TY - BOOK ID - 219038 TI - Stochastic optimization methods PY - 2008 SN - 1281513288 9786611513283 3540794581 3540794573 3642098363 PB - Berlin ; London : Springer, DB - UniCat KW - Business. KW - Operations research. KW - Decision making. KW - Mathematical optimization. KW - Engineering. KW - Computational intelligence. KW - Business and Management. KW - Operation Research/Decision Theory. KW - Optimization. KW - Engineering, general. KW - Computational Intelligence. KW - Optimization (Mathematics) KW - Optimization techniques KW - Optimization theory KW - Systems optimization KW - Mathematical analysis KW - Maxima and minima KW - Operations research KW - Simulation methods KW - System analysis KW - Intelligence, Computational KW - Artificial intelligence KW - Soft computing KW - Construction KW - Industrial arts KW - Technology KW - Deciding KW - Decision (Psychology) KW - Decision analysis KW - Decision processes KW - Making decisions KW - Management KW - Management decisions KW - Choice (Psychology) KW - Problem solving KW - Operational analysis KW - Operational research KW - Industrial engineering KW - Management science KW - Research KW - System theory KW - Trade KW - Economics KW - Commerce KW - Industrial management KW - Decision making KW - Stochastic processes. KW - Random processes KW - Probabilities KW - Operations Research/Decision Theory. UR - https://www.unicat.be/uniCat?func=search&query=sysid:219038 AB - Optimization problems arising in practice involve random model parameters. For the computation of robust optimal solutions, i.e., optimal solutions being insensitive with respect to random parameter variations, appropriate deterministic substitute problems are needed. Based on the probability distribution of the random data, and using decision theoretical concepts, optimization problems under stochastic uncertainty are converted into appropriate deterministic substitute problems. Due to the occurring probabilities and expectations, approximative solution techniques must be applied. Several deterministic and stochastic approximation methods are provided: Taylor expansion methods, regression and response surface methods (RSM), probability inequalities, multiple linearization of survival/failure domains, discretization methods, convex approximation/deterministic descent directions/efficient points, stochastic approximation and gradient procedures, differentiation formulas for probabilities and expectations. ER -