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|.
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页码：1524 / 1538
页数：14
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