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C++ Determine the running times predicted by the detailed model of the computer

ID: 3599239 • Letter: C

Question

C++

Determine the running times predicted by the detailed model of the computer for the following program fragments:

Int   Sum (int x, int n)

{

            int sum = 0;

            for ( int i = 0; i <= n; ++i)

                  sum = sum *x +1;

             return sum;

}

Repeating question 2, using the simplified model of the computer.

Solve the recurrence relation T(n) = 2T(n/2) +1 given that T(1) = 1.

Using the definition of Big O, show that T(n) = 3n2+5n +2 is O(n2)

What is copy constructor? How to use it?

Which grows faster: n*log(n) or n2.

Choose a member function in the Unsorted and Sorted List (Array based and LinkedList based) that we discussed in class. Use diagram or pseudocode to explain how it works and how to implement.

Be able to write recursive function for a given problem.  

Explanation / Answer

Determine the running times predicted by the detailed model of the computer for the following program fragments:

Int   Sum (int x, int n)

{

            int sum = 0; ==> This line runs for 1 time

            for ( int i = 0; i <= n; ++i) ==> This line runs for n+ 1 time, one extrs time because when i = n+1 for loop condition fails

                  sum = sum *x +1;==> This line runs for n time

             return sum;===> This line runs for 1 time

}

T(n) = n +1 + n + 1 + 1
=> O(n)
Overall time complexity is O(n)

Repeating question 2, using the simplified model of the computer.

Solve the recurrence relation T(n) = 2T(n/2) +1 given that T(1) = 1.
We can solve it using Masters method , T(n) is of the form T(n) = aT(n/b) + f(n)
a = 2, b = 2 and  f(n) = 1 , So
g(n) = nlogb a => nlog 22 => => n1 , we can see that g(n)>= f(n) By case 1 of masters theorem we get
T(n) = O(n)

Using the definition of Big O, show that T(n) = 3n2+5n +2 is O(n2)
Answer: f(n) = 3n2+5n +2
We know that if f(n) = O(g(n)) then c*g(n) >=f(n) if there exists c and n0 such that n > n0
Let g(n) = 4n2 , we can clearly see that  4n2 >= 3n2+5n +2  , This we can write
f(n)= O(n2)

Which grows faster: n*log(n) or n2.
Answer: n2 grows faster

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