期刊
SIAM JOURNAL ON IMAGING SCIENCES
卷 3, 期 4, 页码 856-877出版社
SIAM PUBLICATIONS
DOI: 10.1137/090760350
关键词
Bregman; linearized Bregman; compressed sensing; l(1)-minimization; basis pursuit
类别
资金
- NSF [DMS-07-48839, N00014-08-1-1101]
- U.S. ARL and ARO [W911NF-09-1-0383]
- Alfred P. Sloan Research Fellowship
This paper analyzes and improves the linearized Bregman method for solving the basis pursuit and related sparse optimization problems. The analysis shows that the linearized Bregman method has the exact regularization property; namely, it converges to an exact solution of the basis pursuit problem whenever its smooth parameter a is greater than a certain value. The analysis is based on showing that the linearized Bregman algorithm is equivalent to gradient descent applied to a certain dual formulation. This result motivates generalizations of the algorithm enabling the use of gradient-based optimization techniques such as line search, Barzilai-Borwein, limited memory BFGS (L-BFGS), nonlinear conjugate gradient, and Nesterov's methods. In the numerical simulations, the two proposed implementations, one using Barzilai-Borwein steps with nonmonotone line search and the other using L-BFGS, gave more accurate solutions in much shorter times than the basic implementation of the linearized Bregman method with a so-called kicking technique.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据