Time complexity: Best and Worst cases | Quick Sort | Appliedcourse
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- Опубликовано: 17 май 2019
- Chapter Name: Quick Sort
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perfectly done by quick sort with your video.thanks for your explanation
Best video for understanding the time complexity of quick sort algorithm.😎👌👌
You're a legend man. I wish you all the best in your teaching journey cos you're gifted in it.
What an amazing explanation sir! Thank you :)
this ia what we students call a osm techer .love this session .thank you so much sir .
Splendid Explanation!
Nice explanation. Thanks
Good explanation sir 👍
Brilliant explanation!!
Can u tell me which software is being used? I mean how to write with mouse.. sir..are u using any digital pen or something?
That was very helpful
Thanks for help
Thank you very much sir ji
Amazing sir❤
I have question about 4:21 about drawing recursion tree. I always draw them like for each node, calculating the work done in this node. (as shown in Introduction to Algorithms 4th Leiserson Stein Rivest Cormen MIT Press p. 96) using this approach the recursion tree drawn should be n ---> cn, n-1--->c(n-1), n-2 ---> c(n-2), ... 1 ---> 1 but you draw it slightly different. I guess both approach gives the correct results for the example in p. 96 but i am not sure. Can you explain it a bit further?
What is the name for this teaching tool (colour pens)?
Great sir 👏👏
#Excelent!
Good explanation
Thank you sir! Very inspiring!
@Dayton Taylor nobody will give a damn because it’s a scam
@Caden Jaiden yeah, and what a coincidence, you two both joined RUclips one week ago
make a video for heap sort.
great
So,
Almost Best case is average case ?
at 11.46 you have mentioned the depth of the tree i.e log n to the base (10/9). can you please explain how you are calculating this depth?
@Prasanna Sasne In the case when the array is divided into 1/10 and 9/10 of the total size we have to consider the part of the recursion tree with the maximum height, the tree one part is getting divided by 10 and another part by 10/9 so as 10/9
@@vivekmishra007 Damn thanks bro , lucid explanation to the point , I want to be in contact with you , so do you have an email where I can mail you if I have any doubt or we can stick to this RUclips comment section too..
Depends on your comfort
Thanks !
when you analyze the worst case for quicksort, x is the smallest element as you said. so the n1 from Partitioning should be all bigger than x. But you wrote the symptom of
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I was dumb forgot my account password. I would appreciate any tricks you can give me.
@Jerry Kayson instablaster ;)
@River Gus I really appreciate your reply. I found the site through google and im trying it out now.
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@Jerry Kayson glad I could help =)
Bayya how to convert head recursion to tail recursion (tail call optimisation) please make video on it...
🙇🏻♂️
❤️❤️❤️
Kosom the ziognist
brovo
You cannot say that Quick Sort's time complexity is Big-oh of nlogn because it's not. Big-oh represent it's worst case and the worst case time complexity of quick sort is O(n²). To represent the average case you should use Big-Omega which is another symbol: Ω(n log n). Try not to explain difficult subjects to other people if you are not sure about it, it's hard to understand it and it gets even harder if many people say different things about it.
That is common misconception. Big-o does not represent its worst case and big-omega does not represent average case. Big-o is just the upper bound and big-omega is just the lower bound of time complexity. For worst case, quicksort is actually theta(n^2) so it is completely valid to say quicksort's time complexity is O(n^2) when we are considering the worst case. For best case, quicksort's time complexity is theta(nlog(n)) so it is, again, completely valid to say o(nlog(n)) or big-omega(nlog(n)).
@@redkai11 I'm talking about the part that he says that we can call it O(n log n) for the Quick Sort's time complexity ( 13:20 ).
@@uberboy6986 the time complexity of theta(nlog(n)) implies O(nlog(n)) so he is actually right?
@@redkai11 Theta(n log(n)) doesn't implies O(n log(n)). Quick Sort time complexity is O(n^2), Theta(n log(n)) and Omega(n log(n)). Google it.
@@uberboy6986 at this point, I am convinced that you don't know what theta is and its relation to big-o and big-omega. maybe try digging into google more and you will find that anything of theta(f(x)) is also O(f(x)) and big-omega(f(x))