Question

find time complexity in terms of n? use big-O i,j,k,x =0; for i=0; i<=n; i++; for...

find time complexity in terms of n? use big-O

i,j,k,x =0;

for i=0; i<=n; i++;

for j=0, j<=5; j++;

for k=5; k<=n; k=k*5;

x = x*n;

Homework Answers

Answer #1

Please find below explanation:

i,j,k,x =0;

for i=0; i<=n; i++; # time complexity for this line is O(n)

for j=0, j<=5; j++; # time complexity for this line is O(5)

for k=5; k<=n; k=k*5; #time complexity for this line is O(5^k)

x = x*n; # time complexity for this line is O(logn)

for k=5; k<=n; k=k*5; #time complexity for this line is 5 power k which is exponential

because every time the loops execute the number will be multiplied by 5 till n times

5*5

5*5*5

5*5*5*5 this will go 5 power k times

5power k>=n

Overall time complexity is (n)*(5)*(5^k)+logn

remove constant 5 above and we get O(n(5^k)+logn) is the time complexity for the above code.

Know the answer?
Your Answer:

Post as a guest

Your Name:

What's your source?

Earn Coins

Coins can be redeemed for fabulous gifts.

Not the answer you're looking for?
Ask your own homework help question
Similar Questions
What is the time complexity of the following code? (in big-O notaion) sum = 0 count...
What is the time complexity of the following code? (in big-O notaion) sum = 0 count = 0 var1 = 0 for: i=1 to n{ sum = sum+i for j=1 to [(3i+2i)(5n)(2i)]{ var1 = sum+count} } for m = i to j*j{ sum = sum*sum var2 = sum - (count+3)}
Analyze the following code and provide a "Big-O" estimate of its running time in terms of...
Analyze the following code and provide a "Big-O" estimate of its running time in terms of n. Explain your analysis. for ( int i = n; i > 0; i /= 2 ) { for ( int j = 1; j < n; j += 2 ) { for ( int k = 0; k < n; k += 2 ) { ... // constant number of operations } } }
How to measure the time complexity of an algorithm? Identify an important operation in the algorithm...
How to measure the time complexity of an algorithm? Identify an important operation in the algorithm that is executed most frequently. Express the number of times it is executed as a function of N. Convert this expression into the Big-O notation. A. For each of the three fragments of code, what is its worst-case time complexity, in the form "O(…)". (Use the given solution to the first problem as a model)                 //----------------- This is a sample problem – solved ------...
Given the recursive bubble sort algorithm, analyze its time complexity in terms of Big O. bubbleSort(A[],...
Given the recursive bubble sort algorithm, analyze its time complexity in terms of Big O. bubbleSort(A[], n) { // Base case if (n == 1) return;    // One pass of bubble sort. After // this pass, the largest element // is moved (or bubbled) to end. for (i=0; i<n-1; i++) if (A[i] > A[i+1]) swap(A[i], A[i+1]);    // Largest element is fixed, // recur for remaining array bubbleSort(A, n-1); }
Use Big-O Notation to characterize the computational cost of algorithm A1 and A2. The complexity of...
Use Big-O Notation to characterize the computational cost of algorithm A1 and A2. The complexity of A1 is 10000 * n, with n being the number elements processed in A1. The complexity of A2 is 100 * n, with n elements in A2. State in Big-O notation the cost of f1, with f1(x) = log2(2x) – 14x + 3 sqrt(x) + 12x^2 - 150 this is also a theoretical question just answer no code.
Analyze the worst case running time of the following code in “big-Oh” notation in terms of...
Analyze the worst case running time of the following code in “big-Oh” notation in terms of the variable n. a. void fdisp1(int n) { for(int i=0; i < n; i++) { for(int j=0; j < n; j++) { for(int k=0; k < n; k++) { for(int m=0; m < n; m++) { System.out.println("Hi I am Here"); } } } } } b. voidprintAllPossibleOrderedPairs(intarr[], int size) { for (int i = 0; i < size; i++) { for (int j =...
Analyze the following code and provide a "Big-O" estimate of its running time in terms of...
Analyze the following code and provide a "Big-O" estimate of its running time in terms of n. Explain your analysis. for (int i = n; i >= n; i = i/2)           some O(1) time statements; end for
1.) the following code fragments give running time analysis (Big Oh). Explain your answer: sum2 =...
1.) the following code fragments give running time analysis (Big Oh). Explain your answer: sum2 = 0; sum5 = 0; for(i=1; i<=n/2; i++) { sum2 = sum + 2; } for(j=1; j<=n*n; j++) { sum5 = sum + 5; } i think it is O(n^2) since big oh finds the order of worst case which is the the second loop being n^2 complexity. but im not sure. 2.) Give an efficient algorithm along with running time analysis to find the...
What is the time-complexity of the following code? Explain, please. for(i = 1; i < 3n;...
What is the time-complexity of the following code? Explain, please. for(i = 1; i < 3n; i++){ for(j=1; j < 2n; j++){ for(k=1; k < n; k*=2){ m += j; } } }
CountingSort(A, B, k) for i=1 to k C[i]= 0; for j=1 to n C[A[j]] += 1;...
CountingSort(A, B, k) for i=1 to k C[i]= 0; for j=1 to n C[A[j]] += 1; for i=2 to k C[i] = C[i] + C[i-1]; for j=n downto 1 B[C[A[j]]] = A[j]; C[A[j]] -= 1; illustrate the operation of COUNTING-SORT on the array A = {6,0,2,0, 1, 3, 5, 6, 1, 3, 2}. Specifically, show the four arrays A, B, C, and C'.
ADVERTISEMENT
Need Online Homework Help?

Get Answers For Free
Most questions answered within 1 hours.

Ask a Question
ADVERTISEMENT