A Supervised Machine Learning Approach to Variable Branching in Branch-And-Bound

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Learning to Branch in Mixed Integer Programming (2016, methodology): “…also features 7 Min/max for ratios of constraint coeffs. to RHS Minimum and maximum ratios across positive and negative right-hand-sides (RHS) 4 (Alvarez,Louveaux, and Wehenkel 2014) Min/max for one-to-all coefﬁcient ratios The statistics are over the ratios of a variable’s coefﬁcient, to the…”
Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon (2018, background): “On the one hand, Marcos Alvarez et al. (2014, 2016, 2017); Khalil et al. (2016) estimate that strong branching is an eﬀective branching strategy but computationally too expensive and build a machine learning model to approximate it.”
Learning to Search in Branch and Bound Algorithms (2014, background): “The number of branches in this process is NB(m, opt)= m 11−2 1 .”
On learning and branching: a survey (2017, background): “Reliabilitybranchingandinformation-basedbranching(Karzanetal.2009)inspired theworkofMarcosAlvarezetal.”