Regularized Nested Distance for Scenario Tree Reduction


About Event

The Nested Distance is a distance between scenario trees (stochastic processes which are discrete in both space and time) introduced by Pflug and Pichler in 2012 as a refinement of the Wasserstein distance suited for multistage optimization problems. In this presentation, I will make recalls on discrete optimal transport, explain its link with the Wasserstein distance and prove how the Nested Distance is a better tool for multistage optimization purposes. Then I will show how regularized optimal transport allows one to fast compute an approximation of the Nested Distance while retaining its main properties. Lastly, I will present the problem of scenario tree reduction and its relationship with the Nested Distance. The presentation will be in English.


* Os participantes dos seminários não poderão acessar às dependências da FGV usando bermuda, chinelos, blusa modelo top ou cropped, minissaia ou camiseta regata. O uso da máscara e a apresentação do comprovante de vacinação (físico ou digital) serão obrigatórios.

Apoiadores / Parceiros / Patrocinadores


Benoît Tran

Benoît Tran is a former graduate of Université Paris-Saclay in France. He did his Ph.D. in both Ecole Polytechnique and Ecole des Ponts ParisTech on stochastic optimal control. Since 2021, he is a post doctoral researcher at FGV EMap under the supervision of Prof. Vincent Guigues to work on subjects related to multistage stochastic optimization.


Fundação Getulio Vargas

Praia de Botafogo, 190 - sala 537

Informações: emap@fgv.br – 3799-5917


Praia de Botafogo, 190 - sala 537

Informações: emap@fgv.br – 3799-5917