Presented at the MiniconferenceInternational audienceIn this paper we apply distributed sub-gradient methods to optimize global performance in Delay Tolerant Networks (DTNs). These methods rely on simple local node operations and consensus algorithms to average neighbours' information. Existing results for convergence to optimal solutions can only be applied to DTNs in the case of synchronous operation of the nodes and memory-less random meeting processes. In this paper we address both these issues. First, we prove convergence to the optimal solution for a more general class of mobility models. Second, we show that, under asynchronous operations, a direct application of the original sub-gradient method would lead to suboptimal solutions and...
International audienceDistributed learning aims at computing high-quality models by training over sc...
We develop an iterative diffusion mechanism to optimize a global cost function in a distributed mann...
We consider distributed optimization where N nodes in a generic, connected network minimize the sum ...
International audienceIn this paper we address the problem of designing adaptive epidemic-style forw...
This thesis considers optimization problems defined over a network of nodes, where each node knows o...
Delay Tolerant Networks (DTNs) are an emerging type of networks which do not need a predefined infra...
This dissertation contributes toward design, convergence analysis and improving the performance of t...
We consider a multi-agent setting with agents exchanging information over a network to solve a conve...
We consider the problem of cooperatively minimizing the sum of convex functions, where the functions...
We develop and analyze an asynchronous algorithm for distributed convex optimization when the object...
This dissertation considers distributed algorithms for centralized and decentralized networks that s...
This dissertation deals with developing optimization algorithms which can be distributed over a netw...
In decentralized optimization, nodes of a communication network each possess a local objective funct...
In this paper, a joint optimization of link scheduling, routing and replication for delay-tolerant n...
We consider distributed optimization problems in which a number of agents are to seek the global opt...
International audienceDistributed learning aims at computing high-quality models by training over sc...
We develop an iterative diffusion mechanism to optimize a global cost function in a distributed mann...
We consider distributed optimization where N nodes in a generic, connected network minimize the sum ...
International audienceIn this paper we address the problem of designing adaptive epidemic-style forw...
This thesis considers optimization problems defined over a network of nodes, where each node knows o...
Delay Tolerant Networks (DTNs) are an emerging type of networks which do not need a predefined infra...
This dissertation contributes toward design, convergence analysis and improving the performance of t...
We consider a multi-agent setting with agents exchanging information over a network to solve a conve...
We consider the problem of cooperatively minimizing the sum of convex functions, where the functions...
We develop and analyze an asynchronous algorithm for distributed convex optimization when the object...
This dissertation considers distributed algorithms for centralized and decentralized networks that s...
This dissertation deals with developing optimization algorithms which can be distributed over a netw...
In decentralized optimization, nodes of a communication network each possess a local objective funct...
In this paper, a joint optimization of link scheduling, routing and replication for delay-tolerant n...
We consider distributed optimization problems in which a number of agents are to seek the global opt...
International audienceDistributed learning aims at computing high-quality models by training over sc...
We develop an iterative diffusion mechanism to optimize a global cost function in a distributed mann...
We consider distributed optimization where N nodes in a generic, connected network minimize the sum ...