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• Title Page
• Copyright
• Dedication
• About the Authors
• Contents
• Applications Index
• Preface
• Get the most out of MyLab Math
• Resources for Success
• A Note to Students
• Chapter 1: Linear Equations in Linear Algebra
• Introductory Example: Linear Models in Economics and Engineering
• 1.1 Systems of Linear Equations
• 1.2 Row Reduction and Echelon Forms
• 1.3 Vector Equations
• 1.4 The Matrix Equation Ax = b
• 1.5 Solution Sets of Linear Systems
• 1.6 Applications of Linear Systems
• 1.7 Linear Independence
• 1.8 Introduction to Linear Transformations
• 1.9 The Matrix of a Linear Transformation
• 1.10 Linear Models in Business, Science, and Engineering
• Projects
• Supplementary Exercises
• Chapter 2: Matrix Algebra
• Introductory Example: Computer Models in Aircraft Design
• 2.1 Matrix Operations
• 2.2 The Inverse of a Matrix
• 2.3 Characterizations of Invertible Matrices
• 2.4 Partitioned Matrices
• 2.5 Matrix Factorizations
• 2.6 The Leontief Input–Output Model
• 2.7 Applications to Computer Graphics
• 2.8 Subspaces of Rn
• 2.9 Dimension and Rank
• Projects
• Supplementary Exercises
• Chapter 3: Determinants
• Introductory Example: Weighing Diamonds
• 3.1 Introduction to Determinants
• 3.2 Properties of Determinants
• 3.3 Cramer’s Rule, Volume, and Linear Transformations
• Projects
• Supplementary Exercises
• Chapter 4: Vector Spaces
• Introductory Example: Discrete-Time Signals and Digital Signal Processing
• 4.1 Vector Spaces and Subspaces
• 4.2 Null Spaces, Column Spaces, Row Spaces, and Linear Transformations
• 4.3 Linearly Independent Sets; Bases
• 4.4 Coordinate Systems
• 4.5 The Dimension of a Vector Space
• 4.6 Change of Basis
• 4.7 Digital Signal Processing
• 4.8 Applications to Difference Equations
• Projects
• Supplementary Exercises
• Chapter 5: Eigenvalues and Eigenvectors
• Introductory Example: Dynamical Systems and Spotted Owls
• 5.1 Eigenvectors and Eigenvalues
• 5.2 The Characteristic Equation
• 5.3 Diagonalization
• 5.4 Eigenvectors and Linear Transformations
• 5.5 Complex Eigenvalues
• 5.6 Discrete Dynamical Systems
• 5.7 Applications to Differential Equations
• 5.8 Iterative Estimates for Eigenvalues
• 5.9 Applications to Markov Chains
• Projects
• Supplementary Exercises
• Chapter 6: Orthogonality and Least Squares
• Introductory Example: Artificial Intelligence and Machine Learning
• 6.1 Inner Product, Length, and Orthogonality
• 6.2 Orthogonal Sets
• 6.3 Orthogonal Projections
• 6.4 The Gram–Schmidt Process
• 6.5 Least-Squares Problems
• 6.6 Machine Learning and Linear Models
• 6.7 Inner Product Spaces
• 6.8 Applications of Inner Product Spaces
• Projects
• Supplementary Exercises
• Chapter 7: Symmetric Matrices and Quadratic Forms
• Introductory Example: Multichannel Image Processing
• 7.1 Diagonalization of Symmetric Matrices
• 7.2 Quadratic Forms
• 7.3 Constrained Optimization
• 7.4 The Singular Value Decomposition
• 7.5 Applications to Image Processing and Statistics
• Projects
• Supplementary Exercises
• Chapter 8: The Geometry of Vector Spaces
• Introductory Example: The Platonic Solids
• 8.1 Affine Combinations
• 8.2 Affine Independence
• 8.3 Convex Combinations
• 8.4 Hyperplanes
• 8.5 Polytopes
• 8.6 Curves and Surfaces
• Project
• Supplementary Exercises
• Chapter 9: Optimization
• Introductory Example: The Berlin Airlift
• 9.1 Matrix Games
• 9.2 Linear Programming Geometric Method
• 9.3 Linear Programming Simplex Method
• 9.4 Duality
• Project
• Supplementary Exercises
• Appendixes
• Appendix A: Uniqueness of the Reduced Echelon Form
• Appendix B: Complex Numbers
• Credits
• Glossary
• Answers to Odd-Numbered Exercises
• Index
• A
• B
• C
• D
• E
• F
• G
• H
• I
• J
• K
• L
• M
• N
• O
• P
• Q
• R
• S
• T
• U
• V
• W
• Z
• Advice on Reading Linear Algebra