Thanks for the blog post. Unfortunately, I'm outside from using SymPy recently because I'm now mainly involved in projects that use React and Typescript heavily,
However, I particularly find things like mathjs josdejong/mathjs: An extensive math library for JavaScript and Node.js (github.com) <https://github.com/josdejong/mathjs> cortex-js/compute-engine: An engine for symbolic manipulation and numeric evaluation of math formulas expressed with MathJSON (github.com) <https://github.com/cortex-js/compute-engine> to be interesting, and had got me the impression that small symbolic expression system, without automatic evaluation, is still very useful by itself. And although SymPy, being fully featured CAS, with many features, easy to use, had not got a good reputation that it is best at something, or it is best at anything at all. It seemed like having having CAS not being best at performance or having it too opinionated and limits about what kind of math it can do, pushed me away from using general computer algebra systems, to more dedicated matrix, algebra, polynomial libraries. However, I just want to tackle that this problems may come from monolithic software designs of computer algebra systems. Although `arb`, `flint` or `gmpy` achieves best performance at one specific math, However, they don't really seem like restricting what you can build on top of it. I have got a similar opinion, that CAS should be minimal like basic operating system that only connects the modules, and start to have the boundary of what should be inside it or what should be built on top of it. On Monday, August 21, 2023 at 3:35:01 PM UTC-5 da...@dbailey.co.uk wrote: > On 21/08/2023 21:01, Aaron Meurer wrote: > > Thanks Aaron for your amazingly fast response! > > > The main reason Oscar benchmarked matrices is that that's the part of > > SymPy that he's focused on making faster so far. Actually, matrices > > are more important than you'd think. They end up being used internally > > in many calculations, in places like the solvers or integrals, so > > making matrices faster will also make other parts of SymPy faster. > > > > But actually, matrix inverse and your series example are very similar. > > They both produce unwieldy expressions when computed naively. But > > these expressions are much simpler if they are simplified: > > If Oscar had mentioned that, I would never have written that reply - > perhaps he forgot that he was not talking to a SymPy developer! > > Is there a readable account explaining how the internals of SymPy > perform their algebraic/calculus manipulations? > > David > > -- You received this message because you are subscribed to the Google Groups "sympy" group. To unsubscribe from this group and stop receiving emails from it, send an email to sympy+unsubscr...@googlegroups.com. To view this discussion on the web visit https://groups.google.com/d/msgid/sympy/65746075-2500-41c6-bba0-3d1537490ebfn%40googlegroups.com.
