2009 / xvi + 383 pages / Softcover / ISBN: 978-0-898716-66-5 / List Price $101.00 / SIAM Member Price $70.70 / Order Code OT109
Keywords: computational science textbook; scientific computing textbook; case studies in scientific computing; numerical computing; computational linear algebra, optimization, Monte Carlo, differential equations, solution of nonlinear equations
Learning through doing is the foundation of this book, which allows readers to explore case studies as well as expository material. The book provides a practical guide to the numerical solution of linear and nonlinear equations, differential equations, optimization problems, and eigenvalue problems. It treats standard problems and introduces important variants such as sparse systems, differential-algebraic equations, constrained optimization, Monte Carlo simulations, and parametric studies. Stability and error analysis is emphasized, and the MATLAB™ algorithms are grounded in sound principles of software design and in the understanding of machine arithmetic and memory management.
Nineteen case studies allow readers to become familiar with mathematical modeling and algorithm design, motivated by problems in physics, engineering, epidemiology, chemistry, and biology. A website provides solutions to the challenges that are offered throughout the book and also supplies relevant MATLAB codes, derivations, and supplementary notes and slides.
This book is intended as a primary text for courses in numerical analysis, scientific computing, and computational science for advanced undergraduate and early graduate students. Physicists, chemists, biologists, earth scientists, astronomers, and engineers whose work involves numerical computing also will find the book useful as a reference and tool for self-study.
About the Author
Dianne P. O'Leary is Professor of Computer Science at the University of Maryland, College Park, and also holds an appointment in the university's Institute for Advanced Computer Studies (UMIACS) and in the Applied Mathematics and Scientific Computing program. Her research is in computational linear algebra and optimization, with applications to solution of ill-posed problems, image deblurring, information retrieval, and quantum computing.
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