@inproceedings{4e03f55c67e34b03a53415cfbd634ba1,

title = "SI-ADMM: A stochastic inexact admm framework for resolving structured stochastic convex programs",

abstract = "We consider the resolution of the structured stochastic convex program: min E[ f (x;x )]+E[ g(y;x )] such that Ax+By =b. To exploit problem structure and allow for developing distributed schemes, we propose an inexact stochastic generalization in which the subproblems are solved inexactly via stochastic approximation schemes. Based on this framework, we prove the following: (i) when the inexactness sequence satisfies suitable summability properties, the proposed stochastic inexact ADMM (SI-ADMM) scheme produces a sequence that converges to the unique solution almost surely; (ii) if the inexactness is driven to zero at a polynomial (geometric) rate, the sequence converges to the unique solution in a mean-squared sense at a prescribed polynomial (geometric) rate.",

author = "Yue Xie and Shanbhag, {Uday V.}",

year = "2016",

month = jul,

day = "2",

doi = "10.1109/WSC.2016.7822135",

language = "English (US)",

series = "Proceedings - Winter Simulation Conference",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "714--725",

editor = "Roeder, {Theresa M.} and Frazier, {Peter I.} and Robert Szechtman and Enlu Zhou",

booktitle = "2016 Winter Simulation Conference",

address = "United States",

note = "2016 Winter Simulation Conference, WSC 2016 ; Conference date: 11-12-2016 Through 14-12-2016",

}