When Does n lg n Switch from Ω to O?

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In summary, the conversation discusses the asymptotic bounds for n lgn and its relation to the value of a in O(na) and Ω(na). It is mentioned that n lgn = O(n1.261) and n lgn = Ω(n0.797). The question is posed as to where the value of a changes from Ω to O and how it is affected by considering different bases for the logarithm. The conversation concludes with a suggestion to review the mathematical definition of f(x)=O(g(x)) and to use the relation between natural logarithms and other bases to determine asymptotic bounds for logarithms with different bases.
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When considering the asymptotic bounds for n lgn, for what value of a in na does n lgn satisfy O(na) and for what value does it satisfy Ω(na)?

For example n lgn = O(n1.261) but n lgn = Ω(n0.797). Can someone please tell me where for what a does Ω change to O? Also how does the answer change when you consider general logb instead of lg. Thanks!
 
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A good bound that has been found for the logarithm (in a form that might be useful to you) is the following,

##\ln x \leq x^{\frac{1}{e}}##

That of course implies,

##|x \ln x| \leq |x^{\frac{e+1}{e}}|##

Now review the mathematical definition of ##f(x)=O(g(x))##. You'll get the answer for your first question.

If you switch to ##\log_{b} n## you can still express it in terms of the natural logarithm and that will help you determine an asymptotic bound for logarithms with different bases.

Hope this helped.
 
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FAQ: When Does n lg n Switch from Ω to O?

1. What is the meaning of "asymptotic bound" in relation to n lg(n)?

The asymptotic bound of n lg(n) refers to the limiting behavior of the function as n approaches infinity. It is a measure of how quickly the function grows or decreases as n gets larger.

2. How is the asymptotic bound of n lg(n) calculated?

The asymptotic bound of n lg(n) is typically calculated using Big O notation. This notation represents the worst-case scenario for the growth of a function, and in this case, it indicates that the function grows no faster than a linear multiple of n lg(n) as n increases.

3. What are some real-world applications of understanding the asymptotic bound of n lg(n)?

The asymptotic bound of n lg(n) is commonly used in algorithm analysis to determine the time complexity of sorting algorithms. It can also be useful in computer science and engineering for optimizing code and improving performance.

4. How does the asymptotic bound of n lg(n) compare to other asymptotic bounds?

The asymptotic bound of n lg(n) is considered to be less than or equal to the asymptotic bound of n², but greater than the asymptotic bound of n. This means that n lg(n) grows faster than n, but not as fast as n², as n increases.

5. Are there any limitations to using the asymptotic bound of n lg(n)?

While the asymptotic bound of n lg(n) can provide valuable insights into the growth of a function, it is important to note that it does not take into account factors such as hardware limitations or specific input values. Therefore, it should be used as a general measure of growth rather than a precise prediction of performance.

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