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Kernelization for Orthogonality Dimension

Data Structures and Algorithms 2024-08-19 v1

Abstract

The orthogonality dimension of a graph over R\mathbb{R} is the smallest integer dd for which one can assign to every vertex a nonzero vector in Rd\mathbb{R}^d such that every two adjacent vertices receive orthogonal vectors. For an integer dd, the dd-Ortho-DimR_\mathbb{R} problem asks to decide whether the orthogonality dimension of a given graph over R\mathbb{R} is at most dd. We prove that for every integer d3d \geq 3, the dd-Ortho-DimR_\mathbb{R} problem parameterized by the vertex cover number kk admits a kernel with O(kd1)O(k^{d-1}) vertices and bit-size O(kd1logk)O(k^{d-1} \cdot \log k). We complement this result by a nearly matching lower bound, showing that for any ε>0\varepsilon > 0, the problem admits no kernel of bit-size O(kd1ε)O(k^{d-1-\varepsilon}) unless NPcoNP/poly\mathsf{NP} \subseteq \mathsf{coNP/poly}. We further study the kernelizability of orthogonality dimension problems in additional settings, including over general fields and under various structural parameterizations.

Cite

@article{arxiv.2408.08380,
  title  = {Kernelization for Orthogonality Dimension},
  author = {Ishay Haviv and Dror Rabinovich},
  journal= {arXiv preprint arXiv:2408.08380},
  year   = {2024}
}

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29 pages