Lower Bounds on the Convergence Rate of the Markov Symmetric Random Search

被引：2

作者：

Tikhomirov, A. S. ^{[1
]}

机构：

[1] Novgorod State Univ, Velikiy Novgorod 173003, Russia

来源：

COMPUTATIONAL MATHEMATICS AND MATHEMATICAL PHYSICS | 2011年 / 51卷 / 09期

关键词：

random search; global optimization; stochastic optimization; estimate of convergence rate;

D O I：

10.1134/S0965542511090168

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

The convergence rate of the Markov random search algorithms designed for finding the extremizer of a function is investigated. It is shown that, for a wide class of random search methods that possess a natural symmetry property, the number of evaluations of the objective function needed to find the extremizer accurate to cannot grow slower than vertical bar ln epsilon vertical bar.

引用

页码：1524 / 1538

页数：15

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