Texas A&M University at Qatar
Distributed Algorithms for Constrained Optimization over Directed Networks Using Asynchronous Broadcast-Based Algorithm
This talk focuses on distributed convex optimization problems over an unbalanced directed multi-agent network with inequality constraints. The goal is to cooperatively minimize the sum of all locally known convex cost functions. Every single agent in the network only knows its local objective function and local inequality constraint, and is constrained to a privately known convex set. To collaboratively solve the optimization problem, we mainly concentrate on an epigraph form of the original constrained optimization to overcome the unbalancedness of directed networks, and propose a new distributed asynchronous broadcast-based optimization algorithm. The algorithm allows that not only the updates of agents are asynchronous in a distributed fashion, but also the step-sizes of all agents are uncoordinated. An important characteristic of the proposed algorithm is to cope with the constrained optimization problem in the case of unbalanced directed networks whose communications are subjected to possible link failures.
Prof. Tingwen Huang's research focuses on dynamics of nonlinear systems including neural networks, complex networks and multi-agent and their applications to smart grids and cybersecurity. He is the first recipient of Dean’s Fellow for Recognition of Faculty’s Excellence and Achievements awarded by Texas A&M University at Qatar (TAMUQ) in 2014, was bestowed Faculty Research Excellence Award by TAMUQ in 2015, was elected as IEEE Fellow in 2018, conferred Changjiang Chair Professor in 2019 by Ministry of Education of China, was elected as Academician of the International Academy for Systems and Cybernetic Sciences recently.