When evaluating two algorithms, what does Big-O help determine?

When evaluating two algorithms, what does Big-O help determine?

• A. Which algorithm scales better as input size grows
• B. The exact running time for a given input
• C. The memory address of variables
• D. The color of the output

Answer: (A)

Big-O describes how the resources an algorithm uses (time or space) grow as the input size increases. It focuses on the growth rate and compares scalability, ignoring constant factors and lower-order terms so you can see which algorithm handles much larger inputs more efficiently. For example, an algorithm with i.e. linear growth grows much more slowly than one with quadratic growth as n becomes large, so the former scales better. It doesn’t give the exact running time for a specific input or account for machine speed, and it has nothing to do with memory addresses or the color of the output.

Big-O describes how the resources an algorithm uses (time or space) grow as the input size increases. It focuses on the growth rate and compares scalability, ignoring constant factors and lower-order terms so you can see which algorithm handles much larger inputs more efficiently. For example, an algorithm with i.e. linear growth grows much more slowly than one with quadratic growth as n becomes large, so the former scales better. It doesn’t give the exact running time for a specific input or account for machine speed, and it has nothing to do with memory addresses or the color of the output.