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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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TITLE: Learning to Branch in Mixed Integer Programming
ABSTRACT:
Choosing good variables to branch on often leads to a dramatic reduction in terms of the number of nodes needed to solve an instance. The design of strategies for branching in Mixed Integer Programming (MIP) is guided by cycles of parameter tuning and offline experimentation on an extremely heterogeneous testbed, using the average performance. Once devised, these strategies (and the values of their parameters) are essentially input-agnostic. To address these issues, we develop a novel framework for data-driven, on-the-fly design of variable selection strategies. By leveraging recent advances in supervised ranking, we aim to produce strategies that gather the best of all properties: 1) using a small number of search nodes approaching the good performance of SB, 2) maintaining a low computation footprint as in PC, and 3) selecting variables adaptively based on the properties of the given instance. Based on a limited number of observed Strong Branching decisions at the start of the search and a set of dynamic features computed for each candidate variable at a search node, we learn an easy-to-evaluate surrogate function that mimics the SB strategy by solving a ``learning-to-rank'' problem, common in ML. We will show an instantiation of this framework using CPLEX, a state-of-the-art MIP solver, and evaluate performance the MIPLIB benchmark.
This is joint work with Elias Khalil, Le Song, Pierre Le Bodic and George Nemhauser.
