Because that's a long term risk on subtle differences that may crop up. And if the answer is no more complex than “because the language isn’t very good at parallelism”, then all other arguments are completely moot. I don't mean to pile on, but I kinda have to ask, what were you thinking when you wrote this comment? By using our site, you acknowledge that you have read and understand our Cookie Policy, Privacy Policy, and our Terms of Service. “My application is slow, the language sucks!” Doesn’t indicate a very serious investigation into the problem. I say this mostly because my Go code isn't too dissimilar from my Python code (structure, naming, packages). Python's great at running numpy, scikit, and tensorflow. If you're not sure which to choose, learn more about installing packages. Google never used Python for web development, with one major exception, Youtube, an acquisition. For #1, I think it would be a great idea to include advanced algorithms. Recently, the number of algorithms and data structures we use in competitive programming are rapidly growing. (I'm afraid that most of the projects currently use Japanese for discussion, but I think creating issues or PRs in English are always welcome!). You also want to kill it. Diagnosing your problem with the information might be a bit difficult. [0] https://choosealicense.com/no-permission/. The trick with common algorithms isn't about having prepared stuff, but about having the knowledge on the usage, which usually implies having prepared code and definitely implies general preparation. > Trying to make Python fast is a fool’s errand, because Python is slow by design. Currently we are not sure what's the best way to handle precision issues. But if the code contains C extensions, such as NumPy, then PyPy might actually increase the time. But also, the paragraph at the link I posted gives more details that you should read and follow. Or sometimes you use multiple pre-written codes together, the variable names collide, and get annoyed. Isn't this use case the scientific computing use case? Tough call. "Sometimes a problem asks matching on general graphs; you have to find a paper describing it, read it, and implement its really complicated algorithm. PyOxidizer avoids this, but includes the entire standard library in every binary, making them too large by an order of magnitude. > 20% faster is nothing. -v q --nologo 1>&2', bash -c 'cython -3 --embed ./Main.pyx; gcc -O2 -fPIC -I/usr/include/python3.8 -I/usr/local/lib/python3.8/dist-packages/numpy/core/include -o ./a.out ./Main.c -lpython3.8', bash -c 'cat - > /tmp/out; TERM=dumb vim -N -u NONE -i NONE -s ./Main.vim /tmp/out > /dev/null 2>&1; cat /tmp/out', しかし、「面白かった。A問題はox法を使って解いた」「ox法でA問題がTLEだった」「A問題はあることに気がつけば簡単だった」は、問題に言及する内容を含んでいるのでだめです。, WAの回数、TLEの回数、テストケース数など。これらの情報はコンテスト終了まで公開されていません。, 自分の問題に対する思考過程を書くこと。他者へのヒントとなりうるためです。お気をつけください。, 自分で編集、編纂した問題文。問題文の一部だけを切り出したもの。例えば制約のみ書き込む行為など。. > I would certainly hope a minimal HTTP library would be simpler than a suite of functions to manipulate and plot tabular data. There is a great example that solved this issue: C++ STL's set.

I would say there's a small minority of our changes that we can upstream, and eventually we'd like to upstream them. I read the thread, it's from 2016. Maybe researching prior art in the problem domain would be good too. You guys are the best . Just think: if CPython had been GPL-licensed, this would already be open source (and maybe even merged into upstream).
2, by Meet IT), Sublime Text [FastOlympicCoding] — tools for competitive programming. Or sometimes you have to spend time tuning your library by a constant factor. Likely because pytorch uses C++ under the hood. I have tried this command. By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy. You browse the internet (ofc. Our main concern is the unfairness against Java users. Maybe they can invite uwi to help with Java solutions.