131–140 of 188 posts
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I only wish the sarcasm was as painfully obvious.
If that was sarcasm, apologies. In a better world, I'd indeed assume it was, but social justice warriors have spoken to me in that manner with a straight face, and it's hard to know for sure.
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We've banned this account for taking HN threads repeatedly into gender flamewar, and other kinds of flamewar, and ignoring our many requests to stop—such as this explanation from just two weeks ago: https://news.ycombinator.com/item?id=24569144 . Vandalizing HN like this is not cool.
Except maybe for current comment (not really, HN just has more replies on anything these days), nothing actually turned into flamewars, most out of his few "flamewar" comments over many months barely had any replies at all and were just flagged. It seems you are labeling them as flamewar just so you could justify banning him. Possibly because he expressed opinions you consider "politically incorrect" today and you wa…
Anyone who sincerely wanted to follow the rules at https://news.ycombinator.com/newsguidelines.html would be behaving completely differently. Sincere misunderstandings are generally quite easy to clear up. If someone doesn't sincerely want to follow the site guidelines, they shouldn't be posting here. We're trying for a specific kind of community.
Even the worst case is easy to fix. If someone doesn't want to be banned, they're always welcome to email hn@ycombinator.com and give us reason to believe that they'll follow the rules in the future.
This is kind of a hijack, but it's at least related: Are there any websites/apps that can approximate a solution for road destinations in the real world? For example, given a list of cities, an approximate solution on actual roads to visit all of them once?
> able to subtract 0.2 billionth of a trillionth of a trillionth of a percent ... > “This is a result I have wanted all my career,” said David Williamson of Cornell University, who has been studying the traveling salesperson problem since the 1980s I love this. Oh to be a theoretician. Here's hoping for a flood of further improvements.
Meanwhile in an agile dev team.... "can you optimize it in the next sprint?"
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Lately it feels like agile is where innovation goes to die :(
I agree. It’s sad. Agile principles are awesome, but by the time you run them through many company’s implementations of Scrum or SAFe or (God forbid) Jira, you may as well just do waterfall. I don’t think waterfall is necessarily bad, that agile is always right, or that there aren’t other good practices. But practicing waterfall and calling it “agile” is rarely going to end well for anyone.
Or use deep learning to generate solutions 2 orders of magnitude faster with some minor accuracy loss. https://arxiv.org/pdf/1905.05567.pdf
From the paper you linked:
heuristics based solutions are still superior to deep learning based solvers regarding accuracy and execution time for larger problem sizes.
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Looking at the BOTW solution is doesn't seem to take into account the fact that a straight line is not the fastest solution. Climbing is incredibly slow, swimming is slow and perilous, in some places such as the dark forest you can only enter/exit in one place, not randomly, you can travel faster on roads. I guess later in the game one doesn't mind randomly walking right by the castle just because it is the shortest…
Hey man, this is just a person’s fun and imaginative side project. Is there a reason you’re offering such serious and negative critical commentary on this?
While it is wonderful they are getting closer to solving it, if your problem is a simple and practical you can try some alternative algorithms. For fun, I used pytspsa [0] which uses the simulated annealing method to solve the TSP and applied it to BOTW. Simulated annealing was inspired by thermodynamic free energy involved in crystal formation from annealing metal. [1] I combined it with a Breath of the Wild waypoin…
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This is great, thanks for sharing! I had good results with genetic algorithms as well.
There are also TSP-specific heuristics that work well in practice on a lot of large instances, often finding the optimal solution pretty quickly (but with no guarantees of optimality). The first I believe was the Lin-Kernighan heuristic from 1973 (the same Kernighan as the 'K' in K&R C, incidentally). There are fast implementations of some modern improved versions, e.g.: http://akira.ruc.dk/~keld/research/LKH/