Computational and Theoretical Synergies between Linear Optimization and Supervised Machine Learning

Marcos Alvarez, Alejandro

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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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