Matrix Methods: Applied Linear Algebra and Sabermetrics 4th Edition Richard Bronson 0128184205 9780128184202

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Product details:

• ISBN 10: 0128184205
• ISBN 13: 9780128184202
• Author: Richard Bronson

Matrix Methods: Applied Linear Algebra and Sabermetrics, Fourth Edition, provides a unique and comprehensive balance between the theory and computation of matrices. Rapid changes in technology have made this valuable overview on the application of matrices relevant not just to mathematicians, but to a broad range of other fields. Matrix methods, the essence of linear algebra, can be used to help physical scientists– chemists, physicists, engineers, statisticians, and economists– solve real world problems.

Table of contents:

Chapter 1. Matrices
1.1. Basic concepts
Problems 1.1
1.2. Operations
Problems 1.2
1.3. Matrix multiplication
Problems 1.3
1.4. Special matrices
Problems 1.4
1.5. Submatrices and partitioning
Problems 1.5
1.6. Vectors
Problems 1.6
1.7. The geometry of vectors
Problems 1.7
Chapter 2. Simultaneous linear equations
2.1. Linear systems
Problems 2.1
2.2. Solutions by substitution
Problems 2.2
2.3. Gaussian elimination
Problems 2.3
2.4. Pivoting strategies
Problems 2.4
2.5. Linear independence
Problems 2.5
2.6. Rank
Problems 2.6
2.7. Theory of solutions
Problems 2.7
2.8. Final comments on Chapter 2
Chapter 3. The inverse
3.1. Introduction
Problems 3.1
3.2. Calculating inverses
Problems 3.2
3.3. Simultaneous equations
Problems 3.3
3.4. Properties of the inverse
Problems 3.4
3.5. LU decomposition
Problems 3.5
3.6. Final comments on Chapter 3
Chapter 4. An introduction to optimization
4.1. Graphing inequalities
Problems 4.1
4.2. Modeling with inequalities
Problems 4.2
4.3. Solving problems using linear programming
Problems 4.3
4.4. An introduction to the simplex method
Problems 4.4
4.5. Final comments on Chapter 4
Chapter 5. Determinants
5.1. Introduction
Problems 5.1
5.2. Expansion by cofactors
Problems 5.2
5.3. Properties of determinants
Problems 5.3
5.4. Pivotal condensation
Problems 5.4
5.5. Inversion
Problems 5.5
5.6. Cramer’s rule
Problems 5.6
5.7. Final comments on Chapter 5
Chapter 6. Eigenvalues and eigenvectors
6.1. Definitions
Problems 6.1
6.2. Eigenvalues
Problems 6.2
6.3. Eigenvectors
Problems 6.3
6.4. Properties of eigenvalues and eigenvectors
Problems 6.4
6.5. Linearly independent eigenvectors
Problems 6.5
6.6. Power methods
Problems 6.6
Chapter 7. Matrix calculus
7.1. Well-defined functions
Problems 7.1
7.2. Cayley–Hamilton theorem
Problems 7.2
7.3. Polynomials of matrices—distinct eigenvalues
Problems 7.3
7.4. Polynomials of matrices—general case
Problems 7.4
7.5. Functions of a matrix
Problems 7.5
7.6. The function eAt
Problems 7.6
7.7. Complex eigenvalues
Problems 7.7
7.8. Properties of eA
Problems 7.8
7.9. Derivatives of a matrix
Problems 7.9
7.10. Final comments on Chapter 7
Chapter 8. Linear differential equations
8.1. Fundamental form
Problems 8.1
8.2. Reduction of an nth order equation
Problems 8.2
8.3. Reduction of a system
Problems 8.3
8.4. Solutions of systems with constant coefficients
Problems 8.4
8.5. Solutions of systems—general case
Problem 8.5
8.6. Final comments on Chapter 8
Chapter 9. Probability and Markov chains
9.1. Probability: an informal approach
Problems 9.1
9.2. Some laws of probability
Problems 9.2
9.3. Bernoulli trials and combinatorics
Problems 9.3
9.4. Modeling with Markov chains: an introduction
Problems 9.4
9.5. Final comments on Chapter 9
Chapter 10. Real inner products and least squares
10.1. Introduction
Problems 10.1
10.2. Orthonormal vectors
Problems 10.2
10.3. Projections and QR decompositions
Problems 10.3
10.4. The QR algorithm
Problems 10.4
10.5. Least squares
Problems 10.5
Chapter 11. Sabermetrics – An introduction
11.1. Introductory comments
11.2. Some basic measures
11.3. Sabermetrics in the classroom
11.4. Run expectancy matrices
11.5. How to “do” sabermetrics
11.6. Informal reference list
11.7. Testing
Chapter 12. Sabermetrics – A module

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