Given,
Random generate a random number between 1 to n. So, it has to trigger a event to select a number between 1 to n with complexity on constant value.
The function is recursive in nature.
1. The first if condition is initial condition to trap value and end recursion and evaluate the value.
2. Second for block runs from 1 to n take O(n) time and increments the value of s as a sum.
3. Third for block runs const but the line no 9 feed the value into recursion chain with value n bringing near O(log n)
(a). The asymptotic worst case running time of Func5 will be O(n2).
(b). The asymptotic expected running time of Func5 will be O(n log n)
For better understanding we can take example value and go through program step wise and get the value and aggregate the value in terms of n, neglecting constant values.
For each of the following problems, simplify and express your answer as Θ(nk) or Θ(nk(log n...
For each of the following problems write a recurrence relation
describing the running time of each of the following algorithms and
determine the asymptotic complexity of the function defined by the
recurrence relation. Justify your solution using substitution and
carefully computing lower and upper bounds for the sums. Simplify
and express your answer as Θ(n k ) or Θ(n k (log n)) wherever
possible. If the algorithm takes exponential time, then just give
exponential lower bounds.
5. func5 (A,n) /*...
Write a recurrence relation describing the worst case running time of each of the following algorithms, and determine the asymptotic complexity of the function defined by the recurrence relation. Justify your solution by using substitution or a recursion tree. You may NOT use the Master Theorem. Simplify your answers, expressing them in a form such as O(nk) or (nklog n) whenever possible. If the algorithm takes exponential time, then just give an exponential lower bound using the 2 notation. function...
Insertion sort on small arrays in merge sort Although merge-sort runs in Θ(n log n) worst-case time and insertion sort runs in Θ(n 2 ) worst-case time, the constant factors in insertion sort can make it faster in practice for small problem sizes on many machines. Thus, it makes sense to coarsen the leaves of the recursion by using insertion sort within merge sort when subproblems become sufficiently small. Consider a modification to merge sort in which n/k sublists of...
1. For each function defined below, find the value of k such that s(n) = O(nk). For part (a), justify your answer from the definitions of O, O, and by finding explicit constants that work, following the examples in Proofs 7.2.1 and 7.2.3 in the zyBook. Don't just refer to Theorem 7.2.2. For part (b), you do not need to find explicit constants, just explain why your answer is correct. (a) s(n) = (2n + 1)(5n2+1) (b) s(n) = nºt(n)...
1. What is the worst case time complexity of insertion into a binary search tree with n elements? You should use the most accurate asymptotic notation for your answer. 2. A binary search tree is given in the following. Draw the resulting binary search tree (to the right of the given tree) after deleting the node with key value 8. 10 3. You have a sorted array B with n elements, where n is very large. Array C is obtained...
3. Determine the asymptotic complexity of the function defined by the recurrence relation. Justify your solution using expansion/substitution and upper and/or lower bounds, when necessary. You may not use the Master Theorem as justification of your answer. Simplify and express your answer as O(n*) or O(nk log2 n) whenever possible. If the algorithm is exponential just give exponential lower bounds c) T(n) T(n-4) cn, T(0) c' d) T(n) 3T(n/3) c, T() c' e) T(n) T(n-1)T(n-4)clog2n, T(0) c'
3. Determine the...
Suppose the following is a divide-and-conquer algorithm for some problem. "Make the input of size n into 3 subproblems of sizes n/2 , n/4 , n/8 , respectively with O(n) time; Recursively call on these subproblems; and then combine the results in O(n) time. The recursive call returns when the problems become of size 1 and the time in this case is constant." (a) Let T(n) denote the worst-case running time of this approach on the problem of size n....
Give the asymptotic bounds for T(n) in each of the following recurrences. Make your bounds as tight as possible and justify your answers. Assume the base cases T(0)=1 and/or T(1) = 1. 1. T(n) = T(n-1) + 2n 2. T(n) = T(n-2) = 3
Write a recurrence relation describing the worst-case
running time of each of the following algorithms and determine the
asymptotic complexity of the function defined by the recurrence
relation. Justify your solution by using substitution or a
recursion tree. You may NOT use the Master Theorem.
上午1:46 3月21日周四 令52%. " 5. endfor 6. return (r); function func4(A, n) *Aarray of n integers */ 1. if n s 20 then return (A[n]); 4. while (i < n/2) do 7. endwhile 8. x...
Question 1. (1 marks) The following procedure has an input array A[1..n] with n > 2 arbitrary integers. In the pseudo-code, "return” means immediately erit the procedure and then halt. Note that the indices of array A starts at 1. NOTHING(A) 1 n = A. size 2 for i = 1 ton // i=1,2,..., n (including n) 3 for j = 1 ton // j = 1,2,...,n (including n) 4. if A[n - j +1] + j then return 5...