Computational Complexity, Time and Space Complexity of Algorithms - What is 'Big O Notation' about?

The article discusses the computational complexity of algorithms, focusing on two key aspects: time complexity and space complexity. Time complexity measures the amount of time required to execute an algorithm based on the input size. The author explains different classes of time complexity such as O(1), O(n), and O(n^2), describing what these symbols mean and how they affect the algorithm's efficiency. Similarly, space complexity relates to the amount of memory needed to execute the algorithm and is categorized in similar ways. Understanding these concepts is crucial for programmers, as it helps make better decisions when selecting appropriate algorithms for problem-solving in practice. Ultimately, the article emphasizes the significance of complexity analysis in creating efficient applications, which is essential in today's technological landscape.