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, Revised Second Edition
is intended as both a textbook and a reference for computationally
oriented disciplines that need to solve mathematical problems.