Sensitivity Analysis of the Proximal-Based Parallel Decomposition Methods

Abstract EN

The proximal-based parallel decomposition methods were recently proposed to solve structured convex optimization problems.

These algorithms are eligible for parallel computation and can be used efficiently for solving large-scale separable problems.

In this paper, compared with the previous theoretical results, we show that the range of the involved parameters can be enlarged while the convergence can be still established.

Preliminary numerical tests on stable principal component pursuit problem testify to the advantages of the enlargement.