Hybrid Branching

By Tobias Achterberg, Timo Berthold. 2009

108 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Computer Networks and Communications, Industrial and Manufacturing Engineering

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Citations in the corpus

Learning to Branch in Mixed Integer Programming (2016, methodology): “This is the case for modern strategies such as reliability branching (Achterberg, Koch, and Martin 2005) and hybrid branching (Achterberg and Berthold 2009).”
Information-based branching schemes for binary linear mixed integer problems (2009, background): “Identifying a minimum cardinality clause is closely related to identifying a minimally infeasible subsystem of inequalities (IIS) (Gleeson and Ryan (1990), Amaldi et al. (2003)).”
A Supervised Machine Learning Approach to Variable Branching in Branch-And-Bound (2014, background): “Finally, let us mention hybrid branching [1], which is probably today’s state-of-the-art branching strategy.”
MIPLIB 2010 - Mixed Integer Programming Library version 5 (2011, unknown): “is a tie among the top candidates, then a series of secondary criteria should be used to make the final decision [2].”
The SCIP Optimization Suite 3.2 (2016, methodology): “Concerning branching, the customary hybrid branching rule [3] of SCIP is used; node selection is performed with respect to a best estimate criterion—interleaved with best bound and depth-first search phases [1].”
The SCIP Optimization Suite 7.0 (2020, background): “The default hybrid branching rule [4] of SCIP has been extended by the option to adjust the weights of the different scores based on the degree of dual degeneracy in the current LP solution.”
Exact Combinatorial Optimization with Graph Convolutional Neural Networks (2019, methodology): “On the right, a new state s t +1 resulting from branching on the variable a t = x 4 . the core of modern combinatorial optimization solvers, and have been the focus of extensive research [40; 43; 1; 17].”
On learning and branching: a survey (2017, methodology): “Finally, hybrid branching (Achterberg and Berthold 2009) computes for each candidate variable ﬁve different measures, chosen among typical branching scores of MILP, constraint satisfaction and satisﬁability technologies.”