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A way to describe how an algorithm's running time or memory use grows as the input gets larger.
Big-O describes the upper bound of an algorithm's growth rate, ignoring constants and lower-order terms. O(1) means constant time regardless of input size; O(n) means time grows linearly; O(log n) means time grows very slowly as input doubles; O(n²) means time grows with the square of the input.
Step through big-o notation live, one move at a time — this becomes a fully interactive visualizer.
Coming soonn grows (usually a loop or recursive call).n.O(2n + 10) simplifies to O(n).No — it describes how running time scales with input size, not the actual number of seconds. Two O(n) algorithms can have very different real-world speeds.
Big-O gives an upper bound (worst case or general bound); Big-Θ gives a tight bound describing both upper and lower behavior. Most casual usage of "Big-O" actually means Big-Θ.
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