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
May 6, 2016 · Our algorithm includes the method of Chambolle and Pock as a special case and has linear convergence rate when the cost functions are piecewise ...
Abstract—This paper proposes Triangularly Precondi- tioned Primal- Dual algorithm, a new primal-dual algorithm for minimizing the sum of a ...
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Jul 27, 2018 · Our algorithm includes the method of Chambolle and Pock as a special case and has linear convergence rate when the cost functions are piecewise ...
A new randomized block-coordinate primal-dual proximal algorithm for distributed optimization. IEEE Trans. Autom. Control 2019, 64, 4050–4065. [Google ...
Jun 13, 2023 · Abstract: This article develops a novel distributed primal-dua l proximal algorithm (PDPA-Dist) and its corresponding randomized version ...
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 ...
For example in [32] , the authors develop a new primal-dual algorithm that uses the Laplacian of the communication graph as a set of linear constraints to ...
Abstract. In this paper we consider nonconvex optimiza- tion and learning over a network of distributed nodes. We develop a Proximal Primal-Dual Al-.
Feb 1, 2023 · We propose a new primal–dual algorithm, in which the dual update is ... new world of proximal counterparts of variance-reduced SGD-type algorithms ...