Big-O Time Complexity
O(1)
Constant time — stays the same regardless of input size.
O(log n)
Logarithmic — grows very slowly (e.g., binary search).
O((log n)2)
Log-squared — slightly faster growth than log n.
O(√n)
Square root — moderate growth, better than linear.
O(n)
Linear — grows directly with input size.
O(n log n)
Efficient sorting (merge/quick sort average case).
O(n2)
Quadratic — nested loops (bubble sort).
O(n3)
Cubic — very slow for large inputs.
O(2n)
Exponential — doubles each step (very expensive).
O(n!)
Factorial — extremely slow (permutations).