Practical Augmented Lagrangian Methods for Constrained Optimization

This book focuses on Augmented Lagrangian techniques for solving practical constrained optimization problems. The authors rigorously delineate mathematical convergence theory based on sequential optimality conditions and novel constraint qualifications. They also orient the book to practitioners by giving priority to results that provide insight on the practical behavior of algorithms and by providing geometrical and algorithmic interpretations of every mathematical result, and they fully describe a freely available computational package for constrained optimization and illustrate its usefulness with applications.

Audience: The book is aimed at engineers, physicists, chemists, and other practitioners interested in full access to comprehensive and well-documented software for large-scale optimization as well as up-to-date convergence theory and its practical consequences. It will also be of interest to graduate and advanced undergraduate students in mathematics, computer science, applied mathematics, optimization, and numerical analysis.

Table of Contents

Chapter 1: Introduction
Chapter 2: Practical Motivations
Chapter 3: Optimality Conditions
Chapter 4: Model Augmented Lagrangian Algorithm
Chapter 5: Global Minimization Approach
Chapter 6: General Affordable Algorithms
Chapter 7: Boundedness of the Penalty Parameters
Chapter 8: Solving Unconstrained Subproblems
Chapter 9: Solving Constrained Subproblems
Chapter 10: First Approach to Algencan
Chapter 11: Adequate Choice of Subroutines
Chapter 12: Making a Good Choice of Algorithmic Options and Parameters
Chapter 13: Practical Examples Chapter 14: Final Remarks