Comprehensive Analysis of Time and Space Complexity in Algorithm Design

Abstract

The efficiency of algorithms is a central concern in computer science, and understanding their time and space complexity is crucial to developing scalable and efficient solutions. Time complexity refers to the computational time required by an algorithm to solve a problem as a function of its input size, while space complexity deals with the amount of memory used. This review article explores the concepts of time and space complexity, their significance in algorithm design, and the methods used to analyze and optimize them. Additionally, we discuss common complexities in algorithms, strategies to improve efficiency, and real-world applications where these factors are critical.