Binary search algorithm has running time in terms of n O(n) O(Ig(n)) O(n*Ig(n)) O(n^2) O(n^2*Ig(n)) O(n^3) other
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- Sequential search algorithm has running time in terms of n O(n) O(nk) O(n*lg(n)) O(n^2) O(n^2*lg(n)) O(n^3) other Binary search algorithm has running time in terms of n O(n) O(Ig(n)) O(n*lg(n)) O(n^2) O(n^2*lg(n)) O(n^3) otherMergeSort algorithm has running time in terms of n O(n) O(lg(n)) O(n*lg(n)) O(n^2) O(n^2*lg(n)) O(n^3) otherThe big-O time efficiency for performing Binary search for an item in a sorted list is: O(1) O(log(n)) O(n) O(nlog(n))
- Consider the following algorithm that uses a sorted list of n elements (alist). What is the worst case runtime of this algorithm? for each element in alist 1. ask the user for an input, call it value 2. search value in alist using binary search 3. if value exists in alist, print "successful" otherwise print "unsuccessful" Question options: a. O(log n) b. O(n) c. O(n log n) d. O(2^n) e. O(n^2) f. O(1)Show that n!=O(n^n)The number of operations executed by algorithms A is 5n^2 and by algorithm B is 30n^3. Determine n0 such that B is better than A for all n > n0.
- Consider the following algorithm that uses a sorted list of n elements (alist). What is the worst case runtime of this algorithm? for each element in alist 1. ask the user for an input, call it value 2. search value in alist using the binary search algorithm 3. if value exists in alist, print "successful" otherwise print "unsuccessful" Question 10 options: O(2^n) O(n log n) O(log n) O(n^2) O(n) O(1)If lim n->oo (f(n))/(g(n)) = 0 then f(n) = ω( g(n) ) true falseThe time to merge two sorted lists of size n is_ ㅇ(1) O(n) ㅇ(nlogn) o(logn) o(n*n)