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On the Kernelization of Global Constraints

2019-10-29 | |  40 |   35 |   0
Abstract Kernelization is a powerful concept from parameterized complexity theory that captures (a certain idea of) efficient polynomial-time preprocessing for hard decision problems. However, exploiting this technique in the context of constraint programming is challenging. Building on recent results for the VERTEXCOVER constraint, we introduce novel “loss-less” kernelization variants that are tailored for constraint propagation. We showcase the theoretical interest of our ideas on two constraints, VERTEXCOVER and EDGEDOMINATINGSET

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