9,624,186 research outputs found

    Fast Computation of Abelian Runs

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    Given a word ww and a Parikh vector P\mathcal{P}, an abelian run of period P\mathcal{P} in ww is a maximal occurrence of a substring of ww having abelian period P\mathcal{P}. Our main result is an online algorithm that, given a word ww of length nn over an alphabet of cardinality σ\sigma and a Parikh vector P\mathcal{P}, returns all the abelian runs of period P\mathcal{P} in ww in time O(n)O(n) and space O(σ+p)O(\sigma+p), where pp is the norm of P\mathcal{P}, i.e., the sum of its components. We also present an online algorithm that computes all the abelian runs with periods of norm pp in ww in time O(np)O(np), for any given norm pp. Finally, we give an O(n2)O(n^2)-time offline randomized algorithm for computing all the abelian runs of ww. Its deterministic counterpart runs in O(n2logσ)O(n^2\log\sigma) time.Comment: To appear in Theoretical Computer Scienc

    Faster Approximate String Matching for Short Patterns

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    We study the classical approximate string matching problem, that is, given strings PP and QQ and an error threshold kk, find all ending positions of substrings of QQ whose edit distance to PP is at most kk. Let PP and QQ have lengths mm and nn, respectively. On a standard unit-cost word RAM with word size wlognw \geq \log n we present an algorithm using time O(nkmin(log2mlogn,log2mlogww)+n) O(nk \cdot \min(\frac{\log^2 m}{\log n},\frac{\log^2 m\log w}{w}) + n) When PP is short, namely, m=2o(logn)m = 2^{o(\sqrt{\log n})} or m=2o(w/logw)m = 2^{o(\sqrt{w/\log w})} this improves the previously best known time bounds for the problem. The result is achieved using a novel implementation of the Landau-Vishkin algorithm based on tabulation and word-level parallelism.Comment: To appear in Theory of Computing System

    Improved Online Algorithm for Weighted Flow Time

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    We discuss one of the most fundamental scheduling problem of processing jobs on a single machine to minimize the weighted flow time (weighted response time). Our main result is a O(logP)O(\log P)-competitive algorithm, where PP is the maximum-to-minimum processing time ratio, improving upon the O(log2P)O(\log^{2}P)-competitive algorithm of Chekuri, Khanna and Zhu (STOC 2001). We also design a O(logD)O(\log D)-competitive algorithm, where DD is the maximum-to-minimum density ratio of jobs. Finally, we show how to combine these results with the result of Bansal and Dhamdhere (SODA 2003) to achieve a O(log(min(P,D,W)))O(\log(\min(P,D,W)))-competitive algorithm (where WW is the maximum-to-minimum weight ratio), without knowing P,D,WP,D,W in advance. As shown by Bansal and Chan (SODA 2009), no constant-competitive algorithm is achievable for this problem.Comment: 20 pages, 4 figure

    Sharp weighted estimates for multi-frequency Calder\'on-Zygmund operators

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    In this paper we study weighted estimates for the multi-frequency ω\omega-Calder\'{o}n-Zygmund operators TT associated with the frequency set Θ={ξ1,ξ2,,ξN}\Theta=\{\xi_1,\xi_2,\dots,\xi_N\} and modulus of continuity ω\omega satisfying the usual Dini condition. We use the modern method of domination by sparse operators and obtain bounds TLp(w)Lp(w)N1r12[w]Ap/rmax(1,1pr), 1r<p<,\|T\|_{L^p(w)\rightarrow L^p(w)}\lesssim N^{|\frac{1}{r}-\frac{1}{2}|}[w]_{\mathbb{A}_{p/r}}^{max(1,\frac{1}{p-r})},~1\leq r<p<\infty, for the exponents of NN and Ap/r\mathbb{A}_{p/r} characteristic [w]Ap/r[w]_{\mathbb{A}_{p/r}}
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