Learn Merge Sort in just 10 minutes !!
Merge sort is one of the most efficient sorting techniques and it’s based on the “divide and conquer” paradigm.
In merge sort, the problem is divided into two subproblems in every iteration.
Hence efficiency is increased drastically.
Declare left and right var which will mark the extreme indices of the array
Left will be assigned to 0 and right will be assigned to n-1
Find mid = (left +right)/2
Call merge Sort on (left, mid) and (mid+1,rear)
Above will continue till left is less than right
Then we will call merge on the 2 subproblems.
Time Complexity of Merge sort
In the worst case, in every iteration, we are dividing the problem into further 2 subproblems. Hence this will perform log n operations and this has to be done for n iteration resulting in n log n operations total.
In the best case that is sorted array, we can do some modification by using a flag to check whether the lament is already sorted or not
Best Time Complexity: O(nlogn)
Average Time Complexity: O(nlogn)
Worst Time Complexity: O(nlogn)
Merge Sort algorithm example in Hindi | Merge Sort in Data Structure DAA/ADA sorting algo
Merge Sort best case, average case and worst case time complexity
Merge sort recurrence relation T(n) = 2T(n/2) + n
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Step by step instructions showing how to run merge sort.
The “Merge Sort” uses a recursive algorithm to achieve its results. The divide-and-conquer algorithm breaks down a big problem into smaller, more manageable pieces that look similar to the initial problem. It then solves these subproblems recursively and puts their solutions together to solve the original problem.
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Merge Sort in Data Structure | Sorting Algorithms| DSA Full Course
Discussed Merge Sort Algorithm with an example. Step by step instructions on how merging is to be done with the code of Merge Function.
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