The Linearized Bregman Method via Split Feasibility Problems: Analysis and Generalizations
Optimization and Control
2013-09-11 v2 Computer Vision and Pattern Recognition
Numerical Analysis
Numerical Analysis
Abstract
The linearized Bregman method is a method to calculate sparse solutions to systems of linear equations. We formulate this problem as a split feasibility problem, propose an algorithmic framework based on Bregman projections and prove a general convergence result for this framework. Convergence of the linearized Bregman method will be obtained as a special case. Our approach also allows for several generalizations such as other objective functions, incremental iterations, incorporation of non-gaussian noise models or box constraints.
Cite
@article{arxiv.1309.2094,
title = {The Linearized Bregman Method via Split Feasibility Problems: Analysis and Generalizations},
author = {Dirk A. Lorenz and Frank Schöpfer and Stephan Wenger},
journal= {arXiv preprint arXiv:1309.2094},
year = {2013}
}