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  2. Longest common subsequence - Wikipedia

    en.wikipedia.org/wiki/Longest_common_subsequence

    When the length decreases, the sequences must have had a common element. Several paths are possible when two arrows are shown in a cell. Below is the table for such an analysis, with numbers colored in cells where the length is about to decrease. The bold numbers trace out the sequence, (GA). [6]

  3. Cycle detection - Wikipedia

    en.wikipedia.org/wiki/Cycle_detection

    In computer science, cycle detection or cycle finding is the algorithmic problem of finding a cycle in a sequence of iterated function values. For any function f that maps a finite set S to itself, and any initial value x 0 in S , the sequence of iterated function values

  4. Longest common substring - Wikipedia

    en.wikipedia.org/wiki/Longest_common_substring

    The array L stores the length of the longest common suffix of the prefixes S[1..i] and T[1..j] which end at position i and j, respectively. The variable z is used to hold the length of the longest common substring found so far. The set ret is used to hold the set of strings which are of length z.

  5. Longest path problem - Wikipedia

    en.wikipedia.org/wiki/Longest_path_problem

    In graph theory and theoretical computer science, the longest path problem is the problem of finding a simple path of maximum length in a given graph.A path is called simple if it does not have any repeated vertices; the length of a path may either be measured by its number of edges, or (in weighted graphs) by the sum of the weights of its edges.

  6. Clique problem - Wikipedia

    en.wikipedia.org/wiki/Clique_problem

    The simplest nontrivial case of the clique-finding problem is finding a triangle in a graph, or equivalently determining whether the graph is triangle-free. In a graph G with m edges, there may be at most Θ(m 3/2) triangles (using big theta notation to indicate that this bound is tight). The worst case for this formula occurs when G is itself ...

  7. Dynamic programming - Wikipedia

    en.wikipedia.org/wiki/Dynamic_programming

    Find the path of minimum total length between two given nodes and . We use the fact that, if R {\displaystyle R} is a node on the minimal path from P {\displaystyle P} to Q {\displaystyle Q} , knowledge of the latter implies the knowledge of the minimal path from P {\displaystyle P} to R {\displaystyle R} .

  8. LeetCode - Wikipedia

    en.wikipedia.org/wiki/LeetCode

    LeetCode LLC, doing business as LeetCode, is an online platform for coding interview preparation. The platform provides coding and algorithmic problems intended for users to practice coding . [ 1 ] LeetCode has gained popularity among job seekers in the software industry and coding enthusiasts as a resource for technical interviews and coding ...

  9. Longest increasing subsequence - Wikipedia

    en.wikipedia.org/wiki/Longest_increasing_subsequence

    In this variant of the problem, which allows for interesting applications in several contexts, it is possible to devise an optimal selection procedure that, given a random sample of size as input, will generate an increasing sequence with maximal expected length of size approximately . [11] The length of the increasing subsequence selected by ...