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by 7thaccount
2347 days ago
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Oh hello friend, you've piqued my interest as someone interested in D, numerical optimization, and linear algebra. I have some questions though. How do you do numerical optimization in D? Do you somehow wrap Coin-OR's CBC C++ library or lp_solve? What does it mean to call the R functions directly? Do you have an example? I'm going to guess that won't be able to handle the massive and time critical models I use, but am still curious. How do you do linear algebra? Are you binding to BLAS, LAPACK, Armadillo? Or did you write some routines from scratch? |
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For optimization, I was referring to calling into the R API, which exposes the optimization routines in base R (Nelder-Mead, BFGS, Conjugate Gradient, and simple bounds constrainted BFGS). In terms of what it can handle, I guess that's entirely up to what R can handle. Here's the project page, but it looks like I haven't committed to that repo in three years: https://bitbucket.org/bachmeil/dmdoptim/src/master/
If you do try it and have problems with anything, please create an issue so I can fix it or add proper documentation.
I've also used this binding of the nlopt library: http://code.dlang.org/packages/libnlopt
For linear algebra, I built a wrapper on top of the Gretl library http://gretl.sourceforge.net/
There were two reasons for that. First, it offered a really simple LAPACK interface when I was starting out with D, and second, it offers a lot more than just linear algebra.
Is this something I'd recommend to others? I don't know. I built my infrastructure over a period of several years while waiting for my son at his many practices and activities. I also optimize for programmer convenience rather than performance at all costs. The time it takes to write correct, performant code is far more valuable than having code that runs 15% faster.
This has a lot of potential, but ultimately I'm paid to do other things, meaning those things become the priority...