Big O to Runtime Estimator
Estimate actual execution time from Big O notation and input size. Essential for interview discussions. Whether you're solving coding interview problems or studying for exams, understanding how to convert big o notation to estimated runtime is essential.
How It Works
Conversion Logic:
For input size n, compute the number of operations using the Big O function, then multiply by ~10ns per operation.
This is a fundamental skill tested in coding interviews, competitive programming, and CS theory exams.
Step-by-Step Examples
Example 1
Input: O(n) with n = 1,000,000
Output: ~10ms
Explanation: 10^6 operations × 10ns = 10ms
Example 2
Input: O(n²) with n = 10,000
Output: ~1 second
Explanation: 10^8 operations × 10ns = 1s
Example 3
Input: O(n log n) with n = 1,000,000
Output: ~200ms
Explanation: ~2×10^7 operations × 10ns ≈ 200ms
Practice Problems
Test your understanding with these exercises:
Interview tip: Be ready to explain the conversion process step-by-step on a whiteboard.
Implementation Code
python# Big O to Runtime Estimator implementation def convert(input_value): # Apply: For input size n, compute the number of operations using the Big O function, then multiply by ~10ns per operation. pass # Implement the conversion logic
Frequently Asked Questions
How to convert Big O Notation to Estimated Runtime?
Is this conversion asked in interviews?
What are related conversions?
Practice complexity problems on W Code with instant feedback!
Start Learning Free