Poster
A primal-dual method for conic constrained distributed optimization problems
Necdet Serhat Aybat · Erfan Yazdandoost Hamedani
Area 5+6+7+8 #109
Keywords: [ (Other) Classification ] [ Large Scale Learning and Big Data ] [ Convex Optimization ] [ (Other) Optimization ] [ (Application) Privacy, Anonymity, and Security ]
We consider cooperative multi-agent consensus optimization problems over an undirected network of agents, where only those agents connected by an edge can directly communicate. The objective is to minimize the sum of agent-specific composite convex functions over agent-specific private conic constraint sets; hence, the optimal consensus decision should lie in the intersection of these private sets. We provide convergence rates in sub-optimality, infeasibility and consensus violation; examine the effect of underlying network topology on the convergence rates of the proposed decentralized algorithms; and show how to extend these methods to handle time-varying communication networks.
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