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I cannot use gsl_matrix because my app is closed source and, according to this question, if I used GPL code directly, I'd have to make my app open source. And that's a no-no from the higher ups.
So... Does Boost, or even better, TR1, have a library with classes equivalent to gsl_matrix, gsl_vector and other types from the GNU Scientific Library? If there are such classes, how are they called?
Edit: I need to:
Perform dense matrix-vector products and sums (like gsl_blas_dgemv and gsl_blas_dgemm do)
Optionally, solve quadratic programming models.
First of all, there is C interface for BLAS/LAPACK. Some people find it 'hard' to deal with the call signatures which directly mirror the original BLAS ones.
If you're more into fancier side of things, there's Boost uBLAS interface, there's Armadillo, to name just two. Performance-wise, your mileage may vary.
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The TensorFlow white paper mentions that Eigen is used. Are there public explanations for how Eigen was chosen, and are they motivation for using Eigen in TensorFlow C++ op kernels?
I think that one of the key feature that drove the use of Eigen in the first place is because Eigen features its own highly optimized matrix product kernels whereas all other competitors have to be linked to some BLAS libraries. Moreover, the code of Eigen's product kernel is C++ with easy access to low-level internal kernels, so it was 'easy' for them to tweak and extend it to match their needs. This way Google has been able to develop the Tensor module with high CPU performance in a pure header-only fashion. The support for CUDA and now OpenCL via SyCL came later, those are not intrinsic features of Eigen that drove the initial choice.
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Is there an implementation callable from C or C++ that allows the evaluation of the generalized hypergeometric function pFq(a1,...,ap; b1,...,bp; x)?
I tried GSL and Boost, but I don't think the generalized function is available in either of those libraries.
I believe the Arb library, a C library for arbitrary-precision floating-point ball arithmetic developed by the creator of mpmath, now provides an implementation.
I would suggest using this python library for the functions you need. It seems like it has it.
The trick however is you need to be able to call a python script from C++. For that you can use a boost component.
This seems like the easiest solution, even if it is possibly inefficient.
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How might one implement data frame in R, Python, and other languages using C++?
In general, data.frame solves a problem which is solved fundamentally differently in C++ (and other languages) – namely via class hierarchies, or, in the simplest case, via a vector of tuples.
Since you haven’t given specifics it’s hard to know what exactly you are after but if it’s ease of computation, Armadillo is a good linear algebra library for C++ (one among many). I haven’t yet found a good statistics framework for C++ – I suggest simply sticking with R for that.
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I am looking for a C++ library, and I am dealing with convex objective and constraint functions.
I am guessing your problem is non-linear. Where i work, we use SNOPT, Ipopt and another proprietary solver (not for sale). We have also tried and heard good things about Knitro.
As long as your problem is convex, all these solvers work well.
They all have their own API, but they all ask for the same information : values, first and second derivatives.
Assuming your problems are nonlinear, you can use free and open-sourced OPT++, available from Sandia Lab. I have used it in one project in C++ and it was easy to use and worked well.
From what I know, the CPLEX solver is the best convex optimization solver. Its the state of the art in LP solvers. Does convex optimization really well. While looking for it, I see that its IBM's software now. You can find it here : http://www-01.ibm.com/software/integration/optimization/cplex/
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Can anyone point out a good C++ library that can do 2D numerical integration. It needs to be able to accept a 2D array of known values, and the spacing between the points can be assumed to be constant (for a start).
It is preferable that it have a license which allows modifying the code as needed.
It's actually a C library, but if the GPL licensing terms work for you try:
http://www.gnu.org/software/gsl/
You will want to check out the Monte Carlo integration options outlined here:
http://www.gnu.org/software/gsl/manual/html_node/Monte-Carlo-Integration.html
This Fortran library is easy to link to from C++ and is in public domain:
http://gams.nist.gov/cgi-bin/serve.cgi/Module/CMLIB/ADAPT/2967
It's single precision but it's quite easy to modify the sources (get "full sources" and go through every function) to switch to double precision.
http://itpp.sourceforge.net/current/
Try this. It can do what you ask for and more! And you can modify the code as much as you like.
I've read somewhere that you can extract libraries out of GNU Octave's code and use the C++ code in your own applications. I'm not sure if that's an easy task, but you can give it a try if you have the time.