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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
String Representation Based on Substring Equation Systems
[Submitted on 6 Apr 2026 (v1), last revised 5 Aug 2026 (this ver · 2026-04-06 · via cs.DS updates on arXiv.org

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Abstract:Repetitiveness measures quantify how much repetitive structure a string contains and serve as parameters for compressed representations and indexing data structures. Many compression schemes represent strings by recording equalities between identical substrings. We introduce the substring equation system (SES), a general compression scheme that represents a string as the unique solution to substring-equality and character-assignment constraints. We show that every string $w$ has an SES of size $O(\chi(w))$, where $\chi(w)$ is the size of its smallest suffixient set. This result establishes the reachability of $\chi$, which had been an open problem. We also prove that computing the size $s(w)$ of the smallest SES that represents $w$ is NP-hard and $(1 + \varepsilon)$-inapproximable for some fixed constant $\varepsilon > 0$. Finally, we prove that the size $b(w)$ of the smallest bidirectional macro scheme (BMS) representing $w$ satisfies $s(w) \leq b(w) \leq 4s(w)$. Hence, SES and BMS are equivalent up to a constant factor, and this equivalence gives the new bound $b(w) \in O(\chi(w))$.

Submission history

From: Hiroki Shibata [view email]
[v1] Mon, 6 Apr 2026 03:00:33 UTC (99 KB)
[v2] Mon, 13 Apr 2026 05:58:53 UTC (100 KB)
[v3] Sun, 19 Apr 2026 06:24:56 UTC (128 KB)
[v4] Wed, 5 Aug 2026 12:51:10 UTC (200 KB)