Distributed multi-agent optimization with state-dependent communication

Distributed multi-agent optimization with state-dependent communication

Abstract We study distributed algorithms for solving global optimization problems in which the objective function is the sum of local objective functions of agents and the constraint set is given by the intersection of local constraint sets of agents. We assume that each agent knows only his own local objective function and constraint set, and exchanges information with the other agents over a ...

Appendices of “ Distributed Multi - Agent Optimization with State - Dependent Communication ”

Multi-Agent Systems in Distributed Communication Environments

Agents are autonomous and flexible systems situated in an environment where agents act accordingly for achieving their objectives. Composed of several interacting agents, multi-agent systems offer promising engineering solutions for developing robust and scalable systems. A review of agents, multi-agent systems and ontologies is presented. A multi-agent architecture addressing virtual collabora...

Fast Distributed Algorithms for Multi-Agent Optimization

71 TE02 02Grand 2 Fast Distributed Algorithms for Multi-Agent Optimization Cluster: Plenary Invited Session Chair: Lorenz Biegler, Carnegie Mellon University, Pittsburgh, United States of America, [email protected] 1 Fast Distributed Algorithms for Multi-Agent Optimization Asu Özdaglar, Professor, Massachusetts Institute of Technology, 77 Massachusetts Avenue, 32-D630, Cambridge, MA, 02139, Unite...

Distributed Multi-Agent Optimization via Dual Decomposition

In this master thesis, a new distributed multi-agent optimization algorithm is introduced. The algorithm is based upon the dual decomposition of the optimization problem, together with the subgradient method for finding the optimal dual solution. The convergence of the new optimization algorithm is proved for communication networks with bounded timevarying delays, and noisy communication. Furth...