Stochastic Optimization Methods

Stochastic Optimization Methods by Kurt Marti.

Optimization problems arising in practice involve random parameters. For the computation of robust optimal solutions, i.e., optimal solutions being insensitive with respect to random parameter variations, deterministic substitute problems are needed. Based on the distribution of the random data, and...

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Bibliographic Details
Main Author: Marti, Kurt (Author)
Corporate Author: SpringerLink (Online service)
Format: eBook
Language:English
Published: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2005.
Edition:1st ed. 2005.
Series:Springer eBook Collection.
Subjects:
Operations research.
Decision making.
Mathematical optimization.
Computational intelligence.
Electronic resources (E-books)
Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
Table of Contents:
  • Basic Stochastic Optimization Methods
  • Decision/Control Under Stochastic Uncertainty
  • Deterministic Substitute Problems in Optimal Decision Under Stochastic Uncertainty
  • Differentiation Methods
  • Differentiation Methods for Probability and Risk Functions
  • Deterministic Descent Directions
  • Deterministic Descent Directions and Efficient Points
  • Semi-Stochastic Approximation Methods
  • RSM-Based Stochastic Gradient Procedures
  • Stochastic Approximation Methods with Changing Error Variances
  • Technical Applications
  • Approximation of the Probability of Failure/Survival in Plastic Structural Analysis and Optimal Plastic Design.

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