Rates of convergence for a class of global stochastic optimization algorithms

被引：44

作者：

Yin, G ^{[1
]}

机构：

[1] Wayne State Univ, Dept Math, Detroit, MI 48202 USA

来源：

SIAM JOURNAL ON OPTIMIZATION | 1999年 / 10卷 / 01期

关键词：

global optimization; Monte Carlo methods; simulated annealing; rate of convergence; weak convergence;

D O I：

10.1137/S1052623497319225

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

Inspired and motivated by the recent advances in simulated annealing algorithms, this paper analyzes the convergence rates of a class of recursive algorithms for global optimization via Monte Carlo methods. By using perturbed Liapunov function methods, stability results of the algorithms are established. Then the rates of convergence are ascertained by examining the asymptotic properties of suitably scaled estimation error sequences.

引用

页码：99 / 120

页数：22

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