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dc.contributor.authorJiménez-Cordero, María Asunción 
dc.contributor.authorMorales-González, Juan Miguel 
dc.contributor.authorPineda-Morente, Salvador 
dc.date.accessioned2022-09-06T06:30:00Z
dc.date.available2022-09-06T06:30:00Z
dc.date.created2022
dc.date.issued2022-10-11
dc.identifier.urihttps://hdl.handle.net/10630/24891
dc.description.abstractMixed 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 provide an optimal solution if enough time is given, their use in online applications remains a great challenge due to their usual excessive time requirements. To alleviate their computational burden, some machine learning techniques (ML) have been proposed in the literature, using the information provided by previously solved MILP instances. Unfortunately, these techniques report a non-negligible percentage of infeasible or suboptimal instances. By linking mathematical optimization and machine learning, this paper proposes a novel approach that speeds up the traditional constraint generation method, preserving feasibility and optimality guarantees. In particular, we first identify offline the so-called invariant constraint set of past MILP instances. We then train (also offline) a machine learning method to learn an invariant constraint set as a function of the problem parameters of each instance. Next, we predict online an invariant constraint set of the new unseen MILP application and use it to initialize the constraint generation method. This warm-started strategy significantly reduces the number of iterations to reach optimality, and therefore, the computational burden to solve online each MILP problem is significantly reduced. Very importantly, all the feasibility and optimality theoretical guarantees of the traditional constraint generation method are inherited by our proposed methodology. The computational performance of the proposed approach is quantified through synthetic and real-life MILP applications.es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectAprendizaje automático (Inteligencia artificial)es_ES
dc.subject.otherMixed integer linear programminges_ES
dc.subject.otherMachine learninges_ES
dc.subject.otherConstraint generationes_ES
dc.subject.otherWarm-startes_ES
dc.subject.otherFeasibility and optimality guaranteeses_ES
dc.titleWarm-starting constraint generation for mixed-integer optimization: A Machine Learning approaches_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.centroFacultad de Cienciases_ES
dc.identifier.doihttps://doi.org/10.1016/j.knosys.2022.109570
dc.rights.ccAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES


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