Final answer:
When f(n) is O(g(n)), it means that f(n) grows at the same rate or slower than g(n) as n gets very large. This has important implications in algorithm analysis and complexity theory.
Step-by-step explanation:
When we say that f(n) is O(g(n)), we mean that f(n) grows at the same rate or slower than g(n) as n gets very large. This implies that g(n) is an upper bound for f(n), meaning that g(n) is always greater than or equal to f(n) for large enough values of n.
For example, if f(n) = 2n and g(n) =
, we can see that as n gets larger,
grows much faster than 2n. So, we can say that f(n) is O(g(n)).
This has important implications in algorithm analysis and complexity theory, as it helps us determine the efficiency and scalability of algorithms.