SearcharxivSearch

arXiv · 1808.01071

Right-to-left online construction of parameterized position heaps

Abstract

Two strings of equal length are said to parameterized match if there is a bijection that maps the characters of one string to those of the other string, so that two strings become identical. The parameterized pattern matching problem is, given two strings $T$ and $P$, to find the occurrences of substrings in $T$ that parameterized match $P$. Diptarama et al. [Position Heaps for Parameterized Strings, CPM 2017] proposed an indexing data structure called parameterized position heaps, and gave a left-to-right online construction algorithm. In this paper, we present a right-to-left online construction algorithm for parameterized position heaps. For a text string $T$ of length $n$ over two kinds of alphabets $\Sigma$ and $\Pi$ of respective size $\sigma$ and $\pi$, our construction algorithm runs in $O(n \log(\sigma + \pi))$ time with $O(n)$ space. Our right-to-left parameterized position heaps support pattern matching queries in $O(m \log (\sigma + \pi) + m \pi + \mathit{pocc}))$ time, where $m$ is the length of a query pattern $P$ and $\mathit{pocc}$ is the number of occurrences to report. Our construction and pattern matching algorithms are as efficient as Diptarama et al.'s algorithms.

Explore related subjects

Keep this discovery

BibTeXRIS

Noriki Fujisato, Yuto Nakashima, Shunsuke Inenaga, Hideo Bannai, Masayuki Takeda. 2018-08-03. Right-to-left online construction of parameterized position heaps. https://arxiv.org/abs/1808.01071

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Quasi-Monte Carlo Beyond Hardy-Krause II: $(1 + \varepsilon)n$ Samples Suffice

Numerical integration studies how well one can estimate the integral of a function $f$ over $[0,1)^d$ using $n$ sample points. The two classical methods, Monte Carlo (MC) and quasi-Monte Carlo (QMC), have complementary strengths and weaknesses, and a fundamental question is to design an approach that combines the benefits of both. Recently, building on the transference principle in discrepancy theory, Bansal and Jiang~\cite{BJ25a} gave a randomized QMC method that bridges MC and QMC guarantees using only i.i.d.\ samples. Their method also goes beyond the classical Koksma--Hlawka inequality: it achieves integration error $\widetilde{O}_d(\sigma_{\mathsf{SO}}(f)/n)$, where the smoothed-out variation $\sigma_{\mathsf{SO}}(f)$ can be substantially smaller than the Hardy--Krause variation that governs the classical bound. However, their algorithm requires $n^2$ i.i.d.\ samples as input, and this quadratic blowup is inherent to any method based on the transference principle. In this work, we bypass the quadratic blowup: for any constant $\varepsilon > 0$, we show that $(1+\varepsilon)n$ i.i.d.\ samples suffice to both obtain the beyond-Hardy--Krause guarantee of~\cite{BJ25a}, resolving an open problem posed there, and to produce low-discrepancy point sequences. Our algorithms are variants of the online Haar-thinning method of Dwivedi, Feldheim, Gurel-Gurevich, and Ramdas~\cite{DFG+19}.

cs.DS

Single-Exponential Algorithms and a Polynomial Kernel for Strong Connectivity Augmentation

Strong Connectivity Augmentation (SCA) asks whether a directed acyclic graph can be made strongly connected by adding at most $k$ prescribed links whose total weight is within a given budget. Klinkby, Misra, and Saurabh (SODA 2021) gave an $O^*(2^{O(k\log k)})$-time algorithm and asked whether the problem admits a single-exponential parameterized algorithm and a polynomial kernel. We answer both questions affirmatively: SCA can be solved in $O^*(9^k)$ time and admits a polynomial kernel with $O(k^4)$ vertices and $O(k^{16})$ bits. For unweighted SCA, we obtain $O^*(4^k)$ time and a kernel with $O(k^3)$ vertices. Our algorithms are based on a particularly simple reduction to Strongly Connected Spanning Subgraph with two edge costs.

cs.DS