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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
Relating Left and Right Extensions of Maximal Repeats
Shunsuke Inenaga, Dmitry Kosolobov · 2024-10-21 · via cs.DS updates on arXiv.org

The compact directed acyclic word graph (CDAWG) of a string $T$ is an index occupying $O(\mathsf{e})$ space, where $\mathsf{e}$ is the number of right extensions of maximal repeats in $T$. For highly repetitive datasets, the measure $\mathsf{e}$ typically is small compared to the length $n$ of $T$ and, thus, the CDAWG serves as a compressed index. Unlike other compressibility measures (as LZ77, string attractors, BWT runs, etc.), $\mathsf{e}$ is very unstable with respect to reversals: the CDAWG of the reversed string $\overset{{}_{\leftarrow}}{T} = T[n] \cdots T[2] T[1]$ has size $O(\overset{{}_{\leftarrow}}{\mathsf{e}})$, where $\overset{{}_{\leftarrow}}{\mathsf{e}}$ is the number of left extensions of maximal repeats in $T$, and there are strings $T$ with $\frac{\overset{{}_{\leftarrow}}{\mathsf{e}}}{\mathsf{e}} \in Ω(\sqrt{n})$. In this note, we prove that this lower bound is tight: $\frac{\overset{{}_{\leftarrow}}{\mathsf{e}}}{\mathsf{e}} \in O(\sqrt{n})$. Furthermore, given the alphabet size $σ$, we establish the alphabet-dependent bound $\frac{\overset{{}_{\leftarrow}}{\mathsf{e}}}{\mathsf{e}} \le \min\{\frac{2n}σ, σ\}$ and we show that it is asymptotically tight.