Problems and Solutions in Introductory and Advanced Matrix Calculus Downloaded from

Similar documents
Mathematical Modeling and Methods of Option Pricing

Bariatric Surgery. Obesity. Care and. Obesity Care and Bariatric Surgery Downloaded from

E-Commerce Operations Management Downloaded from -COMMERCE. by on 06/15/16. For personal use only.

Applied Linear Algebra I Review page 1

MATHEMATICAL LOGIC FOR COMPUTER SCIENCE

Linear Algebra Review. Vectors

13 MATH FACTS a = The elements of a vector have a graphical interpretation, which is particularly easy to see in two or three dimensions.

Social Services Administration In Hong Kong

Mathematics Course 111: Algebra I Part IV: Vector Spaces

Section Inner Products and Norms

Similarity and Diagonalization. Similar Matrices

Linear Algebra and TI 89

Linear Algebra Notes for Marsden and Tromba Vector Calculus

Introduction to Matrix Algebra

Linear Algebra Done Wrong. Sergei Treil. Department of Mathematics, Brown University

Orthogonal Diagonalization of Symmetric Matrices

4: EIGENVALUES, EIGENVECTORS, DIAGONALIZATION

Linear Algebra Done Wrong. Sergei Treil. Department of Mathematics, Brown University

Similar matrices and Jordan form

A Primer on Index Notation

Linear Algebra: Determinants, Inverses, Rank

SURGICAL CARE MALFORMATIONS

Data Mining: Algorithms and Applications Matrix Math Review

26. Determinants I. 1. Prehistory

Content. Chapter 4 Functions Basic concepts on real functions 62. Credits 11

1 Introduction to Matrices

3. Let A and B be two n n orthogonal matrices. Then prove that AB and BA are both orthogonal matrices. Prove a similar result for unitary matrices.

Continued Fractions and the Euclidean Algorithm

Classification of Cartan matrices

Inner Product Spaces and Orthogonality

LINEAR ALGEBRA W W L CHEN

Data Visualization. Principles and Practice. Second Edition. Alexandru Telea

Vector and Matrix Norms

1 VECTOR SPACES AND SUBSPACES

MATH 304 Linear Algebra Lecture 9: Subspaces of vector spaces (continued). Span. Spanning set.

Mean value theorem, Taylors Theorem, Maxima and Minima.

How To Understand Multivariate Models

Eigenvalues, Eigenvectors, Matrix Factoring, and Principal Components

Quark Confinement and the Hadron Spectrum III

Problems and Solutions in Matrix Calculus

LINEAR ALGEBRA. September 23, 2010

Matrix Differentiation

Matrix Calculations: Applications of Eigenvalues and Eigenvectors; Inner Products

BANACH AND HILBERT SPACE REVIEW

MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS. + + x 2. x n. a 11 a 12 a 1n b 1 a 21 a 22 a 2n b 2 a 31 a 32 a 3n b 3. a m1 a m2 a mn b m

NOTES ON LINEAR TRANSFORMATIONS

Elementary Linear Algebra

α = u v. In other words, Orthogonal Projection

Chapter 6. Orthogonality

2.3. Finding polynomial functions. An Introduction:

October 3rd, Linear Algebra & Properties of the Covariance Matrix

1 Comparing Complex Numbers to Clifford Algebra

Lecture 2 Matrix Operations

[1] Diagonal factorization

by the matrix A results in a vector which is a reflection of the given

Math 131 College Algebra Fall 2015

x = + x 2 + x

Here are some examples of combining elements and the operations used:

A linear combination is a sum of scalars times quantities. Such expressions arise quite frequently and have the form

Review Jeopardy. Blue vs. Orange. Review Jeopardy

The Determinant: a Means to Calculate Volume

Notes on Symmetric Matrices

Eigenvalues and Eigenvectors

Mehtap Ergüven Abstract of Ph.D. Dissertation for the degree of PhD of Engineering in Informatics

GRADES 7, 8, AND 9 BIG IDEAS

ABSTRACT ALGEBRA: A STUDY GUIDE FOR BEGINNERS

ALGEBRAIC EIGENVALUE PROBLEM

Linear Algebra I. Ronald van Luijk, 2012

How To Understand And Solve A Linear Programming Problem

Applied Linear Algebra

MATH 304 Linear Algebra Lecture 18: Rank and nullity of a matrix.

MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS

NANOCOMPUTING. Computational Physics for Nanoscience and Nanotechnology

Math 115A - Week 1 Textbook sections: Topics covered: What is a vector? What is a vector space? Span, linear dependence, linear independence

Brief Introduction to Vectors and Matrices

IRREDUCIBLE OPERATOR SEMIGROUPS SUCH THAT AB AND BA ARE PROPORTIONAL. 1. Introduction

Alabama Department of Postsecondary Education

Notes on Determinant

CONTROLLABILITY. Chapter Reachable Set and Controllability. Suppose we have a linear system described by the state equation

x1 x 2 x 3 y 1 y 2 y 3 x 1 y 2 x 2 y 1 0.

Bindel, Spring 2012 Intro to Scientific Computing (CS 3220) Week 3: Wednesday, Feb 8

Quantum Computing. Robert Sizemore

December 4, 2013 MATH 171 BASIC LINEAR ALGEBRA B. KITCHENS

5. Orthogonal matrices

MA651 Topology. Lecture 6. Separation Axioms.

Lecture 5: Singular Value Decomposition SVD (1)

NEW WORLDS IN C J 1-3. New Worlds in Astroparticle Physics Downloaded from

Quantum Physics II (8.05) Fall 2013 Assignment 4

Finite dimensional C -algebras

A Direct Numerical Method for Observability Analysis

Math 115A HW4 Solutions University of California, Los Angeles. 5 2i 6 + 4i. (5 2i)7i (6 + 4i)( 3 + i) = 35i + 14 ( 22 6i) = i.

1 Sets and Set Notation.

Lecture 1: Schur s Unitary Triangularization Theorem

Elements of Abstract Group Theory

a 11 x 1 + a 12 x a 1n x n = b 1 a 21 x 1 + a 22 x a 2n x n = b 2.

Lecture L3 - Vectors, Matrices and Coordinate Transformations

The Characteristic Polynomial

Recall that two vectors in are perpendicular or orthogonal provided that their dot

Matrix Algebra. Some Basic Matrix Laws. Before reading the text or the following notes glance at the following list of basic matrix algebra laws.

Au = = = 3u. Aw = = = 2w. so the action of A on u and w is very easy to picture: it simply amounts to a stretching by 3 and 2, respectively.

Transcription:

This page intentionally left blank

NEW JERSEY LONDON SINGAPORE BEIJING SHANGHAI HONG KONG TAIPEI CHENNAI TOKYO

Published by World Scientific Publishing Co. Pte. Ltd. 5 Toh Tuck Link, Singapore 596224 USA office: 27 Warren Street, Suite 401-402, Hackensack, NJ 07601 UK office: 57 Shelton Street, Covent Garden, London WC2H 9HE Problems and Solutions in Introductory and Advanced Matrix Calculus Downloaded from www.worldscientific.com Library of Congress Cataloging-in-Publication Data Names: Steeb, W.-H. Hardy, Yorick, 1976 Title: Problems and solutions in introductory and advanced matrix calculus. Description: Second edition / by Willi-Hans Steeb (University of Johannesburg, South Africa & University of South Africa, South Africa), Yorick Hardy (University of Johannesburg, South Africa & University of South Africa, South Africa). New Jersey : World Scientific, 2016. Includes bibliographical references and index. Identifiers: LCCN 2016028706 ISBN 9789813143784 (hardcover : alk. paper) ISBN 9789813143791 (pbk. : alk. paper) Subjects: LCSH: Matrices--Problems, exercises, etc. Calculus. Mathematical physics. Classification: LCC QA188.S664 2016 DDC 512.9/434--dc23 LC record available at https://lccn.loc.gov/2016028706 British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library. Copyright 2017 by World Scientific Publishing Co. Pte. Ltd. All rights reserved. This book, or parts thereof, may not be reproduced in any form or by any means, electronic or mechanical, including photocopying, recording or any information storage and retrieval system now known or to be invented, without written permission from the publisher. For photocopying of material in this volume, please pay a copying fee through the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, USA. In this case permission to photocopy is not required from the publisher. Printed in Singapore

Preface The purpose of this book is to supply a collection of problems in introductory and advanced matrix problems together with their detailed solutions which will prove to be valuable to undergraduate and graduate students as well as to research workers in these fields. Each chapter contains an introduction with the essential definitions and explanations to tackle the problems in the chapter. If necessary, other concepts are explained directly with the present problems. Thus the material in the book is self-contained. The topics range in difficulty from elementary to advanced. Students can learn important principles and strategies required for problem solving. Lecturers will also find this text useful either as a supplement or text, since important concepts and techniques are developed in the problems. A large number of problems are related to applications. Applications include wavelets, linear integral equations, Kirchhoff s laws, global positioning systems, Floquet theory, octonians, random walks, entanglement, tensor decomposition, hyperdeterminant, matrix-valued differential forms, Kronecker product and images. A number of problems useful in quantum physics and graph theory are also provided. Advanced topics include groups and matrices, Lie groups and matrices and Lie algebras and matrices. Exercises for matrix-valued differential forms are also included. In this second edition new problems for braid groups, mutually unbiased bases, vec operator, spectral theorem, binary matrices, nonnormal matrices, wavelets, fractals, matrices and integration are added. Each chapter also contains supplementary problems. Furthermore a number of Maxima and SymbolicC++ programs are added for solving problems. Applications in mathematical and theoretical physics are emphasized. The book can also be used as a text for linear and multilinear algebra or matrix theory. The material was tested in the first author s lectures given around the world. v

Note to the Readers Problems and Solutions in Introductory and Advanced Matrix Calculus Downloaded from www.worldscientific.com The International School for Scientific Computing (ISSC) provides certificate courses for this subject. Please contact the authors if you want to do this course or other courses of the ISSC. e-mail addresses of the first author: steebwilli@gmail.com steeb_wh@yahoo.com e-mail address of the second author: yorickhardy@gmail.com Home page of the first author: http://issc.uj.ac.za vi

Contents Preface v Notation ix 1 Basic Operations 1 2 Linear Equations 47 3 Kronecker Product 71 4 Traces, Determinants and Hyperdeterminants 99 5 Eigenvalues and Eigenvectors 142 6 Spectral Theorem 205 7 Commutators and Anticommutators 217 8 Decomposition of Matrices 241 9 Functions of Matrices 260 10 Cayley-Hamilton Theorem 299 11 Hadamard Product 309 12 Norms and Scalar Products 318 13 vec Operator 340 14 Nonnormal Matrices 355 15 Binary Matrices 365 16 Star Product 371 17 Unitary Matrices 377 18 Groups, Lie Groups and Matrices 398 vii

viii Contents 19 Lie Algebras and Matrices 439 20 Braid Group 466 21 Graphs and Matrices 486 22 Hilbert Spaces and Mutually Unbiased Bases 496 Problems and Solutions in Introductory and Advanced Matrix Calculus Downloaded from www.worldscientific.com 23 Linear Differential Equations 507 24 Differentiation and Matrices 520 25 Integration and Matrices 535 Bibliography 547 Index 551

Notation := is defined as belongs to (a set) / does not belong to (a set) intersection of sets union of sets empty set T S subset T of set S S T the intersection of the sets S and T S T the union of the sets S and T f(s) image of set S under mapping f f g composition of two mappings (f g)(x) = f(g(x)) N set of natural numbers N 0 set of natural numbers including 0 Z set of integers Q set of rational numbers R set of real numbers R + set of nonnegative real numbers C set of complex numbers R n n-dimensional Euclidean space space of column vectors with n real components C n n-dimensional complex linear space space of column vectors with n complex components H Hilbert space S n symmetric group on a set of n symbols i 1 R(z) real part of the complex number z I(z) imaginary part of the complex number z z modulus of complex number z x + iy = (x 2 + y 2 ) 1/2, x, y R x column vector in C n x T transpose of x (row vector) 0 zero (column) vector. norm x y x y scalar product (inner product) in C n x y vector product in R 3 ix

x Notation Problems and Solutions in Introductory and Advanced Matrix Calculus Downloaded from www.worldscientific.com A, B, C m n matrices P n n permutation matrix Π n n projection matrix U n n unitary matrix vec(a) vectorization of matrix A det(a) determinant of a square matrix A tr(a) trace of a square matrix A Pf(A) Pfaffian of square matrix A rank(a) rank of matrix A A T transpose of matrix A A conjugate of matrix A A conjugate transpose of matrix A A 1 inverse of square matrix A (if it exists) I n n n unit matrix I unit operator 0 n n n zero matrix AB matrix product of m n matrix A and n p matrix B A B Hadamard product (entry-wise product) of m n matrices A and B [A, B] := AB BA commutator for square matrices A and B [A, B] + := AB + BA anticommutator for square matrices A and B A B Kronecker product of matrices A and B A B Direct sum of matrices A and B δ jk Kronecker delta with δ jk = 1 for j = k and δ jk = 0 for j k λ eigenvalue ɛ real parameter t time variable Ĥ Hamilton operator The elementary matrices E jk with j = 1,..., m and k = 1,..., n are defined as 1 at entry (j, k) and 0 otherwise. The Pauli spin matrices are defined as ( ) 0 1 σ 1 :=, σ 1 0 2 := ( 0 i i 0 ) ( ) 1 0, σ 3 :=. 0 1