Recently, the number of algorithms and data structures we use in competitive programming are rapidly growing. It would be amazing to have Python bindings for this library. We knew things could get >10X faster with JIT. Pretty much. the project will need to be funded to the tune of $2M. appear to be useful in your problems as well as in every other problems and both of them require preparation. How would Earth turn into debris drifting through space without everything at its surface being destroyed in the process? Not just the package ecosystem, but you also have to have a large number of developers and jobs, so you can do hiring. Also, you ofc. Podcast 286: If you could fix any software, what would you change? For example, we won't use "paste a segtree, then do more implementation after that" kind of problems.
Asking for help, clarification, or responding to other answers. That being said, those libraries are already highly optimized and all the heavy stuff running on C anyways, so making the Python itself faster won't make that much of a difference in those workflows. Is CPython really slow in integrating community contributions? This definition excludes castng, reflection, and dynamic typing. Python's great at running numpy, scikit, and tensorflow. The Overflow #47: How to lead with clarity and empathy in the remote world, Feature Preview: New Review Suspensions Mod UX, Calling a function of a module by using its name (a string). This is why I see little hope for Python, which is to say that while I'm sure it will continue to have a large following for many years a la C, C++, etc, I don't have hope for it being an exciting language or one that is particularly productive. You can also install the in-development version 0.0.1 with: Note, to combine the coverage data from all the tox environments run: Download the file for your platform. I would also expect community-built libraries to have closer-to-optimal perf as more people contribute. "Sometimes a problem asks matching on general graphs; you have to find a paper describing it, read it, and implement its really complicated algorithm. There is a great example that solved this issue: C++ STL's set. I'm not versed in the details of the politics of CPython, but why did this project fork instead of just contributing to CPython? Otherwise, as I say, anything less than a magnitude’s improvement isn’t even worth getting out of bed for. I write Go every day, and come from a Python background. IMO, there are much stronger reasons in favor of developing such a library: This is amazing but I also agree with Radewoosh that there are some issues.
My last impression on Pyston was that it got killed by Dropbox. If the effort were to fail, then CPython's primary maintainers will have wasted a whole bunch of time coordinating with the JIT effort. The Python community was well on their way to creating Python 3 before work started on Unladen Swallow (so it was not in any way "Python's response"). answered May 16 at 18:12. Can't be, insta-rejected.
I may have a blindspot, but the languages that get enough buzz for me to notice are either not competing with Python in important dimensions (e.g. Small mistake: there are references to inferno, the operating system which limbo was a key component in. But it will definitely be confusing if codeforces / other OJs decide to build their own library as well. Wow.
So who does it right? Else how does a project evolve at all? Are priests who committed a sin together able to validly administer the Sacrament of Reconciliation to each other? unused variables cause compilation errors, so things like this slow me down. If you fork it you have to deal with all the legacy code. > I'd love to find a viable competitor to Python that's strictly better than it. I think that this library would only be useful to those who either understand the underlying algorithm as a blackbox, or don't have a good enough implementation in place — so they can always refer the implementation, and make changes to their own. I am playing with the AtCoder Library Practice Contest problems and it turns out the networkx algos have terrible constant factors.
So CPython won't be interested, and many others won't be interested either. Having a lot of competing interpreters/compilers helps define what the core of the language actually is, and which of your assumptions are hinged on that single implementation. You seem like a valued member of the community and this kinda makes me distrust the accuracy of everything else you write. https://github.com/atcoder/ac-library We published the public github repository. I would rather that everyone has access to black-box implementations than some people copy black-boxes and other people spend time in-contest re-implementing well-known algorithms. Ugh.
Was this confirmed elsewhere? Here's one way to implement a template for lazy propagation. Will you make the time limits lower because you think that your implementation is the best (hint: it isn't)? Maybe they can invite uwi to help with Java solutions. Google did not hire Rob Pike with Go in mind. No, usually you should know how to use these algorithms and if you do, then you most likely already have it implemented, so you won't give such problems because they still require knowledge. (Edit: I've broken my own rule and given advice without first asking what kind of programming you do. We will keep the "easy implementation" rule in the future — the next admin maroonrk even says that in order to guarantee that, he is planning to write his solutions to all problems in Python. The strongest of programmers (without any constraint on them being from the same university, country, etc.) Or sometimes you use multiple pre-written codes together, the variable names collide, and get annoyed." But the article just killed me. In apply of document_en/lazysegtree.html, currently it's written that: It applies a[p] = f(a[p]), but in this line of code, d[p] = mapping(f, d[p]);. You browse the internet (ofc. However, I think its not the clean way to do it.
You can easily put a dollar cost on that human time (salary, etc) and multiply it by the number of work units in a year, and you’ve calculated its real-world benefit. The information of OS, pip, and git has been added. For those who don't know how to setup in Dev C++, you can download C++ 17 compiler here and select it in Compiler Options and add the following commands when calling the linker: I have some problems. In their benchmarks PyTorch did not have a speed increase. The Codeforces doesn't have this feature but now suddenly` Atcoder does. For example, even you own a book, there are certain things you can not do, like duplicate it and sell copies. Then, running. What could be the outcome of writing negative things about previous university in an application to another university? If that language turns out to be good for other things too, then great. I was top-1 cf (for a week obviously) 2 years before I started using any prewritten code. What does it have to do with naming servers, can you explain?
It sounds like you’ve already made up your mind about this. I love Go and use it for a bunch of projects, but I've only once wanted to move a project from Python to Go and that was a performance-centric CLI that was only written in Python originally because of how quickly it let us prototype in comparison to Go. E.g. Thanks - Mark Shannon is a core committer, right?
→ Pay attention Before contest 2020 ICPC, COMPFEST 12, Indonesia Multi-Provincial Contest (Unrated, Online Mirror, ICPC Rules, Teams Preferred) 2 days. 100% agree. Pyston is trying to prove that it is possible, but it's not exactly a weekend project. And as much as we want to improve scientific computing in Python, it's very hard since the work is done in C. Our current hope is to help mixed workloads, such as doing a decent amount of data-preprocessing in Python before handing off to C code. These contests may contain some dummy tasks that are irrelevant to the library, so don't try to think problems like "ok, maybe this task requires that library, so the solution should be...". Some of them improve if you can run it in pypy but the libraries aren't officially installed. :(. Look, I’ve written slow interpreters. I think that this library would only be useful to those who either understand the underlying algorithm as a blackbox, or don't have a good enough implementation in place — so they can always refer the implementation, and make changes to their own. > That's not a trade-off I can make, no matter how big the performance improvements. What about lazy propagation? It’s API is a patchwork.