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PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting Algorithms with Polynomially-Improved Approximation Factors for the $2 \rightarrow q$ Norm, and Applications A computational phase transition for learning-to-sample from Ising models Covering vertices by sequential stars Fermi-Dirac machines as quantizations of neurons A Comprehensive Evaluation of Vertex Elimination Algorithms for Algorithmic Differentiation A Tight Bound on Localization of Electrical Flows Optimal Dimension-Free Sampling for Regularized Classification Reducing the Randomness in Partition Oracles for Bounded Degree Minor-Free Graphs Beyond the Half-Approximation: Fair and Efficient Online Class Matching Efficient Uniform Sampling of Surjections via their Profiles Tractable Maximization of Budgeted Phylogenetic Diversity on Networks Utilizing Node Scanwidth Fairness in Aggregation: Optimal Top-$k$ and Improved Full Ranking Learning-Augmented Online Scheduling with Parsimonious Preemption Entropy Equivalence Testing Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees The Secretary Problem with a Stochastic Precursor Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals Efficient Banzhaf-Based Data Valuation for $k$-Nearest Neighbors Classification Block-Sphere Vector Quantization An Approximation Algorithm for Graph Label Selection Iterative Chow Filtering for Learning with Distribution Shift Complexity of Non-Log-Concave Sampling in Fisher Information Stochastic Matching via Local Sparsification Finite Sample Bounds for Learning with Score Matching What is Learnable in Valiant's Theory of the Learnable? Provable Quantization with Randomized Hadamard Transform Min-Max Optimization Requires Exponentially Many Queries Fast and Compact Graph Cuts for the Boykov-Kolmogorov Algorithm A proximal gradient algorithm for composite log-concave sampling
Random Access in Grammar-Compressed Strings: Optimal Trad...
Anouk Duyster, Tomasz Kociumaka · 2026-02-11 · via cs.DS updates on arXiv.org

A Random Access query to a string $T\in [0..σ)^n$ asks for the character $T[i]$ at a given position $i\in [0..n)$. In $O(n\logσ)$ bits of space, this fundamental task admits constant-time queries. While this is optimal in the worst case, much research has focused on compressible strings, hoping for smaller data structures that still admit efficient queries. We investigate the grammar-compressed setting, where $T$ is represented by a straight-line grammar. Our main result is a general trade-off that optimizes Random Access time as a function of string length $n$, grammar size (the total length of productions) $g$, alphabet size $σ$, data structure size $M$, and word size $w=Ω(\log n)$ of the word RAM model. For any $M$ with $g\log n<Mw<n\logσ$, we show an $O(M)$-size data structure with query time $O(\frac{\log(n\logσ\,/\,Mw)}{\log(Mw\,/\,g\log n)})$. Remarkably, we also prove a matching unconditional lower bound that holds for all parameter regimes except very small grammars and relatively small data structures. Previous work focused on query time as a function of $n$ only, achieving $O(\log n)$ time using $O(g)$ space [Bille et al.; SIAM J. Comput. 2015] and $O(\frac{\log n}{\log \log n})$ time using $O(g\log^ε n)$ space for any constant $ε> 0$ [Belazzougui et al.; ESA'15], [Ganardi, Jeż, Lohrey; J. ACM 2021]. The only tight lower bound [Verbin and Yu; CPM'13] was $Ω(\frac{\log n}{\log\log n})$ for $w=Θ(\log n)$, $n^{Ω(1)}\le g\le n^{1-Ω(1)}$, and $M=g\log^{Θ(1)}n$. In contrast, our result yields tight bounds in all relevant parameters and almost all regimes. Our data structure admits efficient deterministic construction. It relies on novel grammar transformations that generalize contracting grammars [Ganardi; ESA'21]. Beyond Random Access, its variants support substring extraction, rank, and select.