A Unified Algorithmic Framework of Symmetric Gauss-Seidel Decomposition Based Proximal Admms for Convex Composite Programming

Liang Chen, Defeng Sun, Kim-Chuan Toh, Ning Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review


This paper aims to present a fairly accessible generalization of several symmetric GaussSeidel decomposition based multi-block proximal alternating direction methods of multipliers (ADMMs) for convex composite optimization problems. The proposed method unifies and refines many constructive techniques that were separately developed for the computational efficiency of multi-block ADMM-type algorithms. Specifically, the majorized augmented Lagrangian functions, the indefinite proximal terms, the inexact symmetric Gauss-Seidel decomposition theorem, the tolerance criteria of approximately solving the subproblems, and the large dual step-lengths, are all incorporated in one algorithmic framework, which we named as sGS-imiPADMM. From the popularity of convergent variants of multi-block ADMMs in recent years, especially for high-dimensional multi-block convex composite conic programming problems, the unification presented in this paper, as well as the corresponding convergence results, may have the great potential of facilitating the implementation of many multi-block ADMMs in various problem settings.
Original languageEnglish
Pages (from-to)739-757
Number of pages18
JournalJournal of Computational Mathematics
Issue number6
Publication statusPublished - 31 Jul 2019


  • Convex optimization
  • Multi-block
  • Alternating direction method of multipliers
  • Symmetric Gauss-Seidel decomposition
  • Majorization

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