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Table of Contents
Preface xvii
Preface to the Second Edition xxi
1 Introduction 1
2 Fundamentals of Unconstrained Optimization 10
3 Line Search Methods 30
4 Trust-Region Methods 66
5 Conjugate Gradient Methods 101
6 Quasi-Newton Methods 135
7 Large-Scale Unconstrained Optimization 164
Summary
Numerical Optimization is a comprehensive book that covers various optimization algorithms and techniques. It includes mathematical formulations, examples, and discussions on constrained and unconstrained optimization, global and local optimization, convexity, and optimization algorithms. The book also delves into line search methods, trust-region methods, conjugate gradient methods, quasi-Newton methods, and large-scale unconstrained optimization. With contributions from multiple authors, including Jorge Nocedal, this book serves as a valuable resource for anyone interested in numerical optimization.