Sulaiman A S A E Alghunaim

Cette personne n’est plus à l’EPFL

Publications associées (12)

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Decentralized Proximal Gradient Algorithms With Linear Convergence Rates

Ali H. Sayed, Kun Yuan, Sulaiman A S A E Alghunaim

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 ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Dual Consensus Proximal Algorithm for Multi-Agent Sharing Problems

Ali H. Sayed, Sulaiman A S A E Alghunaim

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 ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

On the Influence of Bias-Correction on Distributed Stochastic Optimization

Ali H. Sayed, Bicheng Ying, Kun Yuan, Sulaiman A S A E Alghunaim

Various bias-correction methods such as EXTRA, gradient tracking methods, 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 ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Linear convergence of primal-dual gradient methods and their performance in distributed optimization

Ali H. Sayed, Sulaiman A S A E Alghunaim

In this work, we revisit a classical incremental implementation of the primal-descent dual-ascent gradient method used for the solution of equality constrained optimization problems. We provide a short proof that establishes the linear (exponential) conver ...

PERGAMON-ELSEVIER SCIENCE LTD2020

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

Ali H. Sayed, Sulaiman A S A E Alghunaim, Ming Yan

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 ...

IEEE2020

A Proximal Diffusion Strategy for Multiagent Optimization With Sparse Affine Constraints

Ali H. Sayed, Kun Yuan, Sulaiman A S A E Alghunaim

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, ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Distributed Coupled Multiagent Stochastic Optimization

Ali H. Sayed, Sulaiman A S A E Alghunaim

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 ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

A Linearly Convergent Proximal Gradient Algorithm for Decentralized Optimization

Ali H. Sayed, Kun Yuan, Sulaiman A S A E Alghunaim

Decentralized optimization is a powerful paradigm that finds applications in engineering and learning design. This work studies decentralized composite optimization problems with non-smooth regularization terms. Most existing gradient-based proximal decent ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2019

On the Performance of Exact Diffusion over Adaptive Networks

Ali H. Sayed, Sulaiman A S A E Alghunaim, Bicheng Ying, Kun Yuan

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 ...

IEEE2019

Team Policy Learning For Multi-Agent Reinforcement Learning

Ali H. Sayed, Sulaiman A S A E Alghunaim, Lucas Cesar Eduardo Cassano

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 ...

IEEE2019

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