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arXiv · 2302.06926

Lightsolver challenges a leading deep learning solver for Max-2-SAT problems

Hod Wirzberger·Assaf Kalinski·Idan Meirzada·Harel Primack·Yaniv Romano·Chene Tradonsky·Ruti Ben Shlomi
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Abstract

Maximum 2-satisfiability (MAX-2-SAT) is a type of combinatorial decision problem that is known to be NP-hard. In this paper, we compare LightSolver's quantum-inspired algorithm to a leading deep-learning solver for the MAX-2-SAT problem. Experiments on benchmark data sets show that LightSolver achieves significantly smaller time-to-optimal-solution compared to a state-of-the-art deep-learning algorithm, where the gain in performance tends to increase with the problem size.

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Hod Wirzberger, Assaf Kalinski, Idan Meirzada, Harel Primack, Yaniv Romano, Chene Tradonsky, Ruti Ben Shlomi. 2023-02-14. Lightsolver challenges a leading deep learning solver for Max-2-SAT problems. https://arxiv.org/abs/2302.06926

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