291–300 of 551 posts
- Constraint solvers? That's a nice concept, I heard about this once. However, for the purposes of the interview, let's just write some Python code, I wanna see your way of thinking... (I think it's almost impossible to convince your interviewer into constraint solvers, while the concept itself is great)
import z3
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I'm pretty sure that due to Rice's theorem, etc., any finite set of heuristics will always miss some constraint problems that have an efficient solution. There's very rarely a silver bullet when it comes to generic algorithms.
I think they're saying that the types of counter-examples are so pathological in most cases that if you're doing any kind of auto-generation of constraints - for example, a DSL backed by a solver - should have good enough heuristics. Like it might even be the case that certain types of pretty powerful DSLs just never generate "bad structures". I don't know, I've not done research on circuits, but this kind of analysi…
Your best bet using them is when you have a large collection of smaller unstructured problems, most of which align with the heuristics.
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The LC interviews are like testing people how fast they can run 100m after practice, while the real job is a slow arduous never ending jog with multiple detours and stops along the way. But yeah that's the game you have to play now if you want the top $$$ at one of the SMEGMA companies. I wrote (for example) my 2D game engine from scratch (3rd party libs excluded) https://github.com/ensisoft/detonator but would not b…
>The LC interviews are like testing people how fast they can run 100m after practice Ah, but, the road to becoming good at Leetcode/100m sprint is: >a slow arduous never ending jog with multiple detours and stops along the way Hence Leetcode is a reasonably good test for the job. If it didn't actually work, it would've been discarded by companies long ago. Barring a few core library teams, companies don't really care…
A loonnngggg time ago when I was green, and wasn't taught about constraint solving in my State University compsci program, I encountered the problem when trying to help a friend with his idea. He wanted to make an app to help sports club owners schedule players for the day based on a couple simple rules. I thought this was going to be easy, and failed after not realizing what I was up against. At the time I didn't ev…
This might be a dumb question (as I'm not familiar with constraint solvers) but would a linear optimization approach be better? I've used linear optimization for scheduling in the past. The nice thing is that linear optimization handles rule conflicts well, because you just set weights on all your rules and the optimizer will find the "least bad" solution to the conflicts.
But CP (and CP-SAT) solvers tend to do very well on scheduling problems
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I interviewed at an investment bank in London and they asked me pretty hard questions. One was to implement some multithreaded producer consumer thing in C++. I can't remember the details but it was... well you know how writing multithreaded C++ is. I was allowed to look up references at least. Took me maybe 20 minutes and the whole time the interviewer was just sitting on his phone while I wrote it. Weird experience…
If you wrote an MPSC queue (standard question) with multithreaded demo in 20 minutes in C++ you’re pretty hot shit, mate. Their loss. It’s not that it’s hard. But that speed without error is just really good. C++ is particularly unforgiving too.
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The point is to test whether or not you put in the time to sharpen common patterns and also to test your communication ability
Super common patterns like dynamic programming?
In my notes I have roughly 30 patterns to leetcode questions btw.
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The LC interviews are like testing people how fast they can run 100m after practice, while the real job is a slow arduous never ending jog with multiple detours and stops along the way. But yeah that's the game you have to play now if you want the top $$$ at one of the SMEGMA companies. I wrote (for example) my 2D game engine from scratch (3rd party libs excluded) https://github.com/ensisoft/detonator but would not b…
>The LC interviews are like testing people how fast they can run 100m after practice Ah, but, the road to becoming good at Leetcode/100m sprint is: >a slow arduous never ending jog with multiple detours and stops along the way Hence Leetcode is a reasonably good test for the job. If it didn't actually work, it would've been discarded by companies long ago. Barring a few core library teams, companies don't really care…
This that I've singled out above is a very confident statement, considering that inertia in large companies is a byword at this point. Further, "work" could conceivably mean many things in this context, from "per se narrows our massive applicant pool" to "selects for factor X," X being clear only to certain management in certain sectors. Regardless, I agree with those who find it obvious that LC does not ensure a job fit for almost any real-world job.
Interviewer: You can't use a constraint solver
My biggest problem with leetcode type questions is that you can't ask clarifying questions. My mind just doesn't work like most do, and leetcode to some extent seems to rely on people memorizing leetcode type answers. On a few, there's enough context that I can relate real understanding of the problem to, such as the coin example in the article... for others I've seen there's not enough there for me to "get" the ques…
SAT, SMT, and constraint solvers are criminally underutilized in the software industry. We need more education about what they are, how they work, and what sorts of problems they can solve.
At least personally, I've been very underwhelmed by their performance when I've tried using them. Usually past a few dozen variables or so is when I start hitting unacceptable exponential runtimes, especially for problem instances that are unsatisfiable or barely-satisfiable. Maybe their optimizations are well-suited for knapsack problems and other classic OR stuff, but if your problem doesn't fit the mold, then it's…
Of course, NP hard problems become complex at an exponential rate but that doesn't change if you use another exact solving technique.
Using local-search are very useful for scaling but at the cost of proven optimality