arXiv subjects

Tord Stordalen

Publications and source records attributed to Tord Stordalen.

5 recordsLinked to original sources

arXiv2026

Dynamic Range Minimum Queries on the Ultra-Wide Word RAM

We consider the dynamic range minimum problem on the ultra-wide word RAM model of computation. This model extends the classic $w$-bit word RAM model with special ultrawords of length $w^2$ bits that support standard arithmetic and boolean operation and scattered memory access operations that can access $w$ (non-contiguous) locations in memory. The ultra-wide word RAM model captures (and idealizes) modern vector processor architectures. The goal in the dynamic range minimum problem is to maintain an array $A$ of $n$ $w$-bit integers subject to range minimum queries (given indices $i$ and $j$ return a smallest integer in the subarray $A[i..j]$) and updates (given index $i$ and integer $α$ set $A[i] \leftarrow α$). Our main result is a data structure that supports range minimum queries and updates in $O(\log \log \log n)$ time and uses $O(n/\log n)$ space in addition to the input array. This exponentially improves the time of existing techniques. Our result is based on a simple reduction to prefix minimum computations on sequences $O(\log n)$ words combined with a new parallel, recursive implementation of these.

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arXiv2025

Dynamic Indexing Through Learned Indices with Worst-case Guarantees

Indexing data is a fundamental problem in computer science. Recently, various papers apply machine learning to this problem. For a fixed integer $\varepsilon$, a \emph{learned index} is a function $h : \mathcal{U} \rightarrow [0, n]$ where $\forall q \in \mathcal{U}$, $h(q) \in [\text{rank}(q) - \varepsilon, \text{rank}(q) + \varepsilon]$. These works use machine learning to compute $h$. Then, they store $S$ in a sorted array $A$ and access $A[\lfloor h(q) \rfloor]$ to answer queries in $O(k + \varepsilon + \log |h|)$ time. Here, $k$ denotes the output size and $|h|$ the complexity of $h$. Ferragina and Vinciguerra (VLDB 2020) observe that creating a learned index is a geometric problem. They define the PGM index by restricting $h$ to a piecewise linear function and show a linear-time algorithm to compute a PGM index of approximate minimum complexity. Since indexing queries are decomposable, the PGM index may be made dynamic through the logarithmic method. When allowing deletions, range query times deteriorate to worst-case $O(N + \sum\limits_i^{\lceil \log n \rceil } (\varepsilon + \log |h_i|))$ time (where $N$ is the largest size of $S$ seen so far). This paper offers a combination of theoretical insights and experiments as we apply techniques from computational geometry to dynamically maintain an approximately minimum-complexity learned index $h : \mathcal{U} \rightarrow [0, n]$ with $O(\log^2 n)$ update time. We also prove that if we restrict $h$ to lie in a specific subclass of piecewise-linear functions, then we can combine $h$ and hash maps to support queries in $O(k + \varepsilon + \log |h|)$ time (at the cost of increasing $|h|$). We implement our algorithm and compare it to the existing implementation. Our empirical analysis shows that our solution supports more efficient range queries in the special case where the update sequence contains many deletions.

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arXiv2023

Rank and Select on Degenerate Strings

A 'degenerate string' is a sequence of subsets of some alphabet; it represents any string obtainable by selecting one character from each set from left to right. Recently, Alanko et al. generalized the rank-select problem to degenerate strings, where given a character $c$ and position $i$ the goal is to find either the $i$th set containing $c$ or the number of occurrences of $c$ in the first $i$ sets [SEA 2023]. The problem has applications to pangenomics; in another work by Alanko et al. they use it as the basis for a compact representation of 'de Bruijn Graphs' that supports fast membership queries. In this paper we revisit the rank-select problem on degenerate strings, introducing a new, natural parameter and reanalyzing existing reductions to rank-select on regular strings. Plugging in standard data structures, the time bounds for queries are improved exponentially while essentially matching, or improving, the space bounds. Furthermore, we provide a lower bound on space that shows that the reductions lead to succinct data structures in a wide range of cases. Finally, we provide implementations; our most compact structure matches the space of the most compact structure of Alanko et al. while answering queries twice as fast. We also provide an implementation using modern vector processing features; it uses less than one percent more space than the most compact structure of Alanko et al. while supporting queries four to seven times faster, and has competitive query time with all the remaining structures.

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arXiv2022

The Complexity of the Co-Occurrence Problem

Let $S$ be a string of length $n$ over an alphabet $Σ$ and let $Q$ be a subset of $Σ$ of size $q \geq 2$. The 'co-occurrence problem' is to construct a compact data structure that supports the following query: given an integer $w$ return the number of length-$w$ substrings of $S$ that contain each character of $Q$ at least once. This is a natural string problem with applications to, e.g., data mining, natural language processing, and DNA analysis. The state of the art is an $O(\sqrt{nq})$ space data structure that -- with some minor additions -- supports queries in $O(\log\log n)$ time [CPM 2021]. Our contributions are as follows. Firstly, we analyze the problem in terms of a new, natural parameter $d$, giving a simple data structure that uses $O(d)$ space and supports queries in $O(\log\log n)$ time. The preprocessing algorithm does a single pass over $S$, runs in expected $O(n)$ time, and uses $O(d)$ space in addition to the input. Furthermore, we show that $O(d)$ space is optimal and that $O(\log\log n)$-time queries are optimal given optimal space. Secondly, we bound $d = O(\sqrt{nq})$, giving clean bounds in terms of $n$ and $q$ that match the state of the art. Furthermore, we prove that $Ω(\sqrt{nq})$ bits of space is necessary in the worst case, meaning that the $O(\sqrt{nq})$ upper bound is tight to within polylogarithmic factors. All of our results are based on simple and intuitive combinatorial ideas that simplify the state of the art.

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arXiv2022

Predecessor on the Ultra-Wide Word RAM

We consider the predecessor problem on the ultra-wide word RAM model of computation, which extends the word RAM model with 'ultrawords' consisting of $w^2$ bits [TAMC, 2015]. The model supports arithmetic and boolean operations on ultrawords, in addition to 'scattered' memory operations that access or modify $w$ (potentially non-contiguous) memory addresses simultaneously. The ultra-wide word RAM model captures (and idealizes) modern vector processor architectures. Our main result is a simple, linear space data structure that supports predecessor in constant time and updates in amortized, expected constant time. This improves the space of the previous constant time solution that uses space in the order of the size of the universe. Our result holds even in a weaker model where ultrawords consist of $w^{1+ε}$ bits for any $ε> 0 $. It is based on a new implementation of the classic $x$-fast trie data structure of Willard [Inform. Process. Lett. 17(2), 1983] combined with a new dictionary data structure that supports fast parallel lookups.

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