Distributed constrained optimisation over cloud-based multi-agent networks

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.