SearcharxivSearch

arXiv · 2407.08826

The CDAWG Index and Pattern Matching on Grammar-Compressed Strings

Abstract

The compact directed acyclic word graph (CDAWG) is the minimal compact automaton that recognizes all the suffixes of a string. Classically the CDAWG has been implemented as an index of the string it recognizes, requiring $o(n)$ space for a copy of the string $T$ being indexed, where $n=|T|$. In this work, we propose using the CDAWG as an index for grammar-compressed strings. While this enables all analyses supported by the CDAWG on any grammar-compressed string, in this work we specifically consider pattern matching. Using the CDAWG index, pattern matching can be performed on any grammar-compressed string in $\mathcal{O}(\text{ra}(m)+\text{occ})$ time while requiring only $\mathcal{O}(\text{er}(T))$ additional space, where $m$ is the length of the pattern, $\text{ra}(m)$ is the grammar random access time, $\text{occ}$ is the number of occurrences of the pattern in $T$, and $\text{er}(T)$ is the number of right-extensions of the maximal repeats in $T$. Our experiments show that even when using a na\"ive random access algorithm, the CDAWG index achieves state of the art run-time performance for pattern matching on grammar-compressed strings. Additionally, we find that all of the grammars computed for our experiments are smaller than the number of right-extensions in the string they produce and, thus, their CDAWGs are within the best known $\mathcal{O}(\text{er}(T))$ space asymptotic bound.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Alan M. Cleary, Joseph Winjum, Jordan Dood, Shunsuke Inenaga. 2024-07-11. The CDAWG Index and Pattern Matching on Grammar-Compressed Strings. https://arxiv.org/abs/2407.08826

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