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how to prove that nlogn is not Θ(n) without using limits?
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Problem
i'm studying an algorithms designing and analysis , and i've question about Big-theta
how can i prove that nlogn is not Θ(n) without using limits ?
how can i prove that nlogn is not Θ(n) without using limits ?
Solution
Remember that $f \in \Theta(g) \iff f \in O(g) \land g \in O(f)$, so $f \notin \Theta(g) \iff f \notin O(g) \lor g \notin O(f)$.
One definition of big-O notation that doesn't use limits is
$$
f \in O(g) \triangleq \exists c, N, \forall n > N, f(n) N \implies f(n) N) \lor f(n) N) \lor f(n) N) \lor f(n) N) \land \lnot (f(n) N, \lnot (f(n) N, f(n) \geq c \cdot g(n)\\
$$
So, to show that $n \log n \notin \Theta(n)$, you'll want to find a witness $n$ that will be based on a $c$ and $N$ that are abstract, and show that $n \log n \geq c \cdot n$.
Here is an example with some other functions:
Claim: $n^2 \notin O(n)$.
Proof:
Given $c$ and $N$ as above, let
$$n \triangleq \begin{cases}
c & N < 1\\
N c & N \geq 1.
\end{cases}$$
In the first case, $n^2 = c^2 \geq c \cdot c = c \cdot n$.
In the second, $n^2 = N^2 c^2 = N (N c^2) \geq N c^2 = c (c N) = c \cdot n$.
I won't do the example with $n \log n$, because I'd like you to see the principle and I don't want to accidentally do you homework.
One definition of big-O notation that doesn't use limits is
$$
f \in O(g) \triangleq \exists c, N, \forall n > N, f(n) N \implies f(n) N) \lor f(n) N) \lor f(n) N) \lor f(n) N) \land \lnot (f(n) N, \lnot (f(n) N, f(n) \geq c \cdot g(n)\\
$$
So, to show that $n \log n \notin \Theta(n)$, you'll want to find a witness $n$ that will be based on a $c$ and $N$ that are abstract, and show that $n \log n \geq c \cdot n$.
Here is an example with some other functions:
Claim: $n^2 \notin O(n)$.
Proof:
Given $c$ and $N$ as above, let
$$n \triangleq \begin{cases}
c & N < 1\\
N c & N \geq 1.
\end{cases}$$
In the first case, $n^2 = c^2 \geq c \cdot c = c \cdot n$.
In the second, $n^2 = N^2 c^2 = N (N c^2) \geq N c^2 = c (c N) = c \cdot n$.
I won't do the example with $n \log n$, because I'd like you to see the principle and I don't want to accidentally do you homework.
Context
StackExchange Computer Science Q#54063, answer score: 8
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