Consider to prove the result, first prove f(x) = O(g(x)) and then prove g(x) = O(f(x)).
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Understand the Problem
The question is asking to prove the asymptotic relationships between the functions f(x) = log(x^2 + 1) and g(x) = log(x) using big-O notation. It outlines steps to show that f(x) is O(g(x)) and g(x) is O(f(x)), which means they grow at the same rate asymptotically.
Answer
The functions \( f(x) \) and \( g(x) \) grow at the same rate asymptotically, shown by \( f(x) = O(g(x)) \) and \( g(x) = O(f(x)) \).
Answer for screen readers
We have proven that ( f(x) = O(g(x)) ) and ( g(x) = O(f(x)) ), which leads to the conclusion that ( f(x) \sim g(x) ).
Steps to Solve
- Proving $f(x) = O(g(x))$
We need to show that there exist constants $C$ and $k$ such that for all $x > k$, the inequality $|f(x)| \leq C |g(x)|$ holds.
Starting with $f(x) = \log(x^2 + 1)$ and $g(x) = \log(x)$:
Observe that for $x > k$, we can bound $f(x)$ as follows:
$$ x^2 + 1 \leq 2x^2 \quad \text{for} , x > 1 $$
Taking the logarithm, we get:
$$ \log(x^2 + 1) \leq \log(2x^2) = \log(2) + 2\log(x) $$
Thus, we can set $C = 2 + \log(2)$ and $k = 1$.
- Establishing the first inequality
From the previous step, we have:
$$ f(x) \leq C g(x) \quad \text{for} ; x > k $$
So we can conclude that $f(x) = O(g(x))$.
- Proving $g(x) = O(f(x))$
Now we need to show that there exist constants $C'$ and $k'$ such that for all $x > k'$, the inequality $|g(x)| \leq C' |f(x)|$ holds.
Since $g(x) = \log(x)$:
For $x > k'$, we can find a bound for $g(x)$:
$$ \log(x) \leq \log(x^2 + 1) \quad \text{for} ; x > 1 $$
This can be shown as:
$$ x^2 + 1 \geq x^2 \implies \log(x^2 + 1) \geq \log(x^2) $$
So we have:
$$ g(x) \leq f(x) \quad \text{for} ; x > k' $$
- Finishing up the proof
Combining the results from both parts, since $f(x) = O(g(x))$ and $g(x) = O(f(x))$, we conclude that:
$$ f(x) \sim g(x) \quad \text{as} ; x \to \infty $$
Thus, we have established that $f(x)$ and $g(x)$ grow at the same rate asymptotically.
We have proven that ( f(x) = O(g(x)) ) and ( g(x) = O(f(x)) ), which leads to the conclusion that ( f(x) \sim g(x) ).
More Information
This type of asymptotic analysis is useful in computer science, especially in analyzing the efficiency of algorithms. The logarithmic functions grow slower compared to polynomial or exponential functions, highlighting their efficiency.
Tips
- Forgetting to establish both big-O relationships: It is essential to prove both ( f(x) = O(g(x)) ) and ( g(x) = O(f(x)) ) for a complete asymptotic analysis.
- Misapplying logarithmic properties: Ensure that properties of logarithms are used correctly, especially when manipulating inequalities.
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