Suggest an editImprove this articleRefine the answer for “What is time complexity?”. Your changes go to moderation before they’re published.Approval requiredContentWhat you’re changing🇺🇸EN🇺🇦UAPreviewTitle (EN)Short answer (EN)**Time complexity** is a way to assess how an algorithm's running time changes depending on the size of the input data (n). It shows the growth rate of the number of operations, not the actual time in seconds. **Key point:** time complexity answers the question of how much the algorithm slows down as the volume of data grows, and shows whether it will scale.Shown above the full answer for quick recall.Answer (EN)ImageTime complexity is a way to assess **how an algorithm's running time changes depending on the size of the input data (n)**. It shows the *growth rate* of the number of operations, not the actual time in seconds. --- ### **Why it is needed** It allows algorithms to be compared regardless of the computer, language, and implementation. In other words, it answers the question: > *What happens if the data grows by a factor of 10, 100, or 1,000,000?* --- ### **How it is expressed** **Big-O notation** is used - (O(1), O(\log n), O(n), O(n \log n), O(n^2)) and so on. For example: - (O(1)) - the time does not depend on the data size (constant) - (O(n)) - the time grows linearly - (O(n^2)) - quadratic growth (becomes very slow on large data) --- ### **What exactly is measured** The count is of **basic operations**: - comparisons - memory accesses - arithmetic operations - iterations What matters is not the exact number, but **how it grows as n increases**. --- ### **Summary** Time complexity answers the key question: > **how much does the algorithm slow down as the volume of data grows?** It shows whether an algorithm will scale or will "die" on large input data.For the reviewerNote to the moderator (optional)Visible only to the moderator. Helps review go faster.