19 May 2016 Convergence rates of finite difference stochastic approximation algorithms part I: general sampling
Author Affiliations +
Abstract
Stochastic optimization is a fundamental problem that finds applications in many areas including biological and cognitive sciences. The classical stochastic approximation algorithm for iterative stochastic optimization requires gradient information of the sample object function that is typically difficult to obtain in practice. 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. The analysis is carried out under a general framework covering a wide range of updating scenarios. It is shown that the convergence of these algorithms can be accelerated by controlling the implementation of the finite differences.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Liyi Dai, Liyi Dai, } "Convergence rates of finite difference stochastic approximation algorithms part I: general sampling", Proc. SPIE 9871, Sensing and Analysis Technologies for Biomedical and Cognitive Applications 2016, 98710L (19 May 2016); doi: 10.1117/12.2228250; https://doi.org/10.1117/12.2228250
PROCEEDINGS
11 PAGES


SHARE
Back to Top