DISTRIBUTED PROXIMAL-GRADIENT METHOD FOR CONVEX OPTIMIZATION WITH INEQUALITY CONSTRAINTS

We consider a distributed optimization problem over a multi-agent network, in which the sum of several local convex objective functions is minimized subject to global convex inequality constraints. We first transform the constrained optimization problem to an unconstrained one, using the exact penal...

Full description

Saved in:
Bibliographic Details
Published in:The ANZIAM journal Vol. 56; no. 2; pp. 160 - 178
Main Authors: LI, JUEYOU, WU, CHANGZHI, WU, ZHIYOU, LONG, QIANG, WANG, XIANGYU
Format: Journal Article
Language:English
Published: Cambridge, UK Cambridge University Press 01.10.2014
Subjects:
Algorithms
Computer simulation
Constraints
Convergence
Inequalities
Networks
Optimization
State estimation
proximal-gradient method
distributed algorithm
convex optimization
90C25
exact penalty function method
ISSN:1446-1811, 1446-8735
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We consider a distributed optimization problem over a multi-agent network, in which the sum of several local convex objective functions is minimized subject to global convex inequality constraints. We first transform the constrained optimization problem to an unconstrained one, using the exact penalty function method. Our transformed problem has a smaller number of variables and a simpler structure than the existing distributed primal–dual subgradient methods for constrained distributed optimization problems. Using the special structure of this problem, we then propose a distributed proximal-gradient algorithm over a time-changing connectivity network, and establish a convergence rate depending on the number of iterations, the network topology and the number of agents. Although the transformed problem is nonsmooth by nature, our method can still achieve a convergence rate, ${\mathcal{O}}(1/k)$, after $k$ iterations, which is faster than the rate, ${\mathcal{O}}(1/\sqrt{k})$, of existing distributed subgradient-based methods. Simulation experiments on a distributed state estimation problem illustrate the excellent performance of our proposed method.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1446-1811
1446-8735
DOI:10.1017/S1446181114000273