I am not exaggerating but I have seen alot of videos including very professional one like on coursera (University of San diego) but this is the best explanation I have ever had.

@ahmedtamer4620

Жыл бұрын

Now I am a TA at my university and I came here to revise

@anchalpandey9074

Жыл бұрын

couldn't agreee more. don't know why his channel is so underrated I've been studying this for almost an year yet this is the best explanation I've found so far

Of all the lectures, I've understood it by your teaching method. Good job sir!

amazing how relevant this video is

finally somebody who explains what Big O notation is before jumping to exercise. Thank you

beautiful sir this was a huge video in terms of knowledge about big o notation i learned this in just one video and tommorow is my exam 2nd semester software engineering

I'm surprised this channel doesn't have more viewers. Thank you, you explained this very well!

Holy smokes. Now I finally get it. Amazing video, with great visuals. I didn't even understand from Udacity videos

Very helpful! My cs class did not explain very clearly and this made a lot of sense! Thank you for making this video

Nice explanation sir...

Well explained🎉🎉

Best explanation I have seen! Thank you!

The explanation is very good sir. Thank you

Dear Sir, your explanation is very clear. Thank you

sir this is very helpful video , and your explanation is very nice .

Such a great video, about to save me on my exam. Thank you sir!

Thanks sir！better than my 2hours university lecture

Thank you so much

THX DOCTOR

sir ji you are god

Very very nice explanation sir

way of explain is very good

very nicely taught

prety good nd helpfull thxx

Pretty useful video you have over here. Are you teaching a class? What textbooks do you use? Also, if you could do more examples on Big Oh, Big Omega, and Theta, that would be great!

@SunilDhimal

5 жыл бұрын

Thank you! I am following Introduction to Algorithms, Cormen et. al More videos on Asymptotic notations here: Why study Asymptotic: kzread.info/dash/bejne/nGxh0c2Il7rcZrg.html Omega notation: kzread.info/dash/bejne/h6hhttWeqK-nZtY.html Theta notation : kzread.info/dash/bejne/qIOt07JpmruxZcY.html Examples: kzread.info/dash/bejne/eoZhuamip8_PntI.html

Now understood.....

Ma sha Allah

god explanation

Hi sir, could you help, log(2)(n^(2)+1)=O(n)?

hello bro i think 3n+2=O(n^2) have ans : c=4 and n=1; To prove that 3n+2=O(n^2), we need to show that there exists a positive constant c and a non-negative integer n0 such that for all n ≥ n0, the following inequality holds: |3n + 2| ≤ c * n^2 We can start by simplifying the left side of the inequality: |3n + 2| = 3n + 2, since 3n + 2 is non-negative for all n. Next, we can choose c = 4 and n0 = 1. Then, for all n ≥ 1, we have: 3n + 2 ≤ 4n^2 Dividing both sides by n^2, we get: 3/n + 2/n^2 ≤ 4 Since the left side of the inequality is decreasing as n increases, we only need to verify the inequality for n = 1: 3/1 + 2/1^2 = 5 ≤ 4 This is a contradiction, so the inequality cannot hold for any value of n. Therefore, we can conclude that 3n+2 is not O(n^2), and the original statement is false.

Doesn't g(n) become 5n and not 4n? Someone please explain

Nice explanation .... but i have one doubt which is that Big Oh always represent the worst case complexity and this means that the complexity of that algorithm can't be more than that. It can be less But here you are saying that the upper bound can be 0(n2) and 0(n3). i can't be able to understand that can anyone explain this to me ?

@SunilDhimal

Жыл бұрын

We always go for the tightest or the closest upper bound. If f(n) = n^2, we can prove that f(n) = O(n^2) and f(n) = O(n^3). So both n^2 and n^3 are upper bound but we go for the tightest upper bound {bound that is closer to f(n)}. In this case n^2 is the tightest upper bound to f(n).

graph is wrong ,3n+2 running time should be 2 at n=0

@SunilDhimal

2 жыл бұрын

Yes! It is just a representation and not an exact graph