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Iterative Methods for Optimization

Iterative Methods for Optimization

£48.99

Part of Frontiers in Applied Mathematics

  • Date Published: July 1999
  • availability: This item is not supplied by Cambridge University Press in your region. Please contact Soc for Industrial & Applied Mathematics for availability.
  • format: Paperback
  • isbn: 9780898714333

£ 48.99
Paperback

This item is not supplied by Cambridge University Press in your region. Please contact Soc for Industrial & Applied Mathematics for availability.
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About the Authors
  • This book presents a carefully selected group of methods for unconstrained and bound constrained optimization problems and analyzes them in depth both theoretically and algorithmically. It focuses on clarity in algorithmic description and analysis rather than generality, and while it provides pointers to the literature for the most general theoretical results and robust software, the author thinks it is more important that readers have a complete understanding of special cases that convey essential ideas. A companion to Kelley's book, Iterative Methods for Linear and Nonlinear Equations (SIAM, 1995), this book contains many exercises and examples and can be used as a text, a tutorial for self-study, or a reference. Iterative Methods for Optimization does more than cover traditional gradient-based optimization: it is the first book to treat sampling methods, including the Hooke–Jeeves, implicit filtering, MDS, and Nelder–Mead schemes in a unified way.

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    Product details

    • Date Published: July 1999
    • format: Paperback
    • isbn: 9780898714333
    • length: 196 pages
    • dimensions: 251 x 175 x 10 mm
    • weight: 0.36kg
    • availability: This item is not supplied by Cambridge University Press in your region. Please contact Soc for Industrial & Applied Mathematics for availability.
  • Table of Contents

    Preface
    How to Get the Software
    Part I: Optimization of Smooth Functions
    Chapter 1: Basic Concepts
    Chapter 2: Local Convergence of Newton's Method
    Chapter 3: Global Convergence
    Chapter 4: The BFGS Method
    Chapter 5: Simple Bound Constraints
    Part II: Optimization of Noisy Functions
    Chapter 6: Basic Concepts and Goals
    Chapter 7: Implicit Filtering
    Chapter 8: Direct Search Algorithms
    Bibliography
    Index.

  • Author

    C. T. Kelley, North Carolina State University
    C.T. Kelley is a Professor in the Department of Mathematics and Center for Research in Scientific Computation at North Carolina State University. He is a member of the editorial board of the SIAM Journal on Optimization, and the SIAM Journal on Numerical Analysis and is the author of over 100 papers and proceedings articles on numerical and computational mathematics.

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