Big O Complexity Quiz

Overview

Test your understanding of algorithm time and space complexity through code analysis. Examine code snippets and identify their Big O complexity for both time and space. Questions cover common patterns like loops, recursion, data structures, and nested iterations. Each answer includes detailed explanations to help you understand why a particular complexity applies.

Open in new tab

Tips

• Look for loops and count how many times they execute relative to input size n
• Nested loops often indicate polynomial time (O(n²), O(n³))
• Watch for logarithmic patterns - halving the problem each iteration suggests O(log n)
• Space complexity counts auxiliary space (excluding input) - variables, stack frames, data structures
• Common complexities from best to worst: O(1), O(log n), O(n), O(n log n), O(n²), O(2ⁿ), O(n!)