2.)
In above question we have two loops
Time complexity of a loop is given as either having incrementing values or decrementing values is O(n) where n is no of times the loop executes.
In above question we have two loops that is nested loop a loop within a loop
so for every iteration inner loop completes all its iterations
as outer loop is iterating 1 time inner loop iterates n times so when outer loop iterates n times inner loop also iterates making it n*n=n2
Hence the complexity of above one is O(n2)
2. Measure the complexity of the following algorithm: SHOW your work. (15 points) a=1 b 3:...
Using C++ please explain What is the Big-O time complexity of the following code: for (int i=0; i<N; i+=2) { ... constant time operations... Select one: o a. O(n^2) O b. O(log n) c. O(n) O d. 0(1) What is the Big-O time complexity of the following code: for(int i=1; i<N; i*=2) { ... constant time operations... Select one: O O a. O(n^2) b. 0(1) c. O(n) d. O(log n) O What is the Big-O time complexity of the following...
Please show work and solve in Asymptotic complexity using big O notation. (8 pts) Assume n is a power of 2. Determine the time complexity function of the loop for (i=1; i<=n; i=2* i) for (j=1; j<=i; j++) {
3. Analyze the time complexity of the following program segm i = 1; s = 0; while (i<n) { S += i; i *= 2:
II. ALGORITHM COMPLEXITY AND ASYMPTOTIC ANALYSIS The below visual representations of iterative looping structures are provided for Question 3 through Question 20. Algorithm 1 Algorithm 2 log.n 256 Algorithm 3 Algorithm 4 n (10) Match one of our algorithms to the below code snippet. for (int i = 0; i <n; i++) { for(int j = 0; j<n; j++) { for (int k = 0; k<n; k++) { nop++; nop++; nop++; } } } for (int i = 0; i...
1(5 pts): For each code fragment below, give the complexity of the algorithm (O or Θ). Give the tightest possible upper bound as the input size variable increases. The input size variable in these questions is exclusively n. Complexity Code public static int recursiveFunction (int n)f f( n <= 0 ) return 0; return recursiveFunction (n - 1) 1; for(int i 0i <n; i+) j=0; for ( int j k=0; i; k < < j++) for (int j; m <...
FOR ALGORITHM A WORST CASE TIME COMPLEXITY IS DESCRIBED BY RECURRENCE FORMULA T(n)= n/ T (n )thi T (c)=1 if c < 100 FOR ALGORITHM B WORST TIME COMPLEXITY IS DESCRIBED BY RECURRENCE FORMULA T(n) = 2T (2/2) + n/logn ; (c) = 1 fc 2100 WHICH ALGORITHM IS ASYMPTOTICALLY FASTER? WHY?
= Question 20 of 45 (2 points) | Question Attempt: 1 of 1 < 15 16 17 18 19 20 Solve for F. D-(+6) F = 0 . X ?
Using the pseudocode answer these questions Algorithm 1 CS317FinalAlgorithm (A[O..n-1]) ito while i<n - 2 do if A[i]A[i+1] > A[i+2) then return i it i+1 return -1 4. Calculate how many times the comparison A[i]A[i+1] > A[i+2] is done for a worst-case input of size n. Show your work. 5. Calculate how many times the comparison A[i]A[i+1] > A[i+2] is done for a best-case input of size n. Show your work.
Solve ques no. 2 a, b, c, d . Algorithm 1 Sort a list al,..., an for i=1 to n-1 do for j=1 to n-i do if aj > aj+1 then interchange a; and a;+1 end if end for end for (b) Algorithm 1 describes a sorting algorithm called bubble sort for a list al,...,an of at least two numbers. Prove that the algorithm is complete, correct and terminates. (2) Complexity of Algorithms (Learning Target C2) (a) What is the...
Describe the worst case running time of the following pseudocode functions in Big-Oh notation in terms of the variable n. Show your work b) void func(int n) { for (int i = 0; i < n; i = i + 10) { for (int j = 0; j < i; ++i) { System.out.println("i = " + i); System.out.println("j = " + j);