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] ).
Jul 27, 2018 · 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 ...
This paper proposes Triangularly Preconditioned Primal- Dual algorithm, a new primal-dual algorithm for minimizing the sum of a Lipschitz-differentiable ...
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In this paper, we focus on the nonsmooth composite optimization problems over networks, which consist of a smooth term and a nonsmooth term.
We show that our distributed algorithm is easy to implement without the need to perform matrix inversions or inner loops. We demonstrate through computational ...
We develop a Proximal Primal-Dual Al- gorithm (Prox-PDA), which enables the network nodes to distributedly and collectively compute the set of first-order ...