Conjugate Gradient Algorithms in Nonconvex Optimization - Nonconvex Optimization and Its Applications 89 (Paperback)
  • Conjugate Gradient Algorithms in Nonconvex Optimization - Nonconvex Optimization and Its Applications 89 (Paperback)
zoom

Conjugate Gradient Algorithms in Nonconvex Optimization - Nonconvex Optimization and Its Applications 89 (Paperback)

(author)
£149.99
Paperback 478 Pages / Published: 20/11/2010
  • Not available

This product is currently unavailable.

  • This item has been added to your basket
Conjugate direction methods were proposed in the early 1950s. When high speed digital computing machines were developed, attempts were made to lay the fo- dations for the mathematical aspects of computations which could take advantage of the ef?ciency of digital computers. The National Bureau of Standards sponsored the Institute for Numerical Analysis, which was established at the University of California in Los Angeles. A seminar held there on numerical methods for linear equationswasattendedbyMagnusHestenes, EduardStiefel andCorneliusLanczos. This led to the ?rst communication between Lanczos and Hestenes (researchers of the NBS) and Stiefel (of the ETH in Zurich) on the conjugate direction algorithm. The method is attributed to Hestenes and Stiefel who published their joint paper in 1952 [101] in which they presented both the method of conjugate gradient and the conjugate direction methods including conjugate Gram-Schmidt processes. A closelyrelatedalgorithmwasproposedbyLanczos[114]whoworkedonalgorithms for determiningeigenvalues of a matrix. His iterative algorithm yields the similarity transformation of a matrix into the tridiagonal form from which eigenvalues can be well approximated.Thethree-termrecurrencerelationofthe Lanczosprocedurecan be obtained by eliminating a vector from the conjugate direction algorithm scheme. Initially the conjugate gradient algorithm was called the Hestenes-Stiefel-Lanczos method [86].

Publisher: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
ISBN: 9783642099250
Number of pages: 478
Weight: 765 g
Dimensions: 235 x 155 x 25 mm
Edition: Softcover reprint of hardcover 1st ed. 2009


MEDIA REVIEWS

From the reviews:

"The book describes important algorithms for the numerical treatment of unconstrained nonlinear optimization problems with many variables. ... This monograph is suitable as a text for a graduate course in computational optimization. It is useful to anyone active in this field. ... This book is well written and well organized. The argument is clear. Lists of algorithms as well as tables and figures facilitate for the reader the search for desired information in the text. The reference list is comprehensive and contains 214 items." (Sven-Ake Gustafson, Mathematical Reviews, Issue 2009 i)

"It is a very nice written book which can be used by researchers in optimization, in the teaching for seminars and by students ... . Lists of figures, tables and algorithms make this book to a useful compendium for research and teaching. A lot of bibliographical hints with respect to a large reference list make the reader known with the historical development of CG-methods ... . appendices with elements of topology, analysis, linear algebra and numerics of linear algebra make it to a self-contained book." (Armin Hoffmann, Zentralblatt MATH, Vol. 1171, 2009)

You may also be interested in...

Calculus for the Ambitious
Added to basket
Maths for Chemistry
Added to basket
£31.99
Paperback
Schaum's Outline of Calculus
Added to basket
Calculus of Variations
Added to basket
A Student's Guide to Fourier Transforms
Added to basket
Measurement
Added to basket
£16.95
Paperback
An Introduction to Manifolds
Added to basket
Schaums Outline of Tensor Calculus
Added to basket
Calculus
Added to basket
£42.99
Hardback
Maths for Science
Added to basket
£37.99
Paperback
How to Think About Analysis
Added to basket

Please sign in to write a review

Your review has been submitted successfully.