What are the most common search algorithms? (2024)

Linear search is the simplest search algorithm. It works by checking each element of the data one by one until it finds the target or reaches the end. Linear search is easy to implement and does not require any prior sorting or ordering of the data. However, it is also very slow and inefficient, especially for large or unstructured data sets. The time complexity of linear search is O(n), where n is the number of elements in the data.

Binary search is a faster and more efficient search algorithm than linear search. It works by dividing the data into two halves and comparing the target with the middle element. If the target is equal to the middle element, the search is done. If the target is smaller than the middle element, the search continues in the left half. If the target is larger than the middle element, the search continues in the right half. This process is repeated until the target is found or the data is exhausted. Binary search requires that the data is sorted in ascending or descending order before applying the algorithm. The time complexity of binary search is O(log n), where n is the number of elements in the data.

Hash table search is a search algorithm that uses a data structure called a hash table. A hash table is a collection of key-value pairs, where each key is mapped to a unique value by a hash function. A hash function is a mathematical function that converts any input into a fixed-length output, called a hash or a digest. Hash table search works by applying the hash function to the target and looking up the corresponding value in the hash table. If the value exists, the search is successful. If the value does not exist, the search fails. Hash table search is very fast and efficient, as it can find the target in constant time, O(1), regardless of the size of the data. However, hash table search also has some drawbacks, such as the possibility of hash collisions, where two different keys have the same hash, and the need for extra space to store the hash table.

Breadth-first search is a search algorithm that explores a graph or a tree data structure. A graph is a collection of nodes and edges, where nodes represent entities and edges represent connections or relationships. A tree is a special type of graph, where there is one root node and no cycles or loops. Breadth-first search works by starting from a given node, called the source, and visiting all its adjacent nodes, called the neighbors. Then, it visits all the neighbors of the neighbors, and so on, until it reaches the target node or exhausts all the nodes. Breadth-first search can find the shortest path from the source to the target, if it exists, and can also discover all the nodes that are reachable from the source. The time complexity of breadth-first search is O(m + n), where m is the number of edges and n is the number of nodes in the graph.

Depth-first search is another search algorithm that explores a graph or a tree data structure. It works by starting from a given node, called the source, and following one path as deep as possible, until it reaches a dead end or the target node. Then, it backtracks and tries another path, until it visits all the nodes or finds the target. Depth-first search can find any path from the source to the target, but not necessarily the shortest one. It can also detect cycles or loops in the graph, and classify the edges as tree, forward, backward, or cross edges. The time complexity of depth-first search is O(m + n), where m is the number of edges and n is the number of nodes in the graph.

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Algorithms

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