We observe message-efficient distributed algorithms for the Set Cover problem. Given a ground set $U$ of $n$ elements and $m$ subsets of $U$, we aim to find the minimal number of these subsets that contain all elements. In the default distributed setup of this problem, each set has a bidirected communication link with each element it contains. Our first result is a $\tilde{O}(\log^2(\Delta))$-time and $O(\sqrt{\Delta)}(n+m))$-message algorithm with expected approximation ration of $O(\log(\Delta))$ in the $KT_0$ model. The value $\Delta$ denotes the maximal cardinality of each subset. Our algorithm is \emph{almost} optimal with regard to time and message complexity. Further, we present Set Cover algorithm in the Beeping model that only relies on carrier-sensing and can trade runtime for approximation ratio similar to the celebrated algorithm by Kuhn and Wattenhofer [PODC '03].
翻译:我们观察了“ 设置覆盖” 问题的信息高效分布算法 。 如果设定了美元元素和美元子集的地面值, 我们的目标是找到包含所有元素的子集的最小数量。 在默认的分布式设置中, 每组都有与其所含每个元素的双向通信链接。 我们的第一个结果就是$\ tilde{O}( log\2\\\ Delta) 时间和$O( sqrt\Delta)}( n+m) 美元- message 算法, 加上预期的O( log (\ Delta) 近似值配给值在$T_ 0美元模型中。 $\ Delta$ 表示每个子集的最大基点。 我们的算法在时间和电文复杂度方面是最佳的。 此外, 我们在Beping 模型中显示只依赖运货量的“ 设置覆盖算法”, 并可以交易近似Kuhn 和 Wattenhofer [PODC'03] 所夸耀算算算的运行时间 。