In this paper, we consider the consensus problem where a set of nodes cooperate to minimize a global cost. In particular, we consider an online setting and propose an online algorithm based on the alternating direction method of multipliers. Besides, we take into account the asynchronous operation of the nodes. In this context, we prove that the algorithm attains sublinear regret on the objective. Finally, we assess numerically the performance of the algorithm in a distributed sparse regression problem
Abstract—This paper presents a regret analysis on a dis-tributed online optimization problem compute...
This dissertation deals with developing optimization algorithms which can be distributed over a netw...
The alternating direction method of multipliers (ADMM) has recently been recognized as a promising ...
In this paper, we consider the consensus problem where a set of nodes cooperate to minimize a global...
In this paper, we propose (i) a novel distributed algorithm for consensus optimization over networks...
Alternating direction method of multipliers (ADMM) is a popular convex optimization algorithm, which...
Aiming at solving large-scale optimization problems, this paper studies distributed optimization met...
ADMM is a popular algorithm for solving convex optimization problems. Applying this algorithm to dis...
Distributed optimization algorithms are highly attractive for solving big data problems. In particul...
The recent deployment of multi-agent systems in a wide range of scenarios has enabled the solution o...
Abstract—This paper considers the problems of distributed online prediction and optimization. Each n...
In this paper, we focus on an asynchronous distributed optimization problem. In our problem, each no...
Alternating direction method of multipliers (ADMM) is a popular convex optimisation algorithm, which...
We consider a network of agents that are cooperatively solving a global optimization problem, where ...
Distributed computing offers a high degree of flexibility to accom-modate modern learning constraint...
Abstract—This paper presents a regret analysis on a dis-tributed online optimization problem compute...
This dissertation deals with developing optimization algorithms which can be distributed over a netw...
The alternating direction method of multipliers (ADMM) has recently been recognized as a promising ...
In this paper, we consider the consensus problem where a set of nodes cooperate to minimize a global...
In this paper, we propose (i) a novel distributed algorithm for consensus optimization over networks...
Alternating direction method of multipliers (ADMM) is a popular convex optimization algorithm, which...
Aiming at solving large-scale optimization problems, this paper studies distributed optimization met...
ADMM is a popular algorithm for solving convex optimization problems. Applying this algorithm to dis...
Distributed optimization algorithms are highly attractive for solving big data problems. In particul...
The recent deployment of multi-agent systems in a wide range of scenarios has enabled the solution o...
Abstract—This paper considers the problems of distributed online prediction and optimization. Each n...
In this paper, we focus on an asynchronous distributed optimization problem. In our problem, each no...
Alternating direction method of multipliers (ADMM) is a popular convex optimisation algorithm, which...
We consider a network of agents that are cooperatively solving a global optimization problem, where ...
Distributed computing offers a high degree of flexibility to accom-modate modern learning constraint...
Abstract—This paper presents a regret analysis on a dis-tributed online optimization problem compute...
This dissertation deals with developing optimization algorithms which can be distributed over a netw...
The alternating direction method of multipliers (ADMM) has recently been recognized as a promising ...