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Re:Quantum Advantage for NP Approximation by zero_k on Thursday October 10, 2024 @05:31AM (#41797136)
By the way, if you truly care about NP-optimization, e.g. MaxSAT algorithms, there's a lot of good work being done, just check out [1]. It's fun, and useful. You could make >$1B just by slightly improving global logistics via such algorithms. Yes, scalability can be an issue, but it only takes some work to cut the problems into chunks, and do local optimization at each level, rather than total global optimization. Yes, it won't be a global optimum, but likely significantly better than what's out there right now. They already use these algorithms for e.g. train and bus schedule optimization.

[1] https://maxsat-evaluations.github.io/

Re:Quantum Advantage for NP Approximation by hnaccount_rng on Thursday October 10, 2024 @09:31AM (#41798613)
Hypothetically: If a friend with interest in this kind of logistics were to be looking for a job. Which company would one tell said friend to look at?
Re:Quantum Advantage for NP Approximation by zero_k on Thursday October 10, 2024 @10:57AM (#41799511)
Cadence is a good one. There are also bus/train schedule optimization startups/companies. AWS is also hiring in this field, a lot, but I wouldn't recommend it. Even though many people there are very good, and I respect some of the individual people there very much.
Re:Quantum Advantage for NP Approximation by alfiedotwtf on Wednesday October 16, 2024 @11:14PM (#41866096)
I fell in love with optimisation problems but haven’t played with them in a while…

What are some books to read more about non-linear optimisation that’s also non-machine learning (I’ve dabbled a lot with metaheuristics and genetic algorithms) but haven’t implemented a SAT solver (only played with what Prolog gives you)