Contents. Preface for the Instructor. Preface for the Student. xvii. Acknowledgments. 1 Vector Spaces 1 1.A R n and C n 2

Similar documents
ALGEBRA QUALIFYING EXAM PROBLEMS LINEAR ALGEBRA

Math 4153 Exam 3 Review. The syllabus for Exam 3 is Chapter 6 (pages ), Chapter 7 through page 137, and Chapter 8 through page 182 in Axler.

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

Math 113 Final Exam: Solutions

Ir O D = D = ( ) Section 2.6 Example 1. (Bottom of page 119) dim(v ) = dim(l(v, W )) = dim(v ) dim(f ) = dim(v )

LINEAR ALGEBRA 1, 2012-I PARTIAL EXAM 3 SOLUTIONS TO PRACTICE PROBLEMS

Then x 1,..., x n is a basis as desired. Indeed, it suffices to verify that it spans V, since n = dim(v ). We may write any v V as r

FINITE-DIMENSIONAL LINEAR ALGEBRA

Last name: First name: Signature: Student number:

Review problems for MA 54, Fall 2004.

homogeneous 71 hyperplane 10 hyperplane 34 hyperplane 69 identity map 171 identity map 186 identity map 206 identity matrix 110 identity matrix 45

Final Exam, Linear Algebra, Fall, 2003, W. Stephen Wilson

Linear Algebra. Workbook

ABSTRACT ALGEBRA WITH APPLICATIONS

Comprehensive Introduction to Linear Algebra

MATH 221, Spring Homework 10 Solutions

MATRIX AND LINEAR ALGEBR A Aided with MATLAB

Exercise Set 7.2. Skills

Classes of Linear Operators Vol. I

Math 25a Practice Final #1 Solutions

No books, no notes, no calculators. You must show work, unless the question is a true/false, yes/no, or fill-in-the-blank question.

Math 108b: Notes on the Spectral Theorem

Matrix Mathematics. Theory, Facts, and Formulas with Application to Linear Systems Theory. Dennis S. Bernstein

University of Colorado at Denver Mathematics Department Applied Linear Algebra Preliminary Exam With Solutions 16 January 2009, 10:00 am 2:00 pm

Definition (T -invariant subspace) Example. Example

I. Multiple Choice Questions (Answer any eight)

Chap 3. Linear Algebra

NATIONAL UNIVERSITY OF SINGAPORE DEPARTMENT OF MATHEMATICS SEMESTER 2 EXAMINATION, AY 2010/2011. Linear Algebra II. May 2011 Time allowed :

(a) II and III (b) I (c) I and III (d) I and II and III (e) None are true.

Vector Spaces and Linear Transformations

Final A. Problem Points Score Total 100. Math115A Nadja Hempel 03/23/2017

series. Utilize the methods of calculus to solve applied problems that require computational or algebraic techniques..

LAKELAND COMMUNITY COLLEGE COURSE OUTLINE FORM

Lecture 7: Positive Semidefinite Matrices

Conceptual Questions for Review

MATH 423 Linear Algebra II Lecture 33: Diagonalization of normal operators.

Five Mini-Courses on Analysis

The value of a problem is not so much coming up with the answer as in the ideas and attempted ideas it forces on the would be solver I.N.

Introduction to Functional Analysis With Applications

Remark By definition, an eigenvector must be a nonzero vector, but eigenvalue could be zero.

Linear algebra II Homework #1 due Thursday, Feb. 2 A =. 2 5 A = When writing up solutions, write legibly and coherently.

1. General Vector Spaces

Linear Algebra. Min Yan

Linear Systems. Class 27. c 2008 Ron Buckmire. TITLE Projection Matrices and Orthogonal Diagonalization CURRENT READING Poole 5.4

Mobile Robotics 1. A Compact Course on Linear Algebra. Giorgio Grisetti

ANSWERS. E k E 2 E 1 A = B

MATH 583A REVIEW SESSION #1

MATH 240 Spring, Chapter 1: Linear Equations and Matrices

Review of Some Concepts from Linear Algebra: Part 2

HONORS LINEAR ALGEBRA (MATH V 2020) SPRING 2013

Math 102, Winter Final Exam Review. Chapter 1. Matrices and Gaussian Elimination

22m:033 Notes: 7.1 Diagonalization of Symmetric Matrices

Math 1553, Introduction to Linear Algebra

Differential Geometry, Lie Groups, and Symmetric Spaces

Assignment 1 Math 5341 Linear Algebra Review. Give complete answers to each of the following questions. Show all of your work.

The Essentials of Linear State-Space Systems

University of Colorado Denver Department of Mathematical and Statistical Sciences Applied Linear Algebra Ph.D. Preliminary Exam June 8, 2012

Reduction to the associated homogeneous system via a particular solution

DIAGONALIZATION. In order to see the implications of this definition, let us consider the following example Example 1. Consider the matrix

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

University of Colorado Denver Department of Mathematical and Statistical Sciences Applied Linear Algebra Ph.D. Preliminary Exam June 10, 2011

Linear Algebra II Lecture 13

Linear Algebra Practice Problems

LINEAR ALGEBRA BOOT CAMP WEEK 4: THE SPECTRAL THEOREM

Index. for generalized eigenvalue problem, butterfly form, 211

Columbus State Community College Mathematics Department Public Syllabus

GATE Engineering Mathematics SAMPLE STUDY MATERIAL. Postal Correspondence Course GATE. Engineering. Mathematics GATE ENGINEERING MATHEMATICS

MATH 1120 (LINEAR ALGEBRA 1), FINAL EXAM FALL 2011 SOLUTIONS TO PRACTICE VERSION

LINEAR ALGEBRA REVIEW

MATH 304 Linear Algebra Lecture 34: Review for Test 2.

MATRIX LIE GROUPS AND LIE GROUPS

GRE Subject test preparation Spring 2016 Topic: Abstract Algebra, Linear Algebra, Number Theory.

Glossary of Linear Algebra Terms. Prepared by Vince Zaccone For Campus Learning Assistance Services at UCSB

x 3y 2z = 6 1.2) 2x 4y 3z = 8 3x + 6y + 8z = 5 x + 3y 2z + 5t = 4 1.5) 2x + 8y z + 9t = 9 3x + 5y 12z + 17t = 7

MA 265 FINAL EXAM Fall 2012

Remark 1 By definition, an eigenvector must be a nonzero vector, but eigenvalue could be zero.

Linear Algebra Final Exam Solutions, December 13, 2008

Measure, Integration & Real Analysis

1. What is the determinant of the following matrix? a 1 a 2 4a 3 2a 2 b 1 b 2 4b 3 2b c 1. = 4, then det

Hands-on Matrix Algebra Using R

Eigenvalues and eigenvectors

Lecture 10 - Eigenvalues problem

The Spectral Theorem for normal linear maps

(Refer Slide Time: 2:04)

Introduction to Applied Linear Algebra with MATLAB

Linear Algebra 1. M.T.Nair Department of Mathematics, IIT Madras. and in that case x is called an eigenvector of T corresponding to the eigenvalue λ.

Chapter 3 Transformations

1 Last time: least-squares problems

Applied Linear Algebra in Geoscience Using MATLAB

The Jordan Canonical Form

MATH 115A: SAMPLE FINAL SOLUTIONS

Linear Algebra Review. Vectors

Math 414: Linear Algebra II, Fall 2015 Final Exam

Linear algebra and applications to graphs Part 1

MATHEMATICS 217 NOTES

SUMMARY OF MATH 1600

ELEMENTARY MATRIX ALGEBRA

Lecture notes on Quantum Computing. Chapter 1 Mathematical Background

Lecture 19: Isometries, Positive operators, Polar and singular value decompositions; Unitary matrices and classical groups; Previews (1)

Solution for Homework 5

Transcription:

Contents Preface for the Instructor xi Preface for the Student xv Acknowledgments xvii 1 Vector Spaces 1 1.A R n and C n 2 Complex Numbers 2 Lists 5 F n 6 Digression on Fields 10 Exercises 1.A 11 1.B Definition of Vector Space 12 Exercises 1.B 17 1.C Subspaces 18 Sums of Subspaces 20 Direct Sums 21 Exercises 1.C 24 2 Finite-Dimensional Vector Spaces 27 2.A Span and Linear Independence 28 Linear Combinations and Span 28 Linear Independence 32 Exercises 2.A 37

vi Contents 2.B Bases 39 Exercises 2.B 43 2.C Dimension 44 3 Linear Maps 51 Exercises 2.C 48 3.A The Vector Space of Linear Maps 52 Definition and Examples of Linear Maps 52 Algebraic Operations on L.V; W / 55 Exercises 3.A 57 3.B Null Spaces and Ranges 59 Null Space and Injectivity 59 Range and Surjectivity 61 Fundamental Theorem of Linear Maps 63 Exercises 3.B 67 3.C Matrices 70 Representing a Linear Map by a Matrix 70 Addition and Scalar Multiplication of Matrices 72 Matrix Multiplication 74 Exercises 3.C 78 3.D Invertibility and Isomorphic Vector Spaces 80 Invertible Linear Maps 80 Isomorphic Vector Spaces 82 Linear Maps Thought of as Matrix Multiplication 84 Operators 86 Exercises 3.D 88 3.E Products and Quotients of Vector Spaces 91 Products of Vector Spaces 91 Products and Direct Sums 93 Quotients of Vector Spaces 94 Exercises 3.E 98

Contents vii 3.F Duality 101 4 Polynomials 117 The Dual Space and the Dual Map 101 The Null Space and Range of the Dual of a Linear Map 104 The Matrix of the Dual of a Linear Map 109 The Rank of a Matrix 111 Exercises 3.F 113 Complex Conjugate and Absolute Value 118 Uniqueness of Coefficients for Polynomials 120 The Division Algorithm for Polynomials 121 Zeros of Polynomials 122 Factorization of Polynomials over C 123 Factorization of Polynomials over R 126 Exercises 4 129 5 Eigenvalues, Eigenvectors, and Invariant Subspaces 131 5.A Invariant Subspaces 132 Eigenvalues and Eigenvectors 133 Restriction and Quotient Operators 137 Exercises 5.A 138 5.B Eigenvectors and Upper-Triangular Matrices 143 Polynomials Applied to Operators 143 Existence of Eigenvalues 145 Upper-Triangular Matrices 146 Exercises 5.B 153 5.C Eigenspaces and Diagonal Matrices 155 Exercises 5.C 160 6 Inner Product Spaces 163 6.A Inner Products and Norms 164 Inner Products 164 Norms 168 Exercises 6.A 175

viii Contents 6.B Orthonormal Bases 180 Linear Functionals on Inner Product Spaces 187 Exercises 6.B 189 6.C Orthogonal Complements and Minimization Problems 193 Orthogonal Complements 193 Minimization Problems 198 Exercises 6.C 201 7 Operators on Inner Product Spaces 203 7.A Self-Adjoint and Normal Operators 204 Adjoints 204 Self-Adjoint Operators 209 Normal Operators 212 Exercises 7.A 214 7.B The Spectral Theorem 217 The Complex Spectral Theorem 217 The Real Spectral Theorem 219 Exercises 7.B 223 7.C Positive Operators and Isometries 225 Positive Operators 225 Isometries 228 Exercises 7.C 231 7.D Polar Decomposition and Singular Value Decomposition 233 Polar Decomposition 233 Singular Value Decomposition 236 Exercises 7.D 238 8 Operators on Complex Vector Spaces 241 8.A Generalized Eigenvectors and Nilpotent Operators 242 Null Spaces of Powers of an Operator 242 Generalized Eigenvectors 244 Nilpotent Operators 248 Exercises 8.A 249

Contents ix 8.B Decomposition of an Operator 252 Description of Operators on Complex Vector Spaces 252 Multiplicity of an Eigenvalue 254 Block Diagonal Matrices 255 Square Roots 258 Exercises 8.B 259 8.C Characteristic and Minimal Polynomials 261 The Cayley Hamilton Theorem 261 The Minimal Polynomial 262 Exercises 8.C 267 8.D Jordan Form 270 Exercises 8.D 274 9 Operators on Real Vector Spaces 275 9.A Complexification 276 Complexification of a Vector Space 276 Complexification of an Operator 277 The Minimal Polynomial of the Complexification 279 Eigenvalues of the Complexification 280 Characteristic Polynomial of the Complexification 283 Exercises 9.A 285 9.B Operators on Real Inner Product Spaces 287 Normal Operators on Real Inner Product Spaces 287 Isometries on Real Inner Product Spaces 292 Exercises 9.B 294 10 Trace and Determinant 295 10.A Trace 296 Change of Basis 296 Trace: A Connection Between Operators and Matrices 299 Exercises 10.A 304

x Contents 10.B Determinant 307 Determinant of an Operator 307 Determinant of a Matrix 309 The Sign of the Determinant 320 Volume 323 Exercises 10.B 330 Photo Credits 333 Symbol Index 335 Index 337