This algorithm allows the agents to exchange information over networks with time-varying topologies and asymptotically agree on a pair of primal-dual optimal ...

Abstract—We consider a multi-agent convex optimization problem where agents are to minimize a sum of local objective.

Abstract: We consider a multi-agent convex optimization problem where agents are to minimize a sum of local objective functions subject to a global inequality ...

We consider a multi-agent convex optimization problem where agents are to minimize a sum of local objective functions subject to a global inequality ...

Here, we introduce the Distributed Lagrangian Primal-Dual Subgradient Algorithm (DLPDS, ... with a pair of primal and Lagrangian dual optimal solutions and the ...

We devise a distributed primal-dual subgradient algorithm which is based on the characterization of the primal-dual optimal solutions as the saddle points of ...

Abstract—We consider a multi-agent convex optimization problem where the agents are to minimize a sum of local objective functions subject to a global ...

Jan 15, 2010 · Abstract:We consider a general multi-agent convex optimization problem where the agents are to collectively minimize a global objective ...

Abstract—We study a deterministic primal-dual subgradient method for distributed optimization of a separable objective function with global inequality ...

In this paper we propose a novel Augmented Lagrangian Tracking distributed optimization algorithm for solving multi-agent optimization problems.