Driver

\(Ax = \lambda x\) More...

Functions
template<typename scalar_t >
void	slate::heev (HermitianMatrix< scalar_t > &A, std::vector< blas::real_type< scalar_t > > &Lambda, Matrix< scalar_t > &Z, Options const &opts)
	Distributed parallel Hermitian matrix eigen decomposition.

Detailed Description

\(Ax = \lambda x\)

Function Documentation

heev()

template<typename scalar_t >

void slate::heev	(	HermitianMatrix< scalar_t > &	A,
		std::vector< blas::real_type< scalar_t > > &	Lambda,
		Matrix< scalar_t > &	Z,
		Options const &	opts
	)

Distributed parallel Hermitian matrix eigen decomposition.

heev Computes all eigenvalues and, optionally, eigenvectors of a Hermitian matrix A. The matrix A is preliminary reduced to tridiagonal form using a two-stage approach: First stage: reduction to band tridiagonal form (see he2hb); Second stage: reduction from band to tridiagonal form (see hb2st).

Template Parameters

scalar_t

One of float, double, std::complex<float>, std::complex<double>.

Parameters

[in]	A	On entry, the n-by-n Hermitian matrix \(A\). On exit, contents are destroyed.
[out]	Lambda	The vector Lambda of length n. If successful, the eigenvalues in ascending order.
[out]	Z	On entry, if Z is empty, does not compute eigenvectors. Otherwise, the n-by-n matrix \(Z\) to store eigenvectors. On exit, orthonormal eigenvectors of the matrix A.
[in]	opts	Additional options, as map of name = value pairs. Possible options: Option::InnerBlocking: Inner blocking to use for panel. Default 16. Option::MaxPanelThreads: Number of threads to use for panel. Default omp_get_max_threads()/2. Option::Target: Implementation to target. Possible values: HostTask: OpenMP tasks on CPU host [default]. HostNest: nested OpenMP parallel for loop on CPU host. HostBatch: batched BLAS on CPU host. Devices: batched BLAS on GPU device.