How do you define the "order of growth" in algorithm analysis?

The order of growth in algorithm analysis refers to how the runtime or space requirements of an algorithm change as the size of the input increases. This concept is instrumental in understanding the efficiency of an algorithm, especially for large datasets. The order of growth is typically expressed using Big O notation, which provides a high-level understanding of how an algorithm's performance scales.

For instance, if an algorithm has a runtime of O(n), it means that if you double the input size, the runtime will roughly double as well. This relationship helps in comparing the efficiency of different algorithms and in choosing the most appropriate one based on the expected input size. By focusing on the relationship between runtime and input size, one can gauge an algorithm’s scalability, which is vital for performance-critical applications.

The other choices do not accurately capture the essence of order of growth. For example, average time describes only a specific execution scenario, maximum input size does not relate to performance scalability, and algorithm types do not inherently define how their performance changes with input size.