Compact Numerical Methods for Computers
Linear Algebra and Function Minimisation
1st Edition
Published on 13. December 2018
278 pages
978-1-351-45955-6 (ISBN)
Description
This second edition of Compact Numerical Methods for Computers presents reliable yet compact algorithms for computational problems. As in the previous edition, the author considers specific mathematical problems of wide applicability, develops approaches to a solution and the consequent algorithm, and provides the program steps. He emphasizes usefu
Reviews / Votes
Praise for the first edition"Anyone who must solve complex problems on a small computer would be well advised to consult Nash's book for both ideas and actual procedures. Those with the luxury of a large-scale computer for their numerical work will also find much of interest here."
-Peter Castro (Eastman Kodak), Technometrics, 22 February 1980
More details
Edition
1. Auflage
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Professional and scholarly
Product notice
Reflowable
File size
19,39 MB
ISBN-13
978-1-351-45955-6 (9781351459556)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Book
06/2018
1st Edition
Routledge
€163.42
Shipment within 10-20 days
Book
01/1990
2nd Edition
Institute of Physics Publishing
€72.40
Shipment within 3-4 weeks
Person
John C. Nash
Content
A starting point Formal problems in linear algebra The singular-value decomposition and its use to solve least-squares problems Handling larger problems Some comments on the formation of the cross-product matrix ATA Linear equations-a direct approach The Choleski decomposition The symmetric positive definite matrix again The algebraic eigenvalue generalized problem Real symmetric matrices The generalized symmetric matrix eigenvalue problem Optimization and nonlinear equations One-dimensional problems Direct search methods Descent to a minimum I-variable metric algorithms Descent to a minimum II-conjugate gradients Minimizing a nonlinear sum of squares Leftovers The conjugate gradients method applied to problems in linear algebra Appendices Bibliography Index
