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Download QR code; Print/export Download as PDF; Printable version; In other projects ... Pages in category "Problems on strings" The following 11 pages are in this ...
The string spelled by the edges from the root to such a node is a longest repeated substring. The problem of finding the longest substring with at least k {\displaystyle k} occurrences can be solved by first preprocessing the tree to count the number of leaf descendants for each internal node, and then finding the deepest node with at least k ...
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 ...
Since 7 October 2024, Python 3.13 is the latest stable release, and it and, for few more months, 3.12 are the only releases with active support including for bug fixes (as opposed to just for security) and Python 3.9, [55] is the oldest supported version of Python (albeit in the 'security support' phase), due to Python 3.8 reaching end-of-life.
In theoretical computer science, the closest string is an NP-hard computational problem, [1] which tries to find the geometrical center of a set of input strings. To understand the word "center", it is necessary to define a distance between two strings.
After computing E(i, j) for all i and j, we can easily find a solution to the original problem: it is the substring for which E(m, j) is minimal (m being the length of the pattern P.) Computing E ( m , j ) is very similar to computing the edit distance between two strings.
The set of all strings over Σ of length n is denoted Σ n. For example, if Σ = {0, 1}, then Σ 2 = {00, 01, 10, 11}. We have Σ 0 = {ε} for every alphabet Σ. The set of all strings over Σ of any length is the Kleene closure of Σ and is denoted Σ *. In terms of Σ n,
A single edit operation may be changing a single symbol of the string into another (cost W C), deleting a symbol (cost W D), or inserting a new symbol (cost W I). [2] If all edit operations have the same unit costs (W C = W D = W I = 1) the problem is the same as computing the Levenshtein distance of two strings.