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cs.DS updates on arXiv.org

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
Speeding up the AIFV-$2$ dynamic programs by two orders o...
Mordecai Golin, Elfarouk Harb · 2020-02-23 · via cs.DS updates on arXiv.org

AIFV-$2$ codes are a new method for constructing lossless codes for memoryless sources that provide better worst-case redundancy than Huffman codes. They do this by using two code trees instead of one and also allowing some bounded delay in the decoding process. Known algorithms for constructing AIFV-code are iterative; at each step they replace the current code tree pair with a "better" one. The current state of the art for performing this replacement is a pair of Dynamic Programming (DP) algorithms that use $O(n^5)$ time to fill in two tables, each of size $O(n^3)$ (where $n$ is the number of different characters in the source). This paper describes how to reduce the time for filling in the DP tables by two orders of magnitude, down to $O(n^3)$. It does this by introducing a grouping technique that permits separating the $Θ(n^3)$-space tables into $Θ(n)$ groups, each of size $O(n^2)$, and then using Two-Dimensional Range-Minimum Queries (RMQs) to fill in that group's table entries in $O(n^2)$ time. This RMQ speedup technique seems to be new and might be of independent interest.