Graph Coloring Approach to Solving Sudoku with Oscillatory Neural Networks
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Computer Science > Machine Learning
Title:Graph Coloring Approach to Solving Sudoku with Oscillatory Neural Networks
Abstract:Oscillatory Neural Networks (ONNs) present an attractive physics-based computing paradigm rooted in the dynamics of a network of typically fully coupled oscillators aiming to minimize an underlying energy function. In this paper, we propose an ONN-based solver for one well-known constrained combinatorial optimization problem, namely a Sudoku, by formulating the problem as a Graph Coloring problem. By modifying the already existing Graph Coloring solver to a computationally cheaper version and introducing an additional term ensuring the fulfillment of the Sudoku constraints, our solver is shown to significantly outperform the existing HNN- and ONN solvers in terms of accuracy. In particular, we are able to achieve nearly flawless accuracies on $4 \times 4$ as well as rather high accuracies on $9 \times 9$ Sudoku puzzles for different numbers of unknown digits.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2607.15814 [cs.LG] |
| (or arXiv:2607.15814v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2607.15814
arXiv-issued DOI via DataCite (pending registration)
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