Sulaiman A S A E Alghunaim

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This work develops an effective distributed algorithm for the solution of stochastic optimization problems that involve partial coupling among both local constraints and local cost functions. While the collection of networked agents is interested in discov ...

This work considers multi-agent sharing optimization problems, where each agent owns a local smooth function plus a non-smooth function, and the network seeks to minimize the sum of all local functions plus a coupling composite function (possibly non-smoot ...

In this work, a distributed multi-agent optimization problem is studied where different subsets of agents are coupled with each other through affine constraints. Moreover, each agent is only aware of its own contribution to the constraints and only knows w ...

This article develops a proximal primal-dual decentralized strategy for multiagent optimization problems that involve multiple coupled affine constraints, where each constraint may involve only a subset of the agents. The constraints are generally sparse, ...

This work presents a fully distributed algorithm for learning the optimal policy in a multi-agent cooperative reinforcement learning scenario. We focus on games that can only be solved through coordinated team work. We consider situations in which K player ...

This work studies multi-agent sharing optimization problems with the objective function being the sum of smooth local functions plus a convex (possibly non-smooth) function coupling all agents. This scenario arises in many machine learning and engineering ...

This paper develops an effective distributed strategy for the solution of constrained multiagent stochastic optimization problems with coupled parameters across the agents. In this formulation, each agent is influenced by only a subset of the entries of a ...

Various bias-correction methods such as EXTRA, DIGing, and exact diffusion have been proposed recently to solve distributed deterministic optimization problems. These methods employ constant step-sizes and converge linearly to the exact solution under prop ...

This article studies a class of nonsmooth decentralized multiagent optimization problems where the agents aim at minimizing a sum of local strongly-convex smooth components plus a common nonsmooth term. We propose a general primal-dual algorithmic framewor ...

Sulaiman A S A E Alghunaim

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Distributed Coupled Learning Over Adaptive Networks

Dual Consensus Proximal Algorithm for Multi-Agent Sharing Problems

Dual Coupled Diffusion for Distributed Optimization with Affine Constraints

A Proximal Diffusion Strategy for Multiagent Optimization With Sparse Affine Constraints

Team Policy Learning For Multi-Agent Reinforcement Learning

A Multi-Agent Primal-Dual Strategy for Composite Optimization over Distributed Features

Distributed Coupled Multiagent Stochastic Optimization

On the Performance of Exact Diffusion over Adaptive Networks

Decentralized Proximal Gradient Algorithms With Linear Convergence Rates