Time complexity: Best and Worst cases | Quick Sort | Appliedcourse

[@bibhuprasadsubudhi1340]

this ia what we students call a osm techer .love this session .thank you so much sir .

[@frazebean5117]

You're a legend man. I wish you all the best in your teaching journey cos you're gifted in it.

[@nikhilgautam007]

Best video for understanding the time complexity of quick sort algorithm.😎👌👌

[@rohithberavelli1941]

perfectly done by quick sort with your video.thanks for your explanation

[@giantpanda504]

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

[@jerrykayson4145]

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[@rivergus501]

@Jerry Kayson instablaster ;)

[@jerrykayson4145]

@River Gus I really appreciate your reply. I found the site through google and im trying it out now. Looks like it's gonna take quite some time so I will get back to you later with my results.

[@jerrykayson4145]

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[@rivergus501]

@Jerry Kayson glad I could help =)

[@achronicstudent]

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?

[@draaagoo7799]

Brilliant explanation!!

[@debaratighatak2211]

What an amazing explanation sir! Thank you :)

[@prasannasasne81]

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?

[@vivekmishra007]

@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

[@exploringnaturalbeauty2102]

@@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 KZbin comment section too.. Depends on your comfort Thanks !

[@anurag9110]

Nice explanation. Thanks

[@shashipreetham2882]

Splendid Explanation!

[@hakan6449]

Good explanation

[@naniscompass3173]

What is the name for this teaching tool (colour pens)?

[@picklerick3136]

Thank you sir! Very inspiring!

[@picklerick3136]

@Dayton Taylor nobody will give a damn because it’s a scam

[@picklerick3136]

@Caden Jaiden yeah, and what a coincidence, you two both joined KZbin one week ago

[@durgeshbhakta8008]

Good explanation sir 👍

[@jagannathanvijaylakshmi5385]

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?

[@vaishnaviyogendradere17]

Great sir 👏👏

[@sayantaniguha8519]

So, Almost Best case is average case ?

[@sanashoukat477]

make a video for heap sort.

[@RahulGupta-rl8xd]

Thanks for help

[@livingston8267]

Amazing sir❤

[@louerleseigneur4532]

Thank you very much sir ji

[@Arjunsiva]

That was very helpful

[@hirafarman8011]

great

[@gabrielpereiramendes3463]

#Excelent!

[@Momo-hr2yd]

Kosom the ziognist

[@rafiqn2675]

Bayya how to convert head recursion to tail recursion (tail call optimisation) please make video on it...

[@aayushpagare9366]

🙇🏻‍♂️

[@uttamkarmakarece3534]

❤️❤️❤️

[@louerleseigneur4532]

brovo

[@uberboy6986]

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.

[@redkai11]

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)).

[@uberboy6986]

@@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 ).

[@redkai11]

@@uberboy6986 the time complexity of theta(nlog(n)) implies O(nlog(n)) so he is actually right?

[@uberboy6986]

@@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.

[@redkai11]

@@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))