arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2602.05904cs.DS
收藏

改进的基于SDP的3可着色图着色算法

Improved SDP-Based Algorithm for Coloring 3-Colorable Graphs

University of Michigan(密歇根大学)

Nikhil Bansal, Neng Huang, Euiwoong Lee

AI总结:

该研究提出了一种改进的基于SDP的算法,用于将3可着色图用更少的颜色着色,解决了近二十年来该问题的SDP方法的瓶颈。

摘要:

我们提出了一种多项式时间算法,可以将任何具有n个顶点的3可着色图用O(n^{0.19539})种颜色着色,优于Kawarabayashi、Thorup和Yoneda[STOC 2024]之前最好的界$\widetilde{O}(n^{0.19747})$。我们的结果是近二十年来针对该问题的SDP方法的第一个进展。早期的基于SDP的算法由Arora、Chlamtáč和Charikar[STOC 2006]以及Chlamtáč[FOCS 2007]提出,它们依赖于从适当

英文摘要:

We present a polynomial-time algorithm that colors any 3-colorable $n$-vertex graph using $O(n^{0.19539})$ colors, improving upon the previous best bound of $\widetilde{O}(n^{0.19747})$ by Kawarabayashi, Thorup, and Yoneda [STOC 2024]. Our result constitutes the first progress in nearly two decades on SDP-based approaches to this problem. The earlier SDP-based algorithms of Arora, Chlamtáč, and Charikar [STOC 2006] and Chlamtáč [FOCS 2007] rely on extracting a large independent set from a suitably "random-looking" second-level neighborhood, under the assumption that the KMS algorithm [Karger, Motwani, and Sudan, JACM 1998] fails to find one globally. We extend their analysis to third-level neighborhoods. We then come up with a new vector $5/2$-coloring, which allows us to extract a large independent set from some third-level neighborhood. The new vector coloring construction may be of independent interest.

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。

↑