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Show directly that \(f(n)=n^{2}+3 n^{3} \in \Theta\left(n^{3}\right) .\) That is, use the definitions of \(O\) and \(\Omega\) to show that \(f(n)\) is in both \(O\left(n^{3}\right)\) and \(\Omega\left(n^{3}\right)\)

Short Answer

Expert verified
Function \(f(n) = n^{2}+3 n^{3}\) is \(O(n^{3})\) and \(\Omega(n^{3})\), and therefore it is also \(\Theta(n^{3})\).

Step by step solution

01

Proving \(f(n)\) is \(O(n^{3})\)

First, we have to prove \(f(n)\) is \(O(n^{3})\). The definition of Big-O notation says: \(f(n) = O(g(n))\) means there are positive constants \(c\) and \(n_{0}\) such that \(0 \leq f(n) \leq c\cdot g(n)\), for all \(n > n_{0}\). We can see that for \(n^{3}\), the function \(f(n)=n^{2}+3 n^{3}\) will always be less than or equal to \(4n^{3}\), for all \(n > 1\). So here, \(c = 4\) and \(n_{0} = 1\). Hence \(f(n) = O(n^{3})\).
02

Proving \(f(n)\) is \(\Omega(n^{3})\)

Next, we have to prove that \(f(n)\) is \(\Omega(n^{3})\). The definition of Big-Omega notation says: \(f(n) = \Omega(g(n))\) means there are positive constants \(c\) and \(n_{0}\) such that \(0 \leq c\cdot g(n) \leq f(n)\), for all \(n > n_{0}\). We can see that for \(n^{3}\), the function \(f(n)=n^{2}+3 n^{3}\) will always be greater than or equal to \(n^{3}\), for all \(n > 1\). So here, \(c = 1\) and \(n_{0} = 1\). Hence \(f(n) = \Omega(n^{3})\).
03

Conclusion that \(f(n)\) is \(\Theta(n^{3})\)

Since \(f(n)\) is both \(O(n^{3})\) and \(\Omega(n^{3})\), we can conclude that \(f(n) = \Theta(n^{3})\). The function is bounded both above and below by \(n^{3}\), so it is \(\Theta(n^{3})\).

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Most popular questions from this chapter

Discuss the reflexive, symmetric, and transitive properties for asymptotic comparisons \((O, \Omega, \theta, o)\)

Show that the function \(f(n)=\left|n^{2} \sin n\right|\) is in neither \(O(n)\) nor \(\Omega(n)\)

What is the time complexity \(T(n)\) of the nested loops below? For simplicity, you may assume that \(n\) is a power of \(2 .\) That is, \(n=2^{k}\) for some positive integer \(k\) \(:\) \\[ \begin{array}{} \text { for }(i=1 ; i<=n, i++)\\} \\ \ \begin{array}{} j=n \\ \text { while }(j>=1)\\{ \end{array} \end{array} \\] < body of the while loop> \(\quad\) I/ Needs \(\Theta(1)\) \\[ j=\lfloor j / 2\rfloor \\] } }

Algorithm 1.7 (nth Fibonacci Term, Iterative) is clearly linear in \(n,\) but is it a linear-time algorithm? In Section 1.3 .1 we defined the input size as the size of the input. In the case of the \(nth\) Fibonacci term, \(n\) is the input, and the number of bits it takes to encode \(n\) could be used as the input size. Using this measure the size of 64 is \(\lg 64=6,\) and the size of 1024 is \(\lg 1024=\) 10\. Show that Algorithm 1.7 is exponential-time in terms of its input size. Show further that any algorithm for computing the \(nth\) Fibonacci term must be an exponential-time algorithm because the size of the output is exponential in the input size. See Section 9.2 for a related discussion of the input size.

Write an algorithm that prints out all the subsets of three elements of a set of \(n\) elements. The elements of this set are stored in a list that is the input to the algorithm.

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