Name
Abstract | ||
|---|---|---|
Convergence rates are established for an inexact accelerated alternating direction method of multipliers (I-ADMM) for general separable convex optimization with a linear constraint. Both ergodic and non-ergodic iterates are analyzed. Relative to the iteration number k, the convergence rate is O(1/k) in a convex setting and O(1/k(2)) in a strongly convex setting. When an error bound condition holds, the algorithm is 2-step linearly convergent. The I-ADMM is designed so that the accuracy of the inexact iteration preserves the global convergence rates of the exact iteration, leading to better numerical performance in the test problems. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s10589-020-00221-y | COMPUTATIONAL OPTIMIZATION AND APPLICATIONS |
Keywords | DocType | Volume |
Separable convex optimization, Alternating direction method of multipliers, ADMM, Accelerated gradient method, Inexact methods, Global convergence, Convergence rates | Journal | 77 |
Issue | ISSN | Citations |
3 | 0926-6003 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| William W. Hager | 1 | 1603 | 214.67 |
| Zhang Hongchao | 2 | 0 | 0.34 |