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Doctoral thesis (Dissertations and theses)
Computational and Theoretical Synergies between Linear Optimization and Supervised Machine Learning
Marcos Alvarez, Alejandro
2016
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https://hdl.handle.net/2268/204460
 

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Keywords :
mixed-integer programming; linear programming; optimization; supervised machine learning; branch-and-bound; simplex algorithm; reinforcement learning
Disciplines :
Electrical & electronics engineering
Author, co-author :
Marcos Alvarez, Alejandro ;  Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Language :
English
Title :
Computational and Theoretical Synergies between Linear Optimization and Supervised Machine Learning
Defense date :
09 December 2016
Institution :
ULiège - Université de Liège
Degree :
Doctorat en sciences de l'ingénieur (électricité et électronique)
Promotor :
Louveaux, Quentin  ;  Université de Liège - ULiège > Montefiore Institute of Electrical Engineering and Computer Science
Wehenkel, Louis  ;  Université de Liège - ULiège > Montefiore Institute of Electrical Engineering and Computer Science
President :
Geurts, Pierre  ;  Université de Liège - ULiège > Montefiore Institute of Electrical Engineering and Computer Science
Jury member :
Cornélusse, Bertrand  ;  Université de Liège - ULiège > Montefiore Institute of Electrical Engineering and Computer Science
Crama, Yves  ;  Université de Liège - ULiège > HEC Recherche > HEC Recherche: Business Analytics & Supply Chain Management
Ernst, Damien  ;  Université de Liège - ULiège > Montefiore Institute of Electrical Engineering and Computer Science
De Loera, Jesus
Lübbecke, Marco
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since 17 December 2016

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