Nonconvex Optimization and Its Applications

Conjugate Gradient Algorithms in Nonconvex Optimization

Authors: Pytlak, Radoslaw

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About this book

This up-to-date book is on algorithms for large-scale unconstrained and bound constrained optimization. Optimization techniques are shown from a conjugate gradient algorithm perspective.

Large part of the book is devoted to preconditioned conjugate gradient algorithms. In particular memoryless and limited memory quasi-Newton algorithms are presented and numerically compared to standard conjugate gradient algorithms.

The special attention is paid to the methods of shortest residuals developed by the author. Several effective optimization techniques based on these methods are presented.

Because of the emphasis on practical methods, as well as rigorous mathematical treatment of their convergence analysis, the book is aimed at a wide audience. It can be used by researches in optimization, graduate students in operations research, engineering, mathematics and computer science. Practitioners can benefit from numerous numerical comparisons of professional optimization codes discussed in the book.

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-Åke 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)


Table of contents (12 chapters)

Buy this book

eBook $179.00
price for USA (gross)
  • ISBN 978-3-540-85634-4
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $229.00
price for USA
  • ISBN 978-3-540-85633-7
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $229.00
price for USA
  • ISBN 978-3-642-09925-0
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Conjugate Gradient Algorithms in Nonconvex Optimization
Authors
Series Title
Nonconvex Optimization and Its Applications
Series Volume
89
Copyright
2009
Publisher
Springer-Verlag Berlin Heidelberg
Copyright Holder
Springer-Verlag Berlin Heidelberg
eBook ISBN
978-3-540-85634-4
DOI
10.1007/978-3-540-85634-4
Hardcover ISBN
978-3-540-85633-7
Softcover ISBN
978-3-642-09925-0
Series ISSN
1571-568X
Edition Number
1
Number of Pages
XXVI, 478
Topics