A regularized smoothing stochastic approximation (RSSA) algorithm for stochastic variational inequality problems

Farzad Yousefian, Angelia Nedic, Uday V. Shanbhag

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

13 Scopus citations

Abstract

We consider a stochastic variational inequality (SVI) problem with a continuous and monotone mapping over a compact and convex set. Traditionally, stochastic approximation (SA) schemes for SVIs have relied on strong monotonicity and Lipschitzian properties of the underlying map. We present a regularized smoothedSA(RSSA) schemewherein the stepsize, smoothing, and regularization parametersare diminishing sequences. Under suitable assumptions on the sequences, we show that the algorithm generates iterates that converge to a solution in an almost-sure sense. Additionally, we provide rate estimates that relate iterates to their counterparts derived from the Tikhonov trajectory associated with a deterministic problem.

Original languageEnglish (US)
Title of host publicationProceedings of the 2013 Winter Simulation Conference - Simulation
Subtitle of host publicationMaking Decisions in a Complex World, WSC 2013
Pages933-944
Number of pages12
DOIs
StatePublished - Dec 1 2013
Externally publishedYes
Event2013 43rd Winter Simulation Conference - Simulation: Making Decisions in a Complex World, WSC 2013 - Washington, DC, United States
Duration: Dec 8 2013Dec 11 2013

Publication series

NameProceedings of the 2013 Winter Simulation Conference - Simulation: Making Decisions in a Complex World, WSC 2013

Other

Other2013 43rd Winter Simulation Conference - Simulation: Making Decisions in a Complex World, WSC 2013
CountryUnited States
CityWashington, DC
Period12/8/1312/11/13

ASJC Scopus subject areas

  • Modeling and Simulation

Fingerprint Dive into the research topics of 'A regularized smoothing stochastic approximation (RSSA) algorithm for stochastic variational inequality problems'. Together they form a unique fingerprint.

Cite this