A Class of Randomized Primal-Dual Algorithms for Distributed Optimization

Abstract

Based on a preconditioned version of the randomized block-coordinate forward-backward algorithm recently proposed in [Combettes,Pesquet,2014], several variants of block-coordinate primal-dual algorithms are designed in order to solve a wide array of monotone inclusion problems. These methods rely on a sweep of blocks of variables which are activated at each iteration according to a random rule, and they allow stochastic errors in the evaluation of the involved operators. Then, this framework is employed to derive block-coordinate primal-dual proximal algorithms for solving composite convex variational problems. The resulting algorithm implementations may be useful for reducing computational complexity and memory requirements. Furthermore, we show that the proposed approach can be used to develop novel asynchronous distributed primal-dual algorithms in a multi-agent context.

References in corpus (5)

• Generalized Forward-Backward Splitting
• Stability and Performance Limits of Adaptive Primal-Dual Networks
• A Douglas-Rachford Splitting Method for Solving Equilibrium Problems
• Asynchronous Distributed Optimization using a Randomized Alternating Direction Method of Multipliers
• Playing with Duality: An Overview of Recent Primal-Dual Approaches for Solving Large-Scale Optimization Problems

Cited by in corpus (4)

• A Distributed Block-Split Gibbs Sampler with Hypergraph Structure for High-Dimensional Inverse Problems
• A randomized intertial primal-dual fixed point algorithm for monotone inclusions
• Accelerated Primal-Dual Proximal Block Coordinate Updating Methods for Constrained Convex Optimization
• Almost sure convergence of the forward-backward-forward splitting algorithm