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arXiv · 1901.00754

Sparsification of Binary CSPs

Silvia Butti·Stanislav Zivny
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Abstract

A cut $\varepsilon$-sparsifier of a weighted graph $G$ is a re-weighted subgraph of $G$ of (quasi)linear size that preserves the size of all cuts up to a multiplicative factor of $\varepsilon$. Since their introduction by Bencz\'ur and Karger [STOC'96], cut sparsifiers have proved extremely influential and found various applications. Going beyond cut sparsifiers, Filtser and Krauthgamer [SIDMA'17] gave a precise classification of which binary Boolean CSPs are sparsifiable. In this paper, we extend their result to binary CSPs on arbitrary finite domains.

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Silvia Butti, Stanislav Zivny. 2019-01-03. Sparsification of Binary CSPs. https://doi.org/10.1137/19m1242446

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