Which describes algorithm complexity?

Which describes algorithm complexity?

• A. The fastest possible runtime of an algorithm.
• B. A measure of how the running time or space requirements grow with input size (Big-O).
• C. The memory used by the algorithm regardless of input.
• D. A method to optimize code style.

Answer: (B)

Algorithm complexity is about how the resources an algorithm uses change as the input size grows. It expresses this growth with Big-O notation to describe the upper-bound behavior of running time or space (memory), independent of hardware or constant factors. This lets you compare how scalable different algorithms are as inputs get larger. It’s not about the fastest possible runtime for a single input, nor about fixed memory usage for all inputs, and it’s not about code styling or optimization methods. The correct choice captures the idea that complexity measures how time or space grow with input size using Big-O.

Algorithm complexity is about how the resources an algorithm uses change as the input size grows. It expresses this growth with Big-O notation to describe the upper-bound behavior of running time or space (memory), independent of hardware or constant factors. This lets you compare how scalable different algorithms are as inputs get larger. It’s not about the fastest possible runtime for a single input, nor about fixed memory usage for all inputs, and it’s not about code styling or optimization methods. The correct choice captures the idea that complexity measures how time or space grow with input size using Big-O.