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Convergence of a global stochastic optimization algorithm with partial step size restarting

Part of: Limit theorems Sequential methods

Published online by Cambridge University Press:  19 February 2016

G. Yin
G. Yin*
Affiliation:
Wayne State University
*
∗ Postal address: Department of Mathematics, Wayne State University, Detroit, MI 48202, USA. Email address: gyin@math.wayne.edu
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Abstract

This work develops a class of stochastic global optimization algorithms that are Kiefer-Wolfowitz (KW) type procedures with an added perturbing noise and partial step size restarting. The motivation stems from the use of KW-type procedures and Monte Carlo versions of simulated annealing algorithms in a wide range of applications. Using weak convergence approaches, our effort is directed to proving the convergence of the underlying algorithms under general noise processes.

Keywords

Global optimizationMonte Carlo methodsdiffusion; restartingweak convergence

MSC classification

Primary: 60F05: Central limit and other weak theorems 62L20: Stochastic approximation 65K10: Optimization and variational techniques
Type
General Applied Probability
Information
Advances in Applied Probability , Volume 32 , Issue 2 , June 2000 , pp. 480 - 498
Copyright
Copyright © Applied Probability Trust 2000 

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Footnotes

This research was supported in part by the National Science Foundation under grants DMS-9877090 and DMS-9971608.

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