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  2. Longest alternating subsequence - Wikipedia

    en.wikipedia.org/wiki/Longest_Alternating...

    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 substring - Wikipedia

    en.wikipedia.org/wiki/Longest_common_substring

    The picture shows two strings where the problem has multiple solutions. Although the substring occurrences always overlap, it is impossible to obtain a longer common substring by "uniting" them. The strings "ABABC", "BABCA" and "ABCBA" have only one longest common substring, viz. "ABC" of length 3.

  4. Longest increasing subsequence - Wikipedia

    en.wikipedia.org/wiki/Longest_increasing_subsequence

    one of the longest increasing subsequences is 0, 2, 6, 9, 11, 15. This subsequence has length six; the input sequence has no seven-member increasing subsequences. The longest increasing subsequence in this example is not the only solution: for instance, 0, 4, 6, 9, 11, 15 0, 2, 6, 9, 13, 15 0, 4, 6, 9, 13, 15. are other increasing subsequences ...

  5. Longest common subsequence - Wikipedia

    en.wikipedia.org/wiki/Longest_common_subsequence

    Comparison of two revisions of an example file, based on their longest common subsequence (black) A longest common subsequence (LCS) is the longest subsequence common to all sequences in a set of sequences (often just two sequences).

  6. Hunt–Szymanski algorithm - Wikipedia

    en.wikipedia.org/wiki/Hunt–Szymanski_algorithm

    The above algorithm has worst-case time and space complexities of O(mn) (see big O notation), where m is the number of elements in sequence A and n is the number of elements in sequence B. The Hunt–Szymanski algorithm modifies this algorithm to have a worst-case time complexity of O ( mn log m ) and space complexity of O ( mn ) , though it ...

  7. Chvátal–Sankoff constants - Wikipedia

    en.wikipedia.org/wiki/Chvátal–Sankoff_constants

    The problem of computing a longest common subsequence has been well studied in computer science. It can be solved in polynomial time by dynamic programming ; [ 5 ] this basic algorithm has additional speedups for small alphabets (the Method of Four Russians ), [ 6 ] for strings with few differences, [ 7 ] for strings with few matching pairs of ...

  8. Patience sorting - Wikipedia

    en.wikipedia.org/wiki/Patience_sorting

    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).

  9. 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