Distributed Primal-Dual Subgradient Method for Multiagent Optimization via Consensus Algorithms

• Published in: IEEE transactions on systems, man and cybernetics. Part B, Cybernetics Vol. 41; no. 6; pp. 1715 - 1724
• Main Authors: Deming Yuan, Shengyuan Xu, Huanyu Zhao
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.12.2011
• Subjects: Algorithm design and analysis; Approximation algorithms; Average consensus; Convergence; Convex functions; convex optimization; distributed optimization; Lagrangian functions; Optimization; subgradient method; Upper bound
• ISSN: 1083-4419; 1941-0492
• DOI: 10.1109/TSMCB.2011.2160394

This paper studies the problem of optimizing the sum of multiple agents' local convex objective functions, subject to global convex inequality constraints and a convex state constraint set over a network. Through characterizing the primal and dual optimal solutions as the saddle points of the Lagrangian function associated with the problem, we propose a distributed algorithm, named the distributed primal-dual subgradient method, to provide approximate saddle points of the Lagrangian function, based on the distributed average consensus algorithms. Under Slater's condition, we obtain bounds on the convergence properties of the proposed method for a constant step size. Simulation examples are provided to demonstrate the effectiveness of the proposed method.