What primarily dictates the asymptotic scaling measured by Big O notation?
Answer
The number of items, N
Big O notation is fundamentally concerned with the input size N, which dictates the number of steps taken. The magnitude of the data influences the constant factor (how long each step takes), but not the asymptotic scaling.
Related Questions
What does Big O notation focus exclusively on?Which growth category signifies that execution time is independent of the input size N?How does the execution time increase in a logarithmic time complexity, $O(\log N)$?Which operation typically corresponds to linear time complexity, $O(N)$?When simplifying an expression like $5N^2 + 100N + 50$ to Big O notation, what aspects are discarded?What common programming structure often results in quadratic time complexity, $O(N^2)$?What primarily dictates the asymptotic scaling measured by Big O notation?Which type of highly optimized algorithm commonly exhibits quasilinear performance?Under what scenario might an algorithm's observed runtime paradoxically appear to decrease as input size N increases?For small, bounded inputs, what factor often leads to choosing a simpler algorithm over one with superior asymptotic elegance?