Optimization : algorithms and applications

Content: 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.