On stochastic proximal-point method for convex-composite optimization

Angelia Nedich, Tatiana Tatarenko

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

We study stochastic proximal-point method applied to a convex-composite optimization problem, where the objective function is given as the sum of two convex functions, one of which is smooth while the other is not necessarily smooth but has a simple structure for evaluating the proximal operator. The main goal is to investigate a trade-off between the choice of a constant stepsize value and the speed at which the algorithm approaches the optimal points. We consider the case of a strongly convex objective function and make the most standard assumptions on the smooth component function and its stochastic gradient estimates. First of all, we analyze the basic properties of the stochastic proximal-point mapping associated with the procedure under consideration. Based on these properties, we formulate the main result, which provides the explicit condition on the constant stepsize for which the stochastic proximal-point method approaches a σ-neighborhood of the optimal point in expectation, where the parameter σ > 0 is related to the variance of the stochastic gradient estimates. Moreover, the rate at which the σ-neighborhood attracts the iterates is geometric, which allows us to estimate the number of iterations the procedure needs to enter this region (in expectation).

Original languageEnglish (US)
Title of host publication55th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages198-203
Number of pages6
ISBN (Electronic)9781538632666
DOIs
StatePublished - Jul 1 2017
Event55th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2017 - Monticello, United States
Duration: Oct 3 2017Oct 6 2017

Publication series

Name55th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2017
Volume2018-January

Other

Other55th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2017
CountryUnited States
CityMonticello
Period10/3/1710/6/17

    Fingerprint

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Signal Processing
  • Energy Engineering and Power Technology
  • Control and Optimization

Cite this

Nedich, A., & Tatarenko, T. (2017). On stochastic proximal-point method for convex-composite optimization. In 55th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2017 (pp. 198-203). (55th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ALLERTON.2017.8262738