Search-engine optimization, if you want our blog to get noticed. I don’t know how, but your blog is worthy of attention.
At least the one blog I read:
My preferred solver for stiff differential algebraic equations is the Radau5DAE solver, which is available in Fortran and Python, but not yet wrapped or re-written in Julia.
It’s unclear to me if Python is just a wrapper, or is adding to the Fortran code, so that it wouldn’t be enough to just wrap the Fortran code directly (make JLL). Might be easiest would be to use PythonCall.jl, manually, or even a wrapper based on it (it could always be changed later to exclude Python runtime?).
You link indirectly to (and its C-interface):
RADAU5 implicit Runge-Kutta method of order 5 (Radau IIA) for problems of the form My’=f(x,y) with possibly singular matrix M; with dense output (collocation solution). Concerning the linear algebra routines the user has the choice to link the program either with DC_DECSOL and DECSOL or with DC_LAPACK and LAPACK and LAPACKC
Hints for the choice:
a) the LAPACK routines use BLAS
b) the compiler has to accept COMPLEX16 for the LAPACK routines; the DECSOL routines only use REAL8 declarations
That’s ComplexF64 (i.e. of doubles) in Julia lingo, would it help to translate to Julia, make faster (2-4x?), to make generic, supporting ComplexF32 (and even ComplexF16), or would it just be a mistake to lower precision (or not need speedup)?
@chrisrackauckas I see all the Fortran code is FORTRAN77. There was a Fortran-to-Julia for that version, if I recall, made by some Julia people (partly implemented in Perl), could look it up, or was trying, but actually found not one but two more, in case you want to do that (fork the code…, needed in SciMl?):
