Common algorithms include searching algorithms (like binary search), sorting algorithms (like quicksort), and graph algorithms (like Dijkstra's algorithm) for various tasks.

There are many algorithms used in data structures and algorithms (DSA) that serve a wide range of tasks across computer science and software development. Some of the most common algorithms include searching algorithms, sorting algorithms, and graph algorithms. Searching algorithms are fundamental for locating specific elements within data structures. The linear search algorithm checks each element until it finds the target, with a time complexity of O(n). However, for sorted datasets, binary search is much more efficient, with a time complexity of O(log n), as it divides the search interval in half each time. Sorting algorithms are essential for organizing data in a specific order, which is often a preliminary step for searching. Popular sorting algorithms include bubble sort, selection sort, insertion sort, merge sort, and quicksort. Each algorithm has its own time complexity and space requirements, with quicksort generally being the most efficient for average cases with O(n log n) complexity. Graph algorithms are crucial for solving problems related to networks and relationships. Dijkstra's algorithm is widely used for finding the shortest path in weighted graphs, while breadth-first search (BFS) and depth-first search (DFS) are fundamental traversal algorithms used to explore nodes in graphs. Other important algorithms include Kruskal's and Prim's algorithms for finding minimum spanning trees, and the Floyd-Warshall algorithm for finding the shortest paths between all pairs of vertices in a graph. Understanding these algorithms and their applications is vital for developing efficient software solutions and preparing for technical interviews.