DocumentCode :
391363
Title :
Discrete optimization, SPSA and Markov chain Monte Carlo methods
Author :
Gerencser, L. ; Hill, Stacy D.
Author_Institution :
Comput. & Autom. Inst., Hungarian Acad. of Sci., Budapest, Hungary
Volume :
2
fYear :
2002
fDate :
10-13 Dec. 2002
Firstpage :
2346
Abstract :
The minimization of a convex function defined over the grid Zp is considered. A truncated fixed gain simultaneous perturbation stochastic approximation (SPSA) method is proposed and investigated in combination with devices borrowed from the Markov-chain Monte-Carlo literature. In particular, the performance of the proposed method is improved by choosing suitable acceptance probabilities. A new Markovian optimization problem is formulated to get the best rejection probability and gain. A simulation result is presented.
Keywords :
Markov processes; Monte Carlo methods; function approximation; optimisation; probability; Markov-chain; Monte Carlo methods; convergence; convex function; discrete optimization; minimization; rejection probability; simultaneous perturbation stochastic approximation; Automation; Cost function; Grid computing; Optimization methods; Physics computing; Probability distribution; Random variables; Resource management; Stability; Symmetric matrices;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Decision and Control, 2002, Proceedings of the 41st IEEE Conference on
ISSN :
0191-2216
Print_ISBN :
0-7803-7516-5
Type :
conf
DOI :
10.1109/CDC.2002.1184883
Filename :
1184883
Link To Document :
https://search.ricest.ac.ir/dl/search/defaultta.aspx?DTC=49&DC=391363
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