We consider building, given a straight-line program (SLP) consisting of g productions deriving a two-dimensional string T of size N× N, a structure capable of providing random access to any character of T. For one-dimensional strings, it is now known how to build a structure of size 𝒪(g) that provides random access in 𝒪(log N) time. In fact, it is known that this can be obtained by building an equivalent SLP of size 𝒪(g) and depth 𝒪(log N) [Ganardi, Jeż, Lohrey, JACM 2021]. We consider the analogous question for two-dimensional strings: can we build an equivalent SLP of roughly the same size and small depth? We show that the answer is negative: there exists an infinite family of two-dimensional strings of size N× N described by a 2D SLP of size g such that any 2D SLP of depth 𝒪(log N) describing the same string must be of size Ω(g⋅ N/log³N). We complement this with an upper bound showing how to construct such a 2D SLP of size 𝒪(g⋅ N). Next, we observe that one can naturally define a generalization of 2D SLP, which we call 2D SLP with holes. We show that a known general balancing theorem by [Ganardi, Jeż, Lohrey, JACM 2021] immediately implies that, given a 2D SLP of size g deriving a string of size N× N, we can construct a 2D SLP with holes of depth 𝒪(log N) and size 𝒪(g). This allows us to conclude that there is a structure of size 𝒪(g) providing random access in 𝒪(log N) time for such a 2D SLP. Further, this can be extended (analogously as for a 1D SLP) to obtain a structure of size 𝒪(g log^ε N) providing random access in 𝒪(log N/log log N) time, for any ε > 0. The same (optimal) random access time was very recently achieved by [De and Kempa, SODA 2026], but with a significantly larger structure of size 𝒪(g log^{2+ε} N).

We prove that for every integer n > 0 and for every alphabet Σ_k of size k ≥ 3, there exist words of length n whose Burrows-Wheeler Transform (BWT) is totally unclustered, i.e., it consists of exactly n runs with no two consecutive equal symbols. These words represent the worst-case behavior of the clustering effect of the BWT. We also establish a lower bound on their number. This contrasts with the binary case, where the existence of infinitely many totally unclustered BWT images is still an open problem, related to Artin’s conjecture on primitive roots.

Abstract Let $$\varvec{\textsf {Substr}}_{\varvec{k}}\varvec{(X)}$$ Substr k ( X ) denote the set of length- $$\varvec{k}$$ k substrings of a given string $$\varvec{X}$$ X for a given integer $$\varvec{k}>\varvec{0}$$ k > 0 . We study the following basic string problem, called $$\varvec{z}$$ z - Shortest $$\varvec{\mathcal {S}}_{\varvec{k}}$$ S k - Equivalent Strings : Given a set $$\varvec{\mathcal {S}}_{\varvec{k}}$$ S k of $$\varvec{n}$$ n length- $$\varvec{k}$$ k strings and an integer $$\varvec{z}>\varvec{0}$$ z > 0 , list $$\varvec{z}$$ z shortest distinct strings $$\varvec{T}_{\varvec{1}}\varvec{,\ldots ,}\varvec{T}_{\varvec{z}}$$ T 1 , … , T z such that $$\varvec{\textsf {Substr}}_{\varvec{k}}\varvec{(}\varvec{T}_{\varvec{i}}\varvec{)}=\varvec{\mathcal {S}}_{\varvec{k}}$$ Substr k ( T

We define generalized de Bruijn words as those words having a Burrows-Wheeler transform that is a concatenation of permutations of the alphabet. We show that generalized de Bruijn words are in 1-to-1 correspondence with Hamiltonian cycles in the generalized de Bruijn graphs, introduced in the early’80s in the context of network design. When the size of the alphabet is a prime p, we define invertible necklaces as those whose BWT-matrix is non-singular. We show that invertible necklaces of length n correspond to normal bases of the finite field Fpn, and that they form an Abelian group isomorphic to the Reutenauer group RGnp. Using known results in abstract algebra, we can make a bridge between generalized de Bruijn words and invertible necklaces. In particular, we highlight a correspondence between binary de Bruijn words of order d + 1, binary necklaces of length 2d having an odd number of 1’s, invertible BWT matrices of size 2d × 2d, and normal bases of the finite field F22d.

An elastic-degenerate (ED) string T is a sequence of n sets T [ 1 ] , … , T [ n ] containing m strings in total whose cumulative length is N . We call n , m , and N the length, the cardinality and the size of T , respectively. The language of T is defined as L ( T ) = { S 1 ⋯ S n : S i ∈ T [ i ] for all i ∈ [ 1 , n ] } . Given two ED strings, how fast can we check whether the two languages they represent have a nonempty intersection? We call this problem the ED String Intersection (EDSI) problem. For two ED strings T 1 and T 2 of lengths n 1 and n 2 , cardinalities m 1 and m 2 , and sizes N 1 and N 2 , respectively, we show the following: • There is no O ( ( N 1 N 2 ) 1 − ϵ ) -time algorithm, for any ϵ > 0 , for EDSI even if T 1 and T 2 are over a binary alphabet, unless the Strong Exponential-Time Hypothesis is false. • There is no combinatorial O ( ( N 1 + N 2 ) 1.2 − ϵ f ( n 1 , n 2 ) ) -time algorithm, for any ϵ > 0 and any function f , for EDSI even if T 1 and T 2 are over a binary alphabet, unless the Boolean Matrix Multiplication conjecture is false. • An O ( N 1 log ⁡ N 1 log ⁡ n 1 + N 2 log ⁡ N 2 log ⁡ n 2 ) -time algorithm for outputting a compact representation of the intersection language of two unary ED strings. When T 1 and T 2 are given in a compact representation, we show that the problem is NP-complete. • An O ( N 1 m 2 + N 2 m 1 ) -time algorithm for EDSI. • An O ˜ ( N 1 ω − 1 n 2 + N 2 ω − 1 n 1 ) -time algorithm for EDSI, where ω is the matrix multiplication exponent; the O ˜ notation suppresses factors that are polylogarithmic in the input size.

Strings in the real world are often encoded with some level of uncertainty, for example, due to: unreliable data measurements; flexible sequence modeling; or noise introduced for privacy protection. In the character-level uncertainty model, an uncertain string X of length$n$on an alphabetΣ is a sequence of$n$probability distributions over Σ. Given an uncertain string$X$and a weight threshold$\frac {1}{z}\in(0,1)$, we say that pattern$P$occurs in$X$at position$i$, if the product of probabilities of the letters of$P$at positions$i,\ldots, i+ \vert P\vert-1$is at least$\frac {1}{z}$. While indexing standard strings for online pattern searches can be performed in linear time and space, indexing uncertain strings is much more challenging. Specifically, the state-of-the-art index for uncertain strings has$O(nz)$size, requires$O(nz)$time and$O(nz)$space to be constructed, and answers pattern matching queries in the optimal$O(m+ [Occl)$time, where$m$is the length of$P$and$\vert Occ\vert$is the total number of occurrences of$P$in$X$. For large$n$and (moderate)$z$values, this index is completely impractical to construct, which outweighs the benefit of the supported optimal pattern matching queries. We were thus motivated to design a space-efficient index at the expense of slower yet competitive pattern matching queries. We show that when we have at hand a lower bound ℓ on the length of the supported pattern queries, as is often the case in real-world applications, we can slash the index size and the construction space roughly by ℓ. In particular, we propose an index of$Q (n/ \log z)$expected size, which can be constructed using$Q (n/ \log z)$expected space, and supports very fast pattern matching queries in expectation, for patterns of length m ≥ ℓ. We have implemented and evaluated several versions of our index. The best-performing version of our index is up to two orders of magnitude smaller than the state of the art in terms of both index size and construction space, while offering faster or very competitive query and construction times.

Elastic Degenerate (ED) strings and Elastic Founder (EF) graphs are two versions of acyclic components of pangenomes. Both ED strings and EF graphs (which we collectively name variable strings) extend the well-known notion of indeterminate string. Recent work has extensively investigated algorithmic tasks over these structures, and over several other variable strings notions that they generalise. Among such tasks, the basic operation of matching a pattern into a text, which can serve as a toolkit for many pangenomic data analyses using these data structures, deserves special attention. In this paper we: (1) highlight a clear taxonomy within both ED strings and EF graphs ranging through variable strings of all types, from the linear string up to the most general one; (2) investigate the problem PvarT(X,Y) of matching a solid or variable pattern of type X into a variable text of type Y; (3) using as a reference the quadratic conditional lower bounds that are known for PvarT(solid,ED) and PvarT(solid,EF), for all possible types of variable strings X and Y we either prove the quadratic conditional lower bound for PvarT(X,Y), or provide non-trivial, often sub-quadratic, upper bounds, also exploiting the above-mentioned taxonomy.

Abstract An elastic-degenerate (ED) string is a sequence of n finite sets of strings of total length N, introduced to represent a set of related DNA sequences, also known as a pangenome. The ED string matching (EDSM) problem consists in reporting all occurrences of a pattern of length m in an ED text. The EDSM problem has recently received some attention by the combinatorial pattern matching community, culminating in an $$\mathcal {\tilde{O}}(nm^{\omega -1})+\mathcal {O}(N)$$ O ~ ( n m ω - 1 ) + O ( N ) -time algorithm [Bernardini et al., SIAM J. Comput. 2022], where $$\omega $$ ω denotes the matrix multiplication exponent and the $$\mathcal {\tilde{O}}(\cdot )$$ O ~ ( · ) notation suppresses polylog factors. In the k-EDSM problem, the approximate version of EDSM, we are asked to report all pattern occurrences with at most k errors. k-EDSM can be solved in $$\mathcal {O}(k^2mG+kN)$$ O ( k 2 m G + k N ) time, under edit distance, or $$\mathcal {O}(kmG+kN)$$ O ( k m G + k N ) time, under Hamming distance, where G denotes the total number of strings in the ED text [Bernardini et al., Theor. Comput. Sci. 2020]. Unfortunately, G is only bounded by N, and so even for $$k=1$$ k = 1 , the existing algorithms run in $$\varOmega (mN)$$ Ω ( m N ) time in the worst case. In this paper we make progress in this direction. We show that 1-EDSM can be solved in $$\mathcal {O}((nm^2 + N)\log m)$$ O ( ( n m 2 + N ) log m ) or $$\mathcal {O}(nm^3 + N)$$ O ( n m 3 + N ) time under edit distance. For the decision version of the problem, we present a faster $$\mathcal {O}(nm^2\sqrt{\log m} + N\log \log m)$$ O ( n m 2 log m + N log log m ) -time algorithm. We also show that 1-EDSM can be solved in $$\mathcal {O}(nm^2 + N\log m)$$ O ( n m 2 + N log m ) time under Hamming distance. Our algorithms for edit distance rely on non-trivial reductions from 1-EDSM to special instances of classic computational geometry problems (2d rectangle stabbing or 2d range emptiness), which we show how to solve efficiently. In order to obtain an even faster algorithm for Hamming distance, we rely on employing and adapting the k-errata trees for indexing with errors [Cole et al., STOC 2004]. This is an extended version of a paper presented at LATIN 2022.

An elastic-degenerate (ED) string T is a sequence of n sets T[1],…,T[n] containing m strings in total whose cumulative length is N. We call n, m, and N the length, the cardinality and the size of T, respectively. The language of T is defined as ℒ(T) = {S_1 ⋯ S_n : S_i ∈ T[i] for all i ∈ [1,n]}. ED strings have been introduced to represent a set of closely-related DNA sequences, also known as a pangenome. The basic question we investigate here is: Given two ED strings, how fast can we check whether the two languages they represent have a nonempty intersection? We call the underlying problem the ED String Intersection (EDSI) problem. For two ED strings T₁ and T₂ of lengths n₁ and n₂, cardinalities m₁ and m₂, and sizes N₁ and N₂, respectively, we show the following: - There is no 𝒪((N₁N₂)^{1-ε})-time algorithm, thus no 𝒪((N₁m₂+N₂m₁)^{1-ε})-time algorithm and no 𝒪((N₁n₂+N₂n₁)^{1-ε})-time algorithm, for any constant ε > 0, for EDSI even when T₁ and T₂ are over a binary alphabet, unless the Strong Exponential-Time Hypothesis is false. - There is no combinatorial 𝒪((N₁+N₂)^{1.2-ε}f(n₁,n₂))-time algorithm, for any constant ε > 0 and any function f, for EDSI even when T₁ and T₂ are over a binary alphabet, unless the Boolean Matrix Multiplication conjecture is false. - An 𝒪(N₁log N₁log n₁+N₂log N₂log n₂)-time algorithm for outputting a compact (RLE) representation of the intersection language of two unary ED strings. In the case when T₁ and T₂ are given in a compact representation, we show that the problem is NP-complete. - An 𝒪(N₁m₂+N₂m₁)-time algorithm for EDSI. - An Õ(N₁^{ω-1}n₂+N₂^{ω-1}n₁)-time algorithm for EDSI, where ω is the exponent of matrix multiplication; the Õ notation suppresses factors that are polylogarithmic in the input size. We also show that the techniques we develop have applications outside of ED string comparison.

Let Substr_k(X) denote the set of length-k substrings of a given string X for a given integer k > 0. We study the following basic string problem, called z-Shortest 𝒮_k-Equivalent Strings: Given a set 𝒮_k of n length-k strings and an integer z > 0, list z shortest distinct strings T₁,…,T_z such that Substr_k(T_i) = 𝒮_k, for all i ∈ [1,z]. The z-Shortest 𝒮_k-Equivalent Strings problem arises naturally as an encoding problem in many real-world applications; e.g., in data privacy, in data compression, and in bioinformatics. The 1-Shortest 𝒮_k-Equivalent Strings, referred to as Shortest 𝒮_k-Equivalent String, asks for a shortest string X such that Substr_k(X) = 𝒮_k. Our main contributions are summarized below: - Given a directed graph G(V,E), the Directed Chinese Postman (DCP) problem asks for a shortest closed walk that visits every edge of G at least once. DCP can be solved in 𝒪̃(|E||V|) time using an algorithm for min-cost flow. We show, via a non-trivial reduction, that if Shortest 𝒮_k-Equivalent String over a binary alphabet has a near-linear-time solution then so does DCP. - We show that the length of a shortest string output by Shortest 𝒮_k-Equivalent String is in 𝒪(k+n²). We generalize this bound by showing that the total length of z shortest strings is in 𝒪(zk+zn²+z²n). We derive these upper bounds by showing (asymptotically tight) bounds on the total length of z shortest Eulerian walks in general directed graphs. - We present an algorithm for solving z-Shortest 𝒮_k-Equivalent Strings in 𝒪(nk+n²log²n+zn²log n+|output|) time. If z = 1, the time becomes 𝒪(nk+n²log²n) by the fact that the size of the input is Θ(nk) and the size of the output is 𝒪(k+n²).