I wrote a research project in matlab that uses quite a few functions which I do not want to re-implement in C++, so I'm looking for libraries to handle these for me. The functions I need are: (by order of importance)
Hilbert transform
Matrix functions(determinant, inverse, multiplication...)
Finding roots of polynomials(for degrees greater than 5)
FFT
Convolutions
correlation(xcorr in matlab)
I don't know about most of those, but FFTW is the 'fastest Fourier transform in the West'. It is used in the MATLAB implementation of fft().
Once you've got an FFT you can knock off everything save for numbers 2. and 3.
The linear algebra requirement can be met with PETSc www.mcs.anl.gov/petsc/ which supports fftw.
I don't know how you're going to go about the root finding. You'll probably have to code that yourself (bisection, Newton's method etc etc.) but it's the easiest thing on the list to implement by far.
I am not sure about the libraries that are available for use, but if you already have the functions written in matlab there is another option.
If you compile the matlab functions to a dll they can be called by a c++ program. This would allow you to access the matlab functions that you already have without rewriting.
Related
I implemented a program on the GPU (CUDA) which only uses the host (in C++) to start new kernels. During the calculation on the device I need SVD and solving systems of 3x3 (dense) matrices, fixed size.
I've got my own SVD and solver implementation but it is not numerical stable (thus not usable). Due to me being rather new with C++ and CUDA I would prefer to use a library instead. (numerical stuff is very tricky)
Now I have trouble finding that library:
cuSOLVER is not callable from the device
cuLA is not callable form the device (and abandoned so it seems)
Eigen looks promising (should be callable from device?) but it is unclear what the status is on CUDA support (it says experimental). I find people saying it works, others got compile errors?
Preferable I would also being able to do general matrix operations with the library (transpose, inversion, sum, multiply, ...) as my own implementations will likely be less efficient and numerically stable for those.
Any ideas on how to achieve this?
UPDATE:
Seems like Eigen supports basic functions like *,+, transpose and even eigenvalues but SVD, inverse ect is not yet supported. This is at the time of writing.
According to the website, a subset of features works for fixed size matrices (3x3 in your case) from Eigen 3.3. The current stable release is 3.2.6 while 3.3 is in alpha. I don't know if specifically SVD is supported in CUDA. I would recommend trying a small MCVE to see if it works (as well as the other functions you require), and if so, implementing it in your project.
I'm having a similar problem; want to generate random vectors within a kernel function which requires performing cholesky/eigenvalue decompositions of NxN (N<=5) covariance matrices. Since, as you noted, the MAGMA and CULA libraries are not available from the device, and there seems to be no cuSOLVER device API yet, I've resorted to implementing these myself following algorithms outlined in, for example, Numerical Recipes in C. As for solving linear systems, I'd suggest checking out the cuBLAS (level 2 functions), as it provides some basic functionality. If you want to invert matrices, I'd suggest cublasmatinvBatched(). I haven't used it myself, will give it a try during the weekend, but from the description it sounds promising. Hope others will chime into this thread with better solutions...
I need to use Resample as in MATLAB and Octave in c++, the resample is described as below.
"Change the sample rate of x by a factor of p/q. This is performed using a polyphase algorithm. The impulse response h of the antialiasing filter is either specified or either designed with a Kaiser-windowed sinecard."
Is there an equivalent to this approach in c++?? something out of eigen or armadillo or something like that?
Thanks in advance
Motorola has a library that can do polyphase resampling. Also GSL is a good general mathematical C/C++ toolkit, though I don't think it will do what you want it to straight out of the box.
I am writting a program in Visual Studio using MFC dialog based application.
I have 5 matrix in my program where I have to add two of them and multiply other 2 of them and then subtract the result of multiplication from the summed value to get the 5th matrix.
Some time I have to square the summed matrix also so it is quite laborious to write the full code...
So one way is to write the code straight forward in C++ using array...But if I want to multiply two matrices or sum them directly as can be done in MatLab, is it possible in C++?
If yes then how?
Boost has a good library for linear algebra: Boost.uBLAS.
It includes a convenient matrix class, as well as built in matrix arithmetic operations.
Eigen is very powerful and highly optimized. It supports both dynamic matrices (size unknown at compile time) and statically sized matrices. Take a look at the tutorial.
What's a good C++ library for matrix operations
I recommend the gmtl (generic math template library).
I am currently working on a C++-based library for large, sparse linear algebra problems (yes, I know many such libraries exist, but I'm rolling my own mostly to learn about iterative solvers, sparse storage containers, etc..).
I am to the point where I am using my solvers within other programming projects of mine, and would like to test the solvers against problems that are not my own. Primarily, I am looking to test against symmetric sparse systems that are positive definite. I have found several sources for such system matrices such as:
Matrix Market
UF Sparse Matrix Collection
That being said, I have not yet found any sources of good test matrices that include the entire system- system matrix and RHS. This would be great to have in order to check results. Any tips on where I can find such full systems, or alternatively, what I might do to generate a "good" RHS for the system matrices I can get online? I am currently just filling a matrix with random values, or all ones, but suspect that this is not necessarily the best way.
I would suggest using a right-hand-side vector obtained from a predefined 'goal' solution x:
b = A*x
Then you have a goal solution, x, and a resulting solution, x, from the solver.
This means you can compare the error (difference of the goal and resulting solutions) as well as the residuals (A*x - b).
Note that for careful evaluation of an iterative solver you'll also need to consider what to use for the initial x.
The online collections of matrices primarily contain the left-hand-side matrix, but some do include right-hand-sides and also some have solution vectors too.:
http://www.cise.ufl.edu/research/sparse/matrices/rhs.txt
By the way, for the UF sparse matrix collection I'd suggest this link instead:
http://www.cise.ufl.edu/research/sparse/matrices/
I haven't used it yet, I'm about to, but GiNAC seems like the best thing I've found for C++. It is the library used behind Maple for CAS, I don't know the performance it has for .
http://www.ginac.de/
it would do well to specify which kind of problems are you solving...
different problems will require different RHS to be of any use to check validity..... what i'll suggest is get some example code from some projects like DUNE Numerics (i'm working on this right now), FENICS, deal.ii which are already using the solvers to solve matrices... generally they'll have some functionality to output your matrix in some kind of file (DUNE Numerics has functionality to output matrices and RHS in a matlab-compliant files).
This you can then feed to your solvers..
and then again use their the libraries functionality to create output data
(like DUNE Numerics uses a VTK format)... That was, you'll get to analyse data using powerful tools.....
you may have to learn a little bit about compiling and using those libraries...
but it is not much... and i believe the functionality you'll get would be worth the time invested......
i guess even a single well-defined and reasonably complex problem should be good enough for testing your libraries.... well actually two
one for Ax=B problems and another for Ax=cBx (eigenvalue problems) ....
i am trying to implement discrete curve evolution algorithm in c++ do any one help me with psudo code or c code or
some simple steps of your understanding
Discrete Curve Evolution is an algorithm to compute an everywhere convex curve from one that is concave. It moves concave sections of the curve outward along their normal in discrete steps until all concavities are eliminated. It is not a genetic algorithm, the term evolution refers to 'evolving' the position of the curve over time.
Having searched on this for quite some time the best source on the internet is here:
https://cis.temple.edu/~latecki/Software/Evo.zip
This is matlab code so it's not quite what you are looking for but you have three good options:
Port it to C++ (usually not to hard with matlab as long as it doesn't use matrix prims.)
Wrap the matlab code so you can call it from C (matlab provides libraries to do this)
Compile it to an executable and call that from C (matlab also allows this)
Option 2 would require anyone that want's to run it to have a copy of the matlab dynamic library on their computer which may be undesirable. I'm guessing option 3 would require this too, but I only have experience with options 1 and 2. Porting matlab to c++ is usually not that bad; it depends on how much the code utilizes matrix primitives and matrix operations which are easy to use in matlab and hard to use in C++ (because they aren't built-in). Still, I'd recommend giving it the old college try!
If you're just looking for DCE, check out the file evolution.m. That's the function that implements DCE. The full skeleton pruning algorithm this comes from can only be described simply at a high level. The individual steps and parts are QUITE complicated and DCE is only a small piece of that.
Hope this helps! I will be working with this code myself so if I do end up using it in C++ in some way that might help you I will let you know.
I'm not exactly sure what you mean by Discrete Curve evolutionary algorithm, but if you mean a Symbolic regression algorithm, you can start by reading about symbolic regression (or genetic programming in general):
http://en.wikipedia.org/wiki/Symbolic_Regression
There's also some nice existing programs. The Eureqa one has an open API:
http://code.google.com/p/eureqa-api/