[language-switcher]

José M. Amigó, & Osvaldo A. Rosso (2023). Ordinal methods: Concepts, applications, new developments, and challenges—In memory of Karsten Keller (1961–2022). Chaos: An Interdisciplinary Journal of Nonlinear Science, 33.

José María Amigó (Operations Research Center, University Miguel Hernández of Elche) and Osvaldo A. Rosso (Instituto de Física, Universidade Federal de Alagoas (UFAL) and Instituto de Física (IFLP), Universidad Nacional de la Plata)

José M. Amigó, María J. Cánovas, Marco A. López-Cerdá, & Manuel López-Pellicer (2023). Functional Analysis and Continuous Optimization. (Vol. 424) Springer International Publishing.

José M. Amigó (Center of Operations Research, Miguel Hernández University of Elche), María J. Cánovas (Center of Operations Research, Miguel Hernández University of Elche), Marco A. López-Cerdá (Department of Mathematics, Alicante University) and Manuel López-Pellicer (Institute for Pure and Applied Mathematics (IUMPA), Polytechnic University of Valencia)
Abstract:
The book includes selected contributions presented at the «International Meeting […]

Camacho, J., Cánovas, M. J., & Parra, J. (2023). Lipschitz upper semicontinuity in linear optimization via local directional convexity. Optimization, 72(8), 2091–2108.

J.Camacho (Operations Research Center, University Miguel Hernández of Elche), M.J. Cánovas (Operations Research Center, University Miguel Hernández of Elche) and J.Parra (Operations Research Center, University Miguel Hernández of Elche)
Abstract:
This work is focussed on computing the Lipschitz upper semicontinuity modulus of the argmin mapping for canonically perturbed linear programs. The immediate antecedent can be traced out […]

Time Series Data Mining and Its Applications in Real-World Problems

Título: Time series data mining and its applications in real-world problems
Ponente:  Antonio Manuel Durán (Universidad de Córdoba)
Organizador: Jesús Javier Rodríguez Sala
Fecha: Lunes 4 de octubre de 2021 a las 12:00 horas.
Lugar: Online.

Ver el seminario

Abstract: Currently, information systems such as sensors produce a  large amount of data, which is expected to experience exponential  growth in the coming years. These data are often treated […]

Bayesian Decision Tree Ensembling Strategies for Nonparametric Problems

Título: Bayesian Decision Tree Ensembling Strategies for Nonparametric Problems
Ponente:  Antonio Linero (Universidad de Texas en Austin, EEUU)
Organizador: Xavier Barber
Fecha: Viernes 17 de septiembre de 2021 a las 17:00 horas.
Lugar: Online.

Ver el seminario

Abstract: In this talk we will make the case for using Bayesian decision tree ensembles, such as Bayesian additive regression trees (BART), for addressing some fully-nonparametric problems. We present models […]