Final answer:
The running time of pseudocode is often described using big-oh notation, which provides an upper bound on the growth rate of the algorithm's running time. It allows us to compare different algorithms and choose the most efficient one for a given problem.
Step-by-step explanation:
The running time of pseudocode is often described using big-oh notation. Big-oh notation is a mathematical representation used to analyze the efficiency of an algorithm by considering its worst-case scenario. It provides an upper bound on the growth rate of the algorithm's running time.
For example, if an algorithm has a running time of O(n), it means that the running time grows linearly with the input size. If the input size doubles, the running time also doubles. On the other hand, if an algorithm has a running time of O(n^2), it means that the running time grows quadratically with the input size. If the input size doubles, the running time increases by a factor of four.
By analyzing the running time of an algorithm with big-oh notation, we can compare different algorithms and choose the most efficient one for a given problem.