Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Reinforcement Learning for Integer Programming: Learning to Cut   Integer programming (IP) is a general optimization framework widely
applicable to a variety of unstructured and structured problems arising in,
e.g., scheduling, production planning, and graph optimization. As IP models
many provably hard to solve problems, modern IP solvers rely on many
heuristics. These heuristics are usually human-designed, and naturally prone to
suboptimality. The goal of this work is to show that the performance of those
solvers can be greatly enhanced using reinforcement learning (RL). In
particular, we investigate a specific methodology for solving IPs, known as the
Cutting Plane Method. This method is employed as a subroutine by all modern IP
solvers. We present a deep RL formulation, network architecture, and algorithms
for intelligent adaptive selection of cutting planes (aka cuts). Across a wide
range of IP tasks, we show that the trained RL agent significantly outperforms
human-designed heuristics, and effectively generalizes to 10X larger instances
and across IP problem classes. The trained agent is also demonstrated to
benefit the popular downstream application of cutting plane methods in
Branch-and-Cut algorithm, which is the backbone of state-of-the-art commercial
IP solvers.

24
The categories of this paper are: dg-ga
25
The categories of this paper are: patt-sol
18
The categories of this paper are: econ
32
The categories of this paper are: atom-ph
17
The categories of this paper are: q-fin
11
The categories of this paper are: hep-lat
33
The categories of this paper are: plasm-ph
9
The categories of this paper are: nlin
19
The categories of this paper are: eess
23
The categories of this paper are: solv-int
4
The categories of this paper are: cond-mat
13
The categories of this paper are: quant-ph
7
The categories of this paper are: hep-th
30
The categories of this paper are: q-alg
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The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th