Librería Portfolio

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.



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.