Optimization : algorithms and applications
β Scribed by Arora, Rajesh Kumar
- Publisher
- CRC Press
- Year
- 2015
- Tongue
- English
- Leaves
- 454
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Table of Contents
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.
β¦ Subjects
Mathematical optimization. MATLAB. MATHEMATICS / Applied MATHEMATICS / Probability & Statistics / General
π SIMILAR VOLUMES
<P><EM>Choose the Correct Solution Method for Your Optimization Problem</EM></P> <P><STRONG>Optimization: Algorithms and Applications</STRONG> presents a variety of solution techniques for optimization problems, emphasizing concepts rather than rigorous mathematical details and proofs. </P> <P></P>
<p>Stochastic programming is the study of procedures for decision making under the presence of uncertainties and risks. Stochastic programming approaches have been successfully used in a number of areas such as energy and production planning, telecommunications, and transportation. Recently, the pra
<p>Researchers working with nonlinear programming often claim "the word is nonΒ linear" indicating that real applications require nonlinear modeling. The same is true for other areas such as multi-objective programming (there are always several goals in a real application), stochastic programming (a