Answered: MergeSort algorithm has running time in… | bartleby

Related questions

Question

100%

MergeSort 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)
other

Expert Solution

Step by step

Solved in 4 steps

SEE SOLUTION Check out a sample Q&A here

Blurred answer

Similar questions

Consider the following version of insertion sort: Algorithm InsertSort2 (A[0...n − 1]) for i ← 1 to n − 1 do j ← i − 1 while j ≥ 0 and A[j] > A[j + 1] do swap(A[j], A[j + 1]) j ← j − 1 What is its time efficiency? How does it compare to the version given in the text? Apply insertion sort to sort the list E, X, A, M, P, L, E in alphabetical order. Consider the following algorithm to check connectivity of a graph defined by its adjacency matrix. Algorithm Connected(A[0...n - 1, 0...n - 1])// Input: Adjacency matrix A[0...n - 1, 0...n - 1] of an undirected graph G// Output: 1 (true) if G is connected and 0 (false) if it is notif n = 1 return 1 // one-vertex graph is connected by definitionelseif not Connected(A[0...n - 2, 0...n - 2]) return 0else for j ← 0 to n - 2 doif A[n - 1, j] return 1return 0 Does this algorithm work correctly for every undirected graph with n > 0 vertices? If you answer "yes," indicate the algorithm’s…
In the worst case, the number of comparisons needed to search a singly linked list of length n for a given element is?a) log2 nb) n⁄2c) log2 n – 1d) n
Given two sorted arrays a[] and b[], of lengths n1 and n2 and an integer 0≤k<n1+n2, design an algorithm to find a key of rank k. The order of growth of the worst case running time of your algorithm should be log n, where n =n1+n2 using java.
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)
In this problem, consider a non-standard sorting algorithm called the Slow Sort. Given anarray A[1 : n] of n integers, the algorithm is as follows: Slow-Sort(A[1 : n]):1. If n < 100, run merge sort (or selection sort or insertion sort) on A.2. Otherwise, run Slow-Sort(A[1 : n −1]), Slow-Sort(A[2 : n]), and Slow-Sort(A[1 : n −1]) again. Question: Prove the correctness of Slow-Sort.
Determine the big-O notation of the following algorithm: // A is an array with index from 0 to N-1Algorithm C(A, x, a, b); if (b<a) return a m = (b + a)/2 if (A[m] x) return C(A, x, a, m-1) else return C(A, x, m+ 1,b) Please answer max in 15-20 minutes thank u
1- Find the Big -O of the merge sort algorithm using the Iteration Method. T(n) = 2T(n/2)+an +d n>1 cn=1 2- Use the master theorem to prove your answer in part 1
Given A[1...n] which is an increasingly sorted array of distinct positive integers, and t which is an integer; provide an O(n) algorithm deciding whether A contains two distinct elements x and y such that x + y = t.
The worst-time complexity for merge sort is O(1) O O(log2N) O O(N) O(NlogN) O O(N*N)
The amortized time efficiency for performing deletion of a minimum element is?a) O(N)b) O(log N)c) O(N2)d) O(M log N)
In this problem, consider a non-standard sorting algorithm called the Slow Sort. Given anarray A[1 : n] of n integers, the algorithm is as follows: Slow-Sort(A[1 : n]):1. If n < 100, run merge sort (or selection sort or insertion sort) on A.2. Otherwise, run Slow-Sort(A[1 : n −1]), Slow-Sort(A[2 : n]), and Slow-Sort(A[1 : n −1]) again. Question: Write a recurrence for Slow-Sort and use the recursion tree method to solve this recurrenceand find the tightest asymptotic upper bound on the runtime of Slow-Sort.
You are given two sorted arrays A = [1, 2, 3] B = [2, 5, 6, 7], o/p = [1, 2, 2, 3, 5, 6, 7] Write an algorithm to merge these sorted arrays. Mention the time and space complexity of your algorithm