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Conjugate Gradient Algorithms in Nonconvex Optimization

  • Radosław Pytlak

Part of the Nonconvex Optimization and Its Applications book series (NOIA, volume 89)

About this book

Introduction

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.

Keywords

Algebra Bound Constrained Optimization Conjugate Gradient Algorithms Continuous Optimization Large Scale Optimization Nonconvex Optimization Quasi-Newt algorithm algorithms differential equation linear algebra operations research optimization

Authors and affiliations

  • Radosław Pytlak
    • 1
  1. 1.Institute of Automatic Control & RoboticsWarsaw University of TechnologyWarszawaPoland

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-540-85634-4
  • Copyright Information Springer Berlin Heidelberg 2009
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-540-85633-7
  • Online ISBN 978-3-540-85634-4
  • Series Print ISSN 1571-568X
  • Buy this book on publisher's site