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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 ...
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.
[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 ...
The longest repeated substring problem for a string of length can be solved in () time using both the suffix array and the LCP array. It is sufficient to perform a linear scan through the LCP array in order to find its maximum value v m a x {\displaystyle v_{max}} and the corresponding index i {\displaystyle i} where v m a x {\displaystyle v ...
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 ...
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
Naively computing the hash value for the substring s[i+1..i+m] requires O(m) time because each character is examined. Since the hash computation is done on each loop, the algorithm with a naive hash computation requires O(mn) time, the same complexity as a straightforward string matching algorithm. For speed, the hash must be computed in ...
Various implementations exist in different programming languages. In C++ it is part of the Standard Library since C++17 and Boost provides the generic Boyer–Moore search implementation under the Algorithm library. In Go (programming language) there is an implementation in search.go.