Bravo, that was the clearest yet most complete 8 min intro to complexity on RUclips. 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.
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)
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)
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
thanks for the video. Can we use inbuilt functions in Javascript like sort and reverse in the interviews? If so, what should we assume of their complexities?
look up Tim Sort If the question does not ask you to use any sorting algorithm then you may at first use built in ones, then if the interviewer asks you to optimize you can pick an algorithm that suits the question
Some things still don't make sense to me. Where exactly did the 4*4+4 come from for your 1st space complexity example? I get that an int variable has 4 bytes but how did you end up with the operation of multiplying two 4s and then adding them to one 4?
In 4:22 when calculating the time complexity of the sequential loops, once we take c1, c2, c3 aren't we left with two "n " therefore "= o(n^2) . Im not sure my logic is taking me there if someone can explain to me why I'm wrong pls thank you Great video btw thank you .
@@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)
@@ashwanimishra5829 But this is just wrong practically. For example if 1st loop taking 1 second and other one is also taking 1 second to run then total time taken to run will be 2. But according to O(n) time taken will be 1 second
Bravo, that was the clearest yet most complete 8 min intro to complexity on RUclips. 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.
You could also mention the complexities like O(logn), O(nlogn) etc. and the concepts of Master Theorem. It would be better.
Incredible, I find myself at a loss for words to adequately express the precision and clarity of the elucidation.
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)
Excellent explanation and easy to understand. Thank you!
You explained this misery better than my teacher ever could
6:04 Time Complexity for Sorts
Thank You So Much for this wonderful video......................🙏🏻🙏🏻🙏🏻🙏🏻🙏🏻🙏🏻
this guy taught me more in 8 minutes than my professor did in 3 weeks love u bro❤
I appreciate your work. How about the for loops: for(i=1, i
O(n^2)
Perfect explanation...plzz upload more videos on complexities... 👌
best video explaining complexity. the complexity of complexity have been resolved here :D
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)
Thank you very much!!!!❤
thanks! i finally understand bigO notations! the last part also is a big bonus for me. thank you!
It was the best video on RUclips about Time complexity. Best wishes for you bro
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
Simple and great content. I wonder how my prof taught
As time moves 💕
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)
Thank You!
Thanks, that was the most enlightening explanation it to complexity after lot of confusing video explanations on YT. You are the best !
Thank you , you saved me for exam today.
thank you, very good teaching algorithms
This is called clear concept 🙌✨
Best explanation, thank u
Precise illustration. Thank you
I really appreciate the way you teach us. Thanks a lot sir : >)
Great efforts.
Thank you:)
Regards from USA
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...?
very clear and understandable .... thank u for ur vedio
Really to the point and efficient explanation. Kudos
Man! You're the best! This is what i need
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)
thank you, very helpful video.
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
Thanks! this helped a lot for my CS 211 class.
Thanks for the video! It was clear and really practical :)
Best so far. you saved me
Well done
Very clever and clear explanation
Many Thanks
Best video ever watched ❤️
Thank you for the resource!
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...
I was too much Dived into his Teaching .That I heard someone sparking gas Stove lighter at 1:34😂
Thank you for this video.
Very good explanation, Learned in 10mins
Indian Engineer's lives depend on GFG. without it IT industry will fall
You are very clear and easy to understand. Thanks...😍😍😍😍
thanks for the video. Can we use inbuilt functions in Javascript like sort and reverse in the interviews? If so, what should we assume of their complexities?
Any updates? Did you find out?
look up Tim Sort
If the question does not ask you to use any sorting algorithm then you may at first use built in ones, then if the interviewer asks you to optimize you can pick an algorithm that suits the question
How is the sequential O(n)? Does it add up all together? I thought it should be O(2n)
for(i=0; i
when u said welcome to video i felt rly welcomed thank u my friend! also very helpful video!
😂😊😊😊😊😊😊
Wonderful! Best explanation in 8 minutes
Can you do some formula in odd even betting? Like the interesting mall game? I am your new subscriber.. I hope you will reply sir. Thank you!
This is the best explanation i have come across so far
for(int i=0; i
O(n)
Really superb and very useful. Very good explanation...
Some things still don't make sense to me. Where exactly did the 4*4+4 come from for your 1st space complexity example? I get that an int variable has 4 bytes but how did you end up with the operation of multiplying two 4s and then adding them to one 4?
Really good introduction of time complexity. Thank you!
Great video. Thank you so much! Very helpful
Thank you for the great explanation straight to the point.....
In 4:22 when calculating the time complexity of the sequential loops, once we take c1, c2, c3 aren't we left with two "n " therefore "= o(n^2) . Im not sure my logic is taking me there if someone can explain to me why I'm wrong pls thank you
Great video btw thank you .
it is not nested therefore it is not multiplied, it will be just c1+(c2+c3)n => O(n)
Thank you dude...I too had same doubt...but u helped me... 😍
@@Typw thanks a ton
@@Typw why n instead of 2n? ive seen O(2n) lots of times, this is clearly a case
Wonderfully put
Very nice. Thanks
Excellent ! Thanks a lot.
Wonderful explanation! You're the best 💯
Thanks for the explanation of time complexity. But you missed log n and n log n
Best tutorial ever on this topic...
Thanks
I thought you're going to explain the other complexity as well like O(log n), O( n log n) etc...
i wanted that ffs
Really well explained. Thank you!
Such a good way of teaching
Very well done 👍👍👍
This was a wonderful introduction! Thank you
very good explanation and easy for beginners to understand thank you❤️
I think for 3 sequential statements time complexity is 2n+1
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
@@ishika6945 constants aren't important and are ignored in time complexity. It would not impact much or any
@@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)
@@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)
Please make a video on space complexity
bro drink some water
bro my eye scaned the comment unintentionally, and I just couldn't unhear it any more 😂😂😂😂😂😂😂😂😂😂😂😂😂😂😂😂😂
Same😂😂😂😂😂😂
😂
😂😂😂😂😂
I came here to study..now I can't stop laughing
short and great..!!
Thank you for this
Regards from Russia
For Example, 3 (Sequential Statements) should not be "2N" ?? because we are adding up.
2 is again a constant.. I think that's why he dropped it
@@amanbutt7460 but statement is c1+c2n+c3n so we will drop the constant and it will become n+n which is 2n
@@mirzaadilbeg9209 and when it will be n+n=2n we will not consider 2 and still only have O(n) left
Love from Pakistan well brother he is generalized it like it will be usable in every algorithm
The best !
Best lecture 😊
Thanks that video was incredible...your incredible!
Pls upload some video regarding to gate cs
simple and sweet!
Hey priti !!
😘😘😘
Why is c2n+c3n only o(n) should ot be o(2n) assuming we only ignore constant term i particular line of expresson and statement
Finally, I got something that I wanted!!
4:11 - Why for sequential statements, you don't add the n's together to make O(n + n);
I thought the same.
When we add them they become 2n and we can ignore the constant so it all comes down to O(n)
@@ashwanimishra5829 But this is just wrong practically. For example if 1st loop taking 1 second and other one is also taking 1 second to run then total time taken to run will be 2. But according to O(n) time taken will be 1 second
You reached us wrong in sequential statement . Complexity will be O(n+n)
Am I correct or not??
4:20
I also have confusion in sequential statement. He took constant C out, so logically it has to be O(n+n) as a consequence, NO?
kamal boss
Very Helpful.Thanks a lot
Thanks man
Thanks for the video
Thank you!
Why didn't you explain what is this beautiful 'k' character in formulae means?
Smooth ✨
This is a great explanation.
how can we remove time complexity of an sorting algorithm like bubble or selection sort ?? do you have a video on that ??
Thanks, I learned a lot!
Thank you
I understand the topic, thanks :)