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  2. LCP array - Wikipedia

    en.wikipedia.org/wiki/LCP_array

    Deciding if a pattern of length is a substring of a string of length takes (⁡) time if only the suffix array is used. By additionally using the LCP information, this bound can be improved to O ( m + log ⁡ n ) {\displaystyle O(m+\log n)} time. [ 3 ]

  3. Longest common substring - Wikipedia

    en.wikipedia.org/wiki/Longest_common_substring

    One can find the lengths and starting positions of the longest common substrings of and in (+) time with the help of a generalized suffix tree. A faster algorithm can be achieved in the word RAM model of computation if the size σ {\displaystyle \sigma } of the input alphabet is in 2 o ( log ⁡ ( n + m ) ) {\displaystyle 2^{o\left({\sqrt {\log ...

  4. Time complexity - Wikipedia

    en.wikipedia.org/wiki/Time_complexity

    [1]: 226 Since this function is generally difficult to compute exactly, and the running time for small inputs is usually not consequential, one commonly focuses on the behavior of the complexity when the input size increases—that is, the asymptotic behavior of the complexity. Therefore, the time complexity is commonly expressed using big O ...

  5. Longest palindromic substring - Wikipedia

    en.wikipedia.org/wiki/Longest_palindromic_substring

    Longest Palindromic Substring Part II., 2011-11-20, archived from the original on 2018-12-08. A description of Manacher’s algorithm for finding the longest palindromic substring in linear time. Akalin, Fred (2007-11-28), Finding the longest palindromic substring in linear time. An explanation and Python implementation of Manacher's linear ...

  6. Boyer–Moore–Horspool algorithm - Wikipedia

    en.wikipedia.org/wiki/Boyer–Moore–Horspool...

    The algorithm trades space for time in order to obtain an average-case complexity of O(n) on random text, although it has O(nm) in the worst case, where the length of the pattern is m and the length of the search string is n.

  7. Longest common subsequence - Wikipedia

    en.wikipedia.org/wiki/Longest_common_subsequence

    Chowdhury and Ramachandran devised a quadratic-time linear-space algorithm [9] [10] for finding the LCS length along with an optimal sequence which runs faster than Hirschberg's algorithm in practice due to its superior cache performance. [9] The algorithm has an asymptotically optimal cache complexity under the Ideal cache model. [11]

  8. Suffix tree - Wikipedia

    en.wikipedia.org/wiki/Suffix_tree

    String search, in O(m) complexity, where m is the length of the sub-string (but with initial O(n) time required to build the suffix tree for the string) Finding the longest repeated substring; Finding the longest common substring; Finding the longest palindrome in a string

  9. Strand sort - Wikipedia

    en.wikipedia.org/wiki/Strand_sort

    Strand sort is a recursive sorting algorithm that sorts items of a list into increasing order. It has O(n 2) worst-case time complexity, which occurs when the input list is reverse sorted. [1] It has a best-case time complexity of O(n), which occurs when the input is already sorted. [citation needed]