Big O notation is used to describe which aspect of an algorithm?

• A. Disk I/O rate
• B. Graph density
• C. Memory usage
• D. Time complexity

Answer: (D)

Big O notation describes how the running time of an algorithm grows as the input size increases. It focuses on the growth rate of time, not exact times or constants. For example, an algorithm that processes each element once tends to run in linear time, O(n); one with nested loops over the data tends to run in quadratic time, O(n^2); a search in a sorted array often runs in logarithmic time, O(log n). While Big O can also describe memory usage (space complexity), its primary use in this context is to describe time complexity—the way the runtime scales with input size, not disk I/O rate or general memory usage.