Merge sort time complexity analysis

[@Purplajax]

Oh my goodness. I've been looking for a clear explanation of the time complexity without using a recursion tree for so long. This is so helpful!

[@ThisIsTheWay-n5s]

wtf but its a substitution method

[@AkashSardar-hq9pt]

Bro have you got a job?

[@nostromo0211]

Finally a very clear explanation of this derivation. Thank you. Very helpful.

[@ishwarrimal1393]

Thanks. The tutorial was very very simple yet very informative. Loved it

[@Katbrainshabibi]

Best And Authentic Method ...
after a long searched....finally...welldone

[@AmitKumar-ij6ew]

Best and Clear Explanation ever....No shit things ....Completely understood....Thankyou🙏🙏

[@user-xr4dx2nq1b]

Thank u! T(n)=n + nlogn, which simplifies to O(nlogn) because in big O analysis, u ignore smaller terms

[@adityadasgupta5779]

Simple, yet to-the-point. Thanks for such a great explanation.

[@Aksuduvhjrjiwoqo]

Very clear and straightforward! Really appreciated it!

[@shivamyadav-jb6wy]

thanks a lot sir very helpful we need more time complexity analysis on different algorithms.

[@dr.muhammadnaveed951]

An excellent derivation of time complexity of mergesort....Thanks a lot

[@NormanWasHere452]

Wow unbelievably simpler than all the other explanations I've seen, just using some simple maths!

[@gauravvvv]

ah finally someone could explain it in a simple manner. Thanks Sirji.

[@rahulgovindkumar3105]

Thank you so much sir...... was really looking for the explanation of this sort without the recursion tree

[@rushikeshphalke1872]

Just Awesome ! Must watch video. never got this much clear explanation about recurrence relation and how to solve one. Thank you so much.

[@sportszone9664]

I SAW ALOT OF VIDOES FOR ANALYSIS BUT THIS
ONE IS BEST .(THANK YOU SIR)

[@falcxne]

This is an excellent video for the derivation of the Merge Sort Big-O and one of the few that doesn't do a (often hand-waving) recursion tree version. My only suggestion for improving it would be to explain/show explicitly why the merging step is 'n' (as that's crucial for setting up the recurrence relation).

[@Oneofday]

Thanks a lot, your explanation just shed light on my understanding of complexity of recursive algorithm.

[@nguyeninhnguyen6913]

Actually. This is what i was looking for. Verry straightforward.

[@SmartProgramming]

thanks a lot for the explanation sir, keep it up 👍👍👍👍

[@hsl2916]

Best explanation I have heard about merge sort Thank you~

[@_PremKharat]

AWESOME SIR🤩🤩 JUST CLEAR EXPLANATION AND NOTHING ELSE THANK YOU

[@michaelnguyen7950]

A very clear explanation. Thank you so much sir.

[@dudewithayodahat7009]

Thank you so much for this! My professor did not explain this thoroughly at all!

[@arunmahajan809]

Well done, logical and methodical! Thanks a lot.

[@SuperGojeto]

Thank you very much! I was trying to find how it was nlogn and google as well youtube sucked a lot to give a simple explanation. You are the only to explain it to the T literally 😀👍

[@StartPracticing]

Thank you sir

[@perpetual_bias]

thanks for watching? thanks for the clear and concise explanation!

[@khusi8276]

Nice explanation.... Thank you sir✨🙏

[@ParasFtl]

Wow, superb explanation

[@dileepvemula8797]

very good explanation
super

[@j.y.]

The clearest explanation. Thanks!

[@abhishekkorlekar7966]

Thank you sir for the clear and accurate explanation.

[@NotNotNithin]

According to Masters Theorem for division problem the recurrence relation would be O(n^k log^(p+1)n). Here as per the third equation k is 1 and p is 0 and it would result in O(n log n).

[@rohitrout6450]

Uhhh...really really nice..✨❤was struggling a lot with this sort and finally my quest ends here!..Thanks👨🏻‍🦱

[@jn674]

Thank you for the lucid explanation!!!

[@sayantanguha1934]

Excellent explanation.

[@Marshenanigans]

Clean derivation. Congrats.

[@certifiedsmartass4122]

I have to explain this in class today. Thanks for saving my ass 😭.
- Sincerely Ranjit

[@muhammadkhanniazi9428]

Thanks youu soo much sir I got explanation of merge sort time complexity is very clearly

[@rakshithvp1877]

I was looking for this from long time.
Thank you sir

[@machinelearningartificiali5931]

What a beautiful explanation

[@user-snc07]

Awesome explanation sire!

[@Lisa-kk6go]

This is so clear! Thanks a lot!

[@AyandipPal-jd5ch]

Great video sir.

[@taniya-67]

It's an amazing video...❤️❤️

[@bikashpokhrel6563]

thanks its very helpful and clear and perfect explanation ,keep the good work on .thank you !

[@pandu95]

Superb explanation tnk u sir

[@raadonyt]

Very well explained

[@azizdaghfous3385]

Very clear and helpful video . Thank you :)

[@manastripathi6013]

wooww sir really liked it.Thanks for uploading.

[@mxapr6476]

Hey, you have good explanation, please continue doing more videos and make yourself visible on youtube search, to get more views

[@ShubhamKumar-bu5fz]

this video is life saving. thanks

[@PrakharKalani]

Simplest yet best

[@GautamKumar-wx3sm]

How did the last step come?

[@gec549saisneha9]

CLEAR AND CONCISED VIDEO..

[@jenilvaghasiya9892]

very clearly explanation keep it up

[@CBhattacharjee]

Thnak you for the clear explanation.

[@tinegaharon6919]

Wow! You made it easy.

[@younglee9416]

why at last N+N*logN = O(N* log N)? not equal to O(N*(logN+1))?

[@arijitbasu4397]

Best explanation

[@mohitsaraswat8504]

Sir very good explanation thankyou very much

[@osmankhalid2005]

that substitution of n/2^i = 1 at 8:20 is trickier

[@shashankvishwakarma959]

Great Video !!

[@viplovedev]

Shouldn't the splitting part be accounted for as well?

[@crackerahul]

Best explanation ever

[@varshiniv5630]

Very helpfull for me thanks

[@DroidTsuenik9]

Thank you sir. Very clean explanation.

[@Farhankhan-nb3zz]

if we have 2d array .so what is the time complexity analysis

[@Frost571]

very good explanation!

[@NishantMendiratta]

very nicely explained.

[@rohanghosh9700]

Thank u so much sir. Very simple explanation

[@vihaanverma9425]

awesome explanation

[@taniya-67]

It's really very helpful

[@deojeetsarkar2006]

Thank you Sensei

[@preathi5436]

Tq for the clear explanation

[@rahulbansal4584]

Thanks for the video

[@soumelee5661]

good video

[@ahtashama550]

❤❤Deserve appreciation❤❤

[@sharathpalolil571]

thank you

[@yoursrakhi2613]

very much useful sir

[@Rendbot]

What about for the case where n isn't a power of 2?

[@md.marufurrahmanremon3095]

Boss!!!
Salute!!!

[@ashustuti2]

nice bro

[@prabhuprasaddevanaboina3889]

Good. Thank you sir.

[@arnobchowdhury9641]

Thank you sir. This was very helpful.

[@sanasairaazsairaaz9180]

Good explanation sir...Tysm..!!

[@taggebagge]

Brilliant!

[@ravinapit3302]

thanks sir for nice explanation

[@ronnybanerjee6020]

could you please state how to derive the worst case Time Complexity of Merge Sort?

[@SatputeAcademy]

worst and best time complexity is same for merge sort, the derivation given here is for both worst and best cases

[@ronnybanerjee6020]

ok thank you :-)

[@mahbubulhaque3137]

Thank you so much

[@alok7485]

i beg you sir please tell about Graph Searching and Traversal: Overview, Representation of graphs, strongly connected components, Traversal methods (depth first and breadth first search) and its analysis
Back tracking: Overview, 8-queen problem, and Knapsack problem Brach and bound: LC searching Bounding, FIFO branch and bound, LC branch and bound application: 0/1 Knapsack problem, Traveling Salesman Problem

[@sri3884]

Thanks Sir, it is really helpful

[@saurabhsangwan1453]

Thankyou sir! you are awesome

[@taniaroy1829]

Escape + control

[@Ubuchu]

The best

[@mr.mohammedmobinakhtar2617]

can u solve by
substitution method T(n) = T(n - 1) + T(n/2) + n

[@Priyashloklife0724]

I love this video sir, :)

[@SatputeAcademy]

Thanks

[@hamzanazir4412]

sir what do u mean by roughly? b/c of odd no array

[@jaysonmalabanan1905]

sir can you clear me out my class teacher asked me about that answer or this analysis is { n log n ) there is no value in the base of log am very confused ????????

[@sde1983]

In one of the interview I was asked to solve this :(, !! Is it really need to know? Thanks for the explanation

[@ShahidulIslam-dd8he]

Isn't it T(n) = 2T(n/2) + (n-1) and the base case T(1) = 0 ?