Super Linear - Cover:Layout 1 7/17/2008 2:32 PM Page 1 SUPER LINEAR ALGEBRA W. B. Vasantha Kandasamy e-mail: [email protected] web: http://mat.iitm.ac.in/~wbv www.vasantha.net Florentin Smarandache e-mail: [email protected] INFOLEARNQUEST Ann Arbor 2008 This book can be ordered in a paper bound reprint from: Books on Demand ProQuest Information & Learning (University of Microfilm International) 300 N. Zeeb Road P.O. Box 1346, Ann Arbor MI 48106-1346, USA Tel.: 1-800-521-0600 (Customer Service) http://wwwlib.umi.com/bod/ Peer reviewers: Professor Diego Lucio Rapoport Departamento de Ciencias y Tecnologia Universidad Nacional de Quilmes Roque Saen Peña 180, Bernal, Buenos Aires, Argentina Dr.S.Osman, Menofia University, Shebin Elkom, Egypt Prof. Mircea Eugen Selariu, Polytech University of Timisoara, Romania. Copyright 2008 by InfoLearnQuest 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: 1-59973-065-0 ISBN-13: 978-1-59973-065-3 EAN: 9781599730653 Standard Address Number: 297-5092 Printed in the United States of America CONTENTS Preface 5 Chapter One SUPER VECTOR SPACES 7 1.1 Supermatrices 7 1.2 Super Vector Spaces and their Properties 27 1.3 Linear Transformation of Super Vector Spaces 53 1.4 Super Linear Algebra 81 Chapter Two SUPER INNER PRODUCT SUPERSPACES 123 2.1 Super Inner Product Super Spaces and their properties 123 2.2 Superbilinear form 185 2.3 Applications 214 Chapter Three SUGGESTED PROBLEMS 237 FURTHER READING 282 INDEX 287 ABOUT THE AUTHORS 293 PREFACE In this book, the authors introduce the notion of Super linear algebra and super vector spaces using the definition of super matrices defined by Horst (1963). This book expects the readers to be well-versed in linear algebra. Many theorems on super linear algebra and its properties are proved. Some theorems are left as exercises for the reader. These new class of super linear algebras which can be thought of as a set of linear algebras, following a stipulated condition, will find applications in several fields using computers. The authors feel that such a paradigm shift is essential in this computerized world. Some other structures ought to replace linear algebras which are over a century old. Super linear algebras that use super matrices can store data not only in a block but in multiple blocks so it is certainty more powerful than the usual matrices. This book has 3 chapters. Chapter one introduces the notion of super vector spaces and enumerates a number of properties. Chapter two defines the notion of super linear algebra, super inner product spaces and super bilinear forms. Several interesting properties are derived. The main application of these new structures in Markov chains and Leontief economic models are also given in this chapter. The final chapter suggests 161 problems mainly to make the reader understand this new concept and apply them. The authors deeply acknowledge the unflinching support of Dr.K.Kandasamy, Meena and Kama. W.B.VASANTHA KANDASAMY FLORENTIN SMARANDACHE Chapter One SUPER VECTOR SPACES This chapter has four sections. In section one a brief introduction about supermatrices is given. Section two defines the notion of super vector spaces and gives their properties. Linear transformation of super vector is described in the third section. Final section deals with linear algebras. 1.1 Supermatrices Though the study of super matrices started in the year 1963 by Paul Horst. His book on matrix algebra speaks about super matrices of different types and their applications to social problems. The general rectangular or square array of numbers such as ⎡ 1 2 3⎤ ⎡ 2 3 1 4 ⎤ ⎢ ⎥ A = , B = −4 5 6 , ⎢ ⎥ ⎢ ⎥ ⎣−5 0 7 −8⎦ ⎢⎣ 7 −8 11⎥⎦ ⎡−7 2⎤ ⎢ ⎥ 0 ⎢ ⎥ C = [3, 1, 0, -1, -2] and D = ⎢ 2 ⎥ ⎢ ⎥ ⎢ 5 ⎥ ⎢ ⎥ ⎣ −41⎦ 7 are known as matrices. We shall call them as simple matrices [17]. By a simple matrix we mean a matrix each of whose elements are just an ordinary number or a letter that stands for a number. In other words, the elements of a simple matrix are scalars or scalar quantities. A supermatrix on the other hand is one whose elements are themselves matrices with elements that can be either scalars or other matrices. In general the kind of supermatrices we shall deal with in this book, the matrix elements which have any scalar for their elements. Suppose we have the four matrices; ⎡2 −4⎤ ⎡0 40 ⎤ a = , a = 11 ⎢ ⎥ 12 ⎢ ⎥ ⎣0 1 ⎦ ⎣21 −12⎦ ⎡ 3 −1⎤ ⎡ 4 12⎤ ⎢ ⎥ ⎢ ⎥ a = 5 7 and a = −17 6 . 21 ⎢ ⎥ 22 ⎢ ⎥ ⎢⎣−2 9 ⎥⎦ ⎢⎣ 3 11⎥⎦ One can observe the change in notation a denotes a matrix and ij not a scalar of a matrix (1 < i, j < 2). Let ⎡a a ⎤ a = 11 12 ; ⎢ ⎥ ⎣a a ⎦ 21 22 we can write out the matrix a in terms of the original matrix elements i.e., ⎡ 2 −4 0 40 ⎤ ⎢ ⎥ 0 1 21 −12 ⎢ ⎥ a = ⎢ 3 −1 4 12 ⎥. ⎢ ⎥ 5 7 −17 6 ⎢ ⎥ ⎢⎣−2 9 3 11 ⎥⎦ Here the elements are divided vertically and horizontally by thin lines. If the lines were not used the matrix a would be read as a simple matrix. 8 Thus far we have referred to the elements in a supermatrix as matrices as elements. It is perhaps more usual to call the elements of a supermatrix as submatrices. We speak of the submatrices within a supermatrix. Now we proceed on to define the order of a supermatrix. The order of a supermatrix is defined in the same way as that of a simple matrix. The height of a supermatrix is the number of rows of submatrices in it. The width of a supermatrix is the number of columns of submatrices in it. All submatrices with in a given row must have the same number of rows. Likewise all submatrices with in a given column must have the same number of columns. A diagrammatic representation is given by the following figure. In the first row of rectangles we have one row of a square for each rectangle; in the second row of rectangles we have four rows of squares for each rectangle and in the third row of rectangles we have two rows of squares for each rectangle. Similarly for the first column of rectangles three columns of squares for each rectangle. For the second column of rectangles we have two column of squares for each rectangle, and for the third column of rectangles we have five columns of squares for each rectangle. Thus we have for this supermatrix 3 rows and 3 columns. One thing should now be clear from the definition of a supermatrix. The super order of a supermatrix tells us nothing about the simple order of the matrix from which it was obtained 9