Stochastic Optimization Methods, Second Edition

✍ Scribed by Kurt Marti

• Year: 2008
• Tongue: English
• Leaves: 317
• Edition: 2nd ed.
• Category: Library

✦ Synopsis

Optimization problems arising in practice involve random model parameters. For the computation of robust optimal solutions, i.e., optimal solutions being insenistive 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.

✦ Table of Contents

00041uy2JK+cuL......Page 1
00front-matter......Page 2
01......Page 14
02......Page 20
03......Page 51
04......Page 82
05......Page 113
06......Page 161
07......Page 234
back-matter......Page 279

✦ Subjects

Математика;Методы оптимизации;