Branching on nonchimerical fractionalities

31 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Learning to Branch in Mixed Integer Programming (2016, methodology): “For SB, SB+PC and SB+ML, κ is set to 10, and all SB calls are limited to 50 dual simplex iterations, as in (Fischetti and Monaci 2012).”
On learning and branching: a survey (2017, methodology): “Partially following the work of Karzan et al (2009) is the backdoor branching approach of Fischetti and Monaci (2012a). In this work, the collected data include some CERC DATA SCIENCE FOR REAL-TIME DECISION-MAKING DS4DM-2017-004”
Exact Combinatorial Optimization with Graph Convolutional Neural Networks (2019, background): “[29], Fischetti and Monaci [17] and Khalil et al.”
A Supervised Machine Learning Approach to Variable Branching in Branch-And-Bound (2014, methodology): “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 SCIP Optimization Suite 3.2 (2016, background): “In the context of branching rules, further work on cloud branching [13] is planned, as well as the integration of other recent branching developments like nonchimerical branching [29] and an abstract model for the scoring function used in branching [54].”