Article (Scientific journals)
A Machine Learning-Based Approximation of Strong Branching
Marcos Alvarez, Alejandro; Louveaux, Quentin; Wehenkel, Louis
2017In INFORMS Journal on Computing, 29 (1), p. 185-195
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Keywords :
branch-and-bound; strong branching; supervised learning
Abstract :
[en] We present in this paper a new generic approach to variable branching in branch-and-bound for mixed- integer linear problems. Our approach consists in imitating the decisions taken by a good branching strategy, namely strong branching, with a fast approximation. This approximated function is created by a machine learning technique from a set of observed branching decisions taken by strong branching. The philosophy of the approach is similar to reliability branching. However, our approach can catch more complex aspects of observed previous branchings in order to take a branching decision. The experiments performed on randomly generated and MIPLIB problems show promising results.
Disciplines :
Computer science
Author, co-author :
Marcos Alvarez, Alejandro ;  Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Louveaux, Quentin  ;  Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation : Optimisation discrète
Wehenkel, Louis  ;  Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Language :
English
Title :
A Machine Learning-Based Approximation of Strong Branching
Publication date :
January 2017
Journal title :
INFORMS Journal on Computing
ISSN :
1091-9856
eISSN :
1526-5528
Publisher :
INFORMS: Institute for Operations Research
Volume :
29
Issue :
1
Pages :
185-195
Peer reviewed :
Peer Reviewed verified by ORBi
Available on ORBi :
since 18 June 2016

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