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Was (wer) ist search algorithm - definition
![tree]] representing binary search. The array being searched here is <math>[20, 30, 40, 50, 80, 90, 100]</math>, and the target value is <math>40</math>.](https://commons.wikimedia.org/wiki/Special:FilePath/Binary search example tree.svg?width=200 "tree]] representing binary search. The array being searched here is <math>[20, 30, 40, 50, 80, 90, 100]</math>, and the target value is <math>40</math>.")
![[[Binary search tree]]s are searched using an algorithm similar to binary search.](https://commons.wikimedia.org/wiki/Special:FilePath/Binary search tree search 4.svg?width=200 "[[Binary search tree]]s are searched using an algorithm similar to binary search.")
![Visualization of [[exponential search]]ing finding the upper bound for the subsequent binary search](https://commons.wikimedia.org/wiki/Special:FilePath/Exponential search.svg?width=200 "Visualization of [[exponential search]]ing finding the upper bound for the subsequent binary search")
![In [[fractional cascading]], each array has pointers to every second element of another array, so only one binary search has to be performed to search all the arrays.](https://commons.wikimedia.org/wiki/Special:FilePath/Fractional cascading.svg?width=200 "In [[fractional cascading]], each array has pointers to every second element of another array, so only one binary search has to be performed to search all the arrays.")
![Visualization of [[interpolation search]] using linear interpolation. In this case, no searching is needed because the estimate of the target's location within the array is correct. Other implementations may specify another function for estimating the target's location.](https://commons.wikimedia.org/wiki/Special:FilePath/Interpolation search.svg?width=200 "Visualization of [[interpolation search]] using linear interpolation. In this case, no searching is needed because the estimate of the target's location within the array is correct. Other implementations may specify another function for estimating the target's location.")
![[[Uniform binary search]] stores the difference between the current and the two next possible middle elements instead of specific bounds.](https://commons.wikimedia.org/wiki/Special:FilePath/Uniform binary search.svg?width=200 "[[Uniform binary search]] stores the difference between the current and the two next possible middle elements instead of specific bounds.")
Wikipedia
In computer science, a search algorithm is an algorithm designed to solve a search problem. Search algorithms work to retrieve information stored within particular data structure, or calculated in the search space of a problem domain, with either discrete or continuous values.
Although search engines use search algorithms, they belong to the study of information retrieval, not algorithmics.
The appropriate search algorithm often depends on the data structure being searched, and may also include prior knowledge about the data. Search algorithms can be made faster or more efficient by specially constructed database structures, such as search trees, hash maps, and database indexes.
Search algorithms can be classified based on their mechanism of searching into three types of algorithms: linear, binary, and hashing. Linear search algorithms check every record for the one associated with a target key in a linear fashion. Binary, or half-interval, searches repeatedly target the center of the search structure and divide the search space in half. Comparison search algorithms improve on linear searching by successively eliminating records based on comparisons of the keys until the target record is found, and can be applied on data structures with a defined order. Digital search algorithms work based on the properties of digits in data structures by using numerical keys. Finally, hashing directly maps keys to records based on a hash function.
Algorithms are often evaluated by their computational complexity, or maximum theoretical run time. Binary search functions, for example, have a maximum complexity of O(log n), or logarithmic time. In simple terms, the maximum number of operations needed to find the search target is a logarithmic function of the size of the search space.
