Introduction to Scientific Computing (2nd Edition) (2nd Edition)

Author: Michael T. Heath
Publisher:
Publication Date: 2001-10-01
Features: This book comprehensively introduces numerical methods for solving various major problems in scientific computing, including linear and nonlinear equations, least squares methods, eigenvalues, optimization, interpolation, integration, ordinary differential equations, partial differential equations, fast Fourier transforms, and random number generation. The book is designed for readers who use algorithms, focusing on teaching the underlying ideas and principles behind the algorithms rather than detailed analysis of the algorithms. It emphasizes concepts such as sensitivity and ill-conditioning, compares and evaluates different algorithms for the same problem, and enhances readers' appreciation of algorithms. For each type of problem, it provides specialized introductions and discussions on relevant mathematical software, including free software available on the Internet and commercially licensed software platforms, for readers to choose from. The book includes abundant examples and exercises, with over 169 examples, more than 500 thought problems, over 240 practice problems, and over 200 computational problems. This book can serve as a textbook or reference for graduate-level "Numerical Analysis" courses, and it holds significant reference value for scientists and engineers who need to solve computational problems. This book comprehensively introduces numerical methods for solving various major problems in scientific computing, including linear and nonlinear equations, least squares methods, eigenvalues, optimization, interpolation, integration, ordinary differential equations, partial differential equations, fast Fourier transforms, and random number generation. The book's features are: It is designed for readers who use algorithms, focusing on teaching the underlying ideas and principles behind the algorithms rather than detailed analysis of the algorithms. It emphasizes concepts such as sensitivity and ill-conditioning, compares and evaluates different algorithms for the same problem, and enhances readers' appreciation of algorithms. For each type of problem, it provides specialized introductions and discussions on relevant mathematical software, including free software available on the Internet and commercially licensed software platforms, for readers to choose from. The book includes abundant examples and exercises, with 169 examples, more than 500 thought problems, 240 practice problems, and 200 computational problems. This book can serve as a textbook or reference for graduate-level "Numerical Analysis" courses, and it holds significant reference value for scientists and engineers who need to solve computational problems.