Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Large Neighborhood Search Policy for Integer Programming   We propose a deep reinforcement learning (RL) method to learn large
neighborhood search (LNS) policy for integer programming (IP). The RL policy is
trained as the destroy operator to select a subset of variables at each step,
which is reoptimized by an IP solver as the repair operator. However, the
combinatorial number of variable subsets prevents direct application of typical
RL algorithms. To tackle this challenge, we represent all subsets by
factorizing them into binary decisions on each variable. We then design a
neural network to learn policies for each variable in parallel, trained by a
customized actor-critic algorithm. We evaluate the proposed method on four
representative IP problems. Results show that it can find better solutions than
SCIP in much less time, and significantly outperform other LNS baselines with
the same runtime. Moreover, these advantages notably persist when the policies
generalize to larger problems. Further experiments with Gurobi also reveal that
our method can outperform this state-of-the-art commercial solver within the
same time limit.

20
The categories of this paper are: acc-phys
18
The categories of this paper are: econ
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
21
The categories of this paper are: adap-org
36
The categories of this paper are: cmp-lg
37
The categories of this paper are: supr-con
4
The categories of this paper are: cond-mat
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th
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The categories of this paper are: patt-sol
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The categories of this paper are: comp-gas
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The categories of this paper are: dg-ga
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The categories of this paper are: mtrl-th
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The categories of this paper are: bayes-an
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The categories of this paper are: chem-ph