Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Instance-wise algorithm configuration with graph neural networks   We present our submission for the configuration task of the Machine Learning
for Combinatorial Optimization (ML4CO) NeurIPS 2021 competition. The
configuration task is to predict a good configuration of the open-source solver
SCIP to solve a mixed integer linear program (MILP) efficiently. We pose this
task as a supervised learning problem: First, we compile a large dataset of the
solver performance for various configurations and all provided MILP instances.
Second, we use this data to train a graph neural network that learns to predict
a good configuration for a specific instance. The submission was tested on the
three problem benchmarks of the competition and improved solver performance
over the default by 12% and 35% and 8% across the hidden test instances. We
ranked 3rd out of 15 on the global leaderboard and won the student leaderboard.
We make our code publicly available at
\url{https://github.com/RomeoV/ml4co-competition} .

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The categories of this paper are: hep-ex
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The categories of this paper are: acc-phys
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The categories of this paper are: patt-sol
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The categories of this paper are: eess
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The categories of this paper are: nlin
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The categories of this paper are: bayes-an
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The categories of this paper are: q-alg
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The categories of this paper are: gr-qc
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The categories of this paper are: hep-th
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The categories of this paper are: atom-ph
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The categories of this paper are: stat
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The categories of this paper are: nucl-th
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The categories of this paper are: econ
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The categories of this paper are: solv-int
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The categories of this paper are: funct-an
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The categories of this paper are: supr-con