Coloring Fast Without Learning Your Neighbors' Colors

  title={Coloring Fast Without Learning Your Neighbors' Colors},
  author={M. Halld{\'o}rsson and F. Kuhn and Yannic Maus and Alexandre Nolin},
We give an improved randomized CONGEST algorithm for distance-$2$ coloring that uses $\Delta^2+1$ colors and runs in $O(\log n)$ rounds, improving the recent $O(\log \Delta \cdot \log n)$-round algorithm in [Halldorsson, Kuhn, Maus; PODC '20]. We then improve the time complexity to $O(\log \Delta) + 2^{O(\sqrt{\log\log n})}$. 
3 Citations
Efficient CONGEST Algorithms for the Lovasz Local Lemma
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Coloring fast without learning your neighbors
  • 2020
Coloring fast without learning your neighbors' colors
  • 2020
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If Δ is the maximum degree of G, it is shown that there is a randomized CONGEST model algorithm to compute a distance-2 coloring of G with Δ2 + 1 colors in O(log Δ · log n) rounds. Expand
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Distributed Testing of Distance-k Colorings
It is shown that for one natural farness measure, significantly better algorithms are possible for testing distance-3 coloring than for testingdistance-k coloring for \(k \ge 4\), and it is also shown that several farness criteria for measuring the distance to a valid coloring are considered. Expand
Efficient Deterministic Distributed Coloring with Small Bandwidth
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Faster Deterministic Distributed Coloring Through Recursive List Coloring
  • F. Kuhn
  • Computer Science, Mathematics
  • SODA
  • 2020
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