Approximative solutions of stochastic optimization problem
1.
SYSNO ASEP
0348335
Document Type
J - Journal Article
R&D Document Type
Journal Article
Subsidiary J
Článek ve WOS
Title
Approximative solutions of stochastic optimization problem
Author(s)
Lachout, Petr (UTIA-B)
Source Title
Kybernetika. - : Ústav teorie informace a automatizace AV ČR, v. v. i.
- ISSN 0023-5954
Roč. 46, č. 3 (2010), s. 513-523
Number of pages
11 s.
Language
eng - English
Country
CZ - Czech Republic
Keywords
Stochastic optimization problem ; sensitivity ; approximative solution
Subject RIV
BA - General Mathematics
R&D Projects
GA201/08/0539 GA ČR - Czech Science Foundation (CSF)
CEZ
AV0Z10750506 - UTIA-B (2005-2011)
UT WOS
000280425000015
Annotation
The aim of this paper is to present some ideas how to relax the notion of the optimal solution of the stochastic optimization problem. In the deterministic case, $/varepsilon $-minimal solutions and level-minimal solutions are considered as desired relaxations. We call them approximative solutions and we introduce some possibilities how to combine them with randomness. Relations among random versions of approximative solutions and their consistency are presented in this paper. No measurability is assumed, therefore, treatment convenient for nonmeasurable objects is employed.
