This classic volume covers the fundamentals of two closely related topics: linear systems (linear equations and least-squares) and linear programming (optimizing a linear function subject to linear constraints).

This book continues to be essential in many ways.

This book addresses some of the basic questions in numerical analysis: convergence theorems for iterative methods for both linear and nonlinear equations; discretization error, especially for ordinary differential equations; rounding error ...

This Classic edition includes a new appendix which summarizes the major developments since the book was originally published in 1974.

For more than 30 years, this two-volume set has helped prepare graduate students to use partial differential equations and integral equations to handle significant problems arising in applied mathematics, engineering, and the physical ...

The aim of the book is to present mathematical knowledge that is needed in order to understand the art of computing eigenvalues of real symmetric matrices, either all of them or only a few.

a thorough, balanced introduction to both the theoretical and the computational aspects of the topic.

This classic text presents the best practices of developing multigrid solvers for large-scale computational problems in science and engineering.

This book discusses the gradient mappings and minimization, contractions and the continuation property, and degree of a mapping.