This paper presents a linear prioritized local algorithm that computes large independent sets on a random $d$-regular graph with small and fixed degree $d$. We studied experimentally the independence ratio obtained by the algorithm when $ d \in [3,100]$. For all $d \in [5,100]$, our results are larger than lower bounds calculated by exact methods, thus providing improved estimates of lower bounds.

翻译：本文展示了一条线性优先本地算法,该算法用一个随机的以美元为单位的固定数额小于美元(美元)的固定数额(美元)的普通图表来计算大型独立数据集。我们实验研究了算法在以[3,100]美元计算时获得的独立比率。对于所有美元(美元) [5,100]美元,我们的结果大于以精确方法计算的较低界限,从而提供了更精确的下限估计数。