Description
Book SynopsisChoose the Correct Solution Method for Your Optimization Problem
Optimization: Algorithms and Applications presents a variety of solution techniques for optimization problems, emphasizing concepts rather than rigorous mathematical details and proofs.
The book covers both gradient and stochastic methods as solution techniques for unconstrained and constrained optimization problems. It discusses the conjugate gradient method, BroydenFletcherGoldfarbShanno algorithm, Powell method, penalty function, augmented Lagrange multiplier method, sequential quadratic programming, method of feasible directions, genetic algorithms, particle swarm optimization (PSO), simulated annealing, ant colony optimization, and tabu search methods. The author shows how to solve non-convex multi-objective optimization problems using simple modifications of the basic PSO code. The book also introduces multidisciplinary design optimization (MDO) architecturesone of th
Table of Contents
Introduction. 1-D Optimization Algorithms. Unconstrained Optimization. Linear Programming. Guided Random Search Methods. Constrained Optimization. Multiobjective Optimization. Geometric Programming. Multidisciplinary Design Optimization. Integer Programming. Dynamic Programming. Bibliography. Appendices. Index.
