This book differs from traditional numerical analysis texts in that it focuses on the motivation and ideas behind the algorithms presented rather than on detailed analyses of them. It presents a broad overview of methods and software for solving mathematical problems arising in computational modeling and data analysis, including proper problem formulation, selection of effective solution algorithms, and interpretation of results.? In the 20 years since its original publication, the modern, fundamental perspective of this book has aged well, and it continues to be used in the classroom. This Classics edition has been updated to include pointers to Python software and the Chebfun package, expansions on barycentric formulation for Lagrange polynomial interpretation and stochastic methods, and the availability of about 100 interactive educational modules that dynamically illustrate the concepts and algorithms in the book. Scientific Computing: An Introductory Survey, Second Edition is intended as both a textbook and a reference for computationally oriented disciplines that need to solve mathematical problems.
This book conveys the fundamentals of mathematical models, numerical methods and algorithms.
The book provides an introduction to common programming tools and methods in numerical mathematics and scientific computing.
The essential text and reference for modern scientific computing now also covers computational geometry, classification and inference, and much more.
Note that when header files that we have written are included the names of these files are enclosed within quotation marks, in contrast to the system header files such as iostream, fstream and cmath that we have used earlier.
This book is a gentle introduction to such computational methods where the techniques are explained through examples. It is our goal to teach principles and ideas that carry over from field to field.
This book is a practical guide to the numerical solution of linear and nonlinear equations, differential equations, optimization problems, and eigenvalue problems.
This is a textbook that teaches the bridging topics between numerical analysis, parallel computing, code performance, large scale applications.
Accompanying CD-ROM has a software suite containing all the functions and programs discussed.
A book that emphasizes the importance of solving differential equations on a computer, which comprises a large part of what has come to be called scientific computing.
Constructing Probability Boxes and Dempster-Shafer Structures. SAND2003–4015, Albuquerque, NM, ... Dependence in Probabilistic Modeling, Dempster-Shafer Theory, and Probability Bounds Analysis. ... Giaquinta, M. and G. Modica (2007).