I think there is a typo around 11:25. It should be 3n*(40 + n^3/2) = 3n * 40 + 3n^4/2. Isn't it?

Nice comments, everyone seems to understand this. It's always refreshing to find I'm still the dumbest guy on youtube.

This is the first video I've found that actually shows how to find the function of a method. Every other video just goes straight to the math and graphs (which I do understand) however without explaining relation to the code, so this is a life-saver considering I have an exam in 1.5 hours and couldn't figure this out for the life of me. Thank you so much!

Thank you, human. It is the best short explanation I found on the net.

Thanks Bill. This is the proper level of introduction for one of my CS classes.

Thanks for the video, great tutorial as always!

Best Big O video I have come across so far.

Refreshing and easy to follow tutorial, thank you!!

Great examples and explanations!

too good... finally understood with practical examples

Great video! I am going through your playlist as a refresher before I start my masters program. Thank you for creating them. With respect to Big-O notation, however, I believe that you have made a small mistake with semantics. Big-O does not necessarily represent worst-case. Worst, average, and best case generally refer to the state of the input data (i.e., best case for a sorting algorithm would be the scenario where the data is already sorted). Big-O, on the other hand, represents an upper bound on how much time the algorithm will take to process an arbitrarily large input. At least, that is how I understand it.

Big-O doesn't necessarily only indicate the worst case. It indicates the upper bound of the function. Big-O, omega and theta can be used interchangeably to indicate worst, best or average time complexity.

WELL; you're an exellent teacher.

You deserve a reward👍🏻👍🏻👍🏻

can u post another video on this with a some tough examples? i am getting it finally so would like to know it better :)

Nice tutorial...thanks

Is there a typo in the Binary Search pseudocode? At 8:36 in the end of the while loop, the variables "lo" and "hi" should be called "low" and "high", correct?

Great video thanks :)

I like that my maths is bad and I can still follow this, credit to the author for that - thanks.

Best as always

8:28 it should be multiplied by n because we are not considering the time for outer loop... Nsquare is only for inner loop... Please explain me

6:27 can you please explain how it is linear time in right while loop? i is incremented by 3 , so the loop won't run n times. so how it will still give O(n).

Thank you!!

Can I ask? What does it called on constant time to factorial time I just wanted to know because it is necessary for my presentation I want to share to them what does it called .. Thank you.🙏🏾

I was expecting that you will explain why the complexity of binary search is logn.

good job anh

Around 11:25 where it's 3n*(40+n^3/2) breaking down the problem it makes sense: 3n since it's in the outer loop but in the inside loop why is n^3/2, is it because it's nested 2 loops deep? Trying to figure it out but the only logical thing is that it's nested 2 levels deep :/

In big O example where there are two for loops where the inner loop starting value j is sets to i. I want to believe the j value will now be 1. And also the question of what is (n) + (n -1) |+ (n-2) etc how did (n-1) become(n+1) and why are you dividing by 2

Shouldn't there be "3n * 40" instead of (3n/40)? Also, Inner loop runs 41 times instead of 40 So, Eventually making it, 3n*41 which is 123n. Am I right? 11:30 Eventually answer remains o(n^4)

9:04 is a famous algorithm search function. I think I get why you disregard the first half or second half because according to runtime output, the entire for loop will execute regardless whether the value was found or not. To prevent the entire loop from going all the way, and to save time, you change the boundary to mid + 1 or mid - 1 immediately to prevent the entire array from being searched and to save runtime output.

Could you share the slides, plz??

"practical examples coming up..." can i have the link of your examples plz if there is any? thank you

thanks

By dominant, you mean the term in the polynomial with the highest degree , dont you ?

Why is J going from 10 to 50 in the last example? Cheers

Small typo around 3:30, it says quadric instead of quadratic.

So, multiplying two for loops will always equal n²?

kzfaq.info/get/bejne/sLuFnsx20dKsd2Q.html at 8:23 time i understand for inner loop time complexity should be "n(n+1)/2" but shouldnt we multiply external loop "n" complexity to make it n3 ?

Taking notes in the comments to feed the KZfaq algorithm: Complexity= How much time and space does your algorithm need to finish. Big O Notation only cares about the worst case. It gives an upper bound of the complexity as the input size becomes arbitrarily large.

8:21. How does O((n^2)/2 + n/2) get us to O(n^2)?

1:05 Although largely used by computer scientists, I think those notations were invented by mathematicians.

I'm sure you heard this before bu you sound like Christopher Welch from Silicon Valley

charlie?

Pro tip : Play at 1.5x speed.

Cast in the name of god ye not guilty

For the love of god get something that lets you circle or underline the parts you're talking about. This is for the birds.

Am I the only one who is not able to understand mathematical equation at 4:00?

I understand nothing☻

This junk make no sense to me and it's making me mad bro