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Dianne P. O´leary - Scientific Computing with Case Studies - 9780898716665 - V9780898716665
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Scientific Computing with Case Studies

€ 162.59
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Description for Scientific Computing with Case Studies Paperback. A practical guide to the numerical solution of linear and nonlinear equations, differential equations, optimization problems, and eigenvalue problems. Num Pages: 395 pages, col. Illustrations. BIC Classification: PBK; PBKS; TBJ. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly. Dimension: 252 x 171 x 18. Weight in Grams: 814.
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.

Product Details

Publisher
Society for Industrial & Applied Mathematics,U.S. United States
Number of pages
395
Format
Paperback
Publication date
2008
Condition
New
Weight
28g
Number of Pages
399
Place of Publication
New York, United States
ISBN
9780898716665
SKU
V9780898716665
Shipping Time
Usually ships in 7 to 11 working days
Ref
99-1

About Dianne P. O´leary
Dianne Prost O'Leary is a professor of computer science at the University of Maryland, and also holds an appointment in the university's Institute for Advanced Computer Studies (UMIACS) and in the Applied Mathematics and Scientific Computing Program. She earned a B.S. from Purdue University and a Ph.D. from Stanford University. Her research is in computational linear algebra and optimization, with applications to solution of ill-posed problems, image deblurring, information retrieval, and quantum computing. She has authored over 90 research publications on numerical analysis and computational science and 30 publications on education and mentoring.

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