Listar por autor "Esteban-Pérez, Adrián"
Mostrando ítems 1-5 de 5
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Data-driven distributionally robust optimization with Wasserstein metric, moment conditions and robust constraints
Esteban-Pérez, Adrián; Morales-González, Juan Miguel
(2018-07-12)
We consider optimization problems where the information on the uncertain parameters reduces to a finite data sample. Using the Wasserstein metric, a ball in the space of probability distributions centered at the empirical ... -
Distributionally Robust Optimal Power Flow with Contextual Information
Esteban-Pérez, Adrián; Morales-González, Juan Miguel (Elsevier B. V., 2022-10)
In this paper, we develop a distributionally robust chance-constrained formulation of the Optimal Power Flow problem (OPF) whereby the system operator can leverage contextual information. For this purpose, we exploit an ... -
Distributionally robust stochastic programs with side information based on trimmings
Esteban-Pérez, Adrián; Morales-González, Juan Miguel (Springer, 2021-11)
We consider stochastic programs conditional on some covariate information, where the only knowledge of the possible relationship between the uncertain parameters and the covariates is reduced to a finite data sample of ... -
Partition-based distributionally robust optimization via optimal transport with order cone constraints
Esteban-Pérez, Adrián; Morales-González, Juan Miguel (Springer, 2021)
In this paper we wish to tackle stochastic programs affected by ambiguity about the probability law that governs their uncertain parameters. Using optimal transport theory, we construct an ambiguity set that exploits the ... -
Theory and applications of Distributionally Robust Optimization with side data
(UMA Editorial, 2022)Nowadays, a large amount of varied data is being generated which, when made available to the decision maker, constitutes a valuable resource in optimization problems. These data, however, are not free from uncertainty ...