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  2. Longest alternating subsequence - Wikipedia

    en.wikipedia.org/.../Longest_Alternating_Subsequence

    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 ...

  3. Longest common subsequence - Wikipedia

    en.wikipedia.org/wiki/Longest_common_subsequence

    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.

  4. Longest increasing subsequence - Wikipedia

    en.wikipedia.org/wiki/Longest_increasing_subsequence

    In computer science, the longest increasing subsequence problem aims to find a subsequence of a given sequence in which the subsequence's elements are sorted in an ascending order and in which the subsequence is as long as possible. This subsequence is not necessarily contiguous or unique.

  5. Patience sorting - Wikipedia

    en.wikipedia.org/wiki/Patience_sorting

    First, execute the sorting algorithm as described above. The number of piles is the length of a longest subsequence. Whenever a card is placed on top of a pile, put a back-pointer to the top card in the previous pile (that, by assumption, has a lower value than the new card has). In the end, follow the back-pointers from the top card in the ...

  6. Subsequence - Wikipedia

    en.wikipedia.org/wiki/Subsequence

    The longest common subsequence of sequences 1 and 2 is: LCS (SEQ 1,SEQ 2) = CGTTCGGCTATGCTTCTACTTATTCTA. This can be illustrated by highlighting the 27 elements of the longest common subsequence into the initial sequences: SEQ 1 = A CG G T G TCG T GCTATGCT GA T G CT G ACTTAT A T G CTA SEQ 2 = CGTTCGGCTAT C G TA C G TTCTA TT CT A T G ATT T CTA A

  7. Hirschberg's algorithm - Wikipedia

    en.wikipedia.org/wiki/Hirschberg's_algorithm

    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]

  8. Erdős–Szekeres theorem - Wikipedia

    en.wikipedia.org/wiki/Erdős–Szekeres_theorem

    For r = 3 and s = 2, the formula tells us that any permutation of three numbers has an increasing subsequence of length three or a decreasing subsequence of length two. Among the six permutations of the numbers 1,2,3: 1,2,3 has an increasing subsequence consisting of all three numbers; 1,3,2 has a decreasing subsequence 3,2

  9. Longest common substring - Wikipedia

    en.wikipedia.org/wiki/Longest_common_substring

    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 ...

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