Calculating Time Complexity | Data Structures and Algorithms| GeeksforGeeks

[@michaelzumpano7318]

Bravo, that was the clearest yet most complete 8 min intro to complexity on KZbin. Hey, you found a good teaching algorithm - balance time and headspace efficiencies. Props.

[@thembalamimsimango8993]

You explained ten times better than my lecturer did in just 8-minutes, thank you

[@kevindsa57]

After 4 years of Engineering in Computer Science this was the best explanation of Time Complexity thank you !!

[@I_am_FRANCO]

engineering in cs 🤔

[@emrahcansahinoglu2342]

@@I_am_FRANCO In some countries like mine (Türkiye formerly Turkey) the major called Computer Engineering is identical to Computer Science in both curriculum and concept. However, we just took a couple of more courses (in my opinion) from Electrical and Electronics Engineering regarding to the Hardware. Even my degree is given as B.S. in Comp. Eng. I guess he may have a similar situation.

[@meBZcookie]

6:04 Time Complexity for Sorts

[@stephenmusau2394]

The simplest yet most comprehensive explanation that I have come across. Well done sir!

[@W3Fi]

Incredible, I find myself at a loss for words to adequately express the precision and clarity of the elucidation.

[@AV-mb8lv]

Explains O(1), O(n), O(n^2). Then says, "now let's look at the scale of diff. complexities O(1), O(log n), O(n), O(nlog n), O(n^2)" Wait, where are the missing bits? "Now you'll be able to calculate all time complexities of the algorithms"

[@himanshukandwal8710]

for(int i=0; i

[@jqn8361]

@@himanshukandwal8710 O(n), You're going through all the i elements of the list (this is repeated n times + 1 time, just before the for loop breaks), which is a function of n, and assigning them to an integer value, which takes constant time. Searching through the linked list also takes constant time. You're also printing each element, which also takes constant time. So you have T(n) = c1*(n+1) + c2 + c3 + c4 = O(n)

[@biikaaa.6540]

@@himanshukandwal8710 O(n)

[@harshitraj953]

@@himanshukandwal8710 hllo,, sir,, Don't act 😎Smart sir

[@outsiderthevoid8780]

this guy taught me more in 8 minutes than my professor did in 3 weeks love u bro❤

[@bhaskarnarayan2109]

You could also mention the complexities like O(logn), O(nlogn) etc. and the concepts of Master Theorem. It would be better.

[@stith_pragya]

Thank You So Much for this wonderful video......................🙏🏻🙏🏻🙏🏻🙏🏻🙏🏻🙏🏻

[@alekhyarao5431]

Excellent explanation and easy to understand. Thank you!

[@ohmegatech666]

Good video. Quick correction at 5:48, O(n^c) is not exponential, it's polynomial since the exponent is a constant, like O(n^2) or O(n^3). Exponential is when the variable itself is in the exponent like O(2^n)

[@WitchelsArtSchool]

best video explaining complexity. the complexity of complexity have been resolved here :D

[@jerrybear5866]

Thank you , you saved me for exam today.

[@aleksandrakja6792]

You explained this misery better than my teacher ever could

[@LucusJanny]

It was the best video on KZbin about Time complexity. Best wishes for you bro

[@mohammedghabyen721]

Best explanation, thank u

[@laawuni]

Best so far. you saved me

[@twinkleshaw693]

Best video ever watched ❤️

[@ankishgupta1517]

Perfect explanation...plzz upload more videos on complexities... 👌

[@ObsessedMee]

This is called clear concept 🙌✨

[@meghaadikarinayake7067]

Thank you very much!!!!❤

[@meowrice]

thanks! i finally understand bigO notations! the last part also is a big bonus for me. thank you!

[@Tauhid214]

I appreciate your work. How about the for loops: for(i=1, i

[@Millii2703]

O(n^2)

[@floki6939]

Precise illustration. Thank you

[@debsankarpal763]

Wonderful! Best explanation in 8 minutes

[@LilJollyJoker]

Thank You!

[@srichandana5122]

very clear and understandable .... thank u for ur vedio

[@mahendrasahani9148]

thank you, very good teaching algorithms

[@kmishy]

Simple and great content. I wonder how my prof taught

[@kmishy]

As time moves 💕

[@farzamafifi2257]

Evaluate single-core performance for integer computation. Perform two experiments: with task bound to the APP core and separately to the PRO core. Observe if there is a difference in measurements. Propose an algorithm that is able to generate a complexity of integer computation observable and measurable. Perform at least 10 measurements for each experiment. Consider using parts of code for Dhrystone benchmark its is my task is any has source code for in c ++ cause have to run in vrel

[@harshitraj953]

??? Ffff

[@swapnildadamode662]

Man! You're the best! This is what i need

[@ece547teamsioux5]

Great efforts. Thank you:) Regards from USA

[@subbusharma3990]

Thank you for this Regards from Russia

[@msnraju97]

Very good explanation, Learned in 10mins Indian Engineer's lives depend on GFG. without it IT industry will fall

[@hashantha_lk]

Thanks, that was the most enlightening explanation it to complexity after lot of confusing video explanations on YT. You are the best !

[@AdeniranAbisolaAbiwax]

This is the best explanation i have come across so far

[@aladdinalswaifi9655]

Well done Very clever and clear explanation Many Thanks

[@josephbermudez9533]

for 4:10, it is O(2n). Not O(n) Great video, thanks!

[@BigYous]

you drop the constants for big O

[@resep1234]

@@BigYous but if you drop the constants it should still be n+n which is 2n

[@arjunjain7773]

You don't use constants for bigO I don't know why... It's stupid but that's just how it is

[@ognimoddd]

@@arjunjain7773 No, it's not stupid. The constants don't affect it as input gets bigger so it is useless to keep

[@shawmcinroy472]

lepke 2 is a constant...

[@DaBaSoftware]

Thank you for the resource!

[@jamesstark4136]

Thanks! this helped a lot for my CS 211 class.

[@anonymoustravelvidz]

thank you, very helpful video.

[@studentbengal4043]

Best tutorial ever on this topic... Thanks

[@neerajmahapatra5239]

You are very clear and easy to understand. Thanks...😍😍😍😍

[@danialsiddiqi635]

Thank you for this video.

[@pradeepsinghrajput9676]

Really to the point and efficient explanation. Kudos

[@matteooccello491]

Thanks for the video! It was clear and really practical :)

[@manishjoshi4989]

one doubt in sequence time complexity you described c1 + c2 n + c3 n = BigO n time complexity ( doubt is after removing constant from equation why n + n is not 2n)

[@firdousanjum7893]

2 is constant so remove it. It will now b o(n)

[@lakshmibonala2813]

How c1+c2n+c3n = O(n)?? Is it O(2n) by removing constants.

[@Sanatani_kanya_]

C1+n(C2+C3)

[@notnemeziz]

O(2n) and O(n) are the same thing....O here stands for order of...so 2n, 3n or any cn is an order of n ie O(n)

[@shreyankraia9762]

This video explains only O(1),O(n) and O(n^2) Don't waste your time if your looking for other time complexity

[@AhmedAli-kf2wg]

I really appreciate the way you teach us. Thanks a lot sir : >)

[@mathwithmathu1755]

Really superb and very useful. Very good explanation...

[@neyso_dl9810]

I knew that Indians where the best explainer

[@GGanzolo]

Really good introduction of time complexity. Thank you!

[@infi-tech3808]

I believe there was a mistake on 4:23: The time complexity for this sequence of loops would be O(n) + O(n), which simplifies to O(2n) not 0(n)

[@heycalvinnn]

You remove the constant in this case the 2 in 2n so you would be left with O(n)

[@pomegranate8593]

when u said welcome to video i felt rly welcomed thank u my friend! also very helpful video!

[@YeabselamKassaye]

😂😊😊😊😊😊😊

[@jhonbhai8943]

I was too much Dived into his Teaching .That I heard someone sparking gas Stove lighter at 1:34😂

[@shreyapandey1423]

Best lecture 😊

[@LeelaseshuKumar]

Thanks for the explanation of time complexity. But you missed log n and n log n

[@saphalvecham5539]

Wonderfully put

[@AbdurRahman-op8zn]

Excellent ! Thanks a lot.

[@rahulmajumdar5525]

Thank you for the great explanation straight to the point.....

[@sumantabanerjee9728]

for(i=0; i

[@ryanmckenna2047]

Thanks that video was incredible...your incredible!

[@956zombie956]

The best !

[@seemarani2403]

Awesome explanation👌👌

[@hinaafzal4348]

Such a good way of teaching Very well done 👍👍👍

[@atharva..deshmukh]

Finally, I got something that I wanted!!

[@johndavies729]

Really well explained. Thank you!

[@marjoriejetajobe512]

Thanks, I learned a lot!

[@sagarchawla4926]

Smooth ✨

[@amrutapatil1417]

simple and sweet!

[@bhagirathsinhmakwana6085]

Hey priti !!

[@bhagirathsinhmakwana6085]

😘😘😘

[@JPN-bx3yd]

This is a great explanation.

[@ilyas1569]

I understand the topic, thanks :)

[@padigapatinarmada9729]

Simply superb

[@JamesBrodski]

Great video. Thank you so much! Very helpful

[@yaro_05]

Very nice. Thanks

[@martinnieva8484]

This was a wonderful introduction! Thank you

[@sheikhjabed3967]

Very Helpful.Thanks a lot

[@gangadharaswamy8856]

I think for 3 sequential statements time complexity is 2n+1

[@ishika6945]

hey can you tell me why in 4:13 its O(n) and not O(2n) since we are adding na...i do have doubt here. i will be highly obliged if u do reply. thankyou

[@gaminghub23]

@@ishika6945 constants aren't important and are ignored in time complexity. It would not impact much or any

[@gangadharaswamy8856]

​@@ishika6945 c1+c2n+c3n take common factor here n is common so we get =c1+n(c2+c3) Ignore constants c1, c2, c3 Then we have "n" remains There fore time complexity will be 0(n)

[@gangadharaswamy8856]

​@@ishika6945 c1+c2n+c3n =c1+n(c2+c3) Ignore constants c1, c2, c3 Then we have "n" remains There fore time complexity will be 0(n)

[@Edit-your-self]

2x speed better to understand

[@Reacher1998]

Helped a lot, great video 🔥

[@kanishkaralasi7408]

Wonderful explanation! You're the best 💯

[@Umerele1]

man no matter how much i focus i can't understand time complexity 😥

[@cschiud5571]

dog, the prof explained that in like one every week for 3-4 weeks and i just understood it better here after 8 minutes :D

[@dusmannalinwad]

short and great..!!

[@gauria6688]

Please make a video on space complexity

[@shouryaverma6313]

ohh finally I found what I wanted thanks a lot sir

[@haidermunir9341]

So I am here! Thank you FAST-NUCES.

[@AYESHAZAMURD-s4p]

Sir what if in sequential statement the time take by each statement was n+n^2 + n^ 3 then what would be the total Time complexity...?

[@amalkrishnas1696]

Thanks for the video

[@akashp4863]

Thanks man

[@faizanyounas4799]

kamal boss

[@hi-tk4hu]

very good explanation and easy for beginners to understand thank you❤️

[@divinegeorgeninan5822]

Thank you❤

[@swanandjoshi9993]

Thank you

[@MsAdeel66]

i love u dude.

[@code2compass]

How is the sequential O(n)? Does it add up all together? I thought it should be O(2n)

[@no_one4video]

good video

[@krishnavar7219]

great work