Synopses & Reviews
Comprehensive and complete, this overview provides a single-volume treatment of key algorithms and theories. The author provides clear explanations of all theoretical aspects, with rigorous proof of most results. The two-part treatment begins with the derivation of optimality conditions and discussions of convex programming, duality, generalized convexity, and analysis of selected nonlinear programs. The second part concerns techniques for numerical solutions and unconstrained optimization methods, and it presents commonly used algorithms for constrained nonlinear optimization problems. This graduate-level text requires no advanced mathematical background beyond elementary calculus, linear algebra, and real analysis. 1976 edition. 58 figures. 7 tables.
Synopsis
An excellent bridge between principal theories and concepts and their practical implementation, this graduate-level text is a major work by a leading figure in the field of operations research. Unabridged republication of the edition published by Prentice-Hall, Inc., Englewood Cliffs, New Jersey, 1976.
Synopsis
This overview provides a single-volume treatment of key algorithms and theories. Begins with the derivation of optimality conditions and discussions of convex programming, duality, generalized convexity, and analysis of selected nonlinear programs, and then explores techniques for numerical solutions and unconstrained optimization methods. 1976 edition. Includes 58 figures and 7 tables.
Synopsis
This text provides an excellent bridge between principal theories and concepts and their practical implementation. Topics include convex programming, duality, generalized convexity, analysis of selected nonlinear programs, techniques for numerical solutions, and unconstrained optimization methods.
Table of Contents
Author's Preface to the Dover Edition
1. Introduction
II. Analysis
2. Classical Optimization--Unconstrained and Equality Constrained Problems
3. Optimality Conditions for Constrained Extrema
4. Convex Sets and Functions
5. Duality in Nonlinear Convex Programming
6. Generalized Convexity
7. Analysis of Selected Nonlinear Programming Problems
II. Methods
8. One-Dimensional Optimization
9. Multidimensional Unconstrained Optimization Without Derivatives: Empirical and Conjugate Direction Methods
10. Second Derivative, Steepest Descent and Conjugate Gradient Methods
11. Variable Metric Algorithms
12. Penalty Function Methods
13. Solution of Constrained Problems by Extensions of Unconstrained Optimization Techniques
14. Approximation-Type Algorithms
Author Index. Subject Index.