August 26, 2025Open Access

Performance Analysis of Sorting and Searching Algorithms

PPProf.Mrs. Tejaswini.A. PuranikMaharaja Engineering College

Key Points

Sorting algorithms can have time complexities ranging from O(n²) to O(n log n), significantly affecting performance.
Searching algorithms display complexities from O(n) to O(log n), depending on the utilized data structure.
This analysis evaluates the efficiency of popular algorithms like Bubble Sort and Quick Sort, highlighting their differences.
Understanding these complexities is essential for optimizing algorithm performance in various computational tasks.

Abstract

In computer science, the efficiency of algorithms is a critical consideration for optimizing performance. The time and space complexity of sorting and searching algorithms, which are essential to a variety of computational tasks, is frequently the basis for evaluation. Time complexity refers to the amount of time an algorithm takes to complete as a function of the input size, while space complexity indicates the amount of memory the algorithm requires. Bubble Sort, Selection Sort, Merge Sort, and Quick Sort are just a few of the common sorting algorithms included in this investigation. All of these algorithms have time complexities ranging from O(n2) to O (n log n). Similarly, searching algorithms such as Linear Search and Binary Search are examined, with complexities from O(n) to O (log n) depending on the data structure and the algorithm used.

Ask AI

Helpful

Bookmark

View Full Paper