![]() Next we merge the sub-array recursively in a sorted order, so that we finally get a sorted array. We continue to break the subarrays until we reach to a level where each sub array contains 1 element. Next step is to break the given array into two subarrays from the middle element, i.e. Formula yo do so is (p+r)/2 and mark the middle element as m. Next, we find the mid of the array to break it in two halves. We take two variables p & r where p stores the staring index & stores the last index of the array ![]() To sort an array using Merge sort, following is the process Recursively, merge sub-arrays to produce sorted sub-arrays until all the sub-array merges and only one array remains. Divide the unsorted array recursively until 1 element in each sub-array remains.Ģ. Recursively, merge the two halves in a sorted manner, so that only one sorted array is left:ġ. Find the middle index of the array to divide it in two halves:Ĥ. MergeSort(arr, l, r), where l is the index of the first element & r is the index of the last element.ġ. Moving on with this article on Merge Sort in C Merge Sort Algorithm Merge sort is one of the efficient & fastest sorting algorithms with the following time complexity: merge() function merges two sorted sub-arrays into one, wherein it assumes that array and arr are sorted. In Merge sort, we divide the array recursively in two halves, until each sub-array contains a single element, and then we merge the sub-array in a way that it results into a sorted array. Combine: Combining the solutions to get the final result Conquer: Recursively solving the subproblemsģ. Divide: Breaking the problem into subproblemsĢ. One thing to keep in mind while dividing the problems into sub-problems is that, the structure of sub-problems should not change as of the original problem.ġ. In Divide & Conquer algorithm design paradigm, we divide the problems in sub-problems recursively then solve the sub-problems, & at last combine the solutions to find the final result. Following pointers will be covered in this article,īefore we discuss about Merge sort algorithm, let us understand Divide & Conquer technique. This article will help you understand Merge Sort In C in depth. Src/merge-sort.Merge Sort is one of the best examples of Divide & Conquer algorithm. We'll use the same Jest testing environment we've been using throughout the computer science curriculum. Let's start by writing a function that will merge two arrays. We don't need to solve the entire problem at once. Writing A Function to Merge Two ArraysĪs always, we'll start small with our TDD approach. The end result is a fully sorted array - and while the process may seem complicated, it's more efficient than the sorting algorithms we've learned so far - at least for large collections. You can probably guess what happens next: the arrays are merged again - but they are sorted as they're merged. įirst, a merge sort algorithm splits the larger array into smaller and smaller arrays - until each array is only one element.Īs we can see from the illustration, this is the divide part of a merge sort's divide and conquer approach. We'll use the array we've been working with so far with other sort algorithms as an example. We aren't asking you to figure it yourself (so no clues this time) but you are more than welcome to try to write your own solution if you want a challenge.īefore we get started, let's take a look at how a merge sort actually works. So in a best-case or near best-case scenario (where an algorithm is already sorted), the inefficient algorithms we've learned are faster than merge sort.īecause this is a more complex algorithm to implement, we are going to return to our TDD approach. One interesting thing, though, is that the Big O for a merge sort algorithm is always the same - whether that's the best-case or worst-case scenario. The Big O of a merge sort algorithm is O(n * Log n) - which is faster than O(n 2). In this lesson, we'll cover the merge sort algorithm, which uses a divide and conquer approach to break down an array into many arrays of just one element, then sorts and merges the arrays back together until the array we started with is fully sorted. Now we're ready to look at a few algorithms that are both more efficient and more complex. At this point, we've looked at several sorting algorithms that have a Big O of O(N 2): the bubble sort, insertion sort, and selection sort algorithms.
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