Distributed multi-step subgradient optimization for multi-agent system

Abstract

This paper proposes a novel multi-step subgradient scheme to solve distributed optimization problem with a balanced digraph. To this end, matrix diameter and radius are first introduced to describe the convergence rate of the adjacency matrix. Then, we propose a hierarchical and recursive observer to estimate the desired optimal solution, and we prove that the proposed strategy can asymptotically obtain an optimal solution, while all networked systems achieve a consensus among estimates in a finite-time. Simulation results verified the effectiveness of the proposed algorithm.