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Ali H. Sayed

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Asynchronous adaptive networks

Ali H. Sayed

In a recent article [1] we surveyed advances related to adaptation, learning, and optimization over synchronous networks. Various distributed strategies were discussed that enable a collection of networked agents to interact locally in response to streamin ...

Elsevier2018

Distributed Diffusion Adaptation Over Graph Signals

Ali H. Sayed, Jie Chen, Roula Nassif

Most works on graph signal processing assume static graph signals, which is a limitation even in comparison to traditional DSP techniques where signals are modeled as sequences that evolve over time. For broader applicability, it is necessary to develop te ...

IEEE2018

Distributed Coupled Learning Over Adaptive Networks

Ali H. Sayed, Sulaiman A S A E Alghunaim

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 ...

IEEE2018

Efficient Variance-Reduced Learning Over Multi-Agent Networks

Ali H. Sayed, Bicheng Ying, Kun Yuan

This work develops a fully decentralized variance-reduced learning algorithm for multi-agent networks where nodes store and process the data locally and are only allowed to communicate with their immediate neighbors. In the proposed algorithm, there is no ...

IEEE COMPUTER SOC2018

Dual Coupled Diffusion for Distributed Optimization with Affine Constraints

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

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 ...

IEEE2018

Coordinate-Descent Diffusion Learning by Networked Agents

Ali H. Sayed, Bicheng Ying, Chengcheng Wang

This paper examines the mean-square error performance of diffusion stochastic algorithms under a generalized coordinate-descent scheme. In this setting, the adaptation step by each agent is limited to a random subset of the coordinates of its stochastic gr ...

Ieee-Inst Electrical Electronics Engineers Inc2018

Performance limits of stochastic sub-gradient learning, Part II: Multi-agent case

Ali H. Sayed, Bicheng Ying

The analysis in Part I [1] revealed interesting properties for subgradient learning algorithms in the context of stochastic optimization. These algorithms are used when the risk functions are non-smooth or involve non-differentiable components. They have b ...

Elsevier2018

Decentralized Consensus Optimization With Asynchrony and Delays

Ali H. Sayed, Kun Yuan, Tianyu Wu

We propose an asynchronous, decentralized algorithm for consensus optimization. The algorithm runs over a network in which the agents communicate with their neighbors and perform local computation. In the proposed algorithm, each agent can compute and comm ...

2018

Performance limits of stochastic sub-gradient learning, Part I: Single agent case

Ali H. Sayed, Bicheng Ying

In this work and the supporting Part II [1], we examine the performance of stochastic sub-gradient learning strategies under weaker conditions than usually considered in the literature. The new conditions are shown to be automatically satisfied by several ...

Elsevier2018

Distributed Inference over Multitask Graphs under Smoothness

Ali H. Sayed, Stefan Vlaski, Roula Nassif

This paper formulates a multitask optimization problem where agents in the network have individual objectives to meet, or individual parameter vectors to estimate, subject to a smoothness condition over the graph. The smoothness requirement softens the tra ...

IEEE2018