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Specific applications of search algorithms include: Problems in combinatorial optimization, such as: . The vehicle routing problem, a form of shortest path problem; The knapsack problem: Given a set of items, each with a weight and a value, determine the number of each item to include in a collection so that the total weight is less than or equal to a given limit and the total value is as ...
A string-searching algorithm, sometimes called string-matching algorithm, is an algorithm that searches a body of text for portions that match by pattern. A basic example of string searching is when the pattern and the searched text are arrays of elements of an alphabet ( finite set ) Σ.
Download QR code; Print/export Download as PDF; Printable version; ... Reverse-search algorithm; Rocchio algorithm; S. Search game; Search tree; Search-based software ...
To find the exact position of the search key in the list a linear search is performed on the sublist L [(k-1)m, km]. The optimal value of m is √ n, where n is the length of the list L. Because both steps of the algorithm look at, at most, √ n items the algorithm runs in O(√ n) time. This is better than a linear search, but worse than a ...
Trigram search is a method of searching for text when the exact syntax or spelling of the target object is not precisely known [1] or when queries may be regular expressions. [2] It finds objects which match the maximum number of three consecutive character strings (i.e. trigrams ) in the entered search terms, which are generally near matches ...
This algorithm runs in () time. 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.
Nearest neighbor search (NNS), as a form of proximity search, is the optimization problem of finding the point in a given set that is closest (or most similar) to a given point. Closeness is typically expressed in terms of a dissimilarity function: the less similar the objects, the larger the function values.
The similarity of two strings and is determined by this formula: twice the number of matching characters divided by the total number of characters of both strings. The matching characters are defined as some longest common substring [3] plus recursively the number of matching characters in the non-matching regions on both sides of the longest common substring: [2] [4]