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Listar por autor "Jiménez-Cordero, María Asunción"
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Aprendizaje Supervisado: Métodos, Propiedades y Aplicaciones
Valenzuela González, Gema (2022-09-19)El aprendizaje supervisado se centra en el desarrollo, a partir de un conjunto de datos conocido, de un modelo matemático capaz de predecir el valor correspondiente de un nuevo dato. Estudiaremos la construcción de dichos ... -
Cost-driven screening of network constraints for the unit commitment problem
Porras, Álvaro; Pineda-Morente, Salvador; Morales-González, Juan Miguel; Jiménez-Cordero, María Asunción (The Institute of Electrical and Electronics Engineers (IEEE), 2022-03-16)In an attempt to speed up the solution of the unit commitment (UC) problem, both machine-learning and optimization-based methods have been proposed to lighten the full UC formulation by removing as many superfluous line-flow ... -
Desigualdades válidas en programación con variables enteras. Aplicación al problema de la mochila.
Albacete Maza, Lourdes (2023)La programación lineal con variables enteras es una herramienta fundamental en la actualidad debido a su versatilidad en una amplia variedad de disciplinas. Sin embargo, la resolución de este tipo de problemas no es una ... -
Warm-starting constraint generation for mixed-integer optimization: A Machine Learning approach
Jiménez-Cordero, María Asunción; Morales-González, Juan Miguel; Pineda-Morente, Salvador (Elsevier, 2022-10-11)Mixed Integer Linear Programs (MILP) are well known to be NP-hard (Non-deterministic Polynomial-time hard) problems in general. Even though pure optimization-based methods, such as constraint generation, are guaranteed to ... -
Warm-starting constraint generation for mixed-integer optimization: A Machine Learning approach
Jiménez-Cordero, María Asunción; Morales-González, Juan Miguel; Pineda-Morente, Salvador (Elsevier, 2022-10-11)Mixed Integer Linear Programs (MILP) are well known to be NP-hard (Non-deterministic Polynomial-time hard) problems in general. Even though pure optimization-based methods, such as constraint generation, are guaranteed to ...