← 研踪 PaperTrailarXiv:2610.38787 · v1
Dynamic $(Δ+1)$ Vertex Coloring with Adaptive Adversaries: Entropy Compression to the Rescue
首次提交 2026-10-29 03:56
版本更新 2026-10-29 03:56
雷达观测 2026-10-30 10:22
摘要
We present a randomized algorithm for maintaining a $(Δ+1)$ vertex coloring of a fully dynamic graph against an adaptive adversary with $\tilde{O}(\sqrt{n})$ amortized update time. Our algorithm improves upon the recent randomized algorithms of Behnezhad, Rajaraman, and Wasim (SODA 2025) with $\tilde{O}(n^{8/9})$ and Flin and Halldórsson (ICALP 2025) with $\tilde{O}(n^{2/3})$ amortized update time. Our result (nearly) matches a fundamental limit of $O(\sqrt{n})$ update time identified by Flin and Halldórsson and settles their main open question. The previous algorithms' main improvement came from efficient coloring of the dense parts of the graph. Our approach complements these prior work by handling the sparse parts more efficiently, using ideas inspired by the recently developed dynamic entropy compression method. A key new aspect of our work is to tailor this method specifically to dynamic algorithms---as opposed to original local search algorithms---and achieve update times that can be sublinear in the number of involved constraints per update.
作者备注：57 pages, 6 figures, to appear in SODA 2027
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v1 · 2026-10-29 03:56 · 最新观测版本
Dynamic $(Δ+1)$ Vertex Coloring with Adaptive Adversaries: Entropy Compression to the Rescue
We present a randomized algorithm for maintaining a $(Δ+1)$ vertex coloring of a fully dynamic graph against an adaptive adversary with $\tilde{O}(\sqrt{n})$ amortized update time. Our algorithm improves upon the recent randomized algorithms of Behnezhad, Rajaraman, and Wasim (SODA 2025) with $\tilde{O}(n^{8/9})$ and Flin and Halldórsson (ICALP 2025) with $\tilde{O}(n^{2/3})$ amortized update time. Our result (nearly) matches a fundamental limit of $O(\sqrt{n})$ update time identified by Flin and Halldórsson and settles their main open question. The previous algorithms' main improvement came from efficient coloring of the dense parts of the graph. Our approach complements these prior work by handling the sparse parts more efficiently, using ideas inspired by the recently developed dynamic entropy compression method. A key new aspect of our work is to tailor this method specifically to dynamic algorithms---as opposed to original local search algorithms---and achieve update times that can be sublinear in the number of involved constraints per update.
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