<p><span>This textbook is designed for a first course in linear algebra for undergraduate students from a wide range of quantitative and data driven fields. By focusing on applications and implementation, students will be prepared to go on to apply the power of linear algebra in their own discipline
Matrix Methods: Applied Linear Algebra and Sabermetrics
β Scribed by Richard Bronson, Gabriel B. Costa
- Publisher
- Academic Pr
- Year
- 2020
- Tongue
- English
- Leaves
- 494
- Edition
- 4
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Synopsis
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.
- Provides early coverage of applications like Markov chains, graph theory and Leontief Models
- Contains accessible content that requires only a firm understanding of algebra
- Includes dedicated chapters on Linear Programming and Markov Chains
β¦ Table of Contents
Matrix MethodsApplied Linear Algebra and SabermetricsFourth EditionRichard BronsonGabriel B. Costa?
Copyright
Dedications
Preface to the third edition
Preface to the fourth edition
. About the authors
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
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
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
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
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
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
7. Matrix calculus
7.1 Well-defined functions
Problems 7.1
7.2 CayleyβHamilton theorem
Problems 7.2
7.3 Polynomials of matricesβdistinct eigenvalues
Problems 7.3
7.4 Polynomials of matricesβgeneral 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
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 systemsβgeneral case
Problem 8.5
8.6 Final comments on Chapter 8
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
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
11. Sabermetrics β 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 βdoβ sabermetrics
Sabermetrics β the search for objective knowledge about baseball (Bill James)
How to do sabermetrics
11.6 Informal reference list
(A) Websites
(B) Books
11.7 Testing
12. Sabermetrics β A module
12.1 Base stealing runs (BSRs)
12.2 Batting linear weights runs (BLWTS)
12.3 Equivalence coefficient (EC)
12.4 Isolated power (ISO)
12.5 On base average (OBA)
12.6 On base plus slugging (OPS)
12.7 Power factor (PF)
12.8 Power-speed number (PSN)
12.9 Runs created (RC)
12.10 Slugging times on base average (SLOB)
12.11 Total power quotient (TPQ)
12.12 Modified weighted pitcher's rating (MWPR)
12.13 Pitching linear weights runs (PLWTS)
12.14 Walks plus hits per innings pitched (WHIP)
A word on technology
Answers and hints to selected problems
Chapter 1
Section 1.1
Section 1.1
Section 1.2
Section 1.2
Section 1.3
Section 1.3
Section 1.4
Section 1.4
Section 1.5
Section 1.5
Section 1.6
Section 1.6
Section 1.7
Section 1.7
Chapter 2
Section 2.1
Section 2.1
Section 2.2
Section 2.2
Section 2.3
Section 2.3
Section 2.4
Section 2.4
Section 2.5
Section 2.5
Section 2.6
Section 2.6
Section 2.7
Section 2.7
Chapter 3
Section 3.1
Section 3.1
Section 3.2
Section 3.2
Section 3.3
Section 3.3
Section 3.4
Section 3.4
Section 3.5
Section 3.5
Chapter 4
Section 4.1
Section 4.1
Section 4.2
Section 4.2
Section 4.3
Section 4.3
Section 4.4
Section 4.4
Chapter 5
Section 5.1
Section 5.1
Section 5.2
Section 5.2
Section 5.3
Section 5.3
Section 5.4
Section 5.4
Section 5.5
Section 5.5
Section 5.6
Section 5.6
Chapter 6
Section 6.1
Section 6.1
Section 6.2
Section 6.2
Section 6.3
Section 6.3
Section 6.4
Section 6.4
Section 6.5
Section 6.5
Section 6.6
Section 6.6
Chapter 7
Section 7.1
Section 7.1
Section 7.2
Section 7.2
Section 7.3
Section 7.3
Section 7.4
Section 7.4
Section 7.5
Section 7.5
Section 7.6
Section 7.6
Section 7.7
Section 7.7
Section 7.8
Section 7.8
Section 7.9
Section 7.9
Chapter 8
Section 8.1
Section 8.1
Section 8.2
Section 8.2
Section 8.3
Section 8.3
Section 8.4
Section 8.4
Section 8.5
Section 8.5
Chapter 9
Section 9.1
Section 9.1
Section 9.2
Section 9.2
Section 9.3
Section 9.3
Section 9.4
Section 9.4
Chapter 10
Section 10.1
Section 10.1
Section 10.2
Section 10.2
Section 10.3
Section 10.3
Section 10.4
Section 10.4
Section 10.5
Section 10.5
Chapter 11
Chapter 12
Index
A
B
C
D
E
F
G
H
I
L
M
N
O
P
Q
R
S
T
U
V
W
Z
π SIMILAR VOLUMES
<p><span>This textbook is designed for a first course in linear algebra for undergraduate students from a wide range of quantitative and data driven fields. By focusing on applications and implementation, students will be prepared to go on to apply the power of linear algebra in their own discipline
This book presents a substantial part of matrix analysis that is functional analytic in spirit. Topics covered include the theory of majorization, variational principles for eigenvalues, operator monotone and convex functions, and perturbation of matrix functions and matrix inequalities. The book of
"Matrix Methods: Applied Linear Algebra, 3e, as a textbook, provides a unique and comprehensive balance between the theory and computation of matrices. The application of matrices is not just for mathematicians. The use by other disciplines has grown dramatically over the years in response to the ra