Matrix Methods (4th Ed.)
Applied Linear Algebra and Sabermetrics

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Language: English

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512 p. · 19x23.3 cm · Paperback

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.

1. Matrices 2. Simultaneous linear equations 3. The inverse 4. An introduction to optimization 5. Determinants 6. Eigenvalues and eigenvectors 7. Matrix calculus 8. Linear differential equations 9. Probability and Markov chains 10. Real inner products and least square 11. Sabermetrics e An introduction 12. Sabermetrics e A module Appendix: A word on technology Answers and hints to selected problems

Advanced UG and Grad Students in advanced linear algebra, applied linear algebra, and matrix algebra courses

Richard Bronson is a Professor of Mathematics and Computer Science at Fairleigh Dickinson University and is Senior Executive Assistant to the President. Ph.D., in Mathematics from Stevens Institute of Technology. He has written several books and numerous articles on Mathematics. He has served as Interim Provost of the Metropolitan Campus, and has been Acting Dean of the College of Science and Engineering at the university in New Jersey
Gabriel B. Costa is currently a visiting professor at the United States Military Academy at West Point and is on the faculty at Seton Hall. And is an engineer. He holds many titles and fills them with distinction. He has a B.S., M.S. and Ph.D. in Mathematics from Stevens Institute of Technology. He has also co-authored another Academic Press book with Richard Bronson, Matrix Methods.
  • 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