 d 2 b3 p 4 z 0 ° Ž a1 d 2 b 3 p 4 ° 1 if a1 d 2 b3 p 4 0 ¯ K3 : W3 o [0,1] is given by

385

§a¡ ¨ ¸ ¨ a ¸ ­° 1 if a z 0 K3 ¨ a ¸ = Ž a ¨ ¸ ° ¨ a ¸ ¯ 1 if a 0 ¨a¸ Š š

K4 : W4 o [0,1] is defined by §a ¨ a K4 ¨ ¨b ¨ Šc

0 0 0¡ ¸ ­ 1 a 0 0¸ ° if a b c z 0 = Ža b c b a 0¸ ° if a b c 0 ¸ ¯ 1 c b aš

Clearly W = W1K1 , W2 K2 , W3K3 , W4 K4 is special semigroup set fuzzy vector subspace.

Now we proceed on to define the notion of special semigroup set linear algebra.

DEFINITION 4.2.22: Let V = (V1, V2, â&#x20AC;Ś, Vn) be a special semigroup set vector space defined over the set S. Let K = (K1 , K2 ,â&#x20AC;Ś, Kn): V = (V1, V2, â&#x20AC;Ś , Vn) o [0,1] such that Ki : Vi o [0, 1]; i = 1, 2, â&#x20AC;Ś, n.

Ki(ai + bi) t min{Ki(ai), Ki(bi)} Ki(rai) t Ki(ai) for all ai, bi Â? Vi and for all r Â? S; 1d id n.

Then VK = V1K1 ,V2K2 ,...,VnKn is a special semigroup set fuzzy

linear algebra.

We illustrate this by an example.

Example 4.2.28: Let V = (V1, V2, V3, V4, V5) where V1 = {Z12 u Z12 u Z12}, V2 = {all 3 u 3 matrices with entries from Z12}, V3 = {Z12[x] be all polynomials of degree less than or equal to 7},

386

­§ a ¡ ½ °¨ ¸ ° °¨ b ¸ ° °¨ ¸ ° V4 = Ž c a, b,c,d,e � Z12 ž °¨ d ¸ ° °¨ ¸ ° °¯¨Š e ¸š ¿° and V5 = {all 2 u 5 matrices with entries from Z12} be a special semigroup set linear algebra over Z12.

K (K1, K2, K3, K4, K5): V = (V1 V2 V3 V4 V5) o [0, 1]; Ki: Wi o [0, 1]; 1 d i d 5.

K1: W1 o [0, 1] is such that ­ 1 if a b c z 0 ° K1(a b c) = Ž a b c if a b c 0 ¯° 1 K2 : V2 o [0,1] is defined by ªa b c º ­ 1 if 2a 3b 4i z 0 °   K2  d e f  = Ž 2a 3b 4i 1 if 2a 3b 4i 0  g h i Ÿ ¯° K3 : V3 o [0,1] is given by 1 ­ if p(x) z constant ° K3(p(x)) = Ž deg p(x) ° 1 if p(x) is a constant ¯ K4 : V4 o [0,1] is given by

387

ªa º b   ­° 1 if a e z 0 K4  c  = Ž a e   ° 1 if a e 0 d  ¯  e Ÿ

K5 : V5 o [0,1] is defined by ÂŞa K5 ÂŤ 1 ÂŹ b1

a2 b2

a3 b3

a4 b4

­ 1 if 2a1 b5 z 0 a5 º ° = Ž 2a1 b5  b5 Ÿ ° if 2a1 b5 0 ¯ 1

VK = V1K1 , V2 K2 , V3K3 , V4 K4 , V5K5 is a special semigroup set fuzzy linear algebra.

It is important to state that like most of the structures defined in this book; we see special semigroup set fuzzy vector space and special semigroup set fuzzy linear algebra are fuzzy equivalent.

Now we proceed on to define special semigroup set fuzzy linear sub algebras.

DEFINITION 4.2.23: Let V = (V1, V2, â&#x20AC;Ś, Vn) be a special semigroup set linear algebra over a semigroup S. Let W = (W1, W2, â&#x20AC;Ś, Wn) be a special semigroup set linear subalgebra of V over the semigroup S. Define W = (W1, W2, â&#x20AC;Ś, Wn) o [0, 1]; K = (K1, K2, â&#x20AC;Ś, Kn): (W1, W2, â&#x20AC;Ś, Wn) o [0,1]; where Ki : Vi o [0,1]; i = 1, 2, â&#x20AC;Ś, n. such that WK = (W1, W2, â&#x20AC;Ś, Wn)K = W1K1 ,W2K2 ,...,WnKn

is a special semigroup set fuzzy linear algebra then we call WK is a special semigroup set fuzzy linear subalgebra.

We now illustrate this by a simple example.

388

Example 4.2.29: Let V = (V1, V2, V3, V4, V5) where V1 = {Zo u Zo u Zo u Zo u Zo}, V2 = {Zo[x] is a polynomial of degree less than or equal to 12 with coefficients from Zo}, V3 = {all 5 u 5 lower triangular matrices with entries from Zo}, V4 = {3 u 6 matrices with entries from Zo} and

­§ a · ½ °¨ ¸ ° °¨ a ¸ ° °°¨ a ¸ °° V5 = ®¨ ¸ a Zo ¾ °¨ a ¸ ° °¨ a ¸ ° °¨¨ ¸¸ ° °¯© a ¹ ¿° be a special semigroup set linear algebra over S = {1, 2, …, 10}.

W = (W1, W2, W3, W4, W5) (V1, V2, V3, V4, V5) = V where W1 = {Zo u 2Zo u ^ ` u ^ ` u 3Zo} V1, W2 = {all polynomials in x with coefficients from Zo of degree less than or equal to 7} V2, W3 = {all 5u5 lower triangular matrices with entries 3Zo} V3, W4 = {3 u 6 matrices with entries 5Zo} V4 and ­§ a · ½ °¨ ¸ ° °¨ a ¸ ° °°¨ a ¸ °° W5 = ®¨ ¸ a 3Zo ¾ V5, °¨ a ¸ ° °¨ a ¸ ° °¨¨ ¸¸ ° °¯© a ¹ ¿° W is a special semigroup set linear subalgebra of V over the set S = {0, 1, 2, …, 10}.

Define K: W o [0, 1]; by K (K1, K2, K3, K4, K5): (W1,W2,W3,W4,W5) = W o [0, 1], 1 d i d 5.

389

K1: W1 o [0, 1] is defined by ­1 ° if b z 0 K1(a b 0 0 c) = Ž b °¯ 1 if b 0 K2: W2 o [0, 1] is given by 1 ­ if p(x) is not a constant ° K2(p(x)) = Ž deg p(x) °1 if p(x) is a constant ¯

K3 : W3 o [0,1] is defined by ÂŞa ÂŤb ÂŤ K3 ÂŤ c ÂŤ ÂŤd ÂŤÂŹ e

0 0 0 0º b 0 0 0  ­ 1 if a b c d e z 0 ° c c 0 0 = Ž a b c d e  1 if a b c d e 0 d d d 0  °¯ e e e e Ÿ

K4 : W4 o [0,1] is such that ÂŞ a1 K4 ÂŤÂŤ b1 ÂŤÂŹ c1

a2

a3

a4

a5

b2 c2

b3 c3

b4 c4

b5 c5

a6 º ­ 1 if a1 b3 c5 z 0 °  b 6  = Ž a1 b 3 c 5 1 if a1 b3 c5 0 c6 Ÿ °¯

K5 : W5 o [0,1] is given by ªa º a    ­1 a  ° if a z 0 K5   = Ž 3a  a  °¯ 1 if a 0 a     a Ÿ

390

WK W1 , W2 , W3 , W4 , W5 K ,K 1

W

1K1

2 ,..., K5

=

, W2 K2 , W3K3 , W4 K4 , W5K5

is a special semigroup set fuzzy linear subalgebra.

It is pertinent to mention here that the notion of special semigroup set fuzzy vector subspace and special semigroup set fuzzy linear subalgebra are fuzzy equivalent.

Now we proceed on to define the fuzzy notion of special group set vector spaces defined over the group G. DEFINITION 4.2.24: Let V = (V1, V2, …, Vn) be a special group set vector space over the set S. Let K = (K1, K2,…, Kn): V o (V1, V2, … , Vn)o [0, 1] such that Ki: Vi o [0, 1]; i = 1 d i d n.

Ki(ai + bi) t min{Ki(ai), Ki(bi)} Ki(–ai) t Ki(ai); Ki = 0 Ki(rai) t Ki(ai) for all ai, bi Vi and r S. If this is true for every i; i =1,2,…,n.

VK = V1 ,V2 ,...,Vn K ,K ,...,K = V1K1 ,V2K2 ,...,VnKn

1

2

n

is a special group set fuzzy vector space.

We shall illustrate this by a simple example.

Example 4.2.30: Let V = (V1 V2 V3 V4 V5) to be special group set vector space over the set Zo where V1 = {Z u Z u Z u Z u Z u Z}, V2 = {all 3 u 3 matrices with entries from Z},

­§ a · ½ °¨ ¸ ° °¨ b ¸ ° ° ° V3 = ®¨ c ¸ a, b,c,d,e Z ¾ , °¨ d ¸ ° °¨ ¸ ° °¯¨© e ¸¹ ¿° V4 = {all 3 u matrices with entries from Z} and V5 = {all 5 u 5 lower triangular matrices with entries from Z}.

Define K (K1, K2, K3, K4, K5) : V = (V1,V2, V3, V4, V5) o [0,1]; 1 d i d 5.

391

K1: V1 o [0, 1] is such that

­ 1 if a c f z 0 ° K1(a b c d e) = Ž a c f if a c f 0 ¯° 1

K2: V2 o [0, 1] is defined by ªa b c º ­ 1 if a b d i z 0 ° K2  d e f  = Ž a b d i 1 if a b d i 0  g h i Ÿ °¯ K3: V3 o [0,1] is given by ªa º b 1   ­° if 5a 7d e z 0 K3  c  = Ž 5a 7d e   ° 1 if 5a 7d e 0 d  ¯  e Ÿ

K4: V4 o [0, 1] is such that ÂŞ a1 K4 ÂŤÂŤ b1 ÂŤÂŹ c1

a2

a3

a4

b2 c2

b3 c3

b4 c4

a5 º ­ 1 if a 5 b5 c5 z 0 °  b 5  = Ž a 5 b 5 c5 1 if a 5 b5 c5 0 c5 Ÿ °¯

K5: V5 o [0, 1] is given by ÂŞa 0 0 ÂŤb c 0 ÂŤ K5 ÂŤ d e f ÂŤ ÂŤg h i ÂŤÂŹ k l m

0 0º 0 0  ­ 1 if a p z 0 ° 0 0 = Ž a p  j 0  °¯ 1 if a p 0 n p Ÿ

392

Thus VK V1K1 , V2 K2 ,..., V5K5 is a special group set fuzzy vector space.

Now we proceed on to define the notion of special group set fuzzy vector subspace.

DEFINITION 4.2.25: Let V = (V1, V2, …, Vn) be a special group set vector space over the set S. Let W = (W1, W2, …, Wn) (V1, V2, …, Vn) V be a proper subset of V which is a special group set vector subspace of V over S. Let K = (K1, K2, …, Kn): W = (W1, W2, …, Wn) o [0,1] such that Ki: Wi o [0, 1] for every i and WK = W1K1 ,W2K2 ,...,WnKn be

a special group set fuzzy vector space, then we define WK to be a special group set fuzzy vector subspace of V. Now we will illustrate this situation by a simple example.

Example 4.2.31: Let V = (V1, V2, V3, V4, V5) be a space over the set S = Z where V1 = S u S u S u S u S, V2 = {all 3 u 3 matrices with entries from S}, V3 = {all 5 u 2 matrices with entries from S = Z} V4 = {(a a a a a a a) | a S} and

­§ a1 · ½ °¨ ¸ ° °¨ a 2 ¸ ° ° ° V5 = ®¨ a 3 ¸ ai S Z 1 d i d 5¾ . °¨ a ¸ ° °¨ 4 ¸ ° °¯¨© a 5 ¸¹ ¿° Take W = (W1,W2, …,W5) (V1,V2,V3,V4,V5) = V where W1 = S u S u {0} u {0} u S V1, W2 = {all 3 u 3 upper triangular matrices with entries from S} V2, 393

­§ a °¨ °¨ a ° W3 = Ž¨ a °¨ a °¨ °¯¨Š a

½ b¡ ° ¸ b¸ ° ° ¸ b a, b Â? Sž Â&#x17D; V3, ¸ ° b¸ ° b ¸š ¿°

W4 = {(a a a a a a) | a Â? 5S} Â&#x17D; V4 and ­§ a ¡ ½ °¨ ¸ ° °¨ a ¸ ° ° ° W5 = Ž¨ a ¸ a Â? Sž Â&#x17D; V5. °¨ a ¸ ° °¨ ¸ ° °¯¨Š a ¸š ¿° Clearly W = (W1, W2, W3, W4, W5) Â&#x17D; (V1, V2, V3, V4, V5) = V is a special group set vector subspace of V over S. Define K (K1, K2, K3, K4, K5) : W = (W1,W2,W3,W4,W5) o [0,1]; as Ki: Wi o [0, 1] for every i such that K1: W1 o [0,1] is defined by ­ 1 if a b c z 0 ° K1(a b 0 0 c) = ÂŽ a b c °¯ 1 if a b c 0 K2 : W2 o [0,1] is given by §a b c¡ ­ 1 if a d f z 0 ¨ ¸ ° K2 ¨ 0 d e ¸ = ÂŽ a d f ¨ 0 0 f ¸ °¯ 1 if a d f 0 Š š K3 : W3 o [0,1] is given by

394

ÂŞa ÂŤa ÂŤ K3 ÂŤ a ÂŤ ÂŤa ÂŤÂŹ a

bº b  ­ 1 if a b z 0 ° b = Ž a b  b  °¯ 1 if a b 0 b Ÿ

K4 : W4 o [0,1] is given by ­1 ° if a z 0 K4(a a a a a a) = Ž a °¯ 1 if a 0 K5 : W5 o [0,1] is such that ªa º a    ­° 1 if a z 0 K5  a  = Ž 5a   ° 1 if a 0 a  ¯  a Ÿ Thus WK W1K1 , W2 K2 , W3K3 , W4 K4 , W5K5 = (W1K1, W2K2, W3K3, W4K4, W5K5) is a special group set fuzzy vector subspace.

Now we proceed on to define the notion of special group set linear algebra V defined over the group G. DEFINITION 4.2.26: Let V = (V1, V2, â&#x20AC;Ś, Vn) be a special group set linear algebra over the group G. Let K : V = (V1, V2, â&#x20AC;Ś, Vn) o [0,1] be such that K = (K1 , K2 ,â&#x20AC;Ś, Kn): (V1, V2, â&#x20AC;Ś, Vn) o [0, 1] such that Ki:Vi o [0,1] for each i, i = 1, 2, â&#x20AC;Ś, n; satisfying the following conditions Ki(ai + bi) t min{Ki(ai), Ki(bi)} Ki(â&#x20AC;&#x201C; ai)t Ki(ai); Ki (0) = 1 Ki(rai) t Ki(ai)

395

for atleast one pair of ai, bi Â? Vi and for some r Â? G; true for each i, i = 1, 2, â&#x20AC;Ś, n. We call VK = V1K1 ,V2K2 ,...,VnKn to be a pseudo special group set fuzzy linear algebra.

We illustrate this by some simple examples.

Example 4.2.32: Let V = (V1, V2, V3, V4, V5) be a special group set linear algebra over the group Z18 where V1 = Z18 u Z18 u Z18, V2 = {all 4 u 4 matrices with entries from Z18}, V3 = {all 5 u 5 lower triangular matrices with entries from Z18},

­§ a ¡ ½ °¨ ¸ ° °¨ b ¸ ° ° ° V4 = Ž¨ c ¸ a, b,c,d,e � Z18 ž °¨ d ¸ ° °¨ ¸ ° °¯¨Š e ¸š ¿° and ­§ a °¨ °¨ b °°¨ c V5 = Ž¨ °¨ d °¨ e °¨¨ ¯°Š f

½ a¡ ° ¸ b¸ ° °° c¸ ¸ a, b,c,d,e,f � Z18 ž . d¸ ° ° e¸ ¸¸ ° fš ¿°

Define K (K1, K2, K3, K4, K5) : V = (V1,V2,V3,V4,V5) o [0,1]; such that Ki : Vi o [0,1], i = 1, 2, â&#x20AC;Ś, 5.

K1 : V1 o [0,1] is defined by ­ 1 if a b c z 0 ° K1 (a b c) = Ž a b c °¯ 1 if a b c 0

396

K2: V2 o [0, 1] is such that § a b c d¡ 1 ¨ ¸ ­ if d f j m z 0 e f g h¸ ° ¨ d

f

j m K2 = Ž ¨c j k l¸ ° 1 if d f j m 0 ¨ ¸ ¯ Šm n o pš K3: V3 o [0, 1] is defined by ªa b  K3  d  g  k

0 c e h l

0 0º 0 0 0  ­ 1 if a d b g k z 0 ° f 0 0 = Ž a d b g k  1 if a d b g k 0 i j 0  °¯ m n p Ÿ 0

K4: W4 o [0, 1] is given by ªa º b   ­° 1 if a c e z 0 K4  c  = Ž a c e   ° 1 if a c e 0 d  ¯  e Ÿ K5 : W5 o [0, 1] is defined by ªa b  c K5  d e   f

aº b  1 ­ c if a b c d e f z 0 °  = Ža b c d e f d °¯ 1 if a b c d e f 0 e  f Ÿ

397

Clearly VK V1K1 , V2 K2 , V3K3 , V4 K4 , V5K5 is a special group set fuzzy linear algebra.

It is pertinent to record at this juncture the notion of special group set fuzzy vector space and special group set fuzzy linear algebra are fuzzy equivalent. However we wish to state they become the same under the fuzzification.

Now we proceed on to define the notion of special group set fuzzy linear subalgebra.

DEFINITION 4.2.27: Let V = (V1, V2, …, Vn) be a special group set linear algebra defined over the group G. Let W = (W1, W2, …, Wn) (V1, V2, …, Vn) = V such that Wi Vi, i = 1, 2 ,…, n; be a special group set linear subalgebra of V over the same group G. Let K : (K1 , K2 ,…, Kn) : W = (W1, W2, …, Wn) o [0,1] be defined such that Ki : Wi o [0,1] and WK = W1K1 ,W2K2 ,...,WnKn

is a special group set fuzzy linear algebra, then we call W to be a special group set fuzzy linear set subalgebra.

We illustrate this by some an example.

Example 4.2.33: Let V = (V1, V2, V3, V4) be a special group set linear algebra over the group R. Here V1 = R u R u R u R; R the reals under addition, V2 = {all 4 u 4 matrices with entries from R}, ­§ a1 · ½ °¨ ¸ ° °¨ a 2 ¸ ° ° ° V3 = ®¨ a 3 ¸ ai R; 1 d i d 5¾ °¨ a ¸ ° °¨ 4 ¸ ° °¯¨© a 5 ¸¹ ¿°

and V4 = {all 2 u 7 matrices with entries from R}. Choose W = (W1,W2, W3, W4) where W1 = R u {0} u ^ ` u R V1; W2 = {all 4 matrices with entries from R of the form

398

§a ¨ ¨b ¨c ¨ Šd

½ a¡ ° ¸ b b b¸ ° a, b,c,d Â? R ž Â&#x17D; V2, ¸ c c c ° ¸ ° d d dš Âż a

a

­§ a ¡ ½ °¨ ¸ ° °¨ a ¸ ° ° ° W3 = Ž¨ a ¸ a Â? R ž Â&#x17D; V3, °¨ a ¸ ° °¨ ¸ ° °¯¨Š a ¸š ¿° and ­°§ a a a a a a a ¡ ½° W4 = Ž¨ ¸ a, b Â? R ž Â&#x17D; V4, °¯Š b b b b b b b š °¿ so that W is easily verified to be a special group set linear subalgebra of V over the real. Define K (K1, K2, K3, K4) : W = (W1,W2,W3,W4) o [0,1]; Ki : Vi o [0, 1]; i = 1, 2, 3, 4 as follows.

K1 : W1 o [0,1] such that

­ 1 °° a b if a b t 1 K1(a 0 0 b) = Ž a b if a b 1 ° °¯ 1 if a b 0

K2 : W2 o [0,1] is defined by §a ¨ b K2 ¨ ¨c ¨ Šd

a b c d

a b c d

a¡ ­ 1 if a b c d t 1 ¸ ° b¸ °a b c d = Ž c ¸ °a b c d if a b c d 1 ¸ 1 if a b c d 0 d š °¯

399

K3 : W3 o [0, 1] is given by ªa º «a » ­ 1 « » °° a if a t 1 K3 « a » = ® « » °a if a 1 « a » °1 if a 0 «¬ a »¼ ¯

K4 : W4 o [0,1] is given by a ­a ° b if b 1 ªa a a a a a a º ° K4 « b a » = ®b ¬ b b b b b b b ¼ ° a if a d 1 z 0 i.e., b t 1 ° ¯1 if a 0 and b 0 Thus WK W1K1 , W2 K2 , W3K3 , W4 K4 is a special group set fuzzy linear subalgebra.

It is to be observed that the notion of special group set fuzzy vector subspace and special group set fuzzy linear subalgebra are also fuzzy equivalent.

Now we proceed on to define yet another new notion.

DEFINITION 4.2.28: Let V = (V1, V2, …, Vn) 1 1 1 = V1 , V2 ,...,Vn1 V12 , V22 ,..., Vn22 V1n , V2n ,...,Vnnn

be a special semigroup set n-vector space over the set a S. If K (K1 K2 Kn) 1 1 1 = K1 , K 2 ,...,Kn1 K12 , K 22 ,...,Kn22 K1n , K 2n ,...,Knnn :

1 1

1 2

1 n1

= V , V ,...,V

V = (V1, V2, …, Vn) V12 , V22 ,..., Vn22 V1n , V2n ,...,Vnn2

o [0, 1]

400

such that for each i, K iji : V jii o [0, 1]; (1 d ji d ni; 1d i d n) then

VK V1K1 ... VnKn is a special semigroup set fuzzy n-vector space; when n = 2 we get the special semigroup set fuzzy vector bispace. If n = 3 we get the special set fuzzy vector trispace.

If we replace the special semigroup set n-vector space V by a special semigroup set linear n-algebra V and define K = (K1 … Kn): V = (V1, V2, …, Vn) o [0, 1]; we get the special semigroup set fuzzy linear n-algebra. Infact both these concepts are fuzzy equivalent.

We illustrate this by an example.

Example 4.2.34: Let V = (V1 V2 V3 V4) 1 1 1 = V1 , V2 , V3 V12 , V22 V13 , V23 , V33 , V43 V14 , V24 , V34

be a special semigroup set vector 4 space over the semigroup S = Zo = Z + {0}, where V11 = Zo u Zo u Zo , ­§ a · ½ °¨ ¸ ° °¨ b ¸ 1 o° V2 = ® a, b,c,d Z ¾ , °¨¨ c ¸¸ ° °© d ¹ ° ¯ ¿

V31 = {all 3 u 3 matrices with entries from Zo}, V12 = Zo u Zo u Zo u Zo u Zo

and V22 = {5 u 2 matrices with entries from Zo}, V13 = {2 u 6 matrices with entries from Zo }, V23 = { Zo u Zo u Zo u Zo }

V33 = {Zo[x] all polynomials in the variable x with coefficients from Zo of degree less than or equal to four} and

401

­§ a °¨ °¨ b ° 3 V4 = Ž¨ c °¨ d °¨ ¨ ¯°Š e

½ a¡ ° ¸ b¸ ° o° ¸ c a, b,c,d,e � Z ž . ¸ ° d¸ ° e ¸š ¿°

V14 = {6 u 3 matrices with entries from Zo}, V24 = { Zo u Zo}

and V34 = {2 u 7 matrices with entries from Zo}.

Define = K , K , K 1 1

1 2

1 3

K K1 Â&#x2030; K2 Â&#x2030; K3 Â&#x2030; K4) Â&#x2030; K12 , K22 Â&#x2030; K13 , K32 , K33 , K34 Â&#x2030; K14 , K42 , K34 :

V = (V1 Â&#x2030; V2 Â&#x2030; V3 Â&#x2030; V4) = V , V , V31 Â&#x2030; V12 , V22 Â&#x2030; V13 , V23 , V33 , V43 Â&#x2030; 1 1

1 2

V , V , V , V o [0, 1] 4 1

4 2

4 3

4 4

such that Kiji : Vjii o [0,1]; 1 d ji d ni; i = 1, 2, 3, 4. Now K11 : V11 o [0,1] such that

1 ­ if x y z z 0 ° K (x y z) = Ž x y z ° 1 if x y z 0 ¯ 1 1

K12 : V21 o [0,1] defined by ªa º 1   ­ if 2a b c 3d z 0 ° 1 b K2 = Ž 2a b c 3d c ° 1 if 2a b c 3d 0   ¯ d Ÿ

402

K13 : V31 o [0,1] is given by ªa b c º ­ 1 if c e g z 0 ° K  d e f  = Ž c e g  g h i Ÿ °¯ 1 if c e g 0 1 3

K22 : V22 o [0,1] defined by ÂŞa ÂŤc ÂŤ K22 ÂŤ e ÂŤ ÂŤg ÂŤÂŹ i

bº d  ­ 1 if a d e h i z 0 ° f  = Ža d e h i  1 if a d e h i 0 h  °¯ j Ÿ

K12 : V12 o [0,1] such that K12 (x y z Z u) =

1 ­ if x 2y 3z 4Z 5u z 0 °

Ž x 2y 3z 4Z 5u ° 1 if x 2y 3z 4Z 5u 0 ¯ K13 : V13 o [0,1] such that ­ 1 if a l z 0 ªa b c d e f º ° K  = Ža l   g h i j k l Ÿ ° 1 if a l 0 ¯ 3 1

K32 : V23 o [0, 1] defined by

­1 ° if x z 0 K32 (x y z Z) = Ž x °¯ 1 if x 0

403

K33 : V33 o [0, 1] is such that

1 ­ if p(x) z constant ° deg p(x) K (p (x)) = Ž ° 1 if p(x) is a constant ¯ 3 3

K34 : V43 o [0, 1] is such that ÂŞa ÂŤb ÂŤ K34 ÂŤ c ÂŤ ÂŤd ÂŤÂŹ e

aº b  ­ 1 if a d z 0 ° c  = Ž 2a 2d  if a d 0 d  °¯ 1 e Ÿ

K14 : V14 o [0, 1] is defined by

ÂŞa ÂŤd ÂŤ ÂŤg K14 ÂŤ ÂŤj ÂŤm ÂŤ ÂŤÂŹ p

b e h k n q

cº f  1 ­ if a d g h g r z 0 i °  = Ža d g h g r l ° 1 if a d g h g r 0 ¯ o  r Ÿ

K42 : V24 o [0, 1] is given by

­ 1 if x y z 0 ° K (x y) = Ž x y ° 1 if x y 0 ¯ 4 2

K34 : V34 o [0,1] is given by

404

­ 1 if a n z 0 §a b c d e f g¡ ° = K34 ¨ ¸ ÂŽa n Š h i j k l m n š ° 1 if a n 0 ÂŻ Clearly K K1 Â&#x2030; K2 Â&#x2030; K3 Â&#x2030; K4) : V o [0, 1] is such that VK V1K1 Â&#x2030; V2 K2 Â&#x2030; V3K3 Â&#x2030; V4 K4 is a special semigroup set fuzzy vector 4 space.

Now we proceed on to define the notion of special semigroup set vector n-space over a set S. DEFINITION 4.2.29: Let V = (V1, V2, â&#x20AC;Ś, Vn) 1 1 1 = V1 , V2 ,...,Vn1 Â&#x2030; V12 , V22 ,..., Vn22 Â&#x2030; Â&#x2030; V1n , V2n ,...,Vnnn

be a special semigroup set n-vector space over a set S. Let W = (W1, W2, â&#x20AC;Ś, Wn) 1 1 1 = W1 ,W2 ,...,Wn1 Â&#x2030; W12 , W22 ,...,Wn22 Â&#x2030; Â&#x2030; W1n , W2n ,...,Wnnn

Â&#x17D; (V1 Â&#x2030; V2Â&#x2030; â&#x20AC;ŚÂ&#x2030; Vn ) = V be a special semigroup set vector n-subspace of V over the set S. Let K (K1 Â&#x2030; K2 Â&#x2030; Â&#x2030;Kn) 1 1 1 = K1 , K 2 ,...,Kn1 Â&#x2030; K12 , K 22 ,...,Kn22 Â&#x2030; Â&#x2030; K1n , K 2n ,...,Knnn :

1 1

1 2

1 n1

= W ,W ,...,W

W = (W1, W2, â&#x20AC;Ś, Wn) Â&#x2030; W12 , W22 ,...,Wn22 Â&#x2030; Â&#x2030; W1n , W2n ,...,Wnnn

o [0, 1] defined by K iji : V jii o [0,1]; 1d ji d ni; i = 1, 2, â&#x20AC;Ś, n. If WK1

W1 Â&#x2030; W2 Â&#x2030; ... Â&#x2030; Wn (K Â&#x2030;K Â&#x2030;...Â&#x2030;K ) = W1K 1

n

2

1

Â&#x2030; W2K2 Â&#x2030; ... Â&#x2030; WnKn

is a special semigroup set fuzzy vector space then we call WK to be a special semigroup set fuzzy vector n-subspace.

We illustrate this situation by a simple example.

Example 4.2.35: Let V = (V1 Â&#x2030; V2 Â&#x2030; V3 Â&#x2030; V4 Â&#x2030; V5) be a special semigroup set vector 5 space over the set Z20 where 405

V11 = V11 , V21, V31 , V2 = V12 , V22 , V3 = V13 , V23 , V33 , V43 , V4 = V14 , V24 , V5 = V15 , V25 , V35 , V45 , V55

is such that V11 = Z20 u Z20, ­§ a ¡ ½ °¨ ¸ ° V = Ž¨ b ¸ a, b,c � Z20 ž , °¨ c ¸ ° ¯Š š ¿ 1 2

V31 = {all 3 u 3 matrices with entries from Z20}, V12 = Z20 u Z20 u Z20 u Z20, V22 = {all 4 u 4 matrices with entries from Z20}, V13 = Z20 u Z20 u Z20 ­§ a ¡ ½ °¨ ¸ ° °¨ b ¸ ° 3 V2 = Ž a, b,c,d � Z20 ž , °¨¨ c ¸¸ ° °Š d š ° ¯ ¿ ½° °­§ a a a a a ¡ V33 = Ž¨ ¸ a, b � Z20 ž , ¯°Š b b b b b š ¿° V43 = {all 2 u 6 matrices with entries from Z20}, V14 = Z20 u Z20 u Z20 u Z20 u Z20; V24 = {all 2 u 2 matrices with entries from Z20}, V15 = Z20 u Z20 u Z20, V25 = {all 5 u upper triangular matrices with entries from Z20}, ­§ a °¨ °¨ a °°¨ a V35 = Ž¨ °¨ a °¨ a °¨¨ °¯Š a

b¡ ¸ b¸ b¸ ¸ b¸ b¸ ¸ b ¸š

406

½ ° ° °° a, b � Z20 ž , ° ° ° ¿°

V45 = {Z20[x] all polynomials in the variable x with coefficients from Z20} and V55 = {all 4 u 4 symmetric matrices with entries from Z20}. Take W = (W1 Â&#x2030; W2 Â&#x2030; W3 Â&#x2030; W4 Â&#x2030; W5) 1 1 = W1 , W2 , W31 Â&#x2030; W12 , W22 Â&#x2030; W13 , W23 , W33 , W43 Â&#x2030;

W , W Â&#x2030; W , W , W , W , W

4 1

4 2

5 1

5 2

5 3

5 4

5 5

Â&#x17D; (V1 Â&#x2030; V2 Â&#x2030; V3 Â&#x2030; V4 Â&#x2030; V5) is given below; W11 = Z20 u ^ ` Â&#x17D; V11 ,

­§ a ¡ ½ °¨ ¸ ° W = Ž¨ a ¸ a Â? Z20 ž Â&#x17D; V21 , °¨ a ¸ ° ¯Š š Âż 1 2

W31 = {all 3 u 3 upper triangular matrices with entries from Z20} Â&#x17D; V31 , W12 = Z20 u Z20 u ^ ` u ^ ` Â&#x17D; V12 , W22 = {all 4 u 4 upper triangular matrices with entries from Z20} Â&#x17D; V22 , W13 = Z20 u ^ ` u ^ ` Â&#x17D; V13

­§ a ¡ ½ °¨ ¸ ° °¨ a ¸ ° 3 a, b Â? Z20 ž Â&#x17D; V23 , W2 = ÂŽ °¨¨ b ¸¸ ° °Š b š ° ÂŻ Âż ½° °­§ a a a a a ¡ W33 = Ž¨ ¸ a, b Â?{0, 2, 4, 6, 8, 10, 12,14,16,18}ž ¯°Š b b b b b š ¿° Â&#x17D; V33 , W43 = {all 2u6 matrices with entries from {0, 5, 10, 15} Â&#x17D; = } Â&#x17D; V43 , W14 = Z20 u Z20 u Z20 u ^ ` u ^ ` Â&#x17D; V14 , W24 = {all 2u2 upper triangular matrices with entries from Z20} Â&#x17D; V24 ,

407

W15 = Z20 u Z20 u ^ ` V15 , W25 = {all 5u diagonal matrices with entries from Z20} V25 , ­§ a °¨ °¨ a °°¨ a W35 = ®¨ °¨ a °¨ a °¨¨ °¯© a

½ b· ° ¸ b¸ ° °° b¸ 5 ¸ a, b {0,5,10,15} Z20 ¾ V3 , b¸ ° ¸ ° b ¸¸ ° b¹ °¿

W45 = {all polynomials in x of degree less than or equal to 4 with coefficients from Z20} V45 and W55 = {all 4 u4 symmetric matrices with entries from Z20} V55 ; is clearly a special semigroup set vector n-subspace of V over the set Z20. Take K : W o [0, 1] . Define K K1 K2 K3 K4 K5) 1 = K1 , K12 , K13 K12 , K22 K13 , K32 , K33 , K34

K14 , K42 , K34 K15 , K52 , K35 , K54 , K55 :

W = (W1 W2 W3 W4 W5) = W , W , W31 W12 , W22 W13 , W23 , W33 , W43

1 1

1 2

W , W W , W , W , W , W o [0, 1] 4 1

4 2

5 1

5 2

5 3

5 4

5 5

as follows; Kiji : Vjii o [0, 1]; 1 d ji d ni; i = 1, 2, 3, 4, 5 is given by

K11 : W11 o [0,1] such that ­1 ° if a z 0 K (a 0) = ® a °¯ 1 if a 0 1 1

K12 : W21 o [0, 1] is defined by

408

ªa º ­ 1 if a z 0 ° K  a  = Ž 3a  a Ÿ °¯ 1 if a 0 1 2

K13 : W31 o [0,1] is given by ªa b c º ­ 1 if c e f z 0 ° K  0 d e  = Ž c e f if c e f 0  0 0 f Ÿ °¯ 1 1 3

K12 : W12 o [0, 1] such that ­1 if ab z 0 ° K12 (a b 0 0) = Ž ab °¯ 1 if ab 0 K22 : W22 o [0,1] is defined by §a ¨ 0 K22 ¨ ¨0 ¨ Š0

b e 0 0

c d¡ 1 ¸ ­ f g¸ ° if ab cf hi z 0 = Ž ab cf hi h i¸ ° 1 if ab cf hi 0 ¸ ¯ o kš

K13 : W13 o [0,1] is such that ­1 if a z 0 ° K (a 0 0) = Ž 7a °¯ 1 if a 0 3 1

K32 : W23 o [0,1] is given by

409

§a¡ 1 ¨ ¸ ­ if ab a 2 b 2 z 0 ° 3 ¨a¸ K2 = Ž ab a 2 b 2 ¨b¸ ° 1 if ab a 2 b 2 0 ¨ ¸ ¯ b Š š K33 : W33 o [0,1] is such that ­ 1 if ab z 0 §a a a a a¡ ° K ¨ ¸ = Ž 5ab Š b b b b b š ° 1 if ab 0 ¯ 3 3

K34 : W43 o [0,1] is such that ­ 1 if ag fl z 0 ªa b c d e f º ° K34  = Ž ag fl   g h i j k l Ÿ ° 1 if ag fl 0 ¯ K14 : W14 o [0,1] is defined by ­ 1 if abc z 0 ° K (a b c 0 0) = Ž abc °¯ 1 if abc 0 4 1

K42 : W24 o [0,1] is given by ­ 1 if ac b z 0 §a b¡ ° = K42 ¨ ¸ Ž ac b Š 0 c š ° 1 if ac b 0 ¯ K15 : W15 o [0, 1] is such that

­ 1 if 3a 5b z 0 ° K (a b 0) = Ž 3a 5b °¯ 1 if 3a 5b 0 5 1

410

K52 : W25 o [0, 1] is defined by ÂŞa ÂŤ0 ÂŤ K52 ÂŤ 0 ÂŤ ÂŤ0 ÂŤÂŹ 0

0 b 0 0 0

0 0 c 0 0

0 0 0 d 0

0º 0  ­ 1 if abc de z 0 ° 0  = Ž abc de  if abc de 0 0  °¯ 1 e Ÿ

K53 : W35 o [0, 1] is given by ÂŞa ÂŤa ÂŤ ÂŤa K53 ÂŤ ÂŤa ÂŤa ÂŤ ÂŹÂŤ a

bº b  ­ 1 b ° if 6a 3b z 0 =  Ž 6a 3b b ° if 6a 3b 0 ¯ 1 b  b Ÿ

K54 : W45 o [0,1] is given by 1 ­ if p(x) z constant ° deg p(x) K (p (x)) = Ž ° 1 if p(x) is a constant ¯ 5 4

K55 : W55 o [0,1] is given by

ÂŞa ÂŤ 5 ÂŤ0 K5 ÂŤ0 ÂŤ ÂŹ0

0 b 0 0

0 0 c 0

0º ­ 1 0  ° if ad bc z 0 = Ž ad bc 0 ° if ad bc 0  ¯ 1 dŸ

Clearly WK W1K1 Â&#x2030; W2 K2 Â&#x2030; W3K3 Â&#x2030; W4 K4 Â&#x2030; W5K5 is a special semigroup set fuzzy vector 5-subspace.

411

Now we proceed on to define the notion of special semigroup fuzzy linear n-algebra.

DEFINITION 4.2.30: Let V = (V1, V2, …, Vn)

= V11 , V21,...,Vn11 V12 , V22 ,..., Vn22

V

n 1

, V2n ,...,Vnnn

be a special semigroup set linear n-algebra over the semigroup S. Suppose K (K1 K2 Kn) 1 1 1 = K1 , K 2 ,...,Kn1 K12 , K 22 ,...,Kn22 K1n , K 2n ,...,Knnn :

1 1

1 2

1 n1

= W ,W ,...,W

W = (W1, W2, …, Wn) W12 , W22 ,...,Wn22 W1n , W2n ,...,Wnnn

(V1 V2 … Vn ) = V be a special semigroup set vector n-subspace of V over the set S. Let K (K1 K2 Kn) 1 1 1 = K1 , K 2 ,...,Kn1 K12 , K 22 ,...,Kn22 K1n , K 2n ,...,Knnn :

1 1

1 2

1 n1

= V , V ,...,V

V= (V1, V2, …, Vn) V12 , V22 ,..., Vn22 V1n , V2n ,...,Vnn2

o [0, 1] where Ki: Vi o [0,1] is a such that ViKi is a special semigroup set fuzzy linear algebra true for i = 1, 2, …, n. and K iji : V jii o [0, 1]; 1d ji d ni; i = 1, 2, …, n.

We call VK = V1K1 ,V2K2 ,...,VnKn to be a special semigroup set fuzzy linear n-algebra.

It is important at this juncture to mention that the notion of special semigroup set fuzzy vector n-space and special semigroup set fuzzy linear n-algebras are fuzzy equivalent.

However we shall illustrate by an example a special semigroup set fuzzy linear n-algebra.

412

Example 4.2.36: Let

= V , V ,V 1 1

1 2

1 3

V = (V1, V2, V3, V4) V12 , V22 , V32 , V42 V13 , V23

V , V , V , V , V

4 1

4 2

4 3

4 4

4 5

be a special semigroup set linear 4-algebra over the semigroup S = Zo = Z + {0} where V11 = Zo u Zo u Zo u Zo, V21 = {Zo[x] all polynomials in the variable x of degree less than or equal to 5}, V31 = {all 7 u matrices with entries from Zo}, V12 = Zo u Zo u Zo u Zo u Zo u Zo, V22 = {all upper triangular 5 u 5 matrices with entries from Zo}, ­§ a a a a a a · ½ °¨ ¸ o° V = ®¨ b b b b b b ¸ a, b,c Z ¾ , °¨ c c c c c c ¸ ° ¹ ¯© ¿ 2 3

V42 = {all 2 u matrices with entries from Zo},

­§ a °¨ °¨ b °°¨ c V13 = ®¨ °¨ d °¨ e °¨¨ ¯°© f

a b c d e f

½ a· ° ¸ b¸ ° ° c¸ o° ¸ a, b,c,d,e,f Z ¾ , d¸ ° ° e¸ ¸¸ ° f¹ ¿°

V23 = Zo u Zo u Zo, V14 = {Zo[x] all polynomials in the variable x with coefficient from Zo}, V24 = {all 3 u lower triangular matrices with entries from Zo}, V34 = {3 u matrices with entries from Zo},

V44 = {7 u matrices with entries from Zo} and V54 = Zo u Zo u Zo u Zo.

413

Define K K1, K2, K3, K4 K5) = K , K , K Â&#x2030; K12 , K22 , K32 , K42 Â&#x2030; K13 , K32 Â&#x2030; 1 1

1 2

1 3

K , K , K , K , K : V = (V1, V2, V3, V4) = V , V , V Â&#x2030; V , V , V , V Â&#x2030; V , V Â&#x2030; V , V , V , V , V o [0, 1] 4 1

1 1

4 2

1 2

4 3

4 4

1 3

4 1

4 2

4 5

2 1

2 2

2 3

4 3

4 4

4 5

2 4

3 1

3 2

such that, Ki:Vi o [0, 1], 1 d i d 4; Kiji : Vjii o [0, 1]; 1 d ji d ni 1 d i d 4.

Now K11 : V11 o [0, 1] such that ­ 1 if abcd z 0 ° K (a b c d) = Ž abcd °¯ 1 if abcd 0 1 1

K12 : V21 o [0,1] defined by 1 ­ if p(x) z constant ° K (p(x)) = Ž deg p(x) ° 1 if p(x) is a constant ¯ 1 2

K13 : V31 o [0,1] is given by ÂŞa ÂŤc ÂŤ ÂŤe 1 ÂŤ K3 ÂŤ g ÂŤi ÂŤ ÂŤk ÂŤm ÂŹ

bº d  f ­ 1 if abcdef ifklmn z 0  ° h  = Ž abcdef ifklmn 1 if abcdef ifklmn 0 j  °¯  l n Ÿ

K12 : V12 o [0, 1] such that

414

­ abc ° def 1 if def z 0 ° K12 (a b c d e f) = Ž def ° abc 1 if abc z 0 ° ¯ 1 if abc 0 or def

0 or abc def

K22 : V22 o [0, 1] given by ÂŞa ÂŤ0 ÂŤ K22 ÂŤ 0 ÂŤ ÂŤ0 ÂŤÂŹ 0

b e 0 0 0

c d eº f g h  ­ 1 if aehjl z 0 ° h i j  = Ž aehjl  0 j k  °¯ 1 if aehjl 0 0 0 l Ÿ

K32 : V32 o [0, 1] defined by ªa a a a a a º ­ 1 if abc z 0 ° K  b b b b b b  = Ž abc  c c c c c c Ÿ °¯ 1 if abc 0 2 3

K24 : V42 o [0,1] is given by ­ 1 if agbh z 0 ªa b c d e f º ° K  = Ž agbh   g h i j k l Ÿ ° 1 if agbh 0 ¯ 2 4

K13 : V13 o [0,1] defined that

§a ¨ ¨b ¨c K13 ¨ ¨d ¨e ¨¨ Šf

a b c d e f

a¡ ¸ b¸ c¸ ¸= d¸ e¸ ¸ f ¸š

­ 1 if abcdef z 0 ° Ž abcdef °¯ 1 if abcdef 0

415

0

K32 : V23 o [0, 1] is defined by ­ 1 if abc z 0 ° K (a b c) = ® abc °¯ 1 if abc 0 3 2

K14 : V14 o [0, 1] is given by

1 ­ ° deg p(x) if p(x) z constant °° K14 (p(x)) = ® 1 if k z 0 and p(x) k ° k ° if k 0 °¯ 1 K42 : V24 o [0, 1] is such that

ª a 0 0º ­ 1 if abde z 0 ° K «« b c 0 »» = ® abde «¬ d e f »¼ °¯ 1 if abde 0 4 2

K34 : V34 o [0, 1] is defined by

§a b c d e f g· ¨ ¸ K ¨h i j k l m n¸ = ¨p q r s t u v¸ © ¹ ­ 1 ° ajv if ajv z 0 ° ° 1 if ajv 0 and bku z 0 ° ® bku ° 1 if ajv 0 , bku 0, gmt z 0 ° ° gmt ° 1 if ajv 0 , bku 0, gmt 0 ¯ 4 3

416

K44 : V44 o [0, 1] is given by §a h ¡ ¨ ¸ ¨b i ¸ ¨c j ¸ ­ 1 if ah gn dk z 0 ¸ ° 4 ¨ K4 ¨ d k ¸ = Ž ah gn dk ¨ e l ¸ °¯ 1 if ah gn dk 0 ¨ ¸ ¨f m¸ ¨g n ¸ Š š K54 : V54 o [0,1] is defined by

­ 1 if abcd z 0 ° K54 (a b c d) = Ž abcd °¯ 1 if abcd 0 Thus VK = V1K1 ,V2 K2 , V3K3 , V4 K4 is a special semigroup set fuzzy 4-linear algebra. The notion of special semigroup set fuzzy vector n-space and special semigroup set fuzzy linear nalgebra are fuzzy equivalent. Like wise the notion of special semigroup set vector n-subspace and special semigroup set linear n-subalgebras are fuzzy equivalent. Now we leave it for the reader as an exercise to define these notions and construct examples.

Now we proceed on to define the notion of special group set vector space defined over a set S. DEFINITION 4.2.31: Let V = (V1 Â&#x2030; V2 Â&#x2030; â&#x20AC;Ś Â&#x2030; Vn) = V11 , V21,...,Vn11 Â&#x2030; V12 , V22 ,..., Vn22 Â&#x2030; Â&#x2030; V1n , V2n ,...,Vnnn

be a special semigroup set n-vector space over the set S. Let K (K1 Â&#x2030; K2 Â&#x2030; Â&#x2030; Kn)= K11 , K 21 ,...,Kn11 Â&#x2030; K12 , K 22 ,...,Kn22 Â&#x2030; â&#x20AC;Ś

Â&#x2030; K1n , K 2n ,...,Knn : V = (V1, V2, â&#x20AC;Ś, Vn) = V11 , V21,...,Vn1 Â&#x2030; n

V , V ,..., V 2 1

2 2

2 n2

1

Â&#x2030; Â&#x2030; V , V ,...,V n 1

n 2

417

n n2

o [0,1] is such that

Ki:Vi o [0, 1] where ViK is a special group set fuzzy vector i

space, i = 1, 2, â&#x20AC;Ś, n; here K iji : V jii o [0,1] with 1 d ji d ni; i = 1, 2, â&#x20AC;Ś n. We call VK = V1K1 ,V2K2 ,...,VnKn to be a special group set fuzzy vector n-space.

We shall illustrate this by an example.

Example 4.2.37: Let V = (V1 Â&#x2030; V2 Â&#x2030; V3 Â&#x2030; V4 Â&#x2030; V5) = V11 , V21 Â&#x2030; V12 , V22 , V32 Â&#x2030;

V13 , V23 , V33 , V43 Â&#x2030; V14 , V24 Â&#x2030; V15 , V25

be a special group set vector 5-space over the set Z15. Here V11 = Z15 u Z15, V21 = {all 4 u matrices with entries from Z15}, V12 = Z15 u Z15 u Z15 u Z15 , V22 = {Z15[x] all polynomials in x of degree less than or equal to 15 with coefficients from Z15}, V32 = {all 4 u 4 matrices with entries from Z15}, V13 = {all 5 u lower triangular matrices with entries from Z15} V23 = Z15 u Z15 u Z15 u Z15 u Z15

­§ a b ¡ ½ °¨ ° ¸ °¨ a b ¸ ° °¨ a b ¸ ° ° ° ¨ ¸ V33 = Ž¨ a b ¸ a, b � Z15 ž , °¨ a b ¸ ° °¨ ° ¸ °¨ a b ¸ ° °¨ ° ¸ ¯Š a b š ¿

­§ a b c d ¡ ½ ° ° ¨ ¸ V43 = Ž¨ e f g h ¸ a, b,c,d,e,f ,g, h,i, j, k,l � Z15 ž , °¨ i j k l ¸ ° š ¯Š ¿ 418

V14 = Z15 u Z15 u Z15, V24 = {all 4 u diagonal matrices with entries from Z15} V15 = {all 3 u matrices with entries from Z15} and ­§ a ¡ ½ °¨ ¸ ° °¨ a ¸ ° °°¨ a ¸ °° V25 = Ž¨ ¸ a, b Â? Z15 ž . °¨ a ¸ ° °¨ b ¸ ° °¨¨ ¸¸ ° °¯Š b š ¿° Define K K1 Â&#x2030; K2 Â&#x2030; K3 Â&#x2030; K4 Â&#x2030; K5) = K11 , K12 , K13 Â&#x2030; K12 , K22 Â&#x2030; K13 , K32 , K33 , K34 Â&#x2030; K14 , K42 , K34 Â&#x2030;

K , K , K , K , K : V = (V1 Â&#x2030; V2 Â&#x2030; V3 Â&#x2030; V4 Â&#x2030; V5) = V , V Â&#x2030; V , V , V Â&#x2030; V , V , V , V

Â&#x2030; V , V Â&#x2030; V , V o [0,1]; 5 1

5 2

5 3

1 1

5 4

5 5

1 2

2 1

4 1

4 2

2 2

2 3

5 1

3 1

3 2

3 3

3 4

5 2

Ki: Vi o [0,1]; 1 < i < 5, K : Vjii o [0,1]; 1 < ji < ni, 1 < i < 5. i ji

Now K11 : V11 o [0,1] defined by ­1 if ab z 0 ° K11 (a b) = Ž ab °¯ 1 if ab 0 K12 : V21 o [0, 1] is given by ªa b K12  c  d

eº 1 ­ if abcd efgh z 0 f  ° = Ž abcd efgh g ° 1 if abcd efgh 0  ¯ hŸ

419

K12 : V12 o [0,1] is defined by ­ 1 ° abc if abc z 0 ° K12 (a b c d) = ® 1 ° d if abc 0, d z 0 ° ¯ 1 if d 0, abc 0 K22 : V22 o [0,1] such that

­ 1 ° p(x) if p(x) z k, k a constant °° K22 (p(x)) = ® 1 ° k if k z 0 and p(x) k ° if k 0 °¯ 1 K32 : V32 o [0,1] is defined by

­ 1 if afkq z 0 , dgjm 0 ª a b c dº ° « e f g h » ° afkg » = °® 1 K32 « if afkq 0 , dgjm z 0 « i j k l» ° « » ° dgjm ¬m n p q ¼ °¯ 1 if afkq 0 , dgjm 0 K13 : V13 o [0, 1] is given by §a 0 0 ¨ ¨b c 0 3 K1 ¨ d e f ¨ ¨g h i ¨k l m ©

0 0 0 j n

0· ¸ 0¸ 0¸ = ¸ 0¸ p ¸¹

420

­ 1 if abdgk z 0 ° ® abdgk ° 1 if abdgk 0 ¯

K32 : V23 o [0, 1] is defined by ­1 ° if ae z 0 K (a b c d e) = Ž ae °¯ 1 if ae 0 3 2

K33 : V33 o [0, 1] is given by ÂŞa ÂŤa ÂŤ ÂŤa 3 ÂŤ K3 ÂŤ a ÂŤa ÂŤ ÂŤa ÂŤa ÂŹ

bº b  b ­ 1 if a b z 0  ° b = Ž a b b  °¯ 1 if a b 0  b b Ÿ

K34 : V43 o [0, 1] is such that ­1 if a z 0 ªa b c d º ° a ° K34  e f g h  = Ž 1 if b z 0  i j k l Ÿ ° b ° ¯ 1 if a 0 and b 0 K14 : V14 o [0,1] is given by

1 ­ if ab bc ca z 0 ° K14 (a b c) = Ž ab bc ca °¯ 1 if ab bc ca 0 K42 : V24 o [0,1] is such that ªa 0 K42  0  0

0 b 0 0

0 0 c 0

0º ­ 1 0  ° if ad bc z 0 = Ž ad bc 0 ° if ad bc 0  ¯ 1 dŸ 421

K15 : V15 o [0,1] is defined by §a b c d e¡ ¨ ¸ K ¨g h i j k¸ = ¨ l m n p q¸ Š š 5 1

1 ­ ° ab hi pq if ab hi pq z 0 °° 1 Ž ° cd gh mn if ab hi pq 0 and cd gh mn z 0 ° 1 if ab hi pq 0 and cd gh mn 0 °¯ K52 : V25 o [0,1] is such that §a¡ ¨ ¸ ¨a¸ ¨ a ¸ °­ 1 if 4a 2b z 0 5 K2 ¨ ¸ = Ž 4a 2b ¨a¸ ° 1 if 4a 2b 0 ¨b¸ ¯ ¨¨ ¸¸ Šbš Thus VK = V1K1 , V2 K2 , V3K3 , V4 K4 , V5K5 is a special group set fuzzy 5-vector space. One of the major advantages of fuzzifying these concepts is many a times these notions become fuzzy equivalent, there by overcoming some disadvantages in practical application. These new algebraic structures find their application in computer engineering, web testing / processing, in fuzzy models, industries, coding theory and cryptology.

422

Chapter Five

SUGGESTED PROBLEMS

This chapter suggests 66 problems so that the reader becomes familiar with these new concepts 1. Let V = (V1, V2, V3, V4) where V1 = Z+[x], V2 = Z+ u Z+ u Z+ ­°§ a b ¡ ½ ° V3 = Ž¨ ¸ a, b,c,d � Z ž °¯Š c d š ¿° and ½ °­§ a a a a ¡ ° V4 = Ž¨ ¸ a�Z ž ¯°Š a a a a š ¿°

be a special semigroup set vector space over the set {2, 4, 6, â&#x20AC;Ś}. Find a generating 4-subset of V. Is V finite or infinite?

Find a proper special semigroup set vector subspace of V. 2. Obtain some interesting properties about special semigroup set vector spaces.

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3. Find the applications of the special semigroup set vector spaces to industries.

4. Let V = (V1, V2, V3, V4) where V1= Z8, V2 = Z6, V3 = Z9 and V4 = Z12, clearly V1, V2, V3 and V4 are semigroups under modulo addition. V is a special semigroup set vector space over the set S = {0, 1, 3, 5}. a. Find the n-generating subset of V. b. Find proper 4-subset of V which is a special semigroup set vector subspace of V. c. Is V a special semigroup set linear algebra over S? Justify your claim.

5. Let V = (V1, V2, V3, V4, V5) = (Z5, Z7, Z23, Z19, Z11) be a special semigroup set linear algebra over the additive semigroup = {0, 1}. Is V a doubly simple special semigroup set linear algebra or is V a special semigroup set strongly simple linear algebra? Give the generating 5 subset of V over S. Is V finite 5-dimensional?

6. Let V = (V1, V2, V3, V4) = (Z6, Z7, Z8, Z9) be a special semigroup set vector space over the set S = {0, 2, 3, 4}, Find a 4-generating subset of V. What is 4-dimension of V as a special semigroup set vector space over the set? How many special semigroup set vector subspaces V has? Can V have more than one 4-generating subset? Justify your claim.

7. Let

°­§ a ®¨ ¯°© a

§ ­°§ a b · ½° V = ¨ ®¨ a Z6 ¾ , (Z6 u Z6 u Z6), ¸ ¨ °© c a ¹ ¿° ©¯ ­§ a a · ½ °¨ ° ¸ a a¸ ° ° ¨ a a a a· ° °½ °¨ ¸ a Z

, a

Z a a ® ¾ ¸ 6 6¾, a a a a¹ ° ¿° °¨ a a ¸ ¸ °¨ ° °¯¨© a a ¸¹ ¿°

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{Z6 [x]; all polynomials of degree less than or equal to 5}) (V1, V2, V3, V4, V5) be a special semigroup set linear algebra over the semigroup S = Z6. a. Does V have special semigroup set linear subalgebras over S? b. Is V a special semigroup set simple linear algebra over Z6? c. Can V have special subsemigroup set linear subalgebra? d. Find a n-generating subset of V? e. Can V have more than one n-generating subset? f. What is the n-dimension of V? g. Is V a doubly strong special semigroup set linear algebra over S = Z6? h. Is V a special semigroup set strongly simple linear algebra?

8. Let V = (V1, V2, V3, V4, V5) where ­°§ a b · ½° V1 = ®¨ ¸ a, b,c,d Z7 ¾ , V2 = {Z7 u Z7 u Z7 u Z7}, ¯°© c d ¹ ¿° V3 = {Z7 [x]; all polynomials of degree less than or equal to three with coefficients from Z7}, ­§ a °¨ ° a V4 = ®¨ °¨¨ a °© a ¯

½ a· ° ¸ a¸ ° a Z7 ¾ a¸ ° ¸ ° a¹ ¿

and ­§ a 0 b · ½ °¨ ° ¸ V5 = ®¨ 0 c 0 ¸ a, b,c,d Z7 ¾ °¨ b 0 d ¸ ° ¹ ¯© ¿ are semigroups under addition. Thus V = (V1, V2 , … , V5) is a special semigroup set linear algebra over the semigroup Z7. a. Find a 5-generating 5-subset of V.

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b. How many 5-generating 5-subsets of V can be found in V? c. Is V a special semigroup set simple linear algebra? d. Is V a special semigroup set strongly simple linear algebra? e. Is V a doubly simple special semigroup set linear algebra? f. Can V have special semigroup set linear subalgebras?

9. Let V = (V1, V2, V3, V4, V5) , ­§ a a a a a ¡ ½ § ­°§ a b ¡ ½ ¸ ° °¨ ° a, b,c,d � Z ž , Ž¨ a a a a a ¸ a � Z ž , ¨ Ž¨ ¨ °Š c d ¸š ° ¯ ¿° °¨ a a a a a ¸ Š š ¯Š ¿ (Z+ u Z+ u Z+u Z+u Z+), {Z+[x], all polynomials of degree less than or equal to 3},

­§ a °¨ °¨ a ° Ž¨ a °¨ a °¨ °¯Š¨ a

a

a

a

b b b b b b b b b b b b

½¡ a¡ °¸ ¸ b¸ °¸ °¸ b ¸ a, b � Z ž ¸ ¸ °¸ b¸ °¸ b š¸ °¿ ¸š

be a special semigroup set vector space defined over the set Z+. Let W = (W1, W2, W3, W4, W5) where ­§ a °¨ °¨ a ­°§ a a ¡ ½ ° ° W1 = Ž¨ ¸ a � Z ž , W2 = Ž¨ a °¯Š a a š °¿ °¨ a °¨ °¯¨Š a

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½ a a¡ ° ¸ a a¸ ° ° ¸ a a a�Z ž, ¸ ° a a¸ ° a a ¸š ¿°

W3 = {Z+ [x] all polynomials of degree less than or equal to 4 with coefficients from Z+}, W4 = (Z+ u Z+ u Z+u Z+) and ­§ a °¨ °¨ b ° W5 = ®¨ c °¨ d °¨ ¨ ¯°© c

e· ¸ i¸ g j k l ¸ the entries are from Z+} ¸ h k m n¸ i l n p ¸¹ b c f g

d h

be a special semigroup set vector space over the same set S = Z+. How many different types of special semigroup set linear transformations (T1, T2, T3, T4, T5) = T can be got from V to W. If U = (U1 U2, …, U5) is a set linear transformation from W to V can we find any relation between U and V? 10. Let V = (V1, V2, V3) and W = (W1, W2, W3) where V1 = {Z15 u Z15 u Z15}, °­§ a b e · °½ V2 = ®¨ ¸ a, b,c,d Z15 ¾ ¯°© c d f ¹ ¿° and V3 = {Z15 [x] all polynomials of degree less than 5} is a special semigroup set vector space over the set Z15. W = (W1, W2, W3) where ­°§ a b · ½° W1 = ®¨ ¸ a, b,c Z15 ¾ , ¯°© 0 c ¹ ¿° ­§ a a · ½ °¨ ° ¸ W2 = ®¨ a a ¸ a Z15 ¾ °¨ a a ¸ ° ¹ ¯© ¿ and W3 = {Z15 u Z15 u Z15 u Z15 u Z15 u Z15} is again a special semigroup set vector space over the set Z15. Find

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special semigroup linear transformation from V to W. How many such special semigroup linear transformations be made from V to W? Let SH Z12 (V, W) denote the collection of all such special semigroup linear transformations from V to W. What is the algebraic structure enjoyed by SH Z12 (V, W)?

11. Let V = (V1, V2, V3, V4) where ­° § a b ¡ ½ ° V1 = Ž ¨ ¸ a, b,c,d � Z ž , °¯ Š c d š ¿° ­§ a °¨ ° b V2 = Ž¨ °¨¨ c °Š d ¯

½ 0 0 0¡ ° ¸ e 0 0¸ ° a, b,c,d,e,f ,g, p,g, h � Z ž , f g 0¸ ° ¸ ° q p hš ¿

V3 = {Z+ u Z+ u Z+} and V4 = {All polynomial of degree less than or equal to 2 with coefficients from Z+}. V1, V2, V3 and V4 are semigroups under addition. Thus V is a special semigroup set vector space over the set S = Z+. a. Find T = (T1, T2, T3, T4), a special semigroup set linear operator on V. b. If SHoms(V, V) = {Homs(V1, V2), Homs(V2, V4), Homs (V3, V1), Homs(V4, V3)}. What is the algebraic structure of SHoms(V, V)? c. If CS (Homs(V, V)) = {SHoms(V, V)}, what can we say about the algebraic structure of CS (Homs(V, V))?

12. Let V = (V1, V2, V3, V4, V5) where V1 = Z12; V2 = Z14 ; V3 = Z16; V4 = Z7 and V5 = Z10 are semigroups under modulo addition. V is a special semigroup set vector space over the set S = {0, 1}. Find SHoms (V, V) = {Homs (V1, V2), Homs (V2, V3), Homs (V4, V5), Homs (V5, V1), Homs (V3, V4)}. Is Homs (V, V) a special semigroup set vector space over S =

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{0, 1}? What is the structure of CS (Homs (V, V)) = {SHoms (V, V)}?

13. Let V = (V1, V2, V3, V4, V5) where ­§ a b c ¡ ½ °¨ ¸ ° V1 = Ž¨ d e f ¸ a, b,c,d,e,f ,g, h,i � Z ž , °¨ g h i ¸ ° š ¯Š ¿ V2 = {Z+ u Z+ u Z+ u Z+ u Z+}, V3 = {Z+ [x]; all polynomials of degree less than or equal to 5}, ­§ a a a a a ¡ ½ °¨ ¸ ° V4 = Ž¨ b b b b b ¸ a, b,c � Z ž °¨ c c c c c ¸ ° š ¯Š ¿ and ­§ a °¨ ° b V5 = Ž¨ °¨¨ c °Š d ¯

½ 0 0 0¡ ° ¸ e 0 0¸ ° a, b,c,d,e,f ,g, h,i, j � Z ž . f g 0¸ ° ¸ ° h i jš ¿

V1, V2, V3, V4 and V5 are semigroups under addition. V is a special semigroup set linear algebra over the semigroup Z+.

Let W = (W1, W2, W3, W4, W5) where ­§ a °¨ °¨ b ° W1 = Ž¨ b °¨ c °¨ °¯¨Š c

½ a a¡ ° ¸ b b¸ ° ° b b ¸ a, b,c � Z ž , ¸ ° c c¸ ° c c ¸š ¿°

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­§ a a a ¡ ½ °¨ ¸ ° W2 = Ž¨ b b b ¸ a, b,c � Z ž , °¨ c c c ¸ ° š ¯Š ¿ W3 = {2Z+ u Z+ u 3Z+ u 5Z+ u 7Z+}, W4 = {Z+[x]; all polynomials of degree less than or equal to 5} and ­§ 0 a a a ¡ ½ °¨ ° ¸ °¨ b 0 c c ¸ ° W5 = Ž a, b,c,d � Z ž , °¨¨ b c 0 d ¸¸ ° °Š b c d 0 š ° ¯ ¿ W = (W1, W2, W3, W4, W5) is a special semigroup set linear algebra over the same semigroup Z+. Define T = (T1, T2, T3, T4, T5) from V to W such that T is a special semigroup set linear transformation of V to W. If SHom Z (V, V) = { Hom Z (V1, W2), Hom Z (V2, W3), Hom Z (V3, W4), Hom Z (V4, W1), Hom Z (V5, W5)}, prove SHom Z (V, V) is atleast a special semigroup set vector space over the set Z+.

14. Obtain some interesting results about SHoms(V, W) and SHoms (V, V).

15. Let V = V = (V1, V2, V3, V4) where V1 = {Z10 u Z10 u Z10` ­§ a b c ¡ ½ °¨ ° ¸ V2 = Ž¨ d e f ¸ a, b,c,d,e,f ,g, h,i � Z10 ž , °¨ g h i ¸ ° š ¯Š ¿ V3 = {Z10[x]; set of all polynomials of degree less than or equal to 7} and

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­§ a b c d · ½ °¨ ° ¸ ° 0 0 e f¸ ° V4 = ®¨ p,q,g, b,a, bc,de,f Z10 ¾ . ¨ ¸ °¨ 0 0 g h ¸ ° °© 0 0 p q ¹ ° ¯ ¿ V1, V2, V3, V4 are semigroups under addition.

Define T = (T1, T2, T3, T4) from V to V. Suppose SHom Z10 (V, V) = { Hom Z10 (V1, V1), Hom Z10 (V2, V2), Hom Z10 (V3, V3), Hom Z10 (V4, V4)}. Is SHom Z10 (V, V) a special semigroup set linear algebra over the semigroup Z10? What is the algebraic structure of { SHom Z10 (V, V)}?

16. Let V = (V1, V2, V3, V4) be a special semigroup set vector space over the set S. Suppose W = (W1, W2, W3, W4), a proper 4-subset of V and W1 V1, W2 V2, W3 V3 and W4 V4 are subsemigroups and W = (W1, W2, W3, W4) is a special semigroup set vector subspace of V. Define P = (P1, P2, P3, P4) from V into W such that each Pi : Vi o Wi ; 1 d i d 4 is a projection. Prove P. P = P. 17. Let V = (V1, V2, …, Vn) be a special semigroup set linear algebra over a semigroup S. If W = (W1, W2, … , Wn) be a proper subset of V which is a special semigroup set linear subalgebra of V over S. If P = (P1, P2, ... , Pn) is defined such that Pi : Vi o Wi where each Pi is a projection of Vi to Wi, 1 d i d n. Show P is a special semigroup set linear operator on V and P . P = P. 18. Obtain some interesting results about the special semigroup set linear operators which are projection on V. 19. Let V = (V1, …, Vn). If each Vi = W1i " Wtii ; 1 d i d n

then we write V = ( W11 " Wt11 , W12 " Wt22 , … , W1n " Wtnn ) and call this as a special semigroup set direct sum of the special semigroup set linear algebra.

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Using this definition, write V = (V1, V2, V3, V4) where V1 = {Z+ u Z+ u Z+ u Z+ u Z+} ½ °­§ a b ¡ ° V2 = Ž¨ ¸ a, b,c,d � Z ž , ¯°Š c d š ¿° V3 = {(a a a a) | a � Z+} and V4 = {3Z+ u 2Z+ u 5Z+} as a direct sum of special semigroup set linear subalgebras.

20. Let V = (V1, V2, V3) where ­§ a b c ¡ ½ °¨ ° ¸

V1 = Ž¨ d e f ¸ a, b,c,d,e,f ,g, h,i Â? Z Â&#x2030; {0}ž , °¨ g h i ¸ ° š ¯Š Âż V2 = {Z+ Â&#x2030; {0} u Z+ Â&#x2030; {0} u Z+ Â&#x2030; {0}} and ­§ a °¨ ° b V3 = Ž¨ °¨¨ d °Š g ÂŻ

½ 0 0 0¡ ° ¸ c 0 0¸ °

a, b,c,d,e,f ,g, h,i, j Â? Z Â&#x2030; {0}ž e f 0¸ ° ¸ ° h i jš Âż

are semigroups under addition. V = (V1, V2, V3) is a special semigroup set linear algebra over the semigroup Z+ Â&#x2030; {0}. ­ V = °Ž ° ÂŻ

V2 =

§a b c¡ ½ ­ ¨ ¸ ° ° ¨ 0 0 0¸ ž Â&#x2020; ÂŽ ¨ 0 0 0¸ ° ° Š š Âż ÂŻ

^Z

§ 0 0 0¡ ½ ­ ¨ ¸ ° ° ¨d e f ¸ žÂ&#x2020; ÂŽ ¨ 0 0 0¸ ° ° Š š Âż ÂŻ

§ 0 0 0¡ ½ ¨ ¸ ° ¨ 0 0 0¸ ž ¨g h i ¸ ° Š š ¿

= W11 Â&#x2020; W21 Â&#x2020; W31 .

` ^ {I} u Z

Â&#x2030; {0} u {I} u {I} Â&#x2020;

= W Â&#x2020;W 2 1

and

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2 2

Â&#x2030; {0} u Z Â&#x2030; {0}

`

­ ° ° V3 = ÂŽ ° ° ÂŻ ­ ° ° Â&#x2020;ÂŽ ° ° ÂŻ

§a ¨ ¨b ¨d ¨ Šg §0 ¨ ¨0 ¨0 ¨ Š0

0 0 0¡ ¸ 0 0 0¸ 0 0 0¸ ¸ 0 0 0š 0 0 0¡ ¸ 0 0 0¸ 0 f 0¸ ¸ 0 i jš

½ ­ § 0 0 0 0¡ ° ° ¨ ¸ ° ° ¨ 0 c 0 0¸ Â&#x2020; ž ÂŽ ¨ ° ° ¨ 0 e 0 0 ¸¸ ° ° Š 0 h 0 0š Âż ÂŻ ½ ° ° 3 3 3 ž = W1 Â&#x2020; W2 Â&#x2020; W3 ° ° Âż

½ ° ° ž ° ° ¿

Now W = W11 , W22 , W33 is a special semigroup set linear subalgebra of V over Z+ Â&#x2030; {0}. a. Define special semigroup set linear projection P1 from V to W show P1 o P1 = P1. b. Let R = W21 , W12 , W23 be a special semigroup set linear subalgebra of V over Z+ Â&#x2030; {0}. Define a special semigroup set linear projection P2 from V into R. 21. Let V = (V1, V2, V3, V4) where ­§ a b c ¡ ½ °¨ ° ¸

V1 = Ž¨ d e f ¸ a, b,c,d,e,f ,g, h,i Â? Z Â&#x2030; {0}ž , °¨ g h i ¸ ° š ¯Š Âż V2 = {Z+ Â&#x2030; {0} u Z+ Â&#x2030; {0} u Z+ Â&#x2030; {0} u Z+ Â&#x2030; {0}}, °­§ a b c d e f ¡ °½

V3 Ž¨ ¸ a, b,c,d,e,f ,g, h,i, j, k,l Â? Z Â&#x2030; {0}ž ¯°Š g h i j k l š ¿° and ­§ a a ¡ ½ °¨ ° ¸ °¨ a a ¸ ° ° ° V4 = Ž¨ a a ¸ a Â? Z Â&#x2030; {0}ž °¨ a a ¸ ° ¸ °¨ ° °¯¨Š a a ¸š ¿°

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be a semigroup. Write V as a direct sum of special semigroup set linear subalgebras. Define projections using them.

22. Obtain some interesting results about the direct sum of special semigroup set linear algebras.

23. Let V = (V1, V2, V3, V4) be a special semigroup set linear algebra over the semigroup Zo where V1 = {Zo u Zo u Zo u Zo u Zo} where Zo = Z+ Â&#x2030; {0}, V2 = {Zo[x] | all polynomials of degree less than or equal to 4 with coefficients from Z+ Â&#x2030; {0} = Zo}. ­§ a b c ¡ °¨ ¸ V3 = Ž¨ d e f ¸ a, b,c,d,e,f ,g, h,i Â? Zo °¨ g h i ¸ š ¯Š and ­§ a a a ¡ ½ °¨ ° ¸ °¨ a a a ¸ ° °°¨ a a a ¸ ° o° V4 = Ž¨ ¸ aÂ?Z ž . °¨ a a a ¸ ° °¨ a a a ¸ ° ¸¸ °¨¨ ° °¯Š a a a š ¿°

½ ° Z Â&#x2030; {0}ž ° Âż

Find special subsemigroup linear subalgebra over some proper subsemigroup of Zo. Write V also as a direct sum.

Define SHom Zo (V, V) = { Hom Zo (V1, V2), Hom Zo (V2, V1), Hom Zo (V3, V3), Hom Zo (V4, V4)}. Can SHom Zo (V, V) be a special semigroup linear algebra over Zo? Justify your claim.

24. Find some interesting properties of special set vector spaces.

25. Find a special set vector space of dimension (1, 2, 3, 4, 5).

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26. Given V = (V1, V2, V3, V4, V5) where V1 = {So u So u So | So = 3Z+ Â&#x2030; {0}}, ­°§ a b ¡ ½°

V2 = Ž¨ ¸ a, b,c,d Â? 5Z Â&#x2030; {0}ž , °¯Š c d š ¿° °­§ a V3 = Ž¨ 1 ¯°Š a 5

a2 a6

a3 a7

½° a4 ¡

¸ a i Â? Z Â&#x2030; {0};1 d i d 8ž , a8 š ¿°

V4 = {(a a a a a) (a a a a a a a) | ai Â? Z+ Â&#x2030; {0}} and

V5

­ §a b °§ a a a ¡ ¨ °¨ ¸ 0 e Ž¨ a a a ¸ , ¨¨ °¨ a a a ¸ ¨ 0 0 š 0 0 °Š Š ¯

½ c d¡ ° ¸ f g¸ ° a, b,e,c,d,e,f ,g, h,i, j Â? Z Â&#x2030; {0}ž ¸ h i ° ¸ ° 0 fš Âż

is special set vector space over the set So = Z+ Â&#x2030; {0}. Find at least two distinct special set vector subspaces of V. What is the special dimension of V? Find a special generating subset of V. 27. Obtain some interesting properties about the special set linear algebra.

28. Does there exist a special set linear algebra which has no special set linear subalgebra?

29. Let V = (V1, V2, V3, V4, V5, V6) where V1 = {(1 1 1 1 1 1), (0 0 0 0 0 0), (1 1 0 1 1 0), (1 0 1 0 1 0), (1 1 1 1), (0 0 0 0), (1 1 1 0), (0 1 1 0)}, °­§ a V2 = Ž¨ 1 ¯°Š a 5

a2 a6

a3 a7

a4 ¡ ¸ a � Z2 a8 š i

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°½ {0,1} 1 d i d 8ž , ¿°

­°§ a b · ½° V3 = ®¨ ¸ a, b,c,d Z2 {0,1}¾ , °¯© c d ¹ °¿ ­§ a1 °¨ ° a V4 = ®¨ 3 °¨¨ a 5 °© a 7 ¯

a2 · ¸ a4 ¸ a Z2 a6 ¸ i ¸ a8 ¹

½ ° ° {0,1} 1 d i d 8¾ , ° ° ¿

V5 = {all polynomials in the variable x of degree less than or equal to 8 with coefficients from Z2} and V6 = {Z2 u Z2 u Z2 u Z2 u Z2u Z2} be a special set vector space over the set S = {0, 1}. a. Find a special generating subset of V. b. What is the special dimension of V? c. Find atleast 5 distinct special set vector subspaces of V. d. Define a special set linear operator on V. 30. Give some interesting properties about special set n-vector spaces defined over a set S. 31. Give an example of a special set n-linear algebra which is not a special set n-vector space defined over a same set S. 32. Let V = V1 V2 V3 V4 where V1 = V11 , V21 , V2 =

V , V , V , V3 = V , V , V , V , V and V4 = V , V

2 1

2 2

2 3

3 1

3 2

3 3

3 4

3 5

4 1

4 2

defined over the set S = 3Z+ {0}. Take ½° °­§ a b · V11 = ®¨ ¸ a, b,c,d in S¾ , ¯°© c d ¹ ¿°

V21 = {S u S u S}, so that V1 = V11 , V21 is a special set vector space over S. V12 = {5 u 5 matrices with entries from S}. V22 = {S u S u S u S u S}, V32 = {set of all 4 u 2 matrices

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with entries from S}. V42 = {(a a a a), (a a a) | a Â? S} and V52 = {all polynomials in the variable x with coefficients from S of degree less than or equal to 4};V2 = V12 , V22 , V32 , V42 , V52

is a special set vector space over S. Take in V3, V13 = {S u S u S u S}, V23 = {3 u 3 matrices with entries from S}, V33 = {4 u 4 upper triangular matrices with entries from 5}, ­ ½ §a a¡ °§ a a a a ¡ ¨ ° ¸ V = Ž¨ ¸ , ¨ a a ¸ a � Sž °Š a a a a š ¨ a a ¸ ° Š š ¯ ¿ 3 4

and V53 = {S [x]; all polynomials in x with coefficients from S of degree less than or equal to 5}, V3 is a special set vector space over S. Finally define V14 = {S u S u S} and V24 = {3 u upper triangular matrices with entries from S}; V4 is a special set vector space over S. Thus V = V1 Â&#x2030; V2 Â&#x2030; V3 Â&#x2030; V4 is a special set vector 4-space over S. a. Find 3 special set vector 4-subspaces of V. b. Find special set generating 4-subset of V. c. What is special set 4-dimension of V over S? 33. Let V = V1 Â&#x2030; V2 Â&#x2030; V3 Â&#x2030; V4 be a special set 4-vector space defined over the set {0, 1} = S, where V1 = V11 , V21 , V11 = {2 u matrices with entries from S}, V12 = {S u S u S u S u S}, V2 = V12 , V22 , V32 with

­§ a b ¡ ½ °¨ ° ¸

V = Ž¨ c d ¸ a, b,e,c,d,e,f Â? Z Â&#x2030; {0}ž , °¨ e f ¸ ° š ¯Š Âż 2 1

437

V22 = {S [x]; polynomials of degree less than or equal to 7 over S = {0, 1}}, V32 = {7 u matrices with entries from S

= {0, 1}}, V3 = V13 , V23 , V33 , V43 where

­§ a ¡ ½ °¨ ¸ ° °¨ b ¸ ° ° ° V13 = Ž¨ c ¸ a, b,e,c,d,e � S {0,1}ž , °¨ d ¸ ° °¨ ¸ ° °¯¨Š e ¸š °¿ V23 = {set of all 5 u 5 upper triangular matrices with entries from S = {0, 1}},

­§ a b ¡ ½ °¨ ° ¸ ªa a a a º ° °¨ a d ¸ 3 V3 = Ž a, b,d,f ,g � S {0,1} and  ž , b d f g Ÿ ° °¨¨ a f ¸¸ °Š a g š ° ¯ ¿ V43 = {S u S u S} V4 = V14 , V24 , V34 where V14 = {set of all

4 u 4 lower triangular matrices with entries from the set {0, 1}}, V24 = {S u S u S u S} and ­ §a ° ¨ ° ªa a a a a º ¨ a 4 V3 = Ž  , °  a a a a a Ÿ ¨¨ a ° Ša ¯

½ a¡ ° ¸ a¸ °

a Â? Z Â&#x2030; {0}ž . a¸ ° ¸ ° aš Âż

a. Give 3 distinct special set fuzzy vector 4-spaces of V. b. Find at least 2 proper special set vector 4-subspaces of V.

438

c. Find at least 4 proper special set fuzzy vector 4 subspaces of V. d. Define a special set linear 4-operator on V. e. Define a pseudo special set linear 4-operator on V. 34. Obtain some interesting results about special set fuzzy nvector spaces.

35. For the special set linear bialgebra V = V1 V2 given by V1 = V11 , V21, V31 , V41 and V2 = V12 , V22 , V32 , V42 , V52 defined over the set S = Z+ {0} where V11 = {5 u 5 matrices with entries from S}, ½° °­ ª a a a a º V21 = ® « a S¾ , » ¯° ¬ a a a a ¼ ¿° ­ ªa a º ½ °« ° » V = ® « a a » a S¾ , ° «a a » ° ¼ ¯¿ 1 3

V41 = {S[x] set of all polynomials of degree less than or equal to 5}, V12 = {S u S u S u S}, V22 = {set of all 4 u 4 lower triangular matrices with entries from S}, V32 = {all polynomials of degree less than or equal to 3}, ­ ªa º ½ °« » ° ° a ° V42 = ® « » a Zo ¾ « » ° a ° ° «¬ a »¼ ° ¯ ¿ and V52 = {set of all 5 u 7 matrices with entries from Zo}.

Obtain 3 different special set fuzzy linear bialgebras associated with V.

439

Find 5 different special set fuzzy linear subbialgebra associated with a proper set linear subalgebra W = W1 W2.

36. Define a special set trialgebra and illustrate it by an example.

37. Let V = V1 V2 V3 = V11 , V21, V31 V12 , V22 , V32 , V42

V13 , V23 , V33 , V43 , V53 where V11 = {3 u 3 matrices with

entries from S = Zo = Z+ {0}}, V21 = {2 u 6 matrices with entries from S = Zo = Z+ {0}}, V31 = {S u S u S u S}, V12 = {[a a a a a] | a S}, ­ ªa º ½ °« » ° ° a ° V22 = ® « » a S¾ , « » a ° ° ° «¬ a »¼ ° ¯ ¿ V32 = {4 u 4 lower triangular matrices with entries from S}, V42 = {7 u 2 matrices with entries from S}, V13 = S u S u S,

½° °­ ª a a a a a º V23 = ® « a, b S¾ , » ¯° ¬ b b b b b ¼ ¿° ­ªa d º ½ °« ° » V = ® « b e » a, b,c,d,e,f S¾ , °« c f » ° ¼ ¯¿ 3 3

­° ª a V43 = ® « 1 ¯° ¬ a 4

a2 a5

½° a3 º a i S, 1 d i d 6 ¾ » a6 ¼ ¿°

and

440

V53 ={[a a a a a a a] | a Â? S} be a special set linear trialgebra over S = Zo = Z+ Â&#x2030; {0}}.

Find a special set fuzzy linear trialgebra VK. If W is a special set linear trisubalgebra of V find the special set fuzzy linear trisubalgebra WK. Define a special set linear trioperator on V. Find a pseudo special set trivector subspace of P of V and find its fuzzy component, PK. Find a pseudo special set linear trioperator on V. 38. Let V = V1 Â&#x2030; V2 Â&#x2030; V3 Â&#x2030; V4 be a special set vector space over the set S = Z+ Â&#x2030; {0} where ­ ÂŞa Âş ½ ° Âť ° V1 = ÂŽ ÂŤ a Âť , > a a a a @ a Â? Sž , ° ÂŤa Âť ° ÂŻÂŹ Âź Âż ­° ÂŞ a b Âş § a a a ¡ o V2 = ÂŽ ÂŤ ,¨ ¸ a, b,c,d,Â? Z Âť °¯ ÂŹ c d Âź Š a a a š

½° S Z Â&#x2030; {0}ž , °¿

V3 = {all polynomials of degree less than or equal to 4 with coefficient from S} and V4 = {5 u 5 upper triangular matrices with entries from S}.

Find VK the special set fuzzy vector space.

Find a special set vector subspace W of V and find WK the special set fuzzy vector space.

Find at least 4 distinct special set fuzzy vector space.

Find 3 distinct special set fuzzy vector subspaces of W. 39. Obtain some interesting results about special set fuzzy vector spaces.

40. Given V = V1 Â&#x2030; V2 Â&#x2030; V3 is a special set trilinear algebra over the set S = {0, 1} where V1 = {S u S u S u S u S}, V2 = {All 5 u 5 matrices with entries from S} and V3 = {3 u 8 matrices with entries from S}.

441

a. Find VK the special set fuzzy linear trialgebra. b. Find a special set linear trisubalgebra W of V over S = {0, 1}, Find WK the special set fuzzy linear trisubalgebra. c. Find a special set pseudo trivector space W of V and find WK. d. Define a special set linear operator on V. e. Find a special set pseudo linear operator on V. f. Find atleast 3 special set linear subalgebra and their fuzzy analogue.

41. Obtain some interesting properties about the special semigroup set vector n-spaces.

42. Give an example of a special semigroup set vector 6-space.

43. Let V = V1 V2 V3 V4 be a special semigroup set vector 4 space over the set S = Z+ {0} where ­°§ a b · V11 = ®¨ ¸ such that a, b, c, d, S}, ¯°© c d ¹ V21 = {2 u 6 matrices with entries from S}, V31 = S u S u S u S u S and V41 = {7 u 2 matrices with entries from S, i.e., V1

= V11 , V21, V31, V41 and V2 = V12 , V22 , V32 where V12 = {4 u 4 upper triangular matrices with entries from S} V22 = S u S

u S u S u S and V32 = {7 u 3 matrices with entries from S}.

V3 = V13 , V23 where V13 = {all polynomials in the variable

x such that the coefficients are from S and every polynomial is of degree less than 5}, V23 = {4 u 4 lower triangular

matrices with entries from S}, V4 = V14 , V24 , V34 , V44 , V54

where V14 = {3 u 3 upper triangular matrices with entries from S}, V24 = S u S u S u S u S,

442

­ªa a a a a º ½ °« ° » V = ® « b b b b b » a, b,c S¾ , °« c c c c c » ° ¼ ¯¿ 4 3

­ ªa °« ° «a °° « a V44 = ® « ° «a ° «a °« °¯ ¬« a

½ bº ° » b» ° °° b» » a, b S¾ b» ° » ° b » ° b ¼» ¿°

and a. b. c. d. e. f. g.

V54 = {a0 + a1x + a2 x2 + a3 x3 | a0, a1, a2, a3 S}.

Find three proper special semigroup set vector 4subspaces of V. Find a proper generating special semigroup set of V. Write V as a direct sum.

Find SHom (V, V).

Define a pseudo special semigroup set linear 4-operator on V. Can V be made into a special set linear n-algebra?

Define on V a special semigroup set linear 4- operator P such that P2 = P. Is P a projection on V?

44. Prove SHom(V, V) is a special group set vector n-space over a set S if V is a special semigroup set vector n-space over the set S. 45. Is PSHom(V, V) a special semigroup set vector n-space where PSHom(V, V) denotes the collection of all pseudo special semigroup set linear n-operator of V, V a special semigroup set vector space over S. a. What is the difference between SHom(V,V) and PSHom (V, V)? b. Which is of a larger n-dimension SHom(V,V) and PSHom (V, V)?

443

46. Let V = V1 V2 V3 and W = W1 W2 W3 be two special semigroup set vector trispaces over the set {0, 1}.

Here V = V1 V2 V3 1 1 = V1 , V2 V12 , V22 , V32 V13 , V23 , V33

and = W , W 1 1

1 2

W = W1 W2 W3 W12 , W22 , W32 W13 , W23 , W33

with V11 = {S u S u S u S / S = Z+ {0}, V21 = {all 3 u 3 matrices with entries from 2S}, V12 = {S u S u S}, V22 = {all 4 u 4 upper triangular matrices with entries from S}. ­ªa º ½ °« » ° °«b» ° 2 V3 = ® a, b,c,d 5S¾ , °« c » ° ° «¬ d »¼ ° ¯ ¿ V13 = S u S u S u S u S, V23 = {all 3 u 3 lower triangular matrices with entries from S}. ­ªa º ½ ­°§ a b · ½° °« » ° V = ® « b » a, b,c S¾ . W11 = ®¨ ¸ a, b,c,d S¾ , °« c » ° ¯°© c d ¹ ¿° ¯¬ ¼ ¿ 3 3

W12 = {all polynomials in x of degree less than or equal to 8 with coefficients from S},

­°§ a b · ½° W12 = ®¨ ¸ a, b,c S¾ , °¯© 0 c ¹ ¿°

W22 = {5 u 2 matrices with entries from S}, W32 = S u S u S u S,

444

­§ a b c · ½ °¨ ° ¸ W = ®¨ 0 0 d ¸ a, b,c,d,f S¾ , °¨ 0 0 f ¸ ° ¹ ¯© ¿ 3 1

­°§ a W23 = ®¨ 1 ¯°© a 4 and W33 =

a2 a5

^ a

½° a3 · ¸ a i S 1 d i d 6¾ a6 ¹ ¿°

`

a a a S .

a. Find SHom(V, W): Is SHom(V, W) a special semigroup set vector trispace over S? b. Find SHom(V, V). c. Find SHom(W, W). d. Does there exist any relation between SHom(V, V) and SHom(W, W)? e. What is the tridimension of SHom(V, V) and SHom (W, W)? f. Find two special semigroup set vector trisubspaces V1 and V2 of V. g. Find two special semigroup set vector trisubspaces W1 and W2 of W. h. Find SHom(W1, V1). i. Find SHom(W2, V2). j. Is SHom(W1, V1) same as SHom(V1, W1)? Justify your claim. k. Find a special semigroup set linear projection of V into V1.

47. Let V = V1 V2 V3 V4 where V1 = V11 , V21, V31 , V2 = V12 , V22 , V32 , V42 , V3 = V13 , V23 and V4 = V14 , V24 , V34 , V44

be a special semigroup set linear 4-algebra over the semigroup Z12. Here

445

­°§ a b · ½° V11 = ®¨ ¸ a, b,c,d Z12 ¾ , °¯© c d ¹ ¿° V21 = {Z12 u Z12 u Z12}, V31 = {S u S u S u S u S | S = {0, 2, 4, 6, 8, 10} Z15}, ­°§ a 0 · °½ V12 = ®¨ ¸ a, b,c Z12 ¾ , ¯°© b c ¹ ¿°

V22 = {all polynomials in the variable x with coefficients from Z12 of degree less than or equal to 4}, V32 = {Z12 u Z12 u Z12 u Z12}, ­§ a · ½ °¨ ¸ ° ° b ° V42 = ®¨ ¸ such that a, b,c,d Z12 ¾ , °¨¨ c ¸¸ ° °© d ¹ ° ¯ ¿ V13 = {Z12 u S u Z12 u S u Z12u S} where S = {0, 2, 4, 6, 8, 10}}, ­§ a · ½ °¨ ¸ ° °¨ b ¸ ° ° ¨ ¸ c ° °° V23 = ®¨ ¸ such that a, b,c,d,e,f S¾ , °¨ d ¸ ° °¨ e ¸ ° °¨¨ ¸¸ ° ¯°© f ¹ ¿° ­§ a · ½ °¨ ¸ ° °¨ a ¸ ° °¨ ¸ ° 4 4 V1 = {Z12 u Z12}, V2 = ® a a Z12 ¾ , °¨ a ¸ ° °¨ ¸ ° °¯¨© a ¸¹ ¿°

446

3 V34 = ^Z12 [x] , i.e., only polynomials of degree less than or

equal to 3 is taken) and V44 = {2 u 7 matrices with entries from Z12}. a. Find atleast 4 distinct special semigroup set linear 4 subalgebras of V. b. Find SHom (V, V). c. Find PSHom (V, V). d. Find a special semigroup set 4-generator of V over Z12. e. Write V as a direct sum. f. Based on (e) find a special semigroup set linear 4 projection of V. 48. Let V = V1 V2 V3 V4 V5 be a special group set vector 5 space over the group Z15 where V1 = V11 , V21, V31 , V2 = V12 , V22 , V32 , V42 , V3 = V13 , V23 , V4 = V14 , V24 , V34 , V44 and V5 = V15 , V25 , V35

with ½° °­§ a b · V11 = ®¨ ¸ a, b,c,d Z15 ¾ , ¯°© c d ¹ ¿°

­§ a · ½ °¨ ¸ ° ° b ° V12 = ®¨ ¸ a, b,c,d Z15 ¾ , ¨ ¸ °¨ c ¸ ° °© d ¹ ° ¯ ¿ V31 = {Z15 u Z15 u Z15}, °­§ a V12 = ®¨ 1 ¯°© a 4

a2 a5

½° a3 · ¸ a i Z15 ,1 d i d 6 ¾ , a6 ¹ ¿°

447

­§ a1 °¨ ° a V22 = ®¨ 3 °¨¨ a 5 °© a 7 ¯

½ a2 · ° ¸ a4 ¸ ° a i Z15 ,1 d i d 8¾ , ¸ a6 ° ¸ ° a8 ¹ ¿

V32 = {all 3 u 3 lower triangular matrices with entries from

Z15}, V42 = {(a a a a a a) | a Z15}, V13 = {Z15 u Z15 u Z15 u Z15}, V23 = {all 4 u 4 upper triangular matrices with entries from Z15}, V14 = {Z15 u Z15} ­°§ a V24 = ®¨ 1 °¯© a 5

a2

a3

a6

a7

­§ a1 °¨ V = ®¨ a 3 °¨ a ¯© 5 4 3

½° a4 · ¸ a i Z15 ,1 d i d 15¾ , a8 ¹ °¿

½ a2 · ° ¸ a 4 ¸ a i Z15 , 1 d i d 6 ¾ , ° a 6 ¸¹ ¿

V44 = {all 8 u 2 matrices with entries from Z15}; V15 = {Z15

u Z15 u Z15 u Z15 u Z15 u Z15}, V25 = {set of all 2 u matrices with entries from Z15}, and V35 = {(a a a a) | a Z15}. V is also a linear algebra. a. Find for the special group set vector 5-space. The dimension of the special generating set. Find the special group set linear 5-algebra as a generating set and find its dimension. b. Find a special group set vector 5 subspace of V. c. Find a special group set linear 5-algebra of V over Z15.

Find SHom(V, V). Is it a special group set linear 5 algebra over Z15? Find PSHom(V, V). Is PSHom(V, V) also a special group set linear 5-algebra over Z15? d. Define for V as a special group set linear 5-algebra a pseudo special subgroup vector 5-space of V.

448

49. Obtain any interesting property about special group set linear n-algebra defined over a group G. 50. Find for the special group set linear n-algebra V = V1 V2 V3 where V1 = V11 , V21, V31 , V2 = V12 , V22 and V3 = V13 , V23 , V33

with V11 = {Z2 u Z2}; V21 = {Z2[x]; all polynomials of degree less than or equal to 4}, V31 = {all 5 u 5 matrices with entries from Z2}, V12 = {Z2 u Z2 u Z2}, V22 = {all 4 u 4 upper triangular matrices with entries from Z2}, V13 = {Z2 u Z2 u Z2 u Z2}, V23 = {all 3 u matrices with entries from Z2} and ­°§ a b c d · ½° V33 = ®¨ ¸ a, b,c,d,e,f ,g, h Z2 ¾ °¯© e f g h ¹ °¿ over the group Z2 = {0, 1} under addition modulo 2. a. Find 2 special group set linear n-subalgebra. b. Is V simple? c. Is V pseudo simple? d. Find SHom (V, V). e. Find PSHom (V, V).

51. Give some interesting properties about special semigroup set fuzzy linear algebra (vector space).

52. Let V = V1 V2 V3 V4 where V1 = V11 , V21, V31 , V2 = V12 , V22

V3 = V13 , V23 , V33 , V43 and V4 = V14 , V24

with V11 = {Z u Z} V21 = {all 3 u 3 matrices with entries from Z}, ­§ a · ½ °¨ ¸ ° 1 V3 = ®¨ b ¸ a, b,c Z ¾ , °¨ c ¸ ° ¯© ¹ ¿

449

V12 = {Z u Z u Z u Z}, V22 = {all 5 u upper triangular matrices with entries from Z}, V13 = {Z u Z u Z u Z u Z u Z} ­§ a b · ½ °¨ ° ¸ °¨ c d ¸ ° °°¨ e f ¸ °° V23 = ®¨ ¸ a, b,c....k,l Z ¾ , °¨ g h ¸ ° °¨ i j ¸ ° ¸¸ °¨¨ ° °¯© k l ¹ ¿° V33 = {Z[x] all polynomials in the variable x with coefficients from Z of degree less than or equal to 4}, V43 = {All lower triangular 4 u 4 matrices with entries from Z}.

V14 = Z u Z u Z and ­§ a °¨ ° a V24 = ®¨ °¨¨ a °© a ¯

½ b· ° ¸ b¸ ° a, b Z ¾ ¸ b ° ¸ ° b¹ ¿

be a special group set linear 4-algebra over the group Z. a. Define two distinct special group set fuzzy linear 4algebras. b. Find three proper distinct special group set fuzzy linear 4-subalgebras for three distinct special group set linear 4-subalgebras W, U and S. c. Determine a special group set fuzzy linear 4subalgebras K which can be extended to V? d. Can all special group set fuzzy linear 4-subalgebras WK be extended to VK? Justify your claim.

53. Obtain some interesting properties about special semigroup set n-vector spaces (linear algebras).

450

54. Prove every special group set vector n space in general is not a special group set linear n-algebra and every special group set n-linear algebra is a special group set n-vector space.

55. Show every special group set vector n-space over a set is always a special semigroup set vector n-space and vice versa!

56. Give examples of special semigroup set vector n-spaces which are not special group set vector n-spaces.

57. Is every special group set vector n-space special semigroup set vector n space? Justify your claim.

58. Let V and W be two special group set vector 4-space defined over the same set S. Find SHom (V, W). Find a generating 4-set of SHom (V, W).

59. Let V = V1 V2 V3 V4 V5 = V11 , V21, V31 V12 , V22 V13 , V23 , V33

V14 , V24 V15 , V25 , V35 , V45

be a special group set vector 5-space over the set S = Z, where V11 = Z u Z, V21 = {all 3 u 3 matrices with entries from Z}, ­§ a · ½ °¨ ¸ ° 1 V3 = ®¨ b ¸ a, b,c Z ¾ , °¨ c ¸ ° ¯© ¹ ¿ V12 = Z u Z u Z u Z, V22 = {all 2u 2 upper triangular matrices with entries from Z}, V13 = {all 4 u 4 lower triangular matrices with entries from Z},

451

­°§ a a a a a a · ½° V23 = ®¨ ¸ a, b Z ¾ , °¯© b b b b b b ¹ °¿ ­§ a °¨ °¨ a °¨ a °¨ 3 V3 = ®¨ a °¨ b °¨ °¨ b °¨ ¯© b

½ a· ° ¸ a¸ ° ° a¸ ° ¸ a ¸ a, b Z ¾ , ° b¸ ° ¸ b¸ ° ° ¸ b¹ ¿

V14 = Z u Z u Z u Z u Z, V24 = {all 3 u 3 lower triangular matrices with entries from Z}, V15 = {(a a a a a) | a Z}, ­§ a · ½ °¨ ¸ ° °¨ b ¸ ° °¨ c ¸ ° °¨ ¸ ° 5 V2 = ®¨ d ¸ a, b....g Z ¾ . °¨ e ¸ ° °¨ ¸ ° °¨ f ¸ ° °¨ ¸ ° ¯© g ¹ ¿ V35 = {all 2u2 matrices with entries from Z} and V45 = {Z[x] all polynomials of degree less than or equal to 10}. a. Find the special group set 5-dimension of V. b. Find a special group set generating set of V. c. Find SHom (V, V). Is SHom (V, V) finite dimensional. d. Find at least 3 proper distinct special group set vector 5 subspaces of V. W, U and T. e. Define K:V o [0, 1] so that VK is a special group set fuzzy vector 5-space.

452

f.

g. h. i.

j. k. l.

m. n.

o. p.

Will WK, UK, TK be special group set fuzzy vector 5 subspaces where K on V is restricted to WK, UK and TK, be special group set fuzzy vector 5-subspaces where K on V is restricted to W, U and T. Similarly if WKZ UKu and TKT be three distinct special group set fuzzy vector 5-subspaces of V; can their extensions to V be such that WKcZ, UKcu and TKcT where KcZ, Kcu and KcT extended to V be a special group set fuzzy vector 5 spaces.

Find T SHom(V, V) so that T is a special group set 5projecton on V. Write V = V1 V2 V5 as a special group set direct 5-sum of V. Suppose the set Z is replaced by the set S = {0, 1, 2, …, 10} Z. Will V = V1 V2 V5 be a special group set vector 5 space over S? What is (V’s) special dimension when V is defined over S = {0, 1, 2, …, 10}?

Will SHom (V, V) as a special group set 5-vector space over S be finite or infinite? Justify your claim.

Prove by illustrative example V has more than one special group set direct sum as special group set vector 5-subspaces?

Show if T: V o V, U: V o V both T o U and U o T are defined.

Find T and U from V into V i.e., in SHom (V, V) such that i. T o U = U o T ii. T o U z U o T iii. T o T = T and U o U = U iv. U o T = T and T o U = U v. T o T = (0).

Find K: V o [0, 1] so that VK is a special group set fuzzy vector 5-space.

Find for W V two maps K1 and K2 (K zK ) such that WK1 and WK2 are two distinct special group set fuzzy vector 5-subspaces such that K K : W o [0, 1] is an empty intersection on the subset of [0, 1].

453

60. Find some applications of these new structures to industrial problems.

61. Prove if V = V1 V2 Vn is a special group set vector space over a set S by changing the set S to another set T the space can be changed from infinite (finite) special group set n-dimension to finite (infinite) special group set ndimension.

62. Give examples of simple special group set vector n-spaces.

63. Give examples of pseudo simple special group linear nalgebras. Hence or otherwise show the pseudo simple nature is dependent on the group over which the special group linear n-algebras are defined.

64. Is it true all simple special group set linear n-algebras are pseudo simple special group set linear n-algebras? Are these two notions entirely different / distinct?

65. Let V = V1 V2 = V11 , V21, V31 V12 , V22 , V32 , V42 where V11 = {(a a a a) | a Z}, ­§ a · ½ °¨ ¸ ° °¨ a ¸ ° 1 V2 = ® a Z¾ , °¨¨ a ¸¸ ° °© a ¹ ° ¯ ¿ ­§ a a a · ½ ° ° ¨ ¸ V31 = ®¨ a a a ¸ a Z ¾ , °¨ ° ¸ ¯© a a a ¹ ¿

454

­°§ a a · ½° V12 = ®¨ ¸ a Z¾ , °¯© a a ¹ ¿° ­§ a °¨ ° a 2 V2 = ®¨ °¨¨ a ° ¯© a

½ a· ° ¸ a¸ ° a Z¾ , a¸ ° ¸ ° a¹ ¿

V32 = {(a a a a a a) | a Z}

and ­§ a °¨ ° 0 V42 = ®¨ °¨¨ 0 °© 0 ¯

0 a 0 0

0 0 a 0

½ 0· ° ¸ 0¸ ° a Z¾ ¸ 0 ° ¸ ° a¹ ¿

be a special group set linear the group bialgebra over Z. a. Is V simple special group set linear bialgebra? b. Is V a pseudo simple special group set linear bialgebra. c. Find SHom (V, V). d. Is SHom (V, V) a simple special group set linear bialgebra?

66. Let V = V1 V2 V3 = V11 , V21, V31 , V41 V12 , V22 , V32

V13 , V23 be a special group set linear trialgebra over the group Z24, where V11 = {Z24 u Z24u Z24}, V21 = {(set of all 2 u matrices with entries from Z24}, ­§ a · ½ °¨ ¸ ° °¨ b ¸ ° 1 V3 = ® a, b,c,d Z24 ¾ , °¨¨ c ¸¸ ° °© d ¹ ° ¯ ¿

455

V41 = {Z24[x] / polynomials of degree less than or equal to 6 with entries from Z24}, V12 = {Z24 u Z24}, V22 = {all 3 u lower triangular matrices with entries from Z24} and ­§ a °¨ ° c V32 = ®¨ °¨¨ e ° ¯© g

½ b· ° ¸ d¸ ° a, b....h Z24 ¾ , f¸ ° ¸ ° h¹ ¿

V13 = {Z24 u Z24 u Z24 u Z24} and V23 = {all 5 u 5 upper triangular matrices with entries from Z24}. a. Find two distinct special group set linear trisubalgebras W and U of V. b. Find VK, WK and UK the special group set fuzzy linear trialgebra and special group set fuzzy linear trisubalgebra. c. Show SHom(V, V) is also a special group set linear trialgebra over Z24. d. Is V a simple special group set linear trialgebra? e. Is V a pseudo simple special group set linear trialgebra. f. If Z24 is replaced by S = {0, 1} = Z2 will V be a pseudo simple special group set linear trialgebra? g. Will V as a special group set vector trispace over Z2 be a simple special group set vector trispace?

456

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9. GEL'FAND, I.M., Lectures on linear algebra, Interscience, New York, 1961.

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457

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28. PETTOFREZZO, A. J., Elements of Linear Algebra, PrenticeHall, Englewood Cliffs, NJ, 1970.

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33. SMARANDACHE, Florentin (editor), Proceedings of the First International Conference on Neutrosophy, Neutrosophic Logic, Neutrosophic set, Neutrosophic probability and Statistics, December 1-3, 2001 held at the University of New Mexico, published by Xiquan, Phoenix, 2002.

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35. SMARANDACHE, Florentin, An Introduction to Neutrosophy, http://gallup.unm.edu/~smarandache/Introduction.pdf 36. SMARANDACHE, Florentin, Collected Papers II, University of Kishinev Press, Kishinev, 1997.

37. SMARANDACHE, Florentin, Neutrosophic Logic, A Generalization of the Fuzzy Logic, http://gallup.unm.edu/~smarandache/NeutLog.txt 38. SMARANDACHE, Florentin, Neutrosophic Set, A Generalization of the Fuzzy Set, http://gallup.unm.edu/~smarandache/NeutSet.txt 39. SMARANDACHE, Florentin, Neutrosophy : A New Branch of Philosophy, http://gallup.unm.edu/~smarandache/Neutroso.txt 40. SMARANDACHE, Florentin, Special Algebraic Structures, in Collected Papers III, Abaddaba, Oradea, 78-81, 2000.

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41. THRALL, R.M., and TORNKHEIM, L., Vector spaces and matrices, Wiley, New York, 1957.

42. VASANTHA KANDASAMY, W.B., SMARANDACHE, Florentin and K. ILANTHENRAL, Introduction to bimatrices, Hexis, Phoenix, 2005.

43. VASANTHA KANDASAMY, W.B., and FLORENTIN Smarandache, Basic Neutrosophic Algebraic Structures and their Applications to Fuzzy and Neutrosophic Models, Hexis, Church Rock, 2005.

44. VASANTHA KANDASAMY, W.B., and FLORENTIN Smarandache, Fuzzy Cognitive Maps and Neutrosophic Cognitive Maps, Xiquan, Phoenix, 2003.

45. VASANTHA KANDASAMY, W.B., and FLORENTIN Smarandache, Fuzzy Relational Equations and Neutrosophic Relational Equations, Hexis, Church Rock, 2004.

46. VASANTHA KANDASAMY, W.B., Bialgebraic structures and Smarandache bialgebraic structures, American Research Press, Rehoboth, 2003.

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51. VASANTHA KANDASAMY and RAJKUMAR, R. A New class of bicodes and its properties, (To appear).

52. VASANTHA KANDASAMY, W.B., On fuzzy semifields and fuzzy semivector spaces, U. Sci. Phy. Sci., 7, 115-116, 1995.

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54. VASANTHA KANDASAMY, W.B., Semivector spaces over semifields, Zeszyty Nauwoke Politechniki, 17, 43-51, 1993.

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60. VASANTHA KANDASAMY, W.B., SMARANDACHE, Florentin and K. ILANTHENRAL, Set Linear Algebra and Set Fuzzy Linear Algebra, InfoLearnQuest, Phoenix, 2008.

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63. ZELINKSY, D., A first course in Linear Algebra, Academic Press, 1973.

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INDEX

D Direct sum of semigroup linear algebras, 211 Doubly simple special group linear algebra, 249 Doubly simple special semigroup set linear algebra, 226 F Finite dimension group vector space, 212 Fuzzy equivalent, 193-4 Fuzzy set linear algebra, 29 Fuzzy set linear subalgebra, 31 Fuzzy set vector space, 27 Fuzzy set vector subspace, 30 Fuzzy special set vector n-space, 33, 178 G Generating n-set of special semigroup set vector space, 227 Generating set of a set vector space, 20 Group vector space, 211 Group vector subspace, 212

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I Infinite dimension group vector space, 212 L Linearly dependent set of a set vector space, 22 Linearly independent set of a set vector space, 22 P Pseudo direct sum of group linear algebras, 212-3 Pseudo direct union of semigroup vector spaces, 210-1 Pseudo semigroup subvector space, 210 Pseudo simple semigroup linear algebra, 209-10 Pseudo special group set fuzzy linear algebra, 395-6 Pseudo special group set linear n-operator, 374 Pseudo special group set vector n-subspace, 358 Pseudo special quasi semigroup set vector subspace, 242 Pseudo special semigroup set vector subspace, 239-40 Pseudo special set linear n-operator, 141-2 Pseudo special set linear operator, 60 Pseudo special set subsemigroup linear subalgebra, 252 Pseudo special set vector bisubspace, 78 Pseudo special set vector subspace, 49 Pseudo special subgroup set linear n-subalgebra, 360 Pseudo subsemigroup subvector space, 210 Q Quasi special set inverse linear operator, 268 Quasi special set linear bialgebra, 69 S Sectional subset vector sectional subspace, 18 Semigroup linear algebra, 209 Semigroup linear operator, 210

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Semigroup linear subalgebra, 208-9 Semigroup linear transformation, 210 Semigroup linearly independent set, 208 Semigroup subvector space, 208 Semigroup vector space, 207-8 Set annihilator, 27 Set fuzzy linear algebra, 29 Set linear functional, 26 Set linear operator, 23 Set linear transformation, 22-3 Set vector space, 7 Set vector subspace, 12 Simple semigroup linear algebra, 210 Special bigenerator subset bigenerated finitely, 82-3 Special fuzzy set vector space, 33, 157 Special generating subset, 41 Special group set fuzzy linear algebra, 308 Special group set fuzzy linear subalgebra, 310, 398 Special group set fuzzy vector n-space, 417-8 Special group set fuzzy vector space, 391 Special group set fuzzy vector subspace, 307, 393 Special group set linear algebra, 243 Special group set linear n-subalgebra, 355-6 Special group set linear n-transformation, 366-7 Special group set linear operator, 259 Special group set linear subalgebra, 244 Special group set linear transformation, 255-6 Special group set n-linear algebra, 355 Special group set n-vector space, 350 Special group set semisimple vector subspace, 239 Special group set simple vector space, 238 Special group set strong simple vector n-space, 354 Special group set vector n-subspace, 351-2 Special group set vector space, 235 Special group set vector subspace, 236 Special semigroup set fuzzy linear algebra, 297-8 Special semigroup set fuzzy linear n-algebra, 400-1 Special semigroup set fuzzy linear subalgebra, 302, 386-8 Special semigroup set fuzzy n-vector space, 400-1

464

Special semigroup set fuzzy trivector space or vector trispace, 400-1 Special semigroup set fuzzy vector n-subspace, 405 Special semigroup set fuzzy vector space, 380 Special semigroup set fuzzy vector subspace, 295-6, 383-4 Special semigroup set linear algebra, 218 Special semigroup set linear bioperator, 320-321 Special semigroup set linear bitransformation, 320-321 Special semigroup set linear n-algebra, 333 Special semigroup set linear n-transformation, 339-340 Special semigroup set linear operator, 231 Special semigroup set linear sub bialgebra, 318 Special semigroup set linear subalgebra, 221 Special semigroup set linear transformation of special semigroup set linear algebra, 232-3 Special semigroup set linear transformation, 229 Special semigroup set strong simple linear algebra, 222-3 Special semigroup set strong simple vector space, 223-4 Special semigroup set trivector space, 329 Special semigroup set vector bispace, 313 Special semigroup set vector n-space, 330 Special semigroup set vector n-subspace, 330 Special semigroup set vector space, 213 Special semigroup set vector sub bispace, 314-5 Special semigroup set vector subspace, 215 Special set bivector space, 66 Special set direct n-union, 147 Special set direct sum, 61-2 Special set fuzzy linear algebra, 156, 160 Special set fuzzy linear bialgebra, 170 Special set fuzzy linear n-algebra, 199 Special set fuzzy linear sub bialgebra, 173-4 Special set fuzzy linear subalgebra, 162 Special set fuzzy n-linear algebra, 194 Special set fuzzy set vector n-space, 33, 178 Special set fuzzy vector bispace, 162-3 Special set fuzzy vector n-subspace, 183 Special set fuzzy vector subbispace, 166 Special set group fuzzy vector space, 305

465

Special set linear algebra, 45 Special set linear bialgebra, 68, 86 Special set linear bioperator, 97 Special set linear bitransformation, 91-2 Special set linear idempotent operator, 58 Special set linear map, 50-3 Special set linear n-algebra, 114 Special set linear n-operator, 137 Special set linear n-subalgebra, 117-8 Special set linear n-transformation, 131-2 Special set linear operator, 53 Special set linear subalgebra, 46-7 Special set linear subbialgebra, 74 Special set linear transformation, 50 Special set n-dimension generator, 128-9 Special set projection, 63 Special set semigroup linear bialgebra, 317 Special set vector bispace, 33, 66 Special set vector bisubspace, 69-70 Special set vector n-space, 33, 103 Special set vector n-subspace, 106-7 Special set vector space, 33-4 Special set vector subspace, 36 Special set vector transformation, 100 Special subgroup set linear subalgebra, 247 Subgroup vector subspace, 212 Subsemigroup linear subalgebra, 209 Subsemigroup subvector space, 209 Subset vector subspace, 15

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ABOUT THE AUTHORS Dr.W.B.Vasantha Kandasamy is an Associate Professor in the Department of Mathematics, Indian Institute of Technology Madras, Chennai. In the past decade she has guided 12 Ph.D. scholars in the different fields of non-associative algebras, algebraic coding theory, transportation theory, fuzzy groups, and applications of fuzzy theory of the problems faced in chemical industries and cement industries.

She has to her credit 646 research papers. She has guided over 68 M.Sc. and M.Tech. projects. She has worked in collaboration projects with the Indian Space Research Organization and with the Tamil Nadu State AIDS Control Society.

This is her 44th book.

On India's 60th Independence Day, Dr.Vasantha was conferred the Kalpana Chawla Award for Courage and Daring Enterprise by the State Government of Tamil Nadu in recognition of her sustained fight for social justice in the Indian Institute of Technology (IIT) Madras and for her contribution to mathematics.

(The award, instituted in the memory of Indian-American astronaut Kalpana Chawla who died aboard Space Shuttle Columbia). The award carried a cash prize of five lakh rupees (the highest prize-money for any Indian award) and a gold medal.

She can be contacted at vasanthakandasamy@gmail.com Web Site: http://mat.iitm.ac.in/home/wbv/public_html/

Dr. Florentin Smarandache is a Professor of Mathematics at the University of New Mexico in USA. He published over 75 books and 150 articles and notes in mathematics, physics, philosophy, psychology, rebus, literature.

In mathematics his research is in number theory, nonEuclidean geometry, synthetic geometry, algebraic structures, statistics, neutrosophic logic and set (generalizations of fuzzy logic and set respectively), neutrosophic probability (generalization of classical and imprecise probability). Also, small contributions to nuclear and particle physics, information fusion, neutrosophy (a generalization of dialectics), law of sensations and stimuli, etc. He can be contacted at smarand@unm.edu

K. Ilanthenral is the editor of The Maths Tiger, Quarterly Journal of Maths. She can be contacted at ilanthenral@gmail.com

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