arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2205.00441cs.DS
收藏

参数化弦问题的动态数据结构

Dynamic data structures for parameterized string problems

Jędrzej Olkowski, Michał Pilipczuk, Mateusz Rychlicki, Karol Węgrzycki, Anna Zych-Pawlewicz

摘要:

我们重温经典的字符串问题,考虑在该地区的参数化的复杂性,并研究他们通过镜头的动态数据结构。也就是说,我们的目标不是要求一个静态算法来有效地解决给定的实例,而是设计一个数据结构来有效地维护解决方案,或者在实例更新时报告缺乏解决方案。我们首先考虑最接近的字符串问题,我们设计随机动态数据结构与分期付款的更新时间$d^{\mathcal{O}(d)}$和$|Σ|^{\mathcal{O}(d)}$,分别,其中$Σ$是字母表和$d$是假设的最大距离的界限。这些是通过将已知的最接近字符串的静态方法与颜色编码相结合来获得的。接下来,我们注意到,根据Frandsen等人的结果~ [J. ACM'97]可以容易地推断出一个元定理,该元定理为具有$\mathcal{O}(\log \log n)$形式的最坏情况更新时间的参数化字符串问题提供动态数据结构,其中$k$是所讨论的参数,$n$是字符串的长度。我们展示了这个元定理的效用,给出这样的数据结构的问题不相交因子和编辑距离。我们也给这些问题明确的数据结构,最坏情况下的更新时间$\mathcal{O}(k2^{k}\log \log n)$和$\mathcal{O}(k^2\log \log n)$,分别。最后,我们讨论了如何由Amarilli等人引入的下限方法。[ICALP'21]可用于表明,对于参数$k$的恒定值,不太可能获得不相交因子和编辑距离的更新时间$\mathcal{O}(f(k))$。

英文摘要:

We revisit classic string problems considered in the area of parameterized complexity, and study them through the lens of dynamic data structures. That is, instead of asking for a static algorithm that solves the given instance efficiently, our goal is to design a data structure that efficiently maintains a solution, or reports a lack thereof, upon updates in the instance. We first consider the Closest String problem, for which we design randomized dynamic data structures with amortized update times $d^{\mathcal{O}(d)}$ and $|Σ|^{\mathcal{O}(d)}$, respectively, where $Σ$ is the alphabet and $d$ is the assumed bound on the maximum distance. These are obtained by combining known static approaches to Closest String with color-coding. Next, we note that from a result of Frandsen et al.~[J. ACM'97] one can easily infer a meta-theorem that provides dynamic data structures for parameterized string problems with worst-case update time of the form $\mathcal{O}(\log \log n)$, where $k$ is the parameter in question and $n$ is the length of the string. We showcase the utility of this meta-theorem by giving such data structures for problems Disjoint Factors and Edit Distance. We also give explicit data structures for these problems, with worst-case update times $\mathcal{O}(k2^{k}\log \log n)$ and $\mathcal{O}(k^2\log \log n)$, respectively. Finally, we discuss how a lower bound methodology introduced by Amarilli et al.~[ICALP'21] can be used to show that obtaining update time $\mathcal{O}(f(k))$ for Disjoint Factors and Edit Distance is unlikely already for a constant value of the parameter $k$.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑