Which growth category signifies that execution time is independent of the input size N?
Answer
Constant time ($O(1)$)
Constant time, denoted as $O(1)$, describes scenarios where the number of steps required by the algorithm remains fixed, regardless of whether the input size N is small or very large.

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?