Name
Title | ||
|---|---|---|
On The Convergence Rate Of Inexact Majorized Sgs Admm With Indefinite Proximal Terms For Convex Composite Programming |
Abstract | ||
|---|---|---|
In this paper, we propose an inexact majorized symmetric Gauss-Seidel (sGS) alternating direction method of multipliers (ADMM) with indefinite proximal terms for multi-block convex composite programming. This method is a specific form of the inexact majorized ADMM which is further proposed to solve a general two-block separable optimization problem. The new methods adopt certain relative error criteria to solve the involving subproblems approximately, and the step-sizes allow to choose in the scope (0, (1 + <mml:msqrt>5</mml:msqrt>)/2). Under more general conditions, we establish the global convergence and Q-linear convergence rate of the proposed methods. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1142/S0217595920500359 | ASIA-PACIFIC JOURNAL OF OPERATIONAL RESEARCH |
Keywords | DocType | Volume |
Convex composite optimization, indefinite proximal terms, inexact, majorized ADMM, relative error control, symmetric Gauss-Seidel | Journal | 38 |
Issue | ISSN | Citations |
1 | 0217-5959 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Min Li | 1 | 82 | 10.65 |
| Zhongming Wu | 2 | 1 | 2.05 |