Name
Abstract | ||
|---|---|---|
We present a novel framework, namely, accelerated alternating direction method of multipliers (AADMM), for acceleration of linearized ADMM. The basic idea of AADMM is to incorporate a multistep acceleration scheme into linearized ADMM. We demonstrate that for solving a class of convex composite optimization with linear constraints, the rate of convergence of AADMM is better than that of linearized ADMM, in terms of their dependence on the Lipschitz constant of the smooth component. Moreover, AADMM is capable of dealing with the situation when the feasible region is unbounded, as long as the corresponding saddle point problem has a solution. A backtracking algorithm is also proposed for practical performance. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1137/14095697X | SIAM JOURNAL ON IMAGING SCIENCES |
Keywords | Field | DocType |
accelerated gradient method,convex optimization,alternating direction method of multipliers | Mathematical optimization,Saddle point,Mathematical analysis,Feasible region,Lipschitz continuity,Acceleration,Rate of convergence,Numerical analysis,Backtracking,Convex optimization,Mathematics | Journal |
Volume | Issue | ISSN |
8 | 1 | 1936-4954 |
Citations | PageRank | References |
9 | 0.51 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuyuan Ouyang | 1 | 9 | 0.51 |
| Y. Chen | 2 | 127 | 5.88 |
| Guanghui Lan | 3 | 1212 | 66.26 |
| Eduardo Pasiliao Jr. | 4 | 12 | 1.27 |