If my videos have added value to you, join as a contributing member at Patreon: / sunildhimal Learn about Big Oh asymptotic notation - Definition & Example.
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.
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
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
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!
Thank you! I am following Introduction to Algorithms, Cormen et. al More videos on Asymptotic notations here: Why study Asymptotic: czcams.com/video/j8-okOgWv6U/video.html Omega notation: czcams.com/video/Ut-TsexLA6s/video.html Theta notation : czcams.com/video/vOyqP0jXK5c/video.html Examples: czcams.com/video/HR-WGiwlino/video.html
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.
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 ?
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).
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.
Now I am a TA at my university and I came here to revise
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
Dear Sir, your explanation is very clear. Thank you
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
Best explanation I have seen! Thank you!
Very helpful! My cs class did not explain very clearly and this made a lot of sense! Thank you for making this video
Thanks sir！better than my 2hours university lecture
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!
The explanation is very good sir. Thank you
Nice explanation sir...
Well explained🎉🎉
Very very nice explanation sir
very nicely taught
Thank you so much
way of explain is very good
THX DOCTOR
prety good nd helpfull thxx
sir ji you are god
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!
Thank you! I am following Introduction to Algorithms, Cormen et. al
More videos on Asymptotic notations here:
Why study Asymptotic: czcams.com/video/j8-okOgWv6U/video.html
Omega notation: czcams.com/video/Ut-TsexLA6s/video.html
Theta notation : czcams.com/video/vOyqP0jXK5c/video.html
Examples: czcams.com/video/HR-WGiwlino/video.html
Hi sir, could you help, log(2)(n^(2)+1)=O(n)?
god explanation
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.
Now understood.....
Ma sha Allah
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 ?
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
Yes! It is just a representation and not an exact graph