sympy_doc VS SymPy

HackerRank\ (296 comments)

Thank you for your interest. There are some interesting examples in the SWE-bench-lite benchmark which are resolved by AutoCodeRover:

- From sympy: https://github.com/sympy/sympy/issues/13643. AutoCodeRover's patch for it: https://github.com/nus-apr/auto-code-rover/blob/main/results...

- Another one from scikit-learn: https://github.com/scikit-learn/scikit-learn/issues/13070. AutoCodeRover's patch (https://github.com/nus-apr/auto-code-rover/blob/main/results...) modified a few lines below (compared to the developer patch) and wrote a different comment.

There are more examples in the results directory (https://github.com/nus-apr/auto-code-rover/tree/main/results).

That's interesting. You should consider yourself lucky to have met Wolfram employees, as they are obviously vastly outnumbered by users of Mathematica.

I have not met any developers for either of these products but I know that SymPy has a huge list of contributors for a project of its size. See: https://github.com/sympy/sympy/blob/master/AUTHORS

You may not be hearing about SymPy users because SymPy is not a monolithic product. It is a library. If you know mathematicians big into using Python, they are probably aware of SymPy as it is the main attraction when it comes to symbolic computation in Python.

https://github.com/sympy/sympy/issues/9479 suggests that multivariate inequalities are still unsolved in SymPy, though it looks like https://github.com/sympy/sympy/pull/21687 was merged in August. This probably isn't yet implemented in C++ in SymForce yet?

bug report opened https://github.com/sympy/sympy/issues/25507

https://news.ycombinator.com/item?id=36463580

From https://news.ycombinator.com/item?id=36159017 :

> sympy.utilities.lambdify.lambdify() https://github.com/sympy/sympy/blob/a76b02fcd3a8b7f79b3a88df... :

>> """Convert a SymPy expression into a function that allows for fast numeric evaluation [with the CPython math module, mpmath, NumPy, SciPy, CuPy, JAX, TensorFlow, SymPy, numexpr,]*

From https://westurner.github.io/hnlog/#comment-19084622 :

> "latex2sympy parses LaTeX math expressions and converts it into the equivalent SymPy form" and is now merged into SymPy master and callable with sympy.parsing.latex.parse_latex(). It requires antlr-python-runtime to be installed. https://github.com/augustt198/latex2sympy https://github.com/sympy/sympy/pull/13706

ENH: 'generate a Jupyter notebook' (nbformat .ipynb JSON) function from this stem formula

https://en.wikipedia.org/wiki/Vectorization :

> Array programming, a style of computer programming where operations are applied to whole arrays instead of individual elements

> Automatic vectorization, a compiler optimization that transforms loops to vector operations

> Image tracing, the creation of vector from raster graphics

> Word embedding, mapping words to vectors, in natural language processing

> Vectorization (mathematics), a linear transformation which converts a matrix into a column vector

Vector (disambiguation) https://en.wikipedia.org/wiki/Vector

> Vector (mathematics and physics):

> Row and column vectors, single row or column matrices

> Vector space

> Vector field, a vector for each point

And then there are a number of CS usages of the word vector for 1D arrays.

Compute kernel: https://en.m.wikipedia.org/wiki/Compute_kernel

GPGPU > Vectorization, Stream Processing > Compute kernels: https://en.wikipedia.org/wiki/General-purpose_computing_on_g...

sympy.utilities.lambdify.lambdify() https://github.com/sympy/sympy/blob/a76b02fcd3a8b7f79b3a88df... :

> """Convert a SymPy expression into a function that allows for fast numeric evaluation [with the CPython math module, mpmath, NumPy, SciPy, CuPy, JAX, TensorFlow, SymPt, numexpr,]

pyorch lambdify PR, sympytorch: https://github.com/sympy/sympy/pull/20516#issuecomment-78428...

Sympytorch:

> Turn SymPy expressions into PyTorch Modules.

> SymPy floats (optionally) become trainable parameters. SymPy symbols are inputs to the Module.

sympy2jax https://github.com/MilesCranmer/sympy2jax :

> Turn SymPy expressions into parametrized, differentiable, vectorizable, JAX functions.

> All SymPy floats become trainable input parameters. SymPy symbols become columns of a passed matrix.

Look at sympy.isprime for a carefully-optimized pure-Python solution (though if gmpy2 is installed, which it usually is, it will use that instead after trying the easiest cases)