![mergex sort time complexity mergex sort time complexity](https://www.happycoders.eu/wp-content/uploads/2020/07/mergesort_algorithm_divide-v2-800x447.png)
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#MERGEX SORT TIME COMPLEXITY CODE#
Here is the boilerplate code for Merge Sort: const mergeSort = nums =>
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This method takes an input of n-length and breaks the set into smaller subsets and does work on these subsets before applying them back to the full set - hence, “divide and conquer.” Without loss of generality, let’s assume that the first element of A is smaller - we remove this element from A and then append it to C.Merge Sort, known as a “divide and conquer” algorithm, is widely known as the most efficient sorting algorithm.At every step, we compare the first element of A and B and select the smaller one.Create an empty list C to hold the elements of the final list.Let’s call the two sorted lists A and B.The idea is pretty simple and straightforward: Let’s see how we can merge two sorted lists of arbitrary size. We stop when we are left with a single list. We first merge the sorted lists of size 1 to produce sorted lists of size 2, then merge the sorted lists of size 2 to create sorted lists of size 4, and so on. Now, we need to sort the lists in the second row from the bottom, and for that, we will merge the already sorted 1-element lists. In the figure above, we can see that all the lists in the bottom-most row are of size 1 - these can be considered “sorted,” as they contain just one element. Merging of the lists happens from the bottom to the top.
![mergex sort time complexity mergex sort time complexity](https://i.stack.imgur.com/ZFGpn.png)
Step 2: Merging the Sublists to Produce the Sorted ListĪt this step, we already have our sublists sorted. This process continues until the size of the list reduces to 1. The recursive call made to sort the sublists divides the sublists into even smaller sublists. We divide the list into smaller sublists and make a recursive call to sort the sublists. Merge Sort Algorithm Step 1: Dividing the List Into Two Smaller Sublists It’s easier to divide the boxes into smaller chunks, sort those chunks, and then bring the sorted chunks together to get all the boxes in the sorted order. When the number is large, it becomes difficult to sort everything in one go. We may have used a similar technique in real life while sorting a huge number of boxes (or any object). We then sort the sublists and merge them to produce the sorted list. If the list length is odd, the size of the sublists differs by 1. The size of the two sublists is equal when the list is of even length.
![mergex sort time complexity mergex sort time complexity](https://i.stack.imgur.com/9RBT4.jpg)
In the case of merge sort, at each step, we divide the list into two smaller sublists until the size of each list reduces to 1. The problem is divided into multiple subproblems, solved individually, and finally, the result of the subproblems are combined to form the final solution. Merge sort uses the concept of divide and conquer. Therefore, it’s not preferred for sorting an almost sorted array. Merge sort has a consistent speed for a given size of data. Most implementations of this algorithm produce a stable sort, i.e., i.e., the order of any two equal elements in the sorted list and the original list stays the same. Merge sort is a general-purpose comparison-based sorting algorithm - which means that the algorithm compares the elements of the list to sort it. In this article, we will help get one step closer to cracking that interview.
![mergex sort time complexity mergex sort time complexity](https://venturebeat.com/wp-content/uploads/2018/05/screen-shot-2018-05-02-at-2-03-28-pm.jpg)
Sorting algorithm questions are a big part of coding interviews at tech companies.
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If you are a software developer preparing for your next tech interview, then this one algorithm you must review. Merge sort is one of the most efficient and popular sorting algorithms.