Big-O Notation - For Coding Interviews 

Please unban me on discord server.

Please do teach us the math side of these

Can you please make an advanced algorithm evaluation course I know there are multiple ways to measure algorithms some are good for read/writes others for CPU etc. Can you educate us on what type of algorithmic performance would lower cloud bills haha, stick it to the big 3! I believe they ways to measure are called algorithmic efficiencies: time efficiency, space efficiency, complexity theory, functional dominance, asymptotic dominance. Found a site before that compared algorithms that way but forgot the name! :(

NEET coupon is expired it's showing. In India it's very expensive 130 USD it comes around 10,000 INR

@@akshaymalhotra2550 feel free to email me at neetcodebusiness@gmail

​@@Corvurax1In accordance with the sagacious principles elucidated by the eminent philosopher of modern digital discourse, the revered YouTubian's Third Law of Virtual Dynamics declares that within the realm of virtual discourse, for each "wholesome comment" that emanates from the depths of digital appreciation, a commensurate and opposing "vitriolic comment" shall arise, thus maintaining a harmonious equilibrium within the intricate tapestry of online interactions.

As a holder of the master's degree in applied mathematics and software engineer for 25+ years I'd agree with that.

@@sergio_henrique damn your writing skills are fireee

You got your big O ?

also algorithms with complexity related to height of tree

floyd warshall too

yess! please!

thanks stan!

Idiot

Yup this is super important to note, because a lot of times big O is explained as a perform tuning metric. But really it’s about scalability. Scalability is just one aspect of performance tuning. Most of the performance bottlenecks I run into in real world scenarios have more to do with simply not archiving old data or not throwing away bad data. It’s much more common to see a memory or IOPS bottleneck than a cpu bottleneck at the jobs I’ve worked.

There's also much data that however-bad-at-big-O code actually may work fine and efficient at proper low scale while the big-O-good-enough code at that scale would suck.

friend*

I would say that he actually has a deep understand of this. Like Feynmann put it, when you actually know something you can explain it to a 5 year old.

How's it going, Moe?

This video did not talk about the mathematical understanding behind time complexity, which is something you learn in computer science.

But since we are doing it in a while loop, it is n+nlog(n). heappop takes log(n) and since we need to perform that for every element in the heap, it becomes nlog(n)

Deleting from the end of an array should also be constant time. But you're right, nothing is simple.

This might come from the misconception that big O means average case, ignoring its formal definition. He also says most sorting implementations are nlog(n), which again is average case but not big O. Even merge sort, which is actually O(nlog(n)) in *time* complexity, is slower in practice than something like in-place quicksort because of its huge penalty by using linear memory, which is something completely missed here. Same for appending to an array, which is fast until it requires reallocation when it grows beyond its current capacity. Unfortunately though, I suspect many interviewers just want to hear the simplest version possible, like nlog(n) for sorting and O(1) for hash map insertion, which you can do by memorizing these formulas but doesn't imply an understanding of the concept.