GSL is written in C, and I don't know any language more portable than that! gsl_vector and gsl_matrix use a continuous block of doubles, so you can use the FFI to marshall this however you want for efficiency. I'd stick with GSLHaskell until you're ready to optimize the data marshalling though. I like spending my time on interesting things, not reinventing pre-debugged and efficient libraries. I use GSLHaskell in my work and have never had a problem. Dan Chad Scherrer wrote:
Does anyone know of any Haskell code for numeric minimization? I was thinking conjugate gradient would be good, but at this point I'd be happy with anything.
I've found some code written by Tomasz Cholewo at http://ci.uofl.edu/tom/software/Haskell/ but it requires importing his "Arr.lhs" library, which is not publicly available.
The only other thing I've been able to dig up is this www.st.cs.ru.nl/papers/1997/serp97-cgfunctional.ps.gz which suggests Haskell is slow for such problems. I suspect this was an implementation issue, so I don't think their code would be very helpful (though it would be nice to tidy it up and demonstrate the improvement - could it beat the Clean implementation they give?)
The other possibility I was considering was using Alberto Ruiz's wrapper for the GSL library http://dis.um.es/~alberto/GSLHaskell/ The only problems with this are (1) requires having GSL available, so it's not as portable, and (2) does everything in terms of lists, which requires a lot of translations to and from lists (I'm using mutable arrays).
If there's nothing already written that works together, one of these should give me a start, but I'd like to avoid reinventing the wheel if possible.
Thanks!