On a stochastic column-block bregman method for nonlinear systems
For researchers working on sparse solutions in signal processing and image restoration, this method offers a stochastic approach to nonlinear systems, but it is an incremental extension of existing Bregman methods.
The paper proposes a stochastic column-block Bregman method for computing sparse solutions to nonlinear systems, with convergence analysis and an upper bound on convergence rate. Numerical experiments on image recovery demonstrate efficiency.
Sparse solution problems play an important role in both signal processing and image restoration. In this paper, we propose a stochastic column-block nonlinear Bregman method for efficiently computing sparse solutions to nonlinear systems. Under certain assumptions, we analyze the convergence of the proposed method and derive an upper bound for its convergence rate. Numerical experiments, including an image recovery problem, are presented to illustrate the efficiency of the proposed method.