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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
A Succinct Grammar Compression
Yasuo Tabei, Yoshimasa Takabatake, Hiroshi Sakamoto · 2013-04-03 · via cs.DS updates on arXiv.org

We solve an open problem related to an optimal encoding of a straight line program (SLP), a canonical form of grammar compression deriving a single string deterministically. We show that an information-theoretic lower bound for representing an SLP with n symbols requires at least 2n+logn!+o(n) bits. We then present a succinct representation of an SLP; this representation is asymptotically equivalent to the lower bound. The space is at most 2n log {rho}(1 + o(1)) bits for rho leq 2sqrt{n}, while supporting random access to any production rule of an SLP in O(log log n) time. In addition, we present a novel dynamic data structure associating a digram with a unique symbol. Such a data structure is called a naming function and has been implemented using a hash table that has a space-time tradeoff. Thus, the memory space is mainly occupied by the hash table during the development of production rules. Alternatively, we build a dynamic data structure for the naming function by leveraging the idea behind the wavelet tree. The space is strictly bounded by 2n log n(1 + o(1)) bits, while supporting O(log n) query and update time.