Lower bounds on the convergence rate of the Markov symmetric random search

被引：0

作者：

A. S. Tikhomirov

机构：

[1] Novgorod State University,

来源：

Computational Mathematics and Mathematical Physics | 2011年 / 51卷

关键词：

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

D O I：

暂无

中图分类号：

学科分类号：

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 |ln ɛ|.

引用

页码：1524 / 1538

页数：14

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