Bravo, that was the clearest yet most complete 8 min intro to complexity on UA-cam. Hey, you found a good teaching algorithm - balance time and headspace efficiencies. Props.
@@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.
I was struggling to calculate time complexity from a long time . As , I was not able to understand , I was rote memorizing a lot of things . Huge thanks to you for explaining this in such a simple way .
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 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)
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)
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)
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
Bravo, that was the clearest yet most complete 8 min intro to complexity on UA-cam. Hey, you found a good teaching algorithm - balance time and headspace efficiencies. Props.
You explained ten times better than my lecturer did in just 8-minutes, thank you
After 4 years of Engineering in Computer Science this was the best explanation of Time Complexity thank you !!
engineering in cs 🤔
@@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.
Incredible, I find myself at a loss for words to adequately express the precision and clarity of the elucidation.
Thank You So Much for this wonderful video......................🙏🏻🙏🏻🙏🏻🙏🏻🙏🏻🙏🏻
I was struggling to calculate time complexity from a long time . As , I was not able to understand , I was rote memorizing a lot of things . Huge thanks to you for explaining this in such a simple way .
6:04 Time Complexity for Sorts
this guy taught me more in 8 minutes than my professor did in 3 weeks love u bro❤
best video explaining complexity. the complexity of complexity have been resolved here :D
The simplest yet most comprehensive explanation that I have come across. Well done sir!
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"
for(int i=0; i
@@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)
@@himanshukandwal8710 O(n)
@@himanshukandwal8710 hllo,, sir,,
Don't act 😎Smart sir
Best vdo on the topic .. crisp clear and to the point..
thanks! i finally understand bigO notations! the last part also is a big bonus for me. thank you!
Best video ever watched ❤️
You could also mention the complexities like O(logn), O(nlogn) etc. and the concepts of Master Theorem. It would be better.
Excellent explanation and easy to understand. Thank you!
This is called clear concept 🙌✨
Best so far. you saved me
Perfect explanation...plzz upload more videos on complexities... 👌
Thank you very much!!!!❤
Thank You!
It was the best video on UA-cam about Time complexity. Best wishes for you bro
Thank you , you saved me for exam today.
Best explanation, thank u
Simple and great content. I wonder how my prof taught
As time moves 💕
I was too much Dived into his Teaching .That I heard someone sparking gas Stove lighter at 1:34😂
Man! You're the best! This is what i need
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)
Precise illustration. Thank you
very clear and understandable .... thank u for ur vedio
You explained this misery better than my teacher ever could
This is the best explanation i have come across so far
thank you, very good teaching algorithms
Wonderful! Best explanation in 8 minutes
Great efforts.
Thank you:)
Regards from USA
Very good explanation, Learned in 10mins
Indian Engineer's lives depend on GFG. without it IT industry will fall
I really appreciate the way you teach us. Thanks a lot sir : >)
Thank you for the resource!
Thank you for this video.
Thanks, that was the most enlightening explanation it to complexity after lot of confusing video explanations on YT. You are the best !
thank you, very helpful video.
Well done
Very clever and clear explanation
Many Thanks
Thank you for this
Regards from Russia
Really to the point and efficient explanation. Kudos
The best !
when u said welcome to video i felt rly welcomed thank u my friend! also very helpful video!
😂😊😊😊😊😊😊
for 4:10, it is O(2n).
Not O(n)
Great video, thanks!
you drop the constants for big O
@@BigYous but if you drop the constants it should still be n+n which is 2n
You don't use constants for bigO I don't know why... It's stupid but that's just how it is
@@arjunjain7773 No, it's not stupid. The constants don't affect it as input gets bigger so it is useless to keep
lepke 2 is a constant...
Best tutorial ever on this topic...
Thanks
Best lecture 😊
You are very clear and easy to understand. Thanks...😍😍😍😍
Finally, I got something that I wanted!!
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)
2 is constant so remove it. It will now b o(n)
Smooth ✨
Thanks! this helped a lot for my CS 211 class.
Wonderfully put
Excellent ! Thanks a lot.
simple and sweet!
Hey priti !!
😘😘😘
Very nice. Thanks
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)
You remove the constant in this case the 2 in 2n so you would be left with O(n)
Awesome explanation👌👌
Thanks, I learned a lot!
I appreciate your work. How about the for loops: for(i=1, i
O(n^2)
short and great..!!
Really superb and very useful. Very good explanation...
How c1+c2n+c3n = O(n)?? Is it O(2n) by removing constants.
C1+n(C2+C3)
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)
Thanks for the video! It was clear and really practical :)
Thanks man
Amazing ❤❤❤❤❤❤❤❤
Really good introduction of time complexity. Thank you!
ohh finally I found what I wanted
thanks a lot sir
Such a good way of teaching
Very well done 👍👍👍
Thank you for the great explanation straight to the point.....
This is a great explanation.
man no matter how much i focus i can't understand time complexity 😥
Simply superb
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
kamal boss
great work
Really well explained. Thank you!
I understand the topic, thanks :)
This was a wonderful introduction! Thank you
This video explains only O(1),O(n) and O(n^2)
Don't waste your time if your looking for other time complexity
i love u dude.
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
??? Ffff
Helped a lot, great video 🔥
Thanks that video was incredible...your incredible!
Thanks for the video
Good one
for(i=0; i
u rock man
Great video. Thank you so much! Very helpful
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...?
I knew that Indians where the best explainer
Wonderful explanation! You're the best 💯
Thank you❤
very good explanation and easy for beginners to understand thank you❤️
Very Helpful.Thanks a lot
good video
Thank you bro
very nice