Active-constraint variable ordering for faster feasibility of mixed integer linear programs

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Learning to Branch in Mixed Integer Programming (2016, methodology): “We also compute the weighted number of active constraints that x j is in, with the same 4 weightings 24 (Patel and Chinneck 2007) Table 1: Description of the atomic features.”
Information-based branching schemes for binary linear mixed integer problems (2009, background): “Patel and Chinneck (2007) show that one can reach feasibility for MILPs much faster by selecting the branching variable based on its impact on the active constraints in the parent LP relaxation, instead of using the traditional approach of selecting the branching variable based on its impact on the ”
Branching on nonchimerical fractionalities (2012, background): “This is particularly encouraging if one considers the proof-of-concept nature of our implementation.”
A Supervised Machine Learning Approach to Variable Branching in Branch-And-Bound (2014, background): “Many other branching strategies have been developed for the past 15 years, such as inference branching [19], non-chimerical branching [13], active constraint branching [22] or cloud branching [7], but their thorough description is beyond the scope of this paper.”
The feasibility pump (2005, methodology): “Heuristic approaches to general MIP problems have been proposed by several authors, including [1, 2, 3, 6, 8, 9, 10, 11, 12, 13, 17, 16, 20, 21 , 24].”