What is Big O notation and how does it measure the complexity of algorithms?
The article "Big O" by Sam Who explains a fundamental concept of algorithm analysis that is crucial for programmers and software engineers. Big O notation is used to describe how the time or space required for an algorithm grows relative to the size of the input data. In the article, the author presents various types of time complexity, such as O(1), O(n), O(n^2), and others, providing readers with a straightforward way to understand how complex different algorithms can be. Each type of complexity is illustrated with practical examples, making it easier to grasp. Additionally, the article discusses situations where different algorithms become less or more efficient, helping programmers make informed decisions when writing code.