SciPy
SymPy.jl
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SciPy | SymPy.jl | |
---|---|---|
50 | 5 | |
12,459 | 254 | |
1.9% | 0.0% | |
9.9 | 6.9 | |
about 21 hours ago | 5 months ago | |
Python | Julia | |
BSD 3-clause "New" or "Revised" License | MIT License |
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SciPy
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What Is a Schur Decomposition?
I guess it is a rite of passage to rewrite it. I'm doing it for SciPy too together with Propack in [1]. Somebody already mentioned your repo. Thank you for your efforts.
[1]: https://github.com/scipy/scipy/issues/18566
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Fortran codes are causing problems
Fortran codes have caused many problems for the Python package Scipy, and some of them are now being rewritten in C: e.g., https://github.com/scipy/scipy/pull/19121. Not only does R have many Fortran codes, there are also many R packages using Fortran codes: https://github.com/r-devel/r-svn, https://github.com/cran?q=&type=&language=fortran&sort=. Modern Fortran is a fine language but most legacy Fortran codes use the F77 style. When I update the R package quantreg, which uses many Fortran codes, I get a lot of warning messages. Not sure how the Fortran codes in the R ecosystem will be dealt with in the future, but they recently caused an issue in R due to the lack of compiler support for Fortran: https://blog.r-project.org/2023/08/23/will-r-work-on-64-bit-arm-windows/index.html. Some renowned packages like glmnet already have their Fortran codes rewritten in C/C++: https://cran.r-project.org/web/packages/glmnet/news/news.html
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[D] Which BLAS library to choose for apple silicon?
There are several lessons here: a) vanilla conda-forge numpy and scipy versions come with openblas, and it works pretty well, b) do not use netlib unless your matrices are small and you need to do a lot of SVDs, or idek why c) Apple's veclib/accelerate is super fast, but it is also numerically unstable. So much so that the scipy's devs dropped any support of it back in 2018. Like dang. That said, they are apparently are bring it back in, since the 13.3 release of macOS Ventura saw some major improvements in accelerate performance.
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SciPy: Interested in adopting PRIMA, but little appetite for more Fortran code
First, if you read through that scipy issue (https://github.com/scipy/scipy/issues/18118 ) the author was willing and able to relicense PRIMA under a 3-clause BSD license which is perfectly acceptable for scipy.
For the numerical recipes reference, there is a mention that scipy uses a slightly improved version of Powell's algorithm that is originally due to Forman Acton and presumably published in his popular book on numerical analysis, and that also happens to be described & included in numerical recipes. That is, unless the code scipy uses is copied from numerical recipes, which I presume it isn't, NR having the same algorithm doesn't mean that every other independent implementation of that algorithm falls under NR copyright.
- numerically evaluating wavelets?
- Fortran in SciPy: Get rid of linalg.interpolative Fortran code
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Optimization Without Using Derivatives
Reading the discussions under a previous thread titled "More Descent, Less Gradient"( https://news.ycombinator.com/item?id=23004026 ), I guess people might be interested in PRIMA ( www.libprima.net ), which provides the reference implementation for Powell's renowned gradient/derivative-free (zeroth-order) optimization methods, namely COBYLA, UOBYQA, NEWUOA, BOBYQA, and LINCOA.
PRIMA solves general nonlinear optimizaton problems without using derivatives. It implements Powell's solvers in modern Fortran, compling with the Fortran 2008 standard. The implementation is faithful, in the sense of being mathmatically equivalent to Powell's Fortran 77 implementation, but with a better numerical performance. In contrast to the 7939 lines of Fortran 77 code with 244 GOTOs, the new implementation is structured and modularized.
There is a discussion to include the PRIMA solvers into SciPy ( https://github.com/scipy/scipy/issues/18118 ), replacing the buggy and unmaintained Fortran 77 version of COBYLA, and making the other four solvers available to all SciPy users.
- What can I contribute to SciPy (or other) with my pure math skill? I’m pen and paper mathematician
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Emerging Technologies: Rust in HPC
if that makes your eyes bleed, what do you think about this? https://github.com/scipy/scipy/blob/main/scipy/special/specfun/specfun.f (heh)
- Python
SymPy.jl
- Symbolic differentiation in Julia?
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Converting Symbolics.jl Objects to SymPy.jl Objects
I am working on a project which involves calculating the inverse for matrices with symbolic entries. I am using Symbolics.jl to create the symbolic entries. While Symbolics.jl has been great for computing things like determinants and simplifying their results very quickly, there is a lack of finer-grain expression manipulation commands in the module, and thus I would like to convert my symbolic.jl objects to ones readable with SymPy.jl.
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SymPy.jl to calculate the Characteristic polynomial?
This code no longer works! Can I use use SymPy.jl (e.g. A.charpoly() of sage) instead to calculate the char polynomial?
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Packages for basic quantum mechanics?
You can even just import SymPy into Julia and use that for symbolic computation https://github.com/JuliaPy/SymPy.jl
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Doing Symbolic Math with SymPy
Worth noting that Julia's SymPy binding [1] is pretty pretty nice to work with too. If anyone's looking for big Julia project, I think a symbolic math package written fully in Julia would be a really exciting development. As far as I know, there isn't one yet. The better-known symbolic math packages for Julia still use bindings to C++ (SymEngine.jl [2]) or Python (SymPy.jl, Symata.jl [3]).
[1] - https://github.com/JuliaPy/SymPy.jl
What are some alternatives?
SymPy - A computer algebra system written in pure Python
Symata.jl - language for symbolic mathematics
statsmodels - Statsmodels: statistical modeling and econometrics in Python
NumPy - The fundamental package for scientific computing with Python.
ModelingToolkit.jl - An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
SymEngine.jl - Julia wrappers of SymEngine
astropy - Astronomy and astrophysics core library
NeuralPDE.jl - Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
or-tools - Google's Operations Research tools:
PyMC - Bayesian Modeling and Probabilistic Programming in Python