fricas VS Symbolics.jl

But it's integration functionalities are less advanced and comprehensive than those of Fricas. Interestingly, the latter is, like Maxima, implemented using Lisp and stems from a ancient software lineage. Both systems are free and open-source.

Fricas home page: http://fricas.github.io

Some independent integration benchmarks, comparing multiple computer algebra systems: https://www.12000.org/my_notes/CAS_integration_tests/index.h...

Even partial implementations of the Risch algorithm can be pretty daunting but you might look at a couple heuristics that handle the easier bits, like Manel Bronstien's Poor Man's Integrator https://www-sop.inria.fr/cafe/Manuel.Bronstein/pmint/index.html which doesn't need as many algebraic tools, but does need gcd, factor, and solve. I think FriCAS might use this https://github.com/fricas/fricas

Fricas home page: https://fricas.github.io

I used to use Maxima back in the day, which is embedded in Lisp. With a quick googling I found FriCAS https://github.com/fricas/fricas , which aims to be "world class" AND its libraries are built in a strongly-typed DSL called Spad.

BTW this is not a Clojure project. It contains .boot files that look like this and GitHub thinks they're Clojure. Trying to edit the .gitattributes through a PR.

It’s not up to parity with SymPy/Matlab by far yet - here’s the tracking issue on it https://github.com/JuliaSymbolics/Symbolics.jl/issues/59

Hello Juila Community. This is my second day working with Julia, having come over from Sympy due to performance reasons. I am working on a project that requires calculating matrix determinants and adjugates for families of matrices with symbolics entries. I am using Symbolics.jl for the symbols and using Juilia 1.8.2.

After spending some time reading the documentation, it turns out that JuliaSymbolics also lacks factorizations functionality (according to [Link](https://github.com/JuliaSymbolics/Symbolics.jl/issues/59))

You can also get an expression for the partial of β with respect to h using Symbolics.jl:

The example is applied to the right-hand side of a generated mass-matrix ODE (DAE) which is then solved using the adaptive time stepping methods of DifferentialEquations.jl. It's a test example that comes from the robotics / rigid body dynamics simulation groups (specifically interested in control) where they before were generating the governing equations with SymPy, and recently switched to try Symbolics.jl (and we got the example because of some performance issues that needed fixing). The comparison is with and without applying the code simplifier before solving. The table shows an average global induced error of 1e-12 when chopping off the 1e-11 * sin(x) terms and smaller. Thus there's nothing "competitive" against standard adaptive time stepping here: it's used to enhance the simulation of generated models that are simulated with the adaptive time steppers.

Done. https://github.com/JuliaSymbolics/Symbolics.jl/issues/215