We want to efficiently find a specific object in a large unstructured set, which we model by a random $n$-permutation, and we have to do it by revealing just a single element. Clearly, without any help this task is hopeless and the best one can do is select the element at random, and achieve the success probability $\frac{1}{n}$. Can we do better with some small amount of advice about the permutation, even without knowing the object sought? We show that by providing advice of just one integer in $\{0,1,...,n-1\}$, one can improve the success probability considerably, by a $\Theta(\frac{logn}{loglogn})$ factor. We study this and related problems, and show asymptotically matching upper and lower bounds for their optimal probability of success.Our analysis relies on a close relationship of such problems to some intrinsic properties of rendom permutations related to the rencontres number.
翻译:我们希望在大型非结构化的数据集中有效地找到一个特定对象, 我们用随机的 $n- permodation 来模拟, 我们必须通过只透露一个元素来完成。 显然, 没有帮助, 这项任务是没有希望的, 最能做的就是随机选择元素, 并且实现成功概率$\frac{ 1 ⁇ n} 。 我们能否用少量关于调整的建议来做更好, 即使不知道所要的对象? 我们显示, 仅仅提供一个整数的建议 $ 0. 1,..., n-1 $, 一个人就可以通过 $\ Theta (\\\ frac{ logn- logn) $ 来大大提高成功概率 。 我们研究这个和相关问题, 并显示其最佳成功概率在微乎其微的匹配 。 我们的分析依赖于这类问题与与与与串联点数的内属性的密切关联 。