Computing the edit distance of two strings is one of the most basic problems in computer science and combinatorial optimization. Tree edit distance is a natural generalization of edit distance in which the task is to compute a measure of dissimilarity between two (unweighted) rooted trees with node labels. Perhaps the most notable recent application of tree edit distance is in NoSQL big databases, such as MongoDB, where each row of the database is a JSON document represented as a labeled rooted tree, and finding dissimilarity between two rows is a basic operation. Until recently, the fastest algorithm for tree edit distance ran in cubic time (Demaine, Mozes, Rossman, Weimann; TALG'10); however, Mao (FOCS'21) broke the cubic barrier for the tree edit distance problem using fast matrix multiplication. Given a parameter $k$ as an upper bound on the distance, an $O(n+k^2)$-time algorithm for edit distance has been known since the 1980s due to the works of Myers (Algorithmica'86) and Landau and Vishkin (JCSS'88). The existence of an $\tilde{O}(n+\mathrm{poly}(k))$-time algorithm for tree edit distance has been posed as an open question, e.g., by Akmal and Jin (ICALP'21), who gave a state-of-the-art $\tilde{O}(nk^2)$-time algorithm. In this paper, we answer this question positively.
翻译:计算两个字符串的编辑距离是计算机科学和组合优化中最基本的问题之一。 树编辑距离是一种自然的编辑距离的简单化。 在编辑距离中, 任务在于计算两个( 未加权的) 根树与节点标签的不同程度。 也许最近最显著的树编辑距离应用是在NOSQL 大数据库中, 比如 MOngoDB, 数据库的每行文件代表着一个有标签的根树 JSON 文档, 发现两行之间的差异是一个基本操作。 直到最近, 树编辑距离的最快算法在立方时间里运行( Demaine, Mozes, Rossman, Weimann; TALG'10 ; 然而, Mao (FOCS'21) 利用快速矩阵乘法打破了树编辑距离问题的立方屏障 。 如果在距离上以美元为上限的参数, $( n+k) 2美元- 时间算算的编辑距离算法自1980年代开始开放, 因为Myers( Algorica' 86) 和 Lands- dealdeal_deal_deal) au (JCSdeal_deal_Oral_Oral_) romax___ 问题, ial__Bral___________________________________________________________________________________ log____________________________________________________________________________________________________________________________________________