A Dynamic Scheduling Strategy of ADMM Sub-problem Optimization Algorithm Based on Hierarchical Structure

Abstract

The Alternating Direction Method of Multiplier (ADMM) is a simple algorithm to resolve decomposable convex optimization problems, especially effective in solving large-scale problems. However, this algorithm suffers from the straggler problem its updates have to be synchronized. Therefore, the asynchronous ADMM algorithm is proposed. However, the convergence speed of the ADMM algorithm is not very satisfactory. In this paper, we propose a dynamic scheduling strategy for sub-problems-automatically calling different algorithms at different iteration periods of each iteration, and combining this strategy with a hierarchical communication structure. The experiments based on ZiQiang 4000 cluster experimental environment show that the dynamic scheduling strategy based on hierarchical communication structure can solve the ADMM sub-problem and effectively improve the convergence speed and communication efficiency of the algorithm.