Fast Optimization for Mixture Prior Models

Abstract

We consider the minimization of a smooth convex function regularized by the mixture of prior models. This problem is generally difficult to solve even each simpler regularization problem is easy. In this paper, we present two algorithms to effectively solve it. First, the original problem is decomposed into multiple simpler subproblems. Then, these subproblems are efficiently solved by existing techniques in parallel. Finally, the result of the original problem is obtained from the weighted average of solutions of subproblems in an iterative framework. We successfully applied the proposed algorithms to compressed MR image reconstruction and low-rank tensor completion. Numerous experiments demonstrate the superior performance of the proposed algorithm in terms of both the accuracy and computational complexity.