Scientific Computing with Case Studies

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.

Audience: 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.