Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Study of Learning Search Approximation in Mixed Integer Branch and
  Bound: Node Selection in SCIP   In line with the growing trend of using machine learning to help solve
combinatorial optimisation problems, one promising idea is to improve node
selection within a mixed integer programming (MIP) branch-and-bound tree by
using a learned policy. Previous work using imitation learning indicates the
feasibility of acquiring a node selection policy, by learning an adaptive node
searching order. In contrast, our imitation learning policy is focused solely
on learning which of a node's children to select. We present an offline method
to learn such a policy in two settings: one that comprises a heuristic by
committing to pruning of nodes; one that is exact and backtracks from a leaf to
guarantee finding the optimal integer solution. The former setting corresponds
to a child selector during plunging, while the latter is akin to a diving
heuristic. We apply the policy within the popular open-source solver SCIP, in
both heuristic and exact settings. Empirical results on five MIP datasets
indicate that our node selection policy leads to solutions significantly more
quickly than the state-of-the-art precedent in the literature. While we do not
beat the highly-optimised SCIP state-of-practice baseline node selector in
terms of solving time on exact solutions, our heuristic policies have a
consistently better optimality gap than all baselines, if the accuracy of the
predictive model is sufficient. Further, the results also indicate that, when a
time limit is applied, our heuristic method finds better solutions than all
baselines in the majority of problems tested. We explain the results by showing
that the learned policies have imitated the SCIP baseline, but without the
latter's early plunge abort. Our recommendation is that, despite the clear
improvements over the literature, this kind of MIP child selector is better
seen in a broader approach using learning in MIP branch-and-bound tree
decisions.

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The categories of this paper are: q-fin
30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
29
The categories of this paper are: funct-an
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The categories of this paper are: chao-dyn
4
The categories of this paper are: cond-mat
13
The categories of this paper are: quant-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: ao-sci
37
The categories of this paper are: supr-con
3
The categories of this paper are: physics
26
The categories of this paper are: bayes-an
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The categories of this paper are: mtrl-th
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The categories of this paper are: hep-th
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The categories of this paper are: math-ph
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The categories of this paper are: hep-lat