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Paper published in a book (Scientific congresses and symposiums)
Supervised learning of convex piecewise linear approximations of optimization problems
Duchesne, Laurine; Louveaux, Quentin; Wehenkel, Louis
2021In Proceedings of the 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Peer reviewed
 

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Keywords :
Supervised learning; Convex approximations; Input-convex neural networks
Abstract :
[en] We propose to use input convex neural networks (ICNN) to build convex approximations of non-convex feasible sets of optimization problems, in the form of a set of linear equalities and inequalities in a lifted space. Our approach may be tailored to yield both inner- and outer- approximations, or to maximize its accuracy in regions closer to the minimum of a given objective function. We illustrate the method on two-dimensional toy problems and motivate it by various instances of reliability management problems of large-scale electric power systems.
Disciplines :
Computer science
Author, co-author :
Duchesne, Laurine ;  Université de Liège - ULiège > Montefiore Institute
Louveaux, Quentin  ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation : Optimisation discrète
Wehenkel, Louis  ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Méthodes stochastiques
Language :
English
Title :
Supervised learning of convex piecewise linear approximations of optimization problems
Publication date :
2021
Event name :
29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)
Event date :
from 06-10-2021 to 08-10-2021
Main work title :
Proceedings of the 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Peer reviewed :
Peer reviewed
Available on ORBi :
since 04 September 2021

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