Asymptotic Notation

Which of the following case does not exist in complexity theory?

• Best case

• •Worst case

• •Average case

• Null case

int fun(int n)

•{

•int count = 0;

•for (int i = 0; i < n; i++)

•for (int j = i; j > 0; j--)

count = count + 1;

•return count;

•}

• Theta (n)

• Theta (n^2)

• •Theta (n*Logn)

• •Theta (nLognLogn)

What does it mean when we say that an algorithm X is asymptotically more efficient than Y?

• X will always be a better choice for small inputs

• •X will always be a better choice for large inputs

• •Y will always be a better choice for small inputs

• •X will always be a better choice for all inputs

•To measure Time complexity of an algorithm Big O notation is used which:

• describes limiting behaviour of the function

• •characterises a function based on growth of function

• •upper bound on growth rate of the function

• •all of the mentioned

•If for an algorithm time complexity is given by O(1) then complexity of it is:

• •constant

• •polynomial

• •exponential

• •none of the mentioned

•If for an algorithm time complexity is given by O(n) then complexity of it is:

• •constant

• •linear

• •exponential

• •none of the mentioned

space complexity of algorithm means

• space it requires

• time it requires

• hardware it requires

• all of the above

Big

 $\Omega$ defines

• lower bound

• upper bound

• middle bound

• none of the above

This picture represents

• big theta noataion

• big oh notation

• big omega notation

• none of the above

big O represents

• best case scenario

• worst case scenario

• average case scenario

• none of the above