A recent palette sparsification theorem of Assadi, Chen, and Khanna [SODA19] states that in every $n$-vertex graph $G$ with maximum degree $Delta$, sampling $O(log{n})$ colors per each vertex independently from $Delta+1$ colors almost certainly allows for proper coloring of $G$ from the sampled colors. Besides being a combinatorial statement of its own independent interest, this theorem was shown to have various applications to design of algorithms for $(Delta+1)$ coloring in different models of computation on massive graphs such as streaming or sublinear-time algorithms. In this paper, we further study palette sparsification problems: * We prove that for $(1+varepsilon) Delta$ coloring, sampling only $O_{varepsilon}(sqrt{log{n}})$ colors per vertex is sufficient and necessary to obtain a proper coloring from the sampled colors. * A natural family of graphs with chromatic number much smaller than $(Delta+1)$ are triangle-free graphs which are $O(frac{Delta}{ln{Delta}})$ colorable. We prove that sampling $O(Delta^{gamma} + sqrt{log{n}})$ colors per vertex is sufficient and necessary to obtain a proper $O_{gamma}(frac{Delta}{ln{Delta}})$ coloring of triangle-free graphs. * We show that sampling $O_{varepsilon}(log{n})$ colors per vertex is sufficient for proper coloring of any graph with high probability whenever each vertex is sampling from a list of $(1+varepsilon) cdot deg(v)$ arbitrary colors, or even only $deg(v)+1$ colors when the lists are the sets ${1,ldots,deg(v)+1}$. Similar to previous work, our new palette sparsification results naturally lead to a host of new and/or improved algorithms for vertex coloring in different models including streaming and sublinear-time algorithms.
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