Is log n faster than n?
Understand the Problem
The question is asking about the comparison of the logarithmic time complexity 'log n' with linear time complexity 'n' in terms of performance and efficiency as 'n' increases. It seeks to understand which one grows faster, indicating that 'log n' grows much slower than 'n', thus is more efficient for large inputs.
Answer
O(log n) is generally faster than O(n).
The final answer is O(log n) is typically faster than O(n), especially for large datasets.
Answer for screen readers
The final answer is O(log n) is typically faster than O(n), especially for large datasets.
More Information
While O(log n) is typically faster than O(n), this is most evident with large datasets. However, for smaller input sizes, an algorithm with O(n) complexity can sometimes be quicker.
Tips
A common mistake is assuming O(log n) is always faster than O(n) regardless of the input size. Consider the constants and lower-bound efficiencies when the input size is small.
Sources
- Time complexity: Is O(log n) faster than O(n)? - naologic.com
