Name
Abstract | ||
|---|---|---|
In this paper we propose a new algorithm for fast l(1) minimization as frequently arising in compressed sensing. Our method is based on a split Bregman algorithm applied to the dual of the problem of minimizing parallel to u parallel to(1) + 1/2 alpha parallel to u parallel to(2) such that u solves the under-determined linear system Au = f, which was recently investigated in the context of linearized Bregman methods. Furthermore, we provide a convergence analysis for split Bregman methods in general and show with our compressed sensing example that a split Bregman approach to the primal energy can lead to a different type of convergence than split Bregman applied to the dual, thus making the analysis of different ways to minimize the same energy interesting for a wide variety of optimization problems. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1090/S0025-5718-2013-02700-7 | MATHEMATICS OF COMPUTATION |
Keywords | Field | DocType |
l(1) minimization,optimization algorithms,sparsity,compressed sensing,calculus of variation | Convergence (routing),Mathematical optimization,L1 minimization,Linear system,Calculus of variations,Bregman method,Minification,Optimization problem,Compressed sensing,Mathematics | Journal |
Volume | Issue | ISSN |
82 | 284 | 0025-5718 |
Citations | PageRank | References |
4 | 0.42 | 13 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yi Yang | 1 | 92 | 9.96 |
| Michael Möller 0001 | 2 | 71 | 8.70 |
| Stanley Osher | 3 | 7973 | 514.62 |