Details

Linear Algebra


Linear Algebra


1. Aufl.

von: Michael L. O'Leary

103,99 €

Verlag: Wiley
Format: PDF
Veröffentl.: 20.10.2021
ISBN/EAN: 9781119437475
Sprache: englisch
Anzahl Seiten: 464

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Beschreibungen

<b>LINEAR</b> ALGEBRA <p><b>EXPLORE A COMPREHENSIVE INTRODUCTORY TEXT IN LINEAR ALGEBRA WITH COMPELLING SUPPLEMENTARY MATERIALS, INCLUDING A COMPANION WEBSITE AND SOLUTIONS MANUALS</b></p> <p><i>Linear Algebra</i> 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.</p> <p>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. <i>Linear Algebra</i> 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:</p> <ul> <li>A thorough introduction to logic and set theory, as well as descriptions of functions and linear transformations</li> <li>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</li> <li>Discussions of abstract vector spaces, including subspaces, linear independence, dimension, and change of basis</li> <li>A treatment on defining geometries on vector spaces, including the Gram-Schmidt process</li> </ul> <p>Perfect for undergraduate students taking their first course in the subject matter, <i>Linear Algebra</i> will also earn a place in the libraries of researchers in computer science or statistics seeking an accessible and practical foundation in linear algebra.</p>
<p>Preface xi</p> <p>Acknowledgments xv</p> <p>About the Companion Website xvi</p> <p><b>1 Logic and Set Theory 1</b></p> <p>1.1 Statements 1</p> <p>Connectives 2</p> <p>Logical Equivalence 3</p> <p>1.2 Sets and Quantification 7</p> <p>Universal Quantifiers 8</p> <p>Existential Quantifiers 9</p> <p>Negating Quantifiers 10</p> <p>Set-Builder Notation 12</p> <p>Set Operations 13</p> <p>Families of Sets 14</p> <p>1.3 Sets and Proofs 18</p> <p>Direct Proof 20</p> <p>Subsets 22</p> <p>Set Equality 23</p> <p>Indirect Proof 24</p> <p>Mathematical Induction 25</p> <p>1.4 Functions 30</p> <p>Injections 33</p> <p>Surjections 35</p> <p>Bijections and Inverses 37</p> <p>Images and Inverse Images 40</p> <p>Operations 41</p> <p><b>2 Euclidean Space 49</b></p> <p>2.1 Vectors 49</p> <p>Vector Operations 51</p> <p>Distance and Length 57</p> <p>Lines and Planes 64</p> <p>2.2 Dot Product 74</p> <p>Lines and Planes 77</p> <p>Orthogonal Projection 82</p> <p>2.3 Cross Product 88</p> <p>Properties 91</p> <p>Areas and Volumes 93</p> <p><b>3 Transformations and Matrices 99</b></p> <p>3.1 Linear Transformations 99</p> <p>Properties 103</p> <p>Matrices 106</p> <p>3.2 Matrix Algebra 116</p> <p>Addition, Subtraction, and Scalar Multiplication 116</p> <p>Properties 119</p> <p>Multiplication 122</p> <p>Identity Matrix 129</p> <p>Distributive Law 132</p> <p>Matrices and Polynomials 132</p> <p>3.3 Linear Operators 137</p> <p>Reflections 137</p> <p>Rotations 142</p> <p>Isometries 147</p> <p>Contractions, Dilations, and Shears 150</p> <p>3.4 Injections and Surjections 155</p> <p>Kernel 155</p> <p>Range 158</p> <p>3.5 Gauss–Jordan Elimination 162</p> <p>Elementary Row Operations 164</p> <p>Square Matrices 167</p> <p>Nonsquare Matrices 171</p> <p>Gaussian Elimination 177</p> <p><b>4 Invertibility 183</b></p> <p>4.1 Invertible Matrices 183</p> <p>Elementary Matrices 186</p> <p>Finding the Inverse of a Matrix 192</p> <p>Systems of Linear Equations 194</p> <p>4.2 Determinants 198</p> <p>Multiplying a Row by a Scalar 203</p> <p>Adding a Multiple of a Row to Another Row 205</p> <p>Switching Rows 210</p> <p>4.3 Inverses and Determinants 215</p> <p>Uniqueness of the Determinant 216</p> <p>Equivalents to Invertibility 220</p> <p>Products 222</p> <p>4.4 Applications 227</p> <p>The Classical Adjoint 228</p> <p>Symmetric and Orthogonal Matrices 229</p> <p>Cramer’s Rule 234</p> <p>LU Factorization 236</p> <p>Area and Volume 238</p> <p><b>5 Abstract Vectors 245</b></p> <p>5.1 Vector Spaces 245</p> <p>Examples of Vector Spaces 247</p> <p>Linear Transformations 253</p> <p>5.2 Subspaces 259</p> <p>Examples of Subspaces 260</p> <p>Properties 261</p> <p>Spanning Sets 264</p> <p>Kernel and Range 266</p> <p>5.3 Linear Independence 272</p> <p>Euclidean Examples 274</p> <p>Abstract Vector Space Examples 276</p> <p>5.4 Basis and Dimension 281</p> <p>Basis 281</p> <p>Zorn’s Lemma 285</p> <p>Dimension 287</p> <p>Expansions and Reductions 290</p> <p>5.5 Rank and Nullity 296</p> <p>Rank-Nullity Theorem 297</p> <p>Fundamental Subspaces 302</p> <p>Rank and Nullity of a Matrix 304</p> <p>5.6 Isomorphism 310</p> <p>Coordinates 315</p> <p>Change of Basis 320</p> <p>Matrix of a Linear Transformation 324</p> <p><b>6 Inner Product Spaces 335</b></p> <p>6.1 Inner Products 335</p> <p>Norms 341</p> <p>Metrics 342</p> <p>Angles 344</p> <p>Orthogonal Projection 347</p> <p>6.2 Orthonormal Bases 352</p> <p>Orthogonal Complement 355</p> <p>Direct Sum 357</p> <p>Gram–Schmidt Process 361</p> <p>QR Factorization 366</p> <p><b>7 Matrix Theory 373</b></p> <p>7.1 Eigenvectors and Eigenvalues 373</p> <p>Eigenspaces 375</p> <p>Characteristic Polynomial 377</p> <p>Cayley–Hamilton Theorem 382</p> <p>7.2 Minimal Polynomial 386</p> <p>Invariant Subspaces 389</p> <p>Generalized Eigenvectors 391</p> <p>Primary Decomposition Theorem 393</p> <p>7.3 Similar Matrices 402</p> <p>Schur’s Lemma 405</p> <p>Block Diagonal Form 408</p> <p>Nilpotent Matrices 412</p> <p>Jordan Canonical Form 415</p> <p>7.4 Diagonalization 422</p> <p>Orthogonal Diagonalization 426</p> <p>Simultaneous Diagonalization 428</p> <p>Quadratic Forms 432</p> <p>Further Reading 441</p> <p>Index 443</p>
<p><b>MICHAEL L. O’LEARY,</b> 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 <i>A First Course in Mathematical Logic and Set Theory and Revolutions of Geometry</i>, both published by Wiley.</p>
<p><b>EXPLORE A COMPREHENSIVE INTRODUCTORY TEXT IN LINEAR ALGEBRA WITH COMPELLING SUPPLEMENTARY MATERIALS, INCLUDING A COMPANION WEBSITE AND SOLUTIONS MANUALS</b></p><p><i>Linear Algebra</i> 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.</p><p>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. <i>Linear Algebra</i> 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:</p><ul><li>A thorough introduction to logic and set theory, as well as descriptions of functions and linear transformations</li><li>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</li><li>Discussions of abstract vector spaces, including subspaces, linear independence, dimension, and change of basis</li><li>A treatment on defining geometries on vector spaces, including the Gram-Schmidt process</li></ul><p>Perfect for undergraduate students taking their first course in the subject matter, <i>Linear Algebra</i> will also earn a place in the libraries of researchers in computer science or statistics seeking an accessible and practical foundation in linear algebra.</p>

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