Numerical Analysis (3rd Edition)

Author: (USA) J. Ke Ade
Publisher:
Publish Date: 2005-09-01
Features: This book introduces various numerical analysis methods required for scientific computing, discussing them within a rigorous mathematical science context and providing strict proofs of numerical analysis methods. The content is broad and in-depth, with detailed and vivid explanations, including numerous theorems and various exercises. It has been adopted as a textbook by multiple universities in the USA.
Key Features of the Book: Covers a wide range of topics in numerical analysis for scientific computing, in addition to the fundamental knowledge of numerical analysis, it also includes solving systems of linear and nonlinear algebraic equations, numerical differentiation and integration, numerical solutions of ordinary and partial differential equations, and function approximation. It has added content on optimization and provides online resources for further information. Emphasizes the theoretical foundations related to arithmetic. Algorithms are presented in pseudocode, allowing students to immediately implement computer programs using standard languages and interactive packages. This is a numerical analysis textbook that offers more academic discussions on the problems studied, introducing various algorithms and methods related to scientific computing, as well as their mathematical foundations.
Main Content Includes: Computer arithmetic operations, solutions to nonlinear equations, solving systems of linear equations, selected topics in numerical linear algebra, function approximation, numerical differentiation and integration, numerical solutions of ordinary differential equations, numerical solutions of partial differential equations, linear programming, and optimization. Additionally, each chapter is equipped with a large number of exercises, including highly practical computer exercises.
This book can be used as a textbook for senior undergraduate or graduate students in mathematics, engineering technology, natural sciences, computer science, and other related disciplines in numerical analysis courses. It can also serve as a reference for computational mathematicians and engineering technicians.