Computation of Discrete Flows Over Networks via Constrained Wasserstein Barycenters

Autor: Ferran Arque, Cesar Uribe, Carlos Ocampo-Martinez
Rok vydání: 2021
Zdroj: LatinX in AI at International Conference on Machine Learning 2021.
DOI: 10.52591/lxai202107246
Popis: We study a Wasserstein attraction approach for solving dynamic mass transport problems over networks. In the transport problem over networks, we start with a distribution over the set of nodes that needs to be “transported” to a target distribution accounting for the network topology. We exploit the specific structure of the problem, characterized by the computation of implicit gradient steps, and formulate an approach based on discretized flows. As a result, our proposed algorithm relies on the iterative computation of constrained Wasserstein barycenters. We show how the proposed method finds approximate solutions to the network transport problem, taking into account the topology of the network, the capacity of the communication channels, and the capacity of the individual nodes.
Databáze: OpenAIRE