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• W3037883011 abstract "Computing text-to-pattern distances is a fundamental problem in pattern matching. Given a text of length n and a pattern of length m, we are asked to output the distance between the pattern and every n-substring of the text. A basic variant of this problem is computation of Hamming distances, that is counting the number of mismatches (different characters aligned), for each alignment. Other popular variants include (ell _1) distance (Manhattan distance), (ell _2) distance (Euclidean distance) and general (ell _p) distance. While each of those problems trivially generalizes classical pattern-matching, the efficient algorithms for them require a broader set of tools, usually involving both algebraic and combinatorial insights. We briefly survey the history of the problems, and then focus on the progress made in the past few years in many specific settings: fine-grained complexity and lower-bounds, ((1+varepsilon )) multiplicative approximations, k-bounded relaxations, streaming algorithms, purely combinatorial algorithms, and other recently proposed variants." @default.
• W3037883011 created "2020-07-02" @default.
• W3037883011 creator A5020580102 @default.
• W3037883011 date "2020-01-01" @default.
• W3037883011 modified "2023-09-26" @default.
• W3037883011 title "Recent Advances in Text-to-Pattern Distance Algorithms" @default.
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• W3037883011 doi "https://doi.org/10.1007/978-3-030-51466-2_32" @default.
• W3037883011 hasPublicationYear "2020" @default.
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