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arXiv 2002.03766cs.AI
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Testing Unsatisfiability of Constraint Satisfaction Problems via Tensor Products

University of Lethbridge(莱斯布里奇大学);InBridge Inc(英桥公司)

Daya Gaur, Muhammad Khan

英文摘要:

We study the design of stochastic local search methods to prove unsatisfiability of a constraint satisfaction problem (CSP). For a binary CSP, such methods have been designed using the microstructure of the CSP. Here, we develop a method to decompose the microstructure into graph tensors. We show how to use the tensor decomposition to compute a proof of unsatisfiability efficiently and in parallel. We also offer substantial empirical evidence that our approach improves the praxis. For instance, one decomposition yields proofs of unsatisfiability in half the time without sacrificing the quality. Another decomposition is twenty times faster and effective three-tenths of the times compared to the prior method. Our method is applicable to arbitrary CSPs using the well known dual and hidden variable transformations from an arbitrary CSP to a binary CSP.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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