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Article Dans Une Revue Machine Learning: Science and Technology Année : 2023

Machine-learning-assisted Monte Carlo fails at sampling computationally hard problems

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Abstract Several strategies have been recently proposed in order to improve Monte Carlo sampling efficiency using machine learning tools. Here, we challenge these methods by considering a class of problems that are known to be exponentially hard to sample using conventional local Monte Carlo at low enough temperatures. In particular, we study the antiferromagnetic Potts model on a random graph, which reduces to the coloring of random graphs at zero temperature. We test several machine-learning-assisted Monte Carlo approaches, and we find that they all fail. Our work thus provides good benchmarks for future proposals for smart sampling algorithms.

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hal-04028482 , version 1 (14-03-2023)

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Simone Ciarella, Jeanne Trinquier, Martin Weigt, Francesco Zamponi. Machine-learning-assisted Monte Carlo fails at sampling computationally hard problems. Machine Learning: Science and Technology, 2023, 4 (1), pp.010501. ⟨10.1088/2632-2153/acbe91⟩. ⟨hal-04028482⟩
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