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Introduction to Matrix Theory

Autor Arindama Singh
en Limba Engleză Hardback – 17 aug 2021
This book is designed to serve as a textbook for courses offered to undergraduate and postgraduate students enrolled in Mathematics. Using elementary row operations and Gram-Schmidt orthogonalization as basic tools the text develops characterization of equivalence and similarity, and various factorizations such as rank factorization, OR-factorization, Schurtriangularization, Diagonalization of normal matrices, Jordan decomposition, singular value decomposition, and polar decomposition. Along with Gauss-Jordan elimination for linear systems, it also discusses best approximations and least-squares solutions. The book includes norms on matrices as a means to deal with iterative solutions of linear systems and exponential of a matrix. The topics in the book are dealt with in a lively manner. Each section of the book has exercises to reinforce the concepts, and problems have been added at the end of each chapter. Most of these problems are theoretical, and they do not fit into the running text linearly. The detailed coverage and pedagogical tools make this an ideal textbook for students and researchers enrolled in senior undergraduate and beginning postgraduate mathematics courses.
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Specificații

ISBN-13: 9783030804800
ISBN-10: 3030804801
Pagini: 204
Ilustrații: IX, 194 p. 2 illus., 1 illus. in color.
Dimensiuni: 160 x 241 x 17 mm
Greutate: 0.43 kg
Ediția:1st ed. 2021
Editura: Springer
Locul publicării:Cham, Switzerland

Cuprins

Matrix Operations.- Systems of Linear Equations.- Matrix as a Linear Map.- Orthogonality.- Eigenvalues and Eigenvectors.- Canonical Forms.- Norms of Matrices.- Short Bibliography.- Index.

Recenzii

“This is a concise, concrete introduction to matrix theory and linear algebra, designed as a one-semester course for science and engineering students. … The book has a reasonable number of exercises.” (Allen Stenger, MAA Reviews, December 12, 2021)

Notă biografică

M. Thamban Nair is a professor of mathematics at the Indian Institute of Technology Madras, Chennai, India. He completed his Ph.D. at the Indian Institute of Technology Bombay, Mumbai, India, in 1986. His research interests include functional analysis and operator theory, specifically spectral approximation, the approximate solution of integral and operator equations, regularization of inverse and ill-posed problems. He has published three books, including a textbook, Functional Analysis: A First Course (PHI Learning), and a text-cum-monograph, Linear Operator Equations: Approximation and Regularization (World Scientific), and over 90 papers in reputed journals and refereed conference proceedings. He has guided six Ph.D. students and is an editorial board member of the Journal of Analysis and Number Theory, and Journal of Mathematical Analysis. He is a life member of academic bodies such as Indian Mathematical Society and Ramanujan Mathematical Society. Arindama Singh is a professor of mathematics at the Indian Institute of Technology Madras, Chennai, India. He completed his Ph.D. at the Indian Institute of Technology Kanpur, India, in 1990. His research interests include knowledge compilation, singular perturbation, mathematical learning theory, image processing, and numerical linear algebra. He has published five books, including Elements of Computation Theory (Springer), and over 47 papers in reputed journals and refereed conference proceedings. He has guided five Ph.D. students and is a life member of many academic bodies, including Indian Society for Industrial and Applied Mathematics, Indian Society of Technical Education, Ramanujan Mathematical Society, Indian Mathematical Society, and The Association of Mathematics Teachers of India

Textul de pe ultima copertă

This book is designed to serve as a textbook for courses offered to undergraduate and postgraduate students enrolled in Mathematics. Using elementary row operations and Gram-Schmidt orthogonalization as basic tools the text develops characterization of equivalence and similarity, and various factorizations such as rank factorization, OR-factorization, Schurtriangularization, Diagonalization of normal matrices, Jordan decomposition, singular value decomposition, and polar decomposition. Along with Gauss-Jordan elimination for linear systems, it also discusses best approximations and least-squares solutions. The book includes norms on matrices as a means to deal with iterative solutions of linear systems and exponential of a matrix. The topics in the book are dealt with in a lively manner. Each section of the book has exercises to reinforce the concepts, and problems have been added at the end of each chapter. Most of these problems are theoretical, and they do not fit into the running text linearly. The detailed coverage and pedagogical tools make this an ideal textbook for students and researchers enrolled in senior undergraduate and beginning postgraduate mathematics courses.