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Conference Papers Year : 2017

On the Kernelization of Global Constraints

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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Dates and versions

hal-01670318 , version 1 (21-12-2017)

Identifiers

  • HAL Id : hal-01670318 , version 1

Cite

Clément Carbonnel, Emmanuel Hébrard. On the Kernelization of Global Constraints. International Joint Conference on Artificial Intelligence (IJCAI 2017), Aug 2017, Melbourne, Australia. 7p. ⟨hal-01670318⟩
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