Big O Notation Practice Problems with Solutions

Big O notation describes how the running time or space requirements of an algorithm grow as the input size n increases. This guide contains Big O practice problems ranging from beginner to advanced, with step-by-step solutions and explanations. Big O notation? Big O notation describes how the running time or space requirements of an algorithm grow as the input size n increases. This guide contains Big O practice problems ranging from beginner to advanced, with step-by-step solutions and explanations. Complexity Common example O(1) Array access O(log n) Binary search O(n) Linear search O(n log n) Merge sort O(n²) Nested loops O(2ⁿ) Some recursive algorithms O(1) O(log n) O(n) O(n log n) O(n²) O(2ⁿ) n = 64 : (1, log₂n, n) = (1, 6 , 64 ) (n log₂n, n²) = ( 384 , 4,096 ) 2 n = 1.84 × 10 19 n 4 8 16 32 64