Optimization: Algorithms and Applications

Introduction
Historical Review
Optimization Problem
Modeling of the Optimization Problem
Solution with the Graphical Method
Convexity
Gradient Vector, Directional Derivative, and Hessian Matrix
Linear and Quadratic Approximations
Organization of the Book

1-D Optimization Algorithms
Introduction
Test Problem
Solution Techniques
Comparison of Solution Methods

Unconstrained Optimization
Introduction
Unidirectional Search
Test Problem
Solution Techniques
Additional Test Functions
Application to Robotics

Linear Programming
Introduction
Solution with the Graphical Method
Standard Form of an LPP
Basic Solution
Simplex Method
Interior-Point Method
Portfolio Optimization

Guided Random Search Methods
Introduction
Genetic Algorithms
Simulated Annealing
Particle Swarm Optimization
Other Methods

Constrained Optimization
Introduction
Optimality Conditions
Solution Techniques
Augmented Lagrange Multiplier Method
Sequential Quadratic Programming
Method of Feasible Directions
Application to Structural Design

Multiobjective Optimization
Introduction
Weighted Sum Approach
-Constraints Method
Goal Programming
Utility Function Method
Application

Geometric Programming
Introduction
Unconstrained Problem
Dual Problem
Constrained Optimization
Application

Multidisciplinary Design Optimization
Introduction
MDO Architecture
MDO Framework
Response Surface Methodology

Integer Programming
Introduction
Integer Linear Programming
Integer Nonlinear Programming

Dynamic Programming
Introduction
Deterministic Dynamic Programming
Probabilistic Dynamic Programming

Bibliography

Appendix A: Introduction to MATLAB
Appendix B: MATLAB Code
Appendix C: Solutions to Chapter Problems

Index

Chapter Highlights, Formula Charts, and Problems appear at the end of each chapter.