A New Randomized Block-Coordinate Primal-Dual Proximal Algorithm for Distributed Optimization. This paper proposes TriPD, a new primal-dual algorithm for minimizing the sum of a Lipschitz-differentiable convex function and two possibly nonsmooth convex functions, one of which is composed with a linear mapping.
Jun 9, 2017
The goal is to minimize the aggregate of the private cost functions and reach a consensus over a graph. We propose a primal-dual algorithm based on Asymmetric ...
Abstract— We consider a network of agents, each with its own private cost consisting of the sum of two possibly nonsmooth convex functions, one of which is ...
The goal is to minimize the aggregate of the private cost functions and reach a consensus over a graph. We propose a primal-dual algorithm based on Asymmetric ...
This algorithm can be viewed as a special case of [18,Algorithm 1] corresponding to the choice of parameters μ = 0 and θ = λ = 1 (see also [19] ).
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