Parallel multi-splitting proximal method for star networks

Ermin Wei

doi:10.23919/ACC.2017.7963623

Parallel multi-splitting proximal method for star networks

Ermin Wei^*

^*Corresponding author for this work

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

We develop a parallel algorithm based on proximal method to solve the problem of minimizing summation of convex (not necessarily smooth) functions over a star network. We show that this method converges to an optimal solution for any choice of constant stepsize for convex objective functions. Under further assumption of Lipschitz-gradient and strong convexity of objective functions, the method converges linearly.

Original language	English (US)
Title of host publication	2017 American Control Conference, ACC 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4341-4346
Number of pages	6
ISBN (Electronic)	9781509059928
DOIs	https://doi.org/10.23919/ACC.2017.7963623
State	Published - Jun 29 2017
Event	2017 American Control Conference, ACC 2017 - Seattle, United States Duration: May 24 2017 → May 26 2017

Other

Other	2017 American Control Conference, ACC 2017
Country	United States
City	Seattle
Period	5/24/17 → 5/26/17

ASJC Scopus subject areas

Electrical and Electronic Engineering

Access to Document

10.23919/ACC.2017.7963623

Fingerprint Dive into the research topics of 'Parallel multi-splitting proximal method for star networks'. Together they form a unique fingerprint.

Cite this

@inproceedings{abd0b001a840463fa1ddf343be176298,

title = "Parallel multi-splitting proximal method for star networks",

abstract = "We develop a parallel algorithm based on proximal method to solve the problem of minimizing summation of convex (not necessarily smooth) functions over a star network. We show that this method converges to an optimal solution for any choice of constant stepsize for convex objective functions. Under further assumption of Lipschitz-gradient and strong convexity of objective functions, the method converges linearly.",

author = "Ermin Wei",

year = "2017",

month = jun,

day = "29",

doi = "10.23919/ACC.2017.7963623",

language = "English (US)",

pages = "4341--4346",

booktitle = "2017 American Control Conference, ACC 2017",

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

address = "United States",

note = "2017 American Control Conference, ACC 2017 ; Conference date: 24-05-2017 Through 26-05-2017",

}

TY - GEN

T1 - Parallel multi-splitting proximal method for star networks

AU - Wei, Ermin

PY - 2017/6/29

Y1 - 2017/6/29

N2 - We develop a parallel algorithm based on proximal method to solve the problem of minimizing summation of convex (not necessarily smooth) functions over a star network. We show that this method converges to an optimal solution for any choice of constant stepsize for convex objective functions. Under further assumption of Lipschitz-gradient and strong convexity of objective functions, the method converges linearly.

AB - We develop a parallel algorithm based on proximal method to solve the problem of minimizing summation of convex (not necessarily smooth) functions over a star network. We show that this method converges to an optimal solution for any choice of constant stepsize for convex objective functions. Under further assumption of Lipschitz-gradient and strong convexity of objective functions, the method converges linearly.

UR - http://www.scopus.com/inward/record.url?scp=85027044357&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85027044357&partnerID=8YFLogxK

U2 - 10.23919/ACC.2017.7963623

DO - 10.23919/ACC.2017.7963623

M3 - Conference contribution

AN - SCOPUS:85027044357

SP - 4341

EP - 4346

BT - 2017 American Control Conference, ACC 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2017 American Control Conference, ACC 2017

Y2 - 24 May 2017 through 26 May 2017

ER -