This paper reviews the Bregman methods, analyzes the linearized Bregman method, and proposes fast generalization of the latter for solving the basis pursuit and related problems. The analysis shows that the linearized Bregman method has the exact penalty property, namely, it converges to an exact solution of the basis pursuit problem if and only if its regularization parameter $\alpha$ 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 steps, L-BFGS, and nonlinear conjugate gradient steps. In addition, the paper discusses the selection and update of $\alpha$. The analysis and discussions are limited to the l1-norm but can be extended to other l1-like functions.
Rice CAAM Report TR09-02, 2009