### Transcription of Elementary Linear Algebra - Cengage

1 **Elementary** **Linear** **Algebra** SIXTH EDITION. RON LARSON. The Pennsylvania State University The Behrend College DAVI D C. FALVO. The Pennsylvania State University The Behrend College HOUGHTON MIFFLIN HARCOURT PUBLISHING COMPANY Boston New York Publisher: Richard Stratton Senior Sponsoring Editor: Cathy Cantin Senior Marketing Manager: Jennifer Jones Discipline Product Manager: Gretchen Rice King Associate Editor: Janine Tangney Associate Editor: Jeannine Lawless Senior Project Editor: Kerry Falvey Program Manager: Touraj Zadeh Senior Media Producer: Douglas Winicki Senior Content Manager: Maren Kunert Art and Design Manager: Jill Haber Cover Design Manager: Anne S.

2 Katzeff Senior Photo Editor: Jennifer Meyer Dare Senior Composition Buyer: Chuck Dutton New Title Project Manager: Susan Peltier Manager of New Title Project Management: Pat O'Neill Editorial Assistant: Amy Haines Marketing Assistant: Michael Moore Editorial Assistant: Laura Collins Cover image: Carl Reader/age fotostock Copyright 2009 by Houghton Mifflin Harcourt Publishing Company. All rights reserved. No part of this work may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying and recording, or by any information storage or retrieval system without the prior written permission of Houghton Mifflin Harcourt Publishing Company unless such copying is expressly permitted by federal copyright law.

3 Address inquiries to College Permissions, Houghton Mifflin Harcourt Publishing Company, 222 Berkeley Street, Boston, MA 02116-3764. Printed in the Library of Congress Control Number: 2007940572. Instructor's examination copy ISBN-10: 0-547-00481-8. ISBN-13: 978-0-547-00481-5. For orders, use student text ISBNs ISBN-10: 0-618-78376-8. ISBN-13: 978-0-618-78376-2. 123456789-DOC-12 11 10 09 08. Contents A WORD FROM THE AUTHORS vii WHAT IS **Linear** **Algebra** ? xv CHAPTER 1 SYSTEMS OF **Linear** EQUATIONS 1. **introduction** to Systems of **Linear** Equations 1. Gaussian Elimination and Gauss-Jordan Elimination 14. Applications of Systems of **Linear** Equations 29.

4 Review Exercises 41. Project 1 Graphing **Linear** Equations 44. Project 2 Underdetermined and Overdetermined Systems of Equations 45. CHAPTER 2 MATRICES 46. Operations with Matrices 46. Properties of Matrix Operations 61. The Inverse of a Matrix 73. **Elementary** Matrices 87. Applications of Matrix Operations 98. Review Exercises 115. Project 1 Exploring Matrix Multiplication 120. Project 2 Nilpotent Matrices 121. iii iv Contents CHAPTER 3 DETERMINANTS 122. The Determinant of a Matrix 122. Evaluation of a Determinant Using **Elementary** Operations 132. Properties of Determinants 142. **introduction** to Eigenvalues 152. Applications of Determinants 158.

5 Review Exercises 171. Project 1 Eigenvalues and Stochastic Matrices 174. Project 2 The Cayley-Hamilton Theorem 175. Cumulative Test for Chapters 1 3 177. CHAPTER 4 VECTOR SPACES 179. n Vectors in R 179. Vector Spaces 191. Subspaces of Vector Spaces 198. Spanning Sets and **Linear** Independence 207. Basis and Dimension 221. Rank of a Matrix and Systems of **Linear** Equations 232. Coordinates and Change of Basis 249. Applications of Vector Spaces 262. Review Exercises 272. Project 1 Solutions of **Linear** Systems 275. Project 2 Direct Sum 276. CHAPTER 5 INNER PRODUCT SPACES 277. n Length and Dot Product in R 277. Inner Product Spaces 292.

6 Orthonormal Bases: Gram-Schmidt Process 306. Mathematical Models and Least Squares Analysis 320. Applications of Inner Product Spaces 336. Review Exercises 352. Project 1 The QR-Factorization 356. Project 2 Orthogonal Matrices and Change of Basis 357. Cumulative Test for Chapters 4 and 5 359. Contents v CHAPTER 6 **Linear** TRANSFORMATIONS 361. **introduction** to **Linear** Transformations 361. The Kernel and Range of a **Linear** Transformation 374. Matrices for **Linear** Transformations 387. Transition Matrices and Similarity 399. Applications of **Linear** Transformations 407. Review Exercises 416. Project 1 Reflections in the Plane (I) 419.

7 Project 2 Reflections in the Plane (II) 420. CHAPTER 7 EIGENVALUES AND EIGENVECTORS 421. Eigenvalues and Eigenvectors 421. Diagonalization 435. Symmetric Matrices and Orthogonal Diagonalization 446. Applications of Eigenvalues and Eigenvectors 458. Review Exercises 474. Project 1 Population Growth and Dynamical Systems (I) 477. Project 2 The Fibonacci Sequence 478. Cumulative Test for Chapters 6 and 7 479. CHAPTER 8 COMPLEX VECTOR SPACES (online)*. Complex Numbers Conjugates and Division of Complex Numbers Polar Form and DeMoivre's Theorem Complex Vector Spaces and Inner Products Unitary and Hermitian Matrices Review Exercises Project Population Growth and Dynamical Systems (II).

8 Vi Contents CHAPTER 9 **Linear** PROGRAMMING (online)*. Systems of **Linear** Inequalities **Linear** Programming Involving Two Variables The Simplex Method: Maximization The Simplex Method: Minimization The Simplex Method: Mixed Constraints Review Exercises Project Cholesterol Levels CHAPTER 10 NUMERICAL METHODS (online)*. Gaussian Elimination with Partial Pivoting Iterative Methods for Solving **Linear** Systems Power Method for Approximating Eigenvalues Applications of Numerical Methods Review Exercises Project Population Growth APPENDIX MATHEMATICAL INDUCTION AND OTHER A1. FORMS OF PROOFS. ONLINE TECHNOLOGY GUIDE (online)*. ANSWER KEY A9.

9 INDEX A59. *Available online at A Word from the Authors Welcome! We have designed **Elementary** **Linear** **Algebra** , Sixth Edition, for the introductory **Linear** **Algebra** course. Students embarking on a **Linear** **Algebra** course should have a thorough knowledge of **Algebra** , and familiarity with analytic geometry and trigonometry. We do not assume that calculus is a prerequisite for this course, but we do include examples and exercises requir- ing calculus in the text. These exercises are clearly labeled and can be omitted if desired. Many students will encounter mathematical formalism for the first time in this course. As a result, our primary goal is to present the major concepts of **Linear** **Algebra** clearly and concisely.

10 To this end, we have carefully selected the examples and exercises to balance theory with applications and geometrical intuition. The order and coverage of topics were chosen for maximum efficiency, effectiveness, and balance. For example, in Chapter 4 we present the main ideas of vector spaces and bases, beginning with a brief look leading into the vector space concept as a natural exten- sion of these familiar examples. This material is often the most difficult for students, but our approach to **Linear** independence, span, basis, and dimension is carefully explained and illustrated by examples. The eigenvalue problem is developed in detail in Chapter 7, but we lay an intuitive foundation for students earlier in Section , Section , and Chapter 4.