#MERGEX SORT TIME COMPLEXITY SOFTWARE#

Worst Case Complexity - It occurs when the array elements are required to be sorted in reverse order.

The average case time complexity of merge sort is O(n*logn).

Average Case Complexity - It occurs when the array elements are in jumbled order that is not properly ascending and not properly descending.

The best-case time complexity of merge sort is O(n*logn).

Best Case Complexity - It occurs when there is no sorting required, i.e.

We will also see the space complexity of the merge sort. Now, let's see the time complexity of merge sort in best case, average case, and in worst case. After the final merging of above arrays, the array will look like. Now, there is a final merging of the arrays. In the next iteration of combining, now compare the arrays with two data values and merge them into an array of found values in sorted order. After that, compare 40 and 42, and place them sequentially.

Then compare 32 and 17, sort them and put 17 first followed by 32. Then compare 25 and 8, and in the list of two values, put 8 first followed by 25. So, first compare 12 and 31, both are in sorted positions. In combining, first compare the element of each array and then combine them into another array in sorted order. Now, combine them in the same manner they were broken. Now, again divide these arrays to get the atomic value that cannot be further divided. As they are of size 4, so divide them into new arrays of size 2. Now, again divide these two arrays into halves. Merge sort keeps dividing the list into equal parts until it cannot be further divided.ДЄs there are eight elements in the given array, so it is divided into two arrays of size 4. It will be easier to understand the merge sort via an example.ДЄccording to the merge sort, first divide the given array into two equal halves. To understand the working of the merge sort algorithm, let's take an unsorted array. Now, let's see the working of merge sort Algorithm. K = beg /* initial index of merged sub-array */ J = 0 /* initial index of second sub-array */ I = 0, /* initial index of first sub-array */ Int LeftArray, RightArray //temporary arrays Void merge(int a, int beg, int mid, int end) * Function to merge the subarrays of a */ In the following algorithm, arr is the given array, beg is the starting element, and end is the last element of the array.

Now, let's see the algorithm of merge sort. The sorted two-element pairs is merged into the four-element lists, and so on until we get the sorted list. Then we combine the pair of one element lists into two-element lists, sorting them in the process. The sub-lists are divided again and again into halves until the list cannot be divided further. We have to define the merge() function to perform the merging. It divides the given list into two equal halves, calls itself for the two halves and then merges the two sorted halves. It is one of the most popular and efficient sorting algorithm. Merge sort is similar to the quick sort algorithm as it uses the divide and conquer approach to sort the elements. So, it is important to discuss the topic.

#MERGEX SORT TIME COMPLEXITY SOFTWARE#

In coding or technical interviews for software engineers, sorting algorithms are widely asked. This article will be very helpful and interesting to students as they might face merge sort as a question in their examinations. Merge sort is the sorting technique that follows the divide and conquer approach. In this article, we will discuss the merge sort Algorithm.

Mergex sort time complexity