Count Sort Algorithm | Radix Sort Algorithm | Lecture-41 | C++ and DSA Foundation course

Sorting algorithms play a major role in every job interview. They are most frequently asked. And thus it makes it really necessary for us to have a proper understanding of these algorithms.

So far we have studied sorting algorithms like insertion sort, selection sort, bubble sort, etc. Also, we have discussed the divide and conquer approach through which we have done two sorting algorithms, merge sort and quick sort. The best time complexity we have been through so far is O(nlogn).

But in today’s video, we are gonna see some advanced sorting algorithms and then we will analyze their time complexity and will see whether it’s better than the time complexities of the algorithms that we have studied so far.

As always, we will be diving deeper into the logical understanding of these advanced sorting algorithms. Looking into each and every step of each algorithm. Then we will see the line-by-line code in C++, followed by the dry run and proper explanation of each line of code written by Urvi ma’am. Also, we will look at the time and space complexity of each algorithm and see how is it better than others.

Access the notes and assignments for this class for free at : ▶️https://pwskills.com/course/C++--Foundation

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TIME STAMPS:
0:00 Introduction
00:00:21 - Sorting Algorithms done till now
00:00:43 - Improving complexities by today’s algorithms
00:01:03 - Recap
00:02:14 - Today’s Checklist
00:02:22 - Count Sort
00:17:31 - Code
00:27:25 - Dry Run
00:30:31 - Time Complexity
00:32:58 - Space Complexity
00:34:10 - Where can’t we use Count Sort
00:34:36 - Count Sort on Negative Numbers
00:36:28 - Don’t use Count Sort in this scenario
00:37:00 - Radix Sort
00:41:45 - Code
00:55:35 - Dry Run
01:05:15 - Time Complexity
01:06:15 - Space Complexity
01:06:39 - Next Lecture
01:07:04 - Thank you

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