Linear Algebra

LINEAR ALGEBRA

EXPLORE A COMPREHENSIVE INTRODUCTORY TEXT IN LINEAR ALGEBRA WITH COMPELLING SUPPLEMENTARY MATERIALS, INCLUDING A COMPANION WEBSITE AND SOLUTIONS MANUALS

Linear Algebra delivers a fulsome exploration of the central concepts in linear algebra, including multidimensional spaces, linear transformations, matrices, matrix algebra, determinants, vector spaces, subspaces, linear independence, basis, inner products, and eigenvectors. While the text provides challenging problems that engage readers in the mathematical theory of linear algebra, it is written in an accessible and simple-to-grasp fashion appropriate for junior undergraduate students.

An emphasis on logic, set theory, and functions exists throughout the book, and these topics are introduced early to provide students with a foundation from which to attack the rest of the material in the text. Linear Algebra includes accompanying material in the form of a companion website that features solutions manuals for students and instructors. Finally, the concluding chapter in the book includes discussions of advanced topics like generalized eigenvectors, Schur's Lemma, Jordan canonical form, and quadratic forms. Readers will also benefit from the inclusion of:

• A thorough introduction to logic and set theory, as well as descriptions of functions and linear transformations

• An exploration of Euclidean spaces and linear transformations between Euclidean spaces, including vectors, vector algebra, orthogonality, the standard matrix, Gauss-Jordan elimination, inverses, and determinants

• Discussions of abstract vector spaces, including subspaces, linear independence, dimension, and change of basis

• A treatment on defining geometries on vector spaces, including the Gram-Schmidt process

Perfect for undergraduate students taking their first course in the subject matter, Linear Algebra will also earn a place in the libraries of researchers in computer science or statistics seeking an accessible and practical foundation in linear algebra.

Autorentext

MICHAEL L. O'LEARY, is Professor of Mathematics at College of DuPage in Glen Ellyn, Illinois. He received his doctoral degree in mathematics from the University of California, Irvine in 1994 and is the author of A First Course in Mathematical Logic and Set Theory and Revolutions of Geometry, both published by Wiley.

Zusammenfassung
LINEAR ALGEBRA EXPLORE A COMPREHENSIVE INTRODUCTORY TEXT IN LINEAR ALGEBRA WITH COMPELLING SUPPLEMENTARY MATERIALS, INCLUDING A COMPANION WEBSITE AND SOLUTIONS MANUALS

Linear Algebra delivers a fulsome exploration of the central concepts in linear algebra, including multidimensional spaces, linear transformations, matrices, matrix algebra, determinants, vector spaces, subspaces, linear independence, basis, inner products, and eigenvectors. While the text provides challenging problems that engage readers in the mathematical theory of linear algebra, it is written in an accessible and simple-to-grasp fashion appropriate for junior undergraduate students.

An emphasis on logic, set theory, and functions exists throughout the book, and these topics are introduced early to provide students with a foundation from which to attack the rest of the material in the text. Linear Algebra includes accompanying material in the form of a companion website that features solutions manuals for students and instructors. Finally, the concluding chapter in the book includes discussions of advanced topics like generalized eigenvectors, Schur's Lemma, Jordan canonical form, and quadratic forms. Readers will also benefit from the inclusion of:

• A thorough introduction to logic and set theory, as well as descriptions of functions and linear transformations
• An exploration of Euclidean spaces and linear transformations between Euclidean spaces, including vectors, vector algebra, orthogonality, the standard matrix, Gauss-Jordan elimination, inverses, and determinants
• Discussions of abstract vector spaces, including subspaces, linear independence, dimension, and change of basis
• A treatment on defining geometries on vector spaces, including the Gram-Schmidt process
  Perfect for undergraduate students taking their first course in the subject matter, Linear Algebra will also earn a place in the libraries of researchers in computer science or statistics seeking an accessible and practical foundation in linear algebra.

Inhalt

Preface xi

Acknowledgments xv

1 Logic and Set Theory 1

1.1 Statements 1

Connectives 2

Logical Equivalence 3

1.2 Sets and Quantification 7

Universal Quantification 8

Existential Quantification 9

Negating Quantification 10

Set-Builder Notation 12

Set Operations 13

Families of Sets 14

1.3 Sets and Proofs 18

Direct Proof 20

Subsets 22

Set Equality 23

Indirect Proof 24

Mathematical Induction 25

1.4 Functions 30

Injections 33

Surjections 35

Bijections and Inverses 37

Images and Inverse Images 40

Operations 41

2 Euclidean Space 49

2.1 Vectors 49

Vector Operations 51

Distance and Length 57

Lines and Planes 64

2.2 Dot Product 74

Lines and Planes 77

Orthogonal Projection 82

2.3 Cross Product 88

Properties 91

Areas and Volumes 93

3 Transformations and Matrices 99

3.1 Linear Transformations 99

Properties 103

Matrices 106

3.2 Matrix Algebra 116

Addition, Subtraction, and Scalar Multiplication 116

Properties 119

Multiplication 122

Identity Matrix 129

Distributive Law 132

Matrices and Polynomials 132

3.3 Linear Operators 137

Re_ections 137

Rotations 142

Isometries 147

Contractions, Dilations, and Shears 150

3.4 Injections and Surjections 155

Kernel 155

Range 158

3.5 GaussJordan Elimination 162

Elementary Row Operations 164

Square Matrices 167

Nonsquare Matrices 171

Gaussian Elimination 177

4 Invertibility 183

4.1 Invertible Matrices 183

Elementary Matrices 186

Finding the Inverse of a Matrix 192

Systems of Linear Equations 194

4.2 Determinants 198

Multiplying a Row by a Scalar 203

Adding a Multiple of a Row to Another Row 205

Switching Rows 210

4.3 Inverses and Determinants 215

Uniqueness of the Determinant 216

Equivalents to Invertibility 220

Products 222

4.4 Applications 227

The Classical Adjoint 228

Symmetric and Orthogonal Matrices 229

Cramer's Rule 234

LU Factorization 236

Area and Volume 238

5 Abstract Vectors 245

5.1 Vector Spaces 245

Examples of Vector Spaces 247

Linear Transformations 253

5.2 Subspaces 259

Examples of Subspaces 260

Properties 261

Spanning Sets 264

Kernel and Range 266

5.3 Linear Independence 272

Euclidean Examples 274

Abstract Vector Space Examples 276

5.4 Basis and Dimension 281

Basis 281

Zorn's Lemma 285

Dimension 287

Expansions and Reductions 290

5.5 Rank and Nullity 296

Rank-Nullity Theorem 297

Fundamental Subspaces 302

Rank and Nullity of a Matrix 304

5.6 Isomorphism 310

Coordinates 315

Change of Basis 320

Matrix of a Linear Transformation 324

6 Inner Product Spaces 335

6.1 Inner Products 335

Norms 341

Metrics 342

Angles 344

Orthogonal Projection 347

6.2 Orthonormal Bases 352

Orthogonal Complement 355

Direct Sum 357

GramSchmidt Process 361

QR Factorization 366

7 Matrix Theory 373 7.1 Ei...