What Is Asymptotic Analysis? And Why Does It Matter? A Deeper Understanding of Asymptotic Notation.
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First, we must ask what asymptotic means. Well, you have probably heard of the word "asymptote".
An asymptote is a "line that continually approaches a given curve but does not meet it at any finite distance".
Therefore, asymptotic analysis is the analysis of tail behaviors not reaching any finite point. It is a method of describing limiting behavior.
Wall Time vs. Asymptotic Complexity
Well...why not just measure the seconds our code takes to run? And get the Elapsed real time (wall time)?...like Leetcode.
So why do we care about this...well in computer science we often deal with problems that are at a grand scale with inputs to the order of millions and billions.
And thus, the true measure of the efficiency of an algorithm is best expressed in its tail behavior on very large input. It only then shows its true colors.
An Expression of Asymptotic Behaviour
Insertion Sort: 2 * n^2
Merge Sort: 50 * n * log(n)
We have 2 computers:
Computer A: runs 10 Billion instructions / second
Computer B: runs 10 Million instructions / second
Computer A is 1000x faster than Computer B
Computer A runs insertion sort, Computer B runs merge sort
How long will each computer take to sort 10 million numbers?
Computer A: 5.5 hours
Computer B: 20 minutes
A computer that runs 1000x faster lost horrendously to a computer that runs 1000x slower than it.
But the thing is that insertion sort will be faster for an initial amount, but it will lose as the input gets larger (and that's what we care about and what is a true expression of its efficiency).
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Пікірлер: 110
Table of Contents: Quick Intro 0:00 - 0:22 For Loop Madness 0:22 - 0:31 What Does Asymptotic Really Mean? 0:31 - 1:27 Tail Behavior 1:27 - 1:48 Why Elapsed Real Time Is Unreliable 1:48 - 2:31 What We Should Be Interested In 2:31 - 2:55 Racing 2 Computers 2:55 - 3:40 Who Will The Winner Be? 3:40 - 3:52 The Approximate Running Times 3:52 - 4:28 Investigating Linear Functions 4:28 - 5:28 The Graphs Share Something In Common 5:28 - 6:14 Why Do We Drop Constants? 6:14 - 6:40 What I Really Mean When I Say "Linear" Time 6:40 - 7:03 Wrap Up 7:03 - 7:45 Notes: 0:28 -> For those curious, the work that the 2 for loops do would be bounded by O(n). This is because for each of the n iterations of the outer loop, we will perform 10 iterations in the inner loop...do you notice how n does not influence the inner loop? In fact, we notice that the work of 10 (from the inner loop) for each of the outer loop iterations is...constant. Constant in time......O(1) time. So O( n * 1) = O(n). Where n is a measure of an arbitrary input. 2:54 -> Credits to Clyde Kruskal & Mohammad Nayeem Teli (both teachers at the University of Maryland) for the example of Computer A & computer B running 2 different algorithms. That was not an example that I created myself. 5:01 -> no idea why the video looks faded out...camera was acting weird. 6:40 -> Big O is only 1 way we can bound tail behavior. There are others.
Best Explanation EVER!!!! I Mean it! ASYMPTOTICALLY
@BackToBackSWE
4 жыл бұрын
thanks
Thank you SO MUCH! The best introduction to this concept that I have come across.
There are too many sites that try to give a quick crash course a month or two months before the interview, but I really like how dive deeper and take your time to explain things. Also love the fact that your content is more academic in nature. Thank you for your hard work man, its much appreciated, def gonna check out your platform.
@BackToBackSWE
4 жыл бұрын
Sure!
I am getting started with algorithms.Still confused but this video made it a little more clearer. Good job bro
Excellent ! I haven't come across such a fantastic explanation so far !
Thank you so much for helping me understand this concept, this was a great explanation.
did I understand shit? nah did I enjoy this due to your fun energetic personality? hella
@BackToBackSWE
4 жыл бұрын
that's what it's all about
Thank you for the examples that you used. This is such a good and less scary introduction to time complexity analysis
Boyyy YOU GOING CRAZY. Great Explanation!
I really love your videos. Thanks so much for clarifying so many black knowledge holes 🕳 for me!
I was having a problem with understanding what "asymptotic" really mean and you helped me ..thank you very much
love the energy !
That's a great explanation
Thank you very much!
Very good! Of course I Subscribed. I was looking for a function of asymptotic penetration in quantum immersion and you made it very clear. Thanks.
@BackToBackSWE
3 жыл бұрын
thx and thx
Wow this was so clear! I love the graphics popping in and out and the way your voice inflections keep us alert. Great teaching skills!
@BackToBackSWE
7 ай бұрын
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that is very very good explanation
Great explanation
Always a pleasure to watch your video 😇.
By far the best explanation I have encountered!
@BackToBackSWE
4 жыл бұрын
nice
You are doing such an amazing job, keep them video coming...
@BackToBackSWE
4 жыл бұрын
ok
bro ur better than 99% of the online educators and even some professors!
This made it indeed clearer! Thanks a lot
@BackToBackSWE
4 жыл бұрын
nice
Fantastic video, love the ending 😂
ohh my f god! this was so quick, yet so helpful. thank you so much!
@BackToBackSWE
Жыл бұрын
Thank you, means a lot 🎉 You can also check out our free DSA course - backtobackswe.com/
This is mind blowing how you explained it. Keep it up. I subed asap.
such a clear explanation! thanks!!
@BackToBackSWE
4 жыл бұрын
sure
Thanks!
Two years in college and it wasn't this clear for me.....Thanks, man!
@BackToBackSWE
4 жыл бұрын
nice.
Absolutely phenomenal. I just wish you applied examples, to show us how you find the notations. Too bad you didn't cover Recurrence relations as well :(
@BackToBackSWE
4 жыл бұрын
thanks and yeah
Awesome :) Thanks for this.
@BackToBackSWE
4 жыл бұрын
sure
thank you!
Helpful! Thanks
@BackToBackSWE
4 жыл бұрын
sure
Thanks, you explain very clear.
@BackToBackSWE
3 жыл бұрын
sure!
Good video
Thanks 🙏🙏
you are the GOAT
Bro thanks
yo man thanks for blessing up with such informative videos. Your Asypmptotic videos cleared so many confusions. Just one thing, can you make a video explain how to add and mull algos. Like how do we know by analysing algos if its linear,quad,log, n log n etc
@BackToBackSWE
5 жыл бұрын
haha nice, yeah I'll do that
Good explanation Thanks
@BackToBackSWE
4 жыл бұрын
sure
Aaaaaahhhaaaaa!!! I just got it!!! Thanks, man! :D
@BackToBackSWE
4 жыл бұрын
cool
@MichaelWaisJr
4 жыл бұрын
Back To Back SWE Yes, you have contributed greatly to my Mad Ninja Skills! :D Thank you!!
a really great job
@BackToBackSWE
4 жыл бұрын
thx
Thank you!
@BackToBackSWE
4 жыл бұрын
sure
Thank you so much for such a clear explanation :)
@BackToBackSWE
4 жыл бұрын
sure
How did you come up with the 5.5 hours and 20 minutes? Where did you get those values from?
Your channel is a lifesaver for self-taught programmers...
@BackToBackSWE
2 жыл бұрын
Thank You!! Do check out backtobackswe.com/platform/content
For your 100k milestone
@BackToBackSWE
3 жыл бұрын
Ye
Amazing
@BackToBackSWE
4 жыл бұрын
thanks
damn that was good. thanks.
@BackToBackSWE
4 жыл бұрын
sure
Great explanation!
Wow! It's was very clear (a little bit more clear than Udi Manber(1989) hahaha).
@BackToBackSWE
4 жыл бұрын
great
Thanku......
@BackToBackSWE
4 жыл бұрын
thank u
hey bro, i wanna learn asymptotic and bigo notation completly. i watched your video it help me. but i want learn completly. could u suggest books or link or make a complete playlist on it. btw u r awesome in explain topics.
@BackToBackSWE
5 жыл бұрын
I made a video on it a while back but it was badly shot and badly edited (you can find it on the channel). I will redo it but that video (if you can bare the bad quality lighting) is pretty exhaustive.
@lokeshprajapati9197
5 жыл бұрын
@@BackToBackSWE ok i will try or why you not planing to shoot it again. in my college time bigo notation is hard to understand. but now atleast i am understanding it. what is the actual meaning of log(n). Thanx
A very humongous thanks to you 🙌
@BackToBackSWE
4 жыл бұрын
yw
I really appreciate this explanation because you made a point to explain why asymptotic complexity is important
what is "li code" that he talks about at 2:00 ?
Hi, Let's say I was going through a list of numbers and "offering" them into a priority queue whose offer function is log(n), Will this be n*log(n) because I'm doing a log(n) operation n times or will it just be O(n) because the operation itself doesn't matter since the algorithm runs n times? I think I saw you say something about algorithmic complexity vs runtime complexity in one video but I couldn't find it again lol
@BackToBackSWE
5 жыл бұрын
Since you do n operations that take O(log(n)) time it will take O(n * log(n)) time. This does not pertain to heapsort. For heapsort, building the heap takes ϴ(n) time and the extraction phase happens roughly n times and each extraction of the min/max element takes O(1) time (we just pull it from index 0 in the array representing our heap) and then the heapification "downward" of the element swapped to the root will take O(log(n)) time. So yeah, for what you said, O(n * log(n)).
Wow!!
@BackToBackSWE
3 жыл бұрын
yes
U r champ
I wasn't looking for this video and stumbled into it. Your first graph is incorrect. It should be 1/x^2 Hope this helps
Subscribing is a MUST!
@BackToBackSWE
4 жыл бұрын
thanks haha
salamuch homie
cool
Do some examples!!!
@BackToBackSWE
4 жыл бұрын
ok
@ThangHoang-ub8xs
3 жыл бұрын
Be kind, man!
This video must have been shot on the 13th of October...
@BackToBackSWE
4 жыл бұрын
why
If someone asks you what is the asymptotic solution of a given problem? don't tell him a definition of the asymptotic solution instead tell him that at this arbitrary boundary condition 'a' our model should give us an already known solution 'y'. This known solution is maybe from previously developed models or from experiments. Thoughts?
Yep reality is computer code basically holy crap awkward😮
I liked the video, but it was not too clear. You can do it better
@BackToBackSWE
4 жыл бұрын
ok haha