Finally, there is the Grovers algorithm for quantum computers. The sub-structures of a given structure, has the combinatorial search method again with lesser-known types of algorithms as well. There are also local searches, which include meta-heuristics like simulated annealing and the tabu search. Virtual search spaces use search algorithms such as brute-force searches, and heuristics (heuristics is a term used for trying to find the shortest path). The search algorithms used to search in these are the linear search, binary search trees, heaps, hash tables and others. The instances above are basic examples of the applications of search algorithms, but there are four main cases where search algorithms can be used: explicitly stored databases, virtual search spaces, sub-structures of a given structure, and finally the quantum computers of the future.Įxplicitly stored databases include bank account information, electronic documents, and more. The myriads of applications for these search algorithms are still expanding, as anyone can see by looking for information on search algorithms. They can be used for solving things like the popular traveling salesman problem. They are also used for finding information in large databases such as the fingerprint and image databases we sometimes see in crime shows when police are matching the fingerprints of perpetrators. So, what kind of problems could a search algorithm solve? For starters, a sudoku-lover could solve a puzzle in minutes with a search algorithm. To search is “to look at or examine carefully in order to find something concealed.” An algorithm is “a set of rules for solving a problem in a finite number of steps.” Thus, a search algorithm is to look at or examine a problem, with a set of rules for solving said problem, in a finite number of steps.

Applications of linear search