Time Complexity Calculator

Big O Notation describes how the running time or memory usage of an algorithm grows relative to the input size n. It focuses on the dominant term and ignores constants.

Time complexity measures the number of operations performed. A single loop is O(n); two nested loops are O(n²); divide-and-conquer algorithms like binary search are O(log n).

Space complexity measures the additional memory your algorithm uses. Iterative solutions often need O(1) extra space, while recursive ones consume O(n) stack space.