Optimization

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 epsilon-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.