Authors: Wei Xu; Qing Ling; Yongcheng Li; Manxi Wang
Addresses: Department of Automation, University of Science and Technology of China, Hefei, Anhui, 230026, China ' School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, Guangdong, 510006, China ' State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, Luoyang, Henan, 471003, China ' State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, Luoyang, Henan, 471003, China
Abstract: We consider a distributed constrained optimisation problem where a group of distributed agents are interconnected via a cloud center, and collaboratively minimise a network-wide objective function subject to local and global constraints. This paper devotes to developing efficient distributed algorithms that fully utilise the computation abilities of the cloud center and the agents, as well as avoid extensive communications between the cloud center and the agents. We address these issues by introducing two divide-and-conquer techniques, the alternating direction method of multipliers (ADMM) and a primal-dual first-order (PDFO) method, which assign the local objective functions and constraints to the agents while the global ones to the cloud center. Both algorithms are proved to be convergent to the primal-dual optimal solution. Numerical experiments demonstrate the effectiveness of the proposed distributed constrained optimisation algorithms.
Keywords: cloud computing; distributed optimisation; ADMM; alternating direction method of multipliers; PDFO; primal-dual first-order method.
International Journal of Sensor Networks, 2018 Vol.28 No.1, pp.43 - 56
Received: 08 Jan 2017
Accepted: 19 Oct 2017
Published online: 08 Sep 2018 *