# Asymptotic notation

[THIS IS HOMEWORK so please, don't just provide solutions, help me reasoning]

I'm dealing with the following problem:
Let $f(n)$ be the worst case time complexity of an algorithm A. Assume that there exist $c > 0$ and $n_{0}$, such that for each $n > n_{0}$ there exists an input of size $n$ for which A runs in exactly $c\cdot{g(n)}$ steps.

a. Is it necessarily true that $f(n) = \Omega(g(n))$?
b. Assume $f(n) = O(g(n))$. Is it necessarily true that $f(n) = \Theta(g(n))$?

For a. I think it must be true since the $\Omega$ notation has on it all the elements that are either $f(n) = c\cdot g(n)$ or $f(n) > c\cdot g(n)$ so following the assumption leads to $f(n) = c\cdot g(n)$ being always true, therefore making the whole sentence true. (hope I'm right).
For b. I assume $f(n) = O(g(n))$ and $f(n)=c\cdot{g(n)}$ ($\forall{n>n_{0}}$... of course) then I concluded that since (a) is true then $f(n) = \Theta(g(n))$.

any help will be appreciated, thanks.

-
Part b. seems a little strange. If you're already given that $f(n) = c\,g(n)$ when $n$ is large enough why do you need to assume that $f(n) = O(g(n))$? Is that exactly how the question was written? – Antonio Vargas Apr 15 '12 at 13:15
@Antonio yes by 100% – JanosAudron Apr 15 '12 at 13:27
In that case then I think your reasoning is correct. Everything is easy if you know that $f(n) = c \,g(n)$ eventually. – Antonio Vargas Apr 15 '12 at 14:07

The reasoning for $f(n)=c\,g(n)$ is not correct; this equality need not hold under the stated assumptions.

In mathematical terms, the assumption

Assume that there exist $c>0$ and $n_0$, such that for each $n>n_0$ there exists an input of size $n$ for which $A$ runs in exactly $c\,g(n)$ steps.

becomes $$f(n)\ge cg(n)\quad \text{when }\ n>n_0 \tag1$$ because $f$ is the maximal running time among all inputs of size $n$. We cannot conclude that equality holds in (1) because nothing excludes the possibility that an input exists that runs in more than $c\,g(n)$ steps.

a) Yes, compare (1) to the definition of $\Omega$.

b) Yes, $\Theta$ means "$O$ and $\Omega$".

-