Search algorithm

A search algorithm is a specific set of steps used to solve a search problem. These algorithms are designed to retrieve information stored in a particular data structure. They can also calculate values within a search space. This space might contain discrete values or continuous values. While people often associate searching with search engines, those tools actually belong to the field of information retrieval. In computer science, search algorithms are a core part of algorithmics. Choosing the right algorithm depends on how the data is organized. It also depends on what the programmer already knows about that data.

There are several ways these algorithms function. A linear search is the most basic method. It checks every single record one by one until it finds the target key. This is a very direct approach. A binary search, also called a half-interval search, works much faster. It repeatedly targets the center of the search structure. Each step divides the remaining search space in half. This method is much more efficient than checking every item.

Other methods use more complex mathematical rules. Hashing is a method that uses a hash function. This function directly maps keys to specific records. This allows for very fast retrieval of information. Digital search algorithms work differently by using the properties of digits. They use numerical keys to find what they need. Comparison search algorithms are also used to improve upon linear searching. These algorithms work by successively eliminating records. They compare keys until the target record is finally found. This works best on data structures that have a defined order.

Computer scientists evaluate these algorithms using computational complexity. This term refers to the maximum theoretical run time of an algorithm. It measures how many operations are needed to find a target. For example, a binary search has a maximum complexity of O(log n). This is known as logarithmic time. In simple terms, the number of operations grows as a logarithmic function of the search space size. To make searching even faster, engineers use special database structures. These include search trees, hash maps, and database indexes.

Algorithms can be used to search through virtual spaces. These are used in constraint satisfaction problems. In these problems, the goal is to find values that satisfy specific mathematical equations. One basic method is the brute-force search. This is also called a naïve or uninformed search. Other methods use heuristics, which are strategies based on partial knowledge. Local search methods view the search space as a graph. They move from item to item along edges. This can include methods like simulated annealing or genetic programming.

Tree search algorithms are another important category. These view the elements of a space as vertices in a tree. Some methods, like depth-first search, are exhaustive. This means they check everything. Other methods, like backtracking, use heuristics to prune the tree. If given enough time, many of these methods are guaranteed to find the exact solution. This quality is called completeness. These ideas are very important in artificial intelligence. For example, the minimax algorithm helps a computer choose the best move in games like chess.

Search algorithms solve many real-world problems. They are used in combinatorial optimization. This includes the vehicle routing problem and the knapsack problem. The knapsack problem asks how to pick items to maximize value without exceeding a weight limit. Algorithms also help with nurse scheduling and map coloring. They are used in cryptography to factor an integer. Even industrial processes use them. A factory might change temperature or pressure to optimize a chemical reaction.

New technologies are creating even more ways to search. In 1953, the American statistician Jack Kiefer created Fibonacci search. This helps find the maximum of a unimodal function. Researchers are also looking at quantum computers. Grover's algorithm is a search method designed for quantum systems. It is theoretically faster than a linear search. While quantum computing is still largely theoretical, these algorithms help scientists model how such systems might work.