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.
It's fast, which is great. But, you also don't explain things: "In big-O, you can ignore all of these constants"... no you can't; in that special case, you can; though, in general, you cannot.
In general, constants are completely ignored in Big O. Those constants can be significant when viewing it from a non-o perspective. Big O drops the constant because it's not what it was designed for. In big O, O(10¹⁰⁰n) is still just O(n). Linear time. O(10¹⁰⁰) is still just O(1), constant time. So, no, it's not just this one case. It's universal.
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)
@@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.
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)
What is "Big O" ??? Where do You get it from?? From where are you getting those equations from?? Nothing Makes Sense... Oh My GOD... 😭😭😭 Edit :. Ok i watched the whole video and now i understand a lot of it. Lol
-Take these UA-cam instructors (ones that have genuine skill, no nepotism, racism, ageism etc) -Hire them into university with proper wage. -Keep facilities basic and dedicated to teaching skills only -Any student can afford. -stop paying garbage universities for slides ??? -profit -better world, degeneracy removed, education system overhauled. No students struggle. Utopia
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 .
@@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
@@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)
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?
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
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
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.
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)
man no matter how much i focus i can't understand time complexity 😥
You explained ten times better than my lecturer did in just 8-minutes, thank you
You could also mention the complexities like O(logn), O(nlogn) etc. and the concepts of Master Theorem. It would be better.
It's fast, which is great. But, you also don't explain things: "In big-O, you can ignore all of these constants"... no you can't; in that special case, you can; though, in general, you cannot.
In general, constants are completely ignored in Big O. Those constants can be significant when viewing it from a non-o perspective. Big O drops the constant because it's not what it was designed for.
In big O, O(10¹⁰⁰n) is still just O(n). Linear time. O(10¹⁰⁰) is still just O(1), constant time. So, no, it's not just this one case. It's universal.
I was too much Dived into his Teaching .That I heard someone sparking gas Stove lighter at 1:34😂
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
Bruh you licking the mic or something?
6:04 Time Complexity for Sorts
Perfect explanation...plzz upload more videos on complexities... 👌
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 explained this misery better than my teacher ever could
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)
How did my professor overcomplicate this?
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 thought you're going to explain the other complexity as well like O(log n), O( n log n) etc...
i wanted that ffs
Nice explanation now drink some water
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!
Incredible, I find myself at a loss for words to adequately express the precision and clarity of the elucidation.
Who also notice lighter sound at 1:34
This video explains only O(1),O(n) and O(n^2)
Don't waste your time if your looking for other time complexity
What is "Big O" ??? Where do You get it from?? From where are you getting those equations from??
Nothing Makes Sense...
Oh My GOD...
😭😭😭
Edit :. Ok i watched the whole video and now i understand a lot of it. Lol
-Take these UA-cam instructors (ones that have genuine skill, no nepotism, racism, ageism etc)
-Hire them into university with proper wage.
-Keep facilities basic and dedicated to teaching skills only
-Any student can afford.
-stop paying garbage universities for slides
???
-profit
-better world, degeneracy removed, education system overhauled. No students struggle. Utopia
Great efforts.
Thank you:)
Regards from USA
Any one from AIUB?🙂
Yes
The best !
thanks! i finally understand bigO notations! the last part also is a big bonus for me. thank you!
I really appreciate the way you teach us. Thanks a lot sir : >)
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
i understood this in bigO(n) time lol
Why didn't you explain what is this beautiful 'k' character in formulae means?
Simple and great content. I wonder how my prof taught
As time moves 💕
Thanks for the video! It was clear and really practical :)
How is the sequential O(n)? Does it add up all together? I thought it should be O(2n)
This is called clear concept 🙌✨
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
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
Excellent explanation and easy to understand. Thank you!
Thank You So Much for this wonderful video......................🙏🏻🙏🏻🙏🏻🙏🏻🙏🏻🙏🏻
Repent and forsake your sins and follow Jesus now he's coming
Wow sitting in class 1hr 30 m and watch a 8m video…
wouldn't the loop at 2:56 run n+1 times for the outer loop running n times?
Best explanation, thank u
Can anyone plz explain space complexity again not got that 4*4+4
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)
Thanks! this helped a lot for my CS 211 class.
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?
complex code examples make the videos in TC&TP
Thank you for this
Regards from Russia
best video explaining complexity. the complexity of complexity have been resolved here :D
what will be the ans if we take int size = 1 in space complexity
Interesting mall 2015 game..
If i
No
Great video. Thank you so much! Very helpful
i love u dude.
Artificial intelligence🤖🤖 ka support dilva do phir sari duniya following👌 karegi😇😇😇😇 apney subscribe wale ko 👌👌👌
watch abdul dari algo videos and you will become master
u rock man
Smooth ✨
thank you, very helpful video.
Pls upload some video regarding to gate cs
Bewakhoof saratime barbad 😡kardiya
Thanks man
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
Best video ever watched ❤️
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?
Please make a video on space complexity
I really appreciate the way you teach us. Thanks a lot sir : >)
when u said welcome to video i felt rly welcomed thank u my friend! also very helpful video!
😂😊😊😊😊😊😊
👏
thank you! very helpful video
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
Thank you for this video.
4:18 should be 2n no?
he already said you dont use the constant, 2n is just treated as n. The thing i am struggling with is k
Precise illustration. Thank you
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
for(int i=0; i
O(n)
Finally, I got something that I wanted!!
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)
my computer gets disability benefits everytime I visit ur site, why?
In Sequencial example why is the time complexity not equal to n+n?
Very good explanation, Learned in 10mins
Indian Engineer's lives depend on GFG. without it IT industry will fall
Thanks for the explanation of time complexity. But you missed log n and n log n
Well done
Very clever and clear explanation
Many Thanks
Thank you for the resource!
for(i=0; i
simple and sweet!
Hey priti !!
😘😘😘
Real decent.
very nice
Really to the point and efficient explanation. Kudos
I appreciate your work. How about the for loops: for(i=1, i
O(n^2)
It was the best video on UA-cam about Time complexity. Best wishes for you bro
Thank you very much!!!!❤
at 4:19 wouldn't that be n^2?
Tere toothbrush me salt hai?
Thanks that video was incredible...your incredible!
Man! You're the best! This is what i need
Thank You!