The program's run-time is directly proportional to its input size.

Doubling the input size only increases the run time by a constant amount (in this example, 25,000 ns).

In practice, it is set empirically based on the input sizes and the architecture, typically to a value between 4 and 16.

Complexity theory is interested in how algorithms scale with an increase in the input size.

A more accurate analysis would take the depth of the parse tree into account separately from the input size.

The input size is n, so there are 2 possible inputs.

These algorithms have limited memory available to them (much less than the input size) and also limited processing time per item.

Algorithms can be classified by the amount of time they need to complete compared to their input size.

First, it has been shown that the worst case running time of the algorithm is super-polynomial in the input size.

In cryptography, the security parameter is a variable that measures the input size of the problem.