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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 Simple Reduction for Full-Permuted Pattern Matching Pro...
Carl Barton, Ewan Birney, Tomas Fitzgerald · 2019-09-05 · via cs.DS updates on arXiv.org

In this paper we study a variant of string pattern matching which deals with tuples of strings known as \textit{multi-track strings}. Multi-track strings are a generalisation of strings (or \textit{single-track strings}) that have primarily found uses in problems related to searching multiple genomes and music information retrieval. A multi-track string $\mathcal{T} = (t_1, t_2, t_3, \ldots , t_N)$ of length $n$ and track count $N$ is a multi-set of $N$ strings of length $n$ with characters drawn from a common alphabet of size $σ_U$. Given two multi-track strings $\mathcal{T} = (t_1, t_2, t_3, \ldots , t_N)$ and $ \mathcal{P} = (p_1, p_2, p_3, \ldots , p_N)$ of length $n$ and track count $N$, there is a \textit{full-permuted-match} between $\mathcal{P}$ and $\mathcal{T}$ if $t_{r_i} = p_i$ for all $i \in \{1,2,3,\ldots N \}$ and some permutation $(r_1, r_2, r_3\ldots,r_N)$ of $(1, 2, 3,\ldots,N)$, we denote this $\mathcal{P}\asymp\mathcal{T}$. Efficient algorithms for some full-permuted-match problems on multi-track strings have recently been presented. In this paper we show a reduction from a multi-track string of length $n$ and track count $N$ with alphabet size $σ_U$, to a single-track string of length $2n-1$ with alphabet size $σ_U^N$. Through this reduction we allow any string algorithm to be used on multi-track string problems using $\asymp$ as the match relation. For polynomial time algorithms on single-track strings of length $n$ there is a multiplicative penalty of not more than $\mathcal{O}(N)$-time for the same algorithm on mt-strings of length $n$ and track count $N$.