Year: 2015
Type: preprint
Abstract: Recently there has been renewed interests in derivative free approaches to stochastic optimization. In this paper, we examine the rates of convergence for the Kiefer-Wolfowitz algorithm and the mirror descent algorithm, under various updating schemes using finite differences as gradient approximations. It is shown that the convergence of these algorithms can be accelerated by controlling the implementation of the finite differences. Particularly, it is shown that the rate can be increased to $n^{-2/5}$ in general and to $n^{-1/2}$ in Monte Carlo optimization for a broad class of problems, in the iteration number n.
Source: arXiv (Cornell University)
Author: Dai, Liyi
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W2287841963