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Elementary Linear Algebra: Applications Version / Edition 11

Elementary Linear Algebra: Applications Version / Edition 11

by Howard Anton, Chris Rorres


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Product Details

ISBN-13: 9781118434413
Publisher: Wiley
Publication date: 11/28/2013
Edition description: New Edition
Pages: 800
Sales rank: 591,206
Product dimensions: 8.30(w) x 10.30(h) x 1.20(d)

Table of Contents

C H A P T E R 1 Systems of Linear Equations andMatrices

1.1 Introduction to Systems of Linear Equations

1.2 Gaussian Elimination

1.3 Matrices and Matrix Operations

1.4 Inverses; Algebraic Properties of Matrices

1.5 Elementary Matrices and a Method for FindingA−1

1.6 More on Linear Systems and Invertible Matrices

1.7 Diagonal, Triangular, and Symmetric Matrices

1.8 Matrix Transformations

1.9 Applications of Linear Systems

• Network Analysis (Traffic Flow)

• Electrical Circuits

• Balancing Chemical Equations

• Polynomial Interpolation

1.10 Application: Leontief Input-Output Models

C H A P T E R 2 Determinants

2.1 Determinants by Cofactor Expansion

2.2 Evaluating Determinants by Row Reduction

2.3 Properties of Determinants; Cramer’s Rule

C H A P T E R 3 Euclidean Vector Spaces

3.1 Vectors in 2-Space, 3-Space, and n-Space

3.2 Norm, Dot Product, and Distance in Rn

3.3 Orthogonality

3.4 The Geometry of Linear Systems

3.5 Cross Product

C H A P T E R 4 General Vector Spaces

4.1 Real Vector Spaces

4.2 Subspaces

4.3 Linear Independence

4.4 Coordinates and Basis

4.5 Dimension

4.6 Change of Basis

4.7 Row Space, Column Space, and Null Space

4.8 Rank, Nullity, and the Fundamental Matrix Spaces

4.9 Basic Matrix Transformations in R2 and R3

4.10 Properties of Matrix Transformations

4.11 Application: Geometry of Matrix Operators onR2

C H A P T E R 5 Eigenvalues and Eigenvectors

5.1 Eigenvalues and Eigenvectors

5.2 Diagonalization

5.3 Complex Vector Spaces

5.4 Application: Differential Equations

5.5 Application: Dynamical Systems and Markov Chains

C H A P T E R 6 Inner Product Spaces

6.1 Inner Products

6.2 Angle and Orthogonality in Inner Product Spaces

6.3 Gram–Schmidt Process; QR-Decomposition

6.4 Best Approximation; Least Squares

6.5 Application: Mathematical Modeling Using LeastSquares

6.6 Application: Function Approximation; FourierSeries

C H A P T E R 7 Diagonalization and Quadratic Forms

7.1 Orthogonal Matrices

7.2 Orthogonal Diagonalization

7.3 Quadratic Forms

7.4 Optimization Using Quadratic Forms

7.5 Hermitian, Unitary, and Normal Matrices

C H A P T E R 8 General Linear Transformations

8.1 General Linear Transformation

8.2 Compositions and Inverse Transformations

8.3 Isomorphism

8.4 Matrices for General Linear Transformations

8.5 Similarity

C H A P T E R 9 Numerical Methods

9.1 LU-Decompositions

9.2 The Power Method

9.3 Comparison of Procedures for Solving Linear Systems

9.4 Singular Value Decomposition

9.5 Application: Data Compression Using Singular ValueDecomposition

C H A PT E R 10 Applications of Linear Algebra

10.1 Constructing Curves and Surfaces Through SpecifiedPoints

10.2 The Earliest Applications of Linear Algebra

10.3 Cubic Spline Interpolation

10.4 Markov Chains

10.5 Graph Theory

10.6 Games of Strategy

10.7 Leontief Economic Models

10.8 Forest Management

10.9 Computer Graphics

10.10 Equilibrium Temperature Distributions

10.11 Computed Tomography

10.12 Fractals

10.13 Chaos

10.14 Cryptography

10.15 Genetics

10.16 Age-Specific Population Growth

10.17 Harvesting of Animal Populations

10.18 A Least Squares Model for Human Hearing

10.19 Warps and Morphs

10.20 Internet Search Engines

A P P E N D I X A Working with Proofs

A P P E N D I X B Complex Numbers

Answers to Exercises


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