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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
Partial-Match Queries with Random Wildcards: In Tries and...
Junichiro Fukuyama · 2016-01-17 · via cs.DS updates on arXiv.org

Consider an $m$-bit query $q$ to a bitwise trie $T$. A wildcard $*$ is an unspecified bit in $q$ for which the query asks the membership for both cases $*=0$ and $*=1$. It is common that such partial-match queries with wildcards are issued in tries. With uniformly random occurrences of $w$ wildcards in $q$ assumed, the obvious upper bound on the average number of traversal steps in $T$ is $2^w m$. We show that the average does not exceed \[ \frac{m+1}{w+1} \left( 2^{w+2} - 2 w - 4 \right) + m = O \left( \frac{2^w m}{w} \right), \] and equals the value exactly when $T$ includes all the $m$-bit keys as the worst case. Here the query $q$ performs with the naive backtracking algorithm in $T$. It is similarly shown that the average is $O \left( \frac{k^w m}{w} \right)$ in a general trie of maximum out-degree $k$. Our analysis for tries is extended to a distributed hash table (DHT), which is among the most frequently used decentralized data structures in networking. We show, under a natural probabilistic assumption for the largest class of DHTs, that the average number of hops required by an $m$-bit query $q$ to a DHT $D$ with random $w$ wildcards meets the same asymptotic bound. As a result, $q$ is answered with average $O \left( \frac{2^w m}{w} \right)$ hops rather than $Θ\left( 2^w m \right)$ in the four major DHTs Chord, Pastry, Tapestry and Kademlia. In addition, with a uniform key distribution for sufficiently many entries, we prove that a lookup request to the DHT Chord is answered correctly with $O(m)$ hops and probability $1 - 2^{-Ω(m)}$. To the author's knowledge, the probability $1 - 2^{-Ω(m)}$ of correct lookup in Chord has not been identified so far.