Interface with BLAS, LAPACK or ATLAS
For better performance on dense matrices, it is possible to interface some operations of the type gmm::dense_matrix<T> with LAPACK (http://www.netlib.org/lapack/) or ATLAS (http://math-atlas.sourceforge.net/), for T = float, double, std::complex<float> or std::complex<double>. In fact, concerning ATLAS no specific interface has been made untill now, so the fortran interface of ATLAS should be used.
to use this interface you have first to define GMM_USES_LAPACK before including GMM++ files :
#define GMM_USES_LAPACK #include <gmm/gmm.h> ... your code
or specify -DGMM_USES_LAPACK on the command line of your compiler. Of course, you have also to link LAPACK or ATLAS libraries. For example on a standard linux configuration and g++ compiler the adding libraries to link LAPACK are
g++ ... -llapack -lblas -lg2c
and to link ATLAS
g++ ... /usr/lib/atlas/liblapack.a /usr/lib/atlas/libblas.a -latlas -lg2c
Ask your system administrator if this configuration does not work.
The following operations are interfaced:
vect_norm2(std::vector<T>)
vect_sp(std::vector<T>, std::vector<T>)
vect_sp(scaled(std::vector<T>), std::vector<T>)
vect_sp(std::vector<T>, scaled(std::vector<T>))
vect_sp(scaled(std::vector<T>), scaled(std::vector<T>))
vect_hp(std::vector<T>, std::vector<T>)
vect_hp(scaled(std::vector<T>), std::vector<T>)
vect_hp(std::vector<T>, scaled(std::vector<T>))
vect_hp(scaled(std::vector<T>), scaled(std::vector<T>))
add(std::vector<T>, std::vector<T>)
add(scaled(std::vector<T>, a), std::vector<T>)
mult(dense_matrix<T>, dense_matrix<T>, dense_matrix<T>)
mult(transposed(dense_matrix<T>), dense_matrix<T>, dense_matrix<T>)
mult(dense_matrix<T>, transposed(dense_matrix<T>), dense_matrix<T>)
mult(transposed(dense_matrix<T>), transposed(dense_matrix<T>),
dense_matrix<T>)
mult(conjugated(dense_matrix<T>), dense_matrix<T>, dense_matrix<T>)
mult(dense_matrix<T>, conjugated(dense_matrix<T>), dense_matrix<T>)
mult(conjugated(dense_matrix<T>), conjugated(dense_matrix<T>),
dense_matrix<T>)
mult(dense_matrix<T>, std::vector<T>, std::vector<T>)
mult(transposed(dense_matrix<T>), std::vector<T>, std::vector<T>)
mult(conjugated(dense_matrix<T>), std::vector<T>, std::vector<T>)
mult(dense_matrix<T>, scaled(std::vector<T>), std::vector<T>)
mult(transposed(dense_matrix<T>), scaled(std::vector<T>),
std::vector<T>)
mult(conjugated(dense_matrix<T>), scaled(std::vector<T>),
std::vector<T>)
mult_add(dense_matrix<T>, std::vector<T>, std::vector<T>)
mult_add(transposed(dense_matrix<T>), std::vector<T>, std::vector<T>)
mult_add(conjugated(dense_matrix<T>), std::vector<T>, std::vector<T>)
mult_add(dense_matrix<T>, scaled(std::vector<T>), std::vector<T>)
mult_add(transposed(dense_matrix<T>), scaled(std::vector<T>),
std::vector<T>)
mult_add(conjugated(dense_matrix<T>), scaled(std::vector<T>),
std::vector<T>)
mult(dense_matrix<T>, std::vector<T>, std::vector<T>, std::vector<T>)
mult(transposed(dense_matrix<T>), std::vector<T>, std::vector<T>,
std::vector<T>)
mult(conjugated(dense_matrix<T>), std::vector<T>, std::vector<T>,
std::vector<T>)
mult(dense_matrix<T>, scaled(std::vector<T>), std::vector<T>,
std::vector<T>)
mult(transposed(dense_matrix<T>), scaled(std::vector<T>),
std::vector<T>, std::vector<T>)
mult(conjugated(dense_matrix<T>), scaled(std::vector<T>),
std::vector<T>, std::vector<T>)
mult(dense_matrix<T>, std::vector<T>, scaled(std::vector<T>),
std::vector<T>)
mult(transposed(dense_matrix<T>), std::vector<T>,
scaled(std::vector<T>), std::vector<T>)
mult(conjugated(dense_matrix<T>), std::vector<T>,
scaled(std::vector<T>), std::vector<T>)
mult(dense_matrix<T>, scaled(std::vector<T>), scaled(std::vector<T>),
std::vector<T>)
mult(transposed(dense_matrix<T>), scaled(std::vector<T>),
scaled(std::vector<T>), std::vector<T>)
mult(conjugated(dense_matrix<T>), scaled(std::vector<T>),
scaled(std::vector<T>), std::vector<T>)
lower_tri_solve(dense_matrix<T>, std::vector<T>, k, b)
upper_tri_solve(dense_matrix<T>, std::vector<T>, k, b)
lower_tri_solve(transposed(dense_matrix<T>), std::vector<T>, k, b)
upper_tri_solve(transposed(dense_matrix<T>), std::vector<T>, k, b)
lower_tri_solve(conjugated(dense_matrix<T>), std::vector<T>, k, b)
upper_tri_solve(conjugated(dense_matrix<T>), std::vector<T>, k, b)
lu_factor(dense_matrix<T>, std::vector<int>)
lu_solve(dense_matrix<T>, std::vector<T>, std::vector<T>)
lu_solve(dense_matrix<T>, std::vector<int>, std::vector<T>,
std::vector<T>)
lu_solve_transposed(dense_matrix<T>, std::vector<int>, std::vector<T>,
std::vector<T>)
lu_inverse(dense_matrix<T>)
lu_inverse(dense_matrix<T>, std::vector<int>, dense_matrix<T>)
qr_factor(dense_matrix<T>, dense_matrix<T>, dense_matrix<T>)
implicit_qr_algorithm(dense_matrix<T>, std::vector<T>)
implicit_qr_algorithm(dense_matrix<T>, std::vector<T>,
dense_matrix<T>)
implicit_qr_algorithm(dense_matrix<T>, std::vector<std::complex<T> >)
implicit_qr_algorithm(dense_matrix<T>, std::vector<std::complex<T> >,
dense_matrix<T>)
Of course, it is not difficult to interface another operation if needed.
The following interface does not correspond to an algorithm existing in GMM++ :
The interface to gesvd (singular value decomposition).
svd(dense_matrix<T> &X, dense_matrix<T> &U,
dense_matrix<T> &Vt, std::vector<T> sigma);
svd(dense_matrix<std::complex<T> > &X, dense_matrix<std::complex<T> > &U,
dense_matrix<std::complex<T> > &Vt, std::vector<T> sigma);