Ali H. Sayed

TL;DR: A fully distributed adaptive diffusion algorithm based on penalty methods that allows the network to cooperatively optimize the global cost function, which is defined as the sum of the individual costs over the network, subject to all constraints.

TL;DR: An adaptive clustering and learning scheme that allows agents to learn which neighbor they should cooperate with and which other neighbors they should ignore, and enables the agents to identify their clusters and to attain improved learning and estimation accuracy over networks is proposed.

TL;DR: This paper considers learning dictionary models over a network of agents, where each agent is only in charge of a portion of the dictionary elements and generates dual variables that are used by the agents to update their dictionaries without the need to share these dictionaries or even the coefficient models for the training data.

TL;DR: In this paper, the authors examined the mean-square stability and convergence of the learning process of distributed strategies over graphs and identified conditions on the network topology, utilities, and data in order to ensure stability; the results also identified three distinct stages in the learning behavior of multiagent networks related to transient phases I and II and the steady state phase.

TL;DR: This paper proposes a new approach to the analysis of the steady-state performance of constant modulus algorithms (CMA), which are among the most popular adaptive schemes for blind equalization, and bypasses the need for working directly with the weight error covariance matrix.