Understanding Time Complexity and Space Complexity
When it comes to evaluating algorithms, two critical aspects often come into play: time complexity and space complexity. These concepts help in understanding how an algorithm performs in terms of execution time and memory usage, respectively. Let's dive deeper into these essential topics. Time Complexity Time complexity is a measure of the amount of computational time that an algorithm takes to complete as a function of the length of the input. It gives an estimate of the running time of an algorithm in terms of the size of the input data. Time complexity is usually expressed using Big O notation, which provides an upper bound on the running time. Big O Notation Big O notation is used to classify algorithms according to their worst-case or upper bound performance. Here are some common time complexities: O(1) - Constant Time : The execution time is constant and does not change with the size of the input. Example: Accessing an element in an array by index.