Stochastic Optimization Methods

Stochastic Optimization Methods

4.11 - 1251 ratings - Source

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 using decision theoretical concepts, optimization problems under stochastic uncertainty are converted into deterministic substitute problems. Due to the occurring probabilities and expectations, approximative solution techniques must be applied. Deterministic and stochastic approximation methods and their analytical properties are provided: Taylor expansion, regression and response surface methods, probability inequalities, First Order Reliability Methods, convex approximation/deterministic descent directions/efficient points, stochastic approximation methods, differentiation of probability and mean value functions. Convergence results of the resulting iterative solution procedures are given.Optimization problems arising in practice involve random parameters.


Title:Stochastic Optimization Methods
Author: Kurt Marti
Publisher:Springer Science & Business Media - 2005-01-01
ISBN-13:

You must register with us as either a Registered User before you can Download this Book. You'll be greeted by a simple sign-up page.

Once you have finished the sign-up process, you will be redirected to your download Book page.

How it works:
  • 1. Register a free 1 month Trial Account.
  • 2. Download as many books as you like (Personal use)
  • 3. Cancel the membership at any time if not satisfied.


Click button below to register and download Ebook
Privacy Policy | Contact | DMCA