arXiv subjects

Yuki Yonemoto

Publications and source records attributed to Yuki Yonemoto.

7 recordsLinked to original sources

arXiv2026

Quadratic Word Equations with a Linear Side: Polynomial Nielsen Graph Diameter and NP-Completeness

The satisfiability problem for word equations asks whether variables can be replaced by words so that the two sides become equal. For regular word equations, in which each variable occurs at most once on each side, satisfiability is NP-complete. For general quadratic word equations, in which each variable occurs at most twice in total, satisfiability is NP-hard, but its membership in NP remains open. We consider an intermediate class: quadratic word equations with a linear side, where each variable occurs at most once on one designated side. We show that the Nielsen graph of an equation $U=V$ in this class, with total length $N=|U|+|V|$, has diameter $O(N^{12})$, measured over reachable pairs of vertices. Together with the known NP-hardness for regular word equations, this result establishes NP-completeness of satisfiability for this class. We also show that each strongly connected component is isomorphic to the length-preserving reachability graph of a regular equation, and that the condensation graph has depth at most $|U|+2|V|$.

cs.FL↗

arXiv2026

String Matching in (Block) Graphs: A Full Classification by Walk Length

We consider directed graphs in which the nodes are labeled with strings. A walk in such a graph naturally corresponds to the concatenation of the visited nodes' labels. These graphs are widely used in bioinformatics to compactly describe large collections of highly similar genomes. Given such a graph $G=(V,E)$ and a pattern of length $m$, we seek a walk whose corresponding string has an occurrence of the pattern. We call this the SMLG problem. Amir et al. [J. Algorithms, 2000] showed that SMLG can be solved in $\mathcal{O}(m|E| + N)$ time, where $N$ is the total length of all node labels. Equi et al. [ACM Trans. Algorithms, 2023] showed that this is essentially optimal (under SETH). The existing lower bound assumes that the sought walk is of length $Θ(|V|)$. Thus, we might be able to bypass this lower bound by restricting the walk length to $b-1$, which naturally reduces to having as input a directed graph whose set of nodes is partitioned into $b$ blocks. Then, we seek a walk in this graph that starts in the first block and ends in the last block. We call this the $b$-SMBG problem. We provide a more fine-grained classification that essentially settles the complexity of $b$-SMBG parameterized by $b$: (1) We give a near-linear-time algorithm for $b=3$. (2) We show that there is no combinatorial algorithm improving over the state-of-the-art $\mathcal{O}(m|E| + N)$ bound for any $b\ge 4$. (3) We also present a fast matrix multiplication-based algorithm yielding an improvement for $b \in \mathcal{O}(1)$, which is conditionally optimal. (4) Finally, we show that under SETH, for any $b \in ω(\log |V|)$, no algorithm can improve over the state of the art.

cs.DS↗

arXiv2025

Subsequence Matching and LCS under Cartesian-Tree Equivalence

Two strings of the same length are said to Cartesian-tree match (CT-match) if their Cartesian-trees are isomorphic [Park et al., TCS 2020]. Cartesian-tree matching is a natural model that allows for capturing similarities of numerical sequences. Oizumi et al. [CPM 2022] showed that subsequence pattern matching under CT-matching model (CT-MSeq) can be solved in $O(nm \log \log n)$ time, where $n$ and $m$ are text and pattern lengths, respectively. This current article follows this line of research, and gives the following new results: (1) An $O(nm)$-time CT-MSeq algorithm for binary alphabets; (2) An $O((nm)^{1-ε})$-time conditional lower bound for the CT-MSeq problem on alphabets of size 4, for any constant $ε> 0$, under the Orthogonal Vector Hypothesis (OVH). Further, we introduce the new problem of longest common subsequence under CT-matching (CT-LCS) for two given strings $S$ and $T$ of length $n$, and present the following results: (3) An $O(n^6)$-time CT-LCS algorithm for general ordered alphabets; (4) An $O(n^2 / \log n)$-time CT-LCS algorithm for binary alphabets; (5) An $O(n^{2-ε})$-time conditional lower bound for the CT-LCS problem on alphabets of size 5, for any constant $ε> 0$, under OVH.

cs.DS↗

arXiv2025

Subsequence Matching and LCS with Segment Number Constraints

The longest common subsequence (LCS) is a fundamental problem in string processing which has numerous algorithmic studies, extensions, and applications. A sequence $u_1, \ldots, u_f$ of $f$ strings s said to be an ($f$-)segmentation of a string $P$ if $P = u_1 \cdots u_f$. Li et al. [BIBM 2022] proposed a new variant of the LCS problem for given strings $T_1, T_2$ and an integer $f$, which we hereby call the segmental LCS problem (SegLCS), of finding (the length of) a longest string $P$ that has an $f$-segmentation which can be embedded into both $T_1$ and $T_2$. Li et al. [IJTCS-FAW 2024] gave a dynamic programming solution that solves SegLCS in $O(fn_1n_2)$ time with $O(fn_1 + n_2)$ space, where $n_1 = |T_1|$, $n_2 = |T_2|$, and $n_1 \le n_2$. Recently, Banerjee et al. [ESA 2024] presented an algorithm which, for a constant $f \geq 3$, solves SegLCS in $\tilde{O}((n_1n_2)^{1-(1/3)^{f-2}})$ time. In this paper, we deal with SegLCS as well as the problem of segmental subsequence pattern matching, SegE, that asks to determine whether a pattern $P$ of length $m$ has an $f$-segmentation that can be embedded into a text $T$ of length $n$. When $f = 1$, this is equivalent to substring matching, and when $f = |P|$, this is equivalent to subsequence matching. Our focus in this article is the case of general values of $f$, and our main contributions are threefold: (1) $O((mn)^{1-ε})$-time conditional lower bound for SegE under the strong exponential-time hypothesis (SETH), for any constant $ε> 0$. (2) $O(mn)$-time algorithm for SegE. (3) $O(fn_2(n_1 - \ell+1))$-time algorithm for SegLCS where $\ell$ is the solution length.

cs.DS↗

arXiv2024

Simple Linear-time Repetition Factorization

A factorization $f_1, \ldots, f_m$ of a string $w$ of length $n$ is called a repetition factorization of $w$ if $f_i$ is a repetition, i.e., $f_i$ is a form of $x^kx'$, where $x$ is a non-empty string, $x'$ is a (possibly-empty) proper prefix of $x$, and $k \geq 2$. Dumitran et al. [SPIRE 2015] presented an $O(n)$-time and space algorithm for computing an arbitrary repetition factorization of a given string of length $n$. Their algorithm heavily relies on the Union-Find data structure on trees proposed by Gabow and Tarjan [JCSS 1985] that works in linear time on the word RAM model, and an interval stabbing data structure of Schmidt [ISAAC 2009]. In this paper, we explore more combinatorial insights into the problem, and present a simple algorithm to compute an arbitrary repetition factorization of a given string of length $n$ in $O(n)$ time, without relying on data structures for Union-Find and interval stabbing. Our algorithm follows the approach by Inoue et al. [ToCS 2022] that computes the smallest/largest repetition factorization in $O(n \log n)$ time.

cs.DS↗

arXiv2024

Faster Space-Efficient STR-IC-LCS Computation

One of the most fundamental method for comparing two given strings $A$ and $B$ is the longest common subsequence (LCS), where the task is to find (the length) of an LCS of $A$ and $B$. In this paper, we deal with the STR-IC-LCS problem which is one of the constrained LCS problems proposed by Chen and Chao [J. Comb. Optim, 2011]. A string $Z$ is said to be an STR-IC-LCS of three given strings $A$, $B$, and $P$, if $Z$ is a longest string satisfying that (1) $Z$ includes $P$ as a substring and (2) $Z$ is a common subsequence of $A$ and $B$. We present three efficient algorithms for this problem: First, we begin with a space-efficient solution which computes the length of an STR-IC-LCS in $O(n^2)$ time and $O((\ell+1)(n-\ell+1))$ space, where $\ell$ is the length of an LCS of $A$ and $B$ of length $n$. When $\ell = O(1)$ or $n-\ell = O(1)$, then this algorithm uses only linear $O(n)$ space. Second, we present a faster algorithm that works in $O(nr/\log{r}+n(n-\ell+1))$ time, where $r$ is the length of $P$, while retaining the $O((\ell+1)(n-\ell+1))$ space efficiency. Third, we give an alternative algorithm that runs in $O(nr/\log{r}+n(n-\ell'+1))$ time with $O((\ell'+1)(n-\ell'+1))$ space, where $\ell'$ denotes the STR-IC-LCS length for input strings $A$, $B$, and $P$.

cs.DS↗

arXiv2023

Computing SEQ-IC-LCS of Labeled Graphs

We consider labeled directed graphs where each vertex is labeled with a non-empty string. Such labeled graphs are also known as non-linear texts in the literature. In this paper, we introduce a new problem of comparing two given labeled graphs, called the SEQ-IC-LCS problem on labeled graphs. The goal of SEQ-IC-LCS is to compute the the length of the longest common subsequence (LCS) $Z$ of two target labeled graphs $G_1 = (V_1, E_1)$ and $G_2 = (V_2, E_2)$ that includes some string in the constraint labeled graph $G_3 = (V_3, E_3)$ as its subsequence. Firstly, we consider the case where $G_1$, $G_2$ and $G_3$ are all acyclic, and present algorithms for computing their SEQ-IC-LCS in $O(|E_1||E_2||E_3|)$ time and $O(|V_1||V_2||V_3|)$ space. Secondly, we consider the case where $G_1$ and $G_2$ can be cyclic and $G_3$ is acyclic, and present algorithms for computing their SEQ-IC-LCS in $O(|E_1||E_2||E_3| + |V_1||V_2||V_3|\log|Σ|)$ time and $O(|V_1||V_2||V_3|)$ space, where $Σ$ is the alphabet.

cs.DS↗