A dual approach for optimal algorithms in distributed optimization over networks

We study dual-based algorithms for distributed convex optimization problems over networks, where the objective is to minimize a sum ∑ i = 1 m f i ( z ) of functions over in a network. We provide complexity bounds for four different cases, namely: each function f i is strongly convex and smooth, each function is either strongly convex or smooth, and when it is convex but neither strongly convex nor smooth. Our approach is based on the dual of an...