Paper Info
Title
A class of decomposition methods for convex optimization and monotone variational inclusions via the hybrid inexact proximal point framework
Abstract
We show that the decomposition method for convex programming proposed by Chen and Teboulle can be regarded as a special case in the hybrid inexact proximal point framework. We further demonstrate that the more general decomposition algorithms for variational inequalities introduced by Tseng are also either a special case in this framework or very closely related to it. This analysis provides a new insight into the nature of those decomposition schemes, as well as paves the way for deriving more practical methods by solving subproblems approximately (for example, using appropriate bundle methods). As a by-product, we also improve some convergence results and extend the approach to a more general class of problems.
Year
DOI
Venue
2004
10.1080/1055678042000218957
OPTIMIZATION METHODS & SOFTWARE
Keywords
Field
DocType
maximal monotone operator,variational inclusion,decomposition,enlargement of operator,hybrid inexact proximal point method,bundle method
Convergence (routing),Mathematical optimization,Proximal Gradient Methods,Decomposition method (constraint satisfaction),Convex optimization,Monotone polygon,Mathematics,Bundle,Special case,Variational inequality
Journal
Volume
Issue
ISSN
19
5
1055-6788
Citations 
PageRank 
References 
7
0.68
13
Authors
1
Name
Order
Citations
PageRank
M. V. Solodov160072.47