TasGrid::TasmanianDenseSolver Namespace Reference

Methods for dense linear algebra. More...

Functions
void	solveLeastSquares (int n, int m, const double A[], double b[], double *x)
	Least squares solver, used to infer anisotropic coefficients and thus rarely exceeds 10 - 20. More...
void	solveLeastSquares (AccelerationContext const *acceleration, int n, int m, double A[], double b[], double *x)
	The same solver, but uses LAPACK dgels method (if enabled).
template<typename scalar_type >
void	solvesLeastSquares (AccelerationContext const *acceleration, int n, int m, scalar_type A[], int nrhs, scalar_type B[])
	Least squares solver, operates on multiple right-hand sides and row-major matrices. More...
template<typename scalar_type >
void	solvesLeastSquaresGPU (AccelerationContext const *acceleration, int n, int m, scalar_type A[], int nrhs, scalar_type B[])
	Overload that accepts arrays on the GPU device.

Detailed Description

Methods for dense linear algebra.

Most of these build on BLAS and LAPACK, but some come with basic reference implementations. For example, the general least-squares is used with a single right-hand-size and size of x of 10 - 20, hence a reference solution is sufficient.

Function Documentation

solvesLeastSquares()

template<typename scalar_type >

void TasGrid::TasmanianDenseSolver::solvesLeastSquares	(	AccelerationContext const *	acceleration,
		int	n,
		int	m,
		scalar_type	A[],
		int	nrhs,
		scalar_type	B[]
	)

Least squares solver, operates on multiple right-hand sides and row-major matrices.

Solves where A is an n by m matrix (row major format), B is n by nrhs. The main difference in this overload is that the matrices are row-major format.