Justify $\Theta(n^7 \log n)$ for $g(n)$

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In summary, the conversation discusses finding the asymptotic complexity of a function and justifying why $n^7 \log n$ is the dominant term. The participants go through the definition and use various arguments to show that all terms in the function are smaller than $n^7 \log n$. Finally, they conclude that $g(n)=\Theta(n^7 \log n)$ based on the definition of asymptotic complexity.
  • #1
evinda
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Hi! (Smile)

I want to find the asymptotic complexity ($\Theta$) of the function $g(n)=8n^7 \log n+18 \log{13}+16 \log n^n+12 n^{\frac{5}{2}}$.

We see that the dominant term is $n^7 \log n$, but how could we justify it? (Thinking)

Does this mean that : $g(n)=\Theta(n^7 \log n)$ ? :confused:
 
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  • #2
According to definition we have to find constants such that

\(\displaystyle c_1 f(n) \leq g(n) \leq c_2 f(n) \)

is held true for all $n\geq N_0$ , Can you find such constants ?
 
  • #3
ZaidAlyafey said:
According to definition we have to find constants such that

\(\displaystyle c_1 f(n) \leq g(n) \leq c_2 f(n) \)

is held true for all $n\geq N_0$ , Can you find such constants ?

Yes, but.. how can I explain that $n^7 \log n$ is the dominant term? (Thinking)
 
  • #4
You can try to verify it by definition, that is,
$$g(n) = \Theta(n^7 \log n) \Leftrightarrow \exists c>0, \exists n_0 \in \mathbb{N}, \forall n \geq n_0: 0 \leq g(n) \leq c n^7 \log n$$

I would do the following: try to show that all terms (with exception of the first term obviously) of $g(n)$ are smaller than $n^7 \log n$ for a certain $n_0 \in \mathbb{N}$.

First, note that $\forall n \geq 2: 18 \log 13 \leq n^7 \log n$. This follows from the fact that $\log n$ and $n^7$ are increasing functions forall $n \in \mathbb{N}_0$.

Second, we can write $16 \log n^n = 16n \log n$. We want to search for which values of $n \in \mathbb{N}_0$ we have: $16 n \log n \leq n^7 \log n$ or equivalently $16 n \leq n^7$. Since $n^7$ increases much faster than $16n$ for the natural numbers (without zero) we can conlude that $\forall n \geq 2: 16n \leq n^7$, therefore $ \forall n \geq 2: 16n \log n \leq n^7 \log n$.

For the last term we can use a similar argument to show that $\forall n \geq 2: 12n^{\frac{5}{2}} \leq n^7 \log n$.

At this point we have shown that all terms are smaller than $n^7 \log n$.
What's the conclusion now?
 
  • #5
At the beginning, before I verify that $g(n)=\Theta(n^7 \log n)$ can I just say the following?

We see that the dominant term of the function $g(n)$ is $n^7 \log n$, so: $g(n)=\Theta(n^7 \log n)$ ?

Or is there a better way to explain why we suppose that $g(n)=\Theta(n^7 \log n)$? (Thinking)
 
  • #6
By definition

$$T(n) = \Theta( G(n) ) \, \implies \,\, \lim \frac{T(n)}{G(n)} \to c \, ; \,\, 0<c<\infty $$

Suppose that $f(n) = a(n) +g(n)$ where $a(n)$ is the dominant term so

$$\lim_{n \to \infty } \frac{a(n)+g(n)}{a(n)} = 1+ 0 = 1 $$

So $T(n) = \Theta (a(n)) \blacksquare $.
 

FAQ: Justify $\Theta(n^7 \log n)$ for $g(n)$

1. What is the definition of $\Theta(n^7 \log n)$?

The notation $\Theta(n^7 \log n)$ is used to describe the time complexity of an algorithm, where the runtime is directly proportional to $n^7 \log n$. This means that as the input size increases, the runtime of the algorithm will also increase at the same rate.

2. How is the runtime of an algorithm determined to be $\Theta(n^7 \log n)$?

The runtime of an algorithm is determined by analyzing the number of operations it performs as the input size increases. In the case of $\Theta(n^7 \log n)$, the algorithm will have a runtime of $n^7 \log n$ operations.

3. What does the $\Theta$ notation represent in $\Theta(n^7 \log n)$?

The $\Theta$ notation represents the tightest bound on the runtime of an algorithm. In this case, it means that the algorithm has an upper and lower bound of $n^7 \log n$ operations.

4. Can $\Theta(n^7 \log n)$ be simplified further?

No, $\Theta(n^7 \log n)$ is already in its simplest form. The $\Theta$ notation represents both the upper and lower bound of the algorithm's runtime, so it cannot be simplified further.

5. How does the $\log n$ term affect the overall time complexity of an algorithm?

The $\log n$ term has a smaller impact on the overall time complexity compared to other terms such as $n^7$. This is because the logarithm function grows at a slower rate than polynomial functions, such as $n^7$. However, as the input size increases, the logarithm term will eventually have a significant impact on the algorithm's runtime.

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