Synopses & Reviews
Presents recent developments of key topics in nonlinear programming using a logical and self-contained format. Divided into three sections that deal with convex analysis, optimality conditions and duality, computational techniques. Precise statements of algorithms are given along with convergence analysis. Each chapter contains detailed numerical examples, graphical illustrations and numerous exercises to aid readers in understanding the concepts and methods discussed.
Description
Includes bibliographical references (p. 576-625) and index.
Table of Contents
CONVEX ANALYSIS.
Convex Sets.
Convex Functions and Generalizations.
OPTIMALITY CONDITIONS AND DUALITY.
The Fritz John and the Karush-Kuhn-Tucker Optimality Conditions.
Constraint Qualifications.
Lagrangian Duality and Saddle Point Optimality Conditions.
ALGORITHMS AND THEIR CONVERGENCE.
The Concept of an Algorithm.
Unconstrained Optimization.
Penalty and Barrier Functions.
Methods of Feasible Directions.
Linear Complementary Problem, and Quadratic, Separable, Fractional, and Geometric Programming.
Appendices.
Bibliography.
Index.