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The longest alternating subsequence problem has also been studied in the setting of online algorithms, in which the elements of are presented in an online fashion, and a decision maker needs to decide whether to include or exclude each element at the time it is first presented, without any knowledge of the elements that will be presented in the future, and without the possibility of recalling ...
A longest common subsequence (LCS) is the longest subsequence common to all sequences in a set of sequences (often just two sequences). It differs from the longest common substring : unlike substrings, subsequences are not required to occupy consecutive positions within the original sequences.
In computer science, the Hunt–Szymanski algorithm, [1] [2] also known as Hunt–McIlroy algorithm, is a solution to the longest common subsequence problem.It was one of the first non-heuristic algorithms used in diff which compares a pair of files each represented as a sequence of lines.
One application of the algorithm is finding sequence alignments of DNA or protein sequences. It is also a space-efficient way to calculate the longest common subsequence between two sets of data such as with the common diff tool. The Hirschberg algorithm can be derived from the Needleman–Wunsch algorithm by observing that: [3]
OPAL — an SIMD C/C++ library for massive optimal sequence alignment; diagonalsw — an open-source C/C++ implementation with SIMD instruction sets (notably SSE4.1) under the MIT license; SSW — an open-source C++ library providing an API to an SIMD implementation of the Smith–Waterman algorithm under the MIT license
In combinatorics, a Davenport–Schinzel sequence is a sequence of symbols in which the number of times any two symbols may appear in alternation is limited. The maximum possible length of a Davenport–Schinzel sequence is bounded by the number of its distinct symbols multiplied by a small but nonconstant factor that depends on the number of alternations that are allowed.
The longest common substrings of a set of strings can be found by building a generalized suffix tree for the strings, and then finding the deepest internal nodes which have leaf nodes from all the strings in the subtree below it. The figure on the right is the suffix tree for the strings "ABAB", "BABA" and "ABBA", padded with unique string ...
In computer science, the longest repeated substring problem is the problem of finding the longest substring of a string that occurs at least twice. This problem can be solved in linear time and space Θ ( n ) {\displaystyle \Theta (n)} by building a suffix tree for the string (with a special end-of-string symbol like '$' appended), and finding ...