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An anatree [1] is a data structure designed to solve anagrams. Solving an anagram is the problem of finding a word from a given list of letters. These problems are commonly encountered in word games like Scrabble or in newspaper crossword puzzles. The problem for the wordwheel also has the condition that the central letter appear in all the ...
A better solution, which was proposed by Sellers, [2] relies on dynamic programming. It uses an alternative formulation of the problem: for each position j in the text T and each position i in the pattern P , compute the minimum edit distance between the i first characters of the pattern, P i {\displaystyle P_{i}} , and any substring T j ...
In computer science, the two-way string-matching algorithm is a string-searching algorithm, discovered by Maxime Crochemore and Dominique Perrin in 1991. [1] It takes a pattern of size m, called a “needle”, preprocesses it in linear time O(m), producing information that can then be used to search for the needle in any “haystack” string, taking only linear time O(n) with n being the ...
HackerRank categorizes most of their programming challenges into a number of core computer science domains, [3] including database management, mathematics, and artificial intelligence. When a programmer submits a solution to a programming challenge, their submission is scored on the accuracy of their output.
An anagram is a word or phrase formed by rearranging the letters of a different word or phrase, typically using all the original letters exactly once. [1] For example, the word anagram itself can be rearranged into the phrase "nag a ram"; which is an Easter egg suggestion in Google after searching for the word "anagram". [2]
Jumble: a kind of word game in which the solution of a puzzle is its anagram; Chronogram: a phrase or sentence in which some letters can be interpreted as numerals and rearranged to stand for a particular date; Gramogram: a word or sentence in which the names of the letters or numerals are used to represent the word
Alternative ()-time solutions were provided by Jeuring (1994), and by Gusfield (1997), who described a solution based on suffix trees. A faster algorithm can be achieved in the word RAM model of computation if the size σ {\displaystyle \sigma } of the input alphabet is in 2 o ( log n ) {\displaystyle 2^{o(\log n)}} .
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 ...