Classes
struct	Instances
	Instances. More...

struct	Instances< K, 1 >

class	InvariantsEvaluator2D

class	InvariantsEvaluator3D

class	KernelMap
	KernelMap class which creates an unordered_map of the Keys/Kernels. More...

Functions
template<int dim>
MFEM_HOST_DEVICE real_t	DistanceSquared (const real_t x, const real_t y)
	Compute the square of the Euclidean distance to another vector.

template<int dim>
MFEM_HOST_DEVICE void	Diag (const real_t c, real_t *data)
	Creates n x n diagonal matrix with diagonal elements c.

template<int dim>
MFEM_HOST_DEVICE void	Subtract (const real_t a, const real_t x, const real_t y, real_t *z)
	Vector subtraction operation: z = a * (x - y)

template<int dim>
MFEM_HOST_DEVICE void	AddMultVWt (const real_t v, const real_t w, real_t *VWt)
	Dense matrix operation: VWt += v w^t.

template<int H, int W, typename T >
MFEM_HOST_DEVICE void	FNorm (real_t &scale_factor, real_t &scaled_fnorm2, const T *data)

template<int H, int W, typename T >
MFEM_HOST_DEVICE real_t	FNorm (const T *data)
	Compute the Frobenius norm of the matrix.

template<int H, int W, typename T >
MFEM_HOST_DEVICE real_t	FNorm2 (const T *data)
	Compute the square of the Frobenius norm of the matrix.

template<typename T >
MFEM_HOST_DEVICE real_t	Norml2 (const int size, const T *data)
	Returns the l2 norm of the Vector with given size and data.

template<typename TA , typename TX , typename TY >
MFEM_HOST_DEVICE void	Mult (const int height, const int width, const TA data, const TX x, TY *y)
	Matrix vector multiplication: y = A x, where the matrix A is of size height x width with given data, while x and y specify the data of the input and output vectors.

template<typename TA , typename TX , typename TY >
MFEM_HOST_DEVICE void	MultTranspose (const int height, const int width, const TA data, const TX x, TY *y)
	Matrix transpose vector multiplication: y = At x, where the matrix A is of size height x width with given data, while x and y specify the data of the input and output vectors.

template<typename T >
MFEM_HOST_DEVICE void	Symmetrize (const int size, T *data)
	Symmetrize a square matrix with given size and data: A -> (A+A^T)/2.

template<int dim, typename T >
MFEM_HOST_DEVICE T	Det (const T *data)
	Compute the determinant of a square matrix of size dim with given data.

template<int dim, typename T >
MFEM_HOST_DEVICE void	CalcInverse (const T data, T inv_data)
	Return the inverse of a matrix with given size and data into the matrix with data inv_data.

template<int dim, typename T >
MFEM_HOST_DEVICE void	CalcAdjugate (const T data, T adj_data)
	Return the adjugate of a matrix.

template<typename TALPHA , typename TA , typename TB , typename TC >
MFEM_HOST_DEVICE void	Add (const int height, const int width, const TALPHA alpha, const TA Adata, const TB Bdata, TC *Cdata)
	Compute C = A + alphaB, where the matrices A, B and C are of size height* x width with data Adata, Bdata and Cdata.

template<typename TALPHA , typename TBETA , typename TA , typename TB , typename TC >
MFEM_HOST_DEVICE void	Add (const int height, const int width, const TALPHA alpha, const TA Adata, const TBETA beta, const TB Bdata, TC *Cdata)
	Compute C = alphaA + betaB, where the matrices A, B and C are of size height x width with data Adata, Bdata and Cdata.

template<typename TA , typename TB >
MFEM_HOST_DEVICE void	Add (const int height, const int width, const TA Adata, TB Bdata)
	Compute B += A, where the matrices A and B are of size height x width with data Adata and Bdata.

template<typename TA , typename TB >
MFEM_HOST_DEVICE void	Add (const int height, const int width, const real_t alpha, const TA Adata, TB Bdata)
	Compute B +=alphaA, where the matrices A and B are of size height* x width with data Adata and Bdata.

template<typename TA , typename TB >
MFEM_HOST_DEVICE void	Set (const int height, const int width, const real_t alpha, const TA Adata, TB Bdata)
	Compute B = alphaA, where the matrices A and B are of size height* x width with data Adata and Bdata.

template<typename TA , typename TB , typename TC >
MFEM_HOST_DEVICE void	AddMult (const int Aheight, const int Awidth, const int Bwidth, const TB Bdata, const TC Cdata, TA *Adata, const TB alpha, const TA beta)
	Matrix-matrix multiplication: A = alpha * B * C + beta * A, where the matrices A, B and C are of sizes Aheight x Awidth, Aheight x Bwidth and Bwidth x Awidth, respectively.

template<typename TA , typename TB , typename TC >
MFEM_HOST_DEVICE void	Mult (const int Aheight, const int Awidth, const int Bwidth, const TB Bdata, const TC Cdata, TA *Adata)
	Matrix-matrix multiplication: A = B * C, where the matrices A, B and C are of sizes Aheight x Awidth, Aheight x Bwidth and Bwidth x Awidth, respectively.

template<typename TA , typename TB , typename TC >
MFEM_HOST_DEVICE void	MultABt (const int Aheight, const int Awidth, const int Bheight, const TA Adata, const TB Bdata, TC *ABtdata)
	Multiply a matrix of size Aheight x Awidth and data Adata with the transpose of a matrix of size Bheight x Awidth and data Bdata: A * Bt. Return the result in a matrix with data ABtdata.

template<typename TA , typename TB , typename TC >
MFEM_HOST_DEVICE void	AddMultAtB (const int Aheight, const int Awidth, const int Bwidth, const TA Adata, const TB Bdata, TC *Cdata, const TB alpha, const TA beta)
	Compute C = alphaAtB + beta*C.

template<typename TA , typename TB , typename TC >
MFEM_HOST_DEVICE void	MultAtB (const int Aheight, const int Awidth, const int Bwidth, const TA Adata, const TB Bdata, TC *AtBdata)
	Multiply the transpose of a matrix of size Aheight x Awidth and data Adata with a matrix of size Aheight x Bwidth and data Bdata: At * B. Return the result in a matrix with data AtBdata.

template<typename TA , typename TB , typename TC >
MFEM_HOST_DEVICE void	AddMultAtB (const int Aheight, const int Awidth, const int Bwidth, const TA Adata, const TB Bdata, TC *AtBdata)
	Multiply the transpose of a matrix of size Aheight x Awidth and data Adata with a matrix of size Aheight x Bwidth and data Bdata: At * B. Add the result to the matrix with data AtBdata.

template<int HEIGHT, int WIDTH>
MFEM_HOST_DEVICE void	CalcLeftInverse (const real_t data, real_t left_inv)
	Given a matrix of size 2x1, 3x1, or 3x2, compute the left inverse.

template<int dim>
MFEM_HOST_DEVICE void	CalcEigenvalues (const real_t data, real_t lambda, real_t *vec)

template<int dim>
MFEM_HOST_DEVICE real_t	CalcSingularvalue (const real_t *data, const int i)
	Return the i'th singular value of the matrix of size dim with given data.

template<>
MFEM_HOST_DEVICE void	CalcLeftInverse< 2, 1 > (const real_t d, real_t left_inv)

template<>
MFEM_HOST_DEVICE void	CalcLeftInverse< 3, 1 > (const real_t d, real_t left_inv)

template<>
MFEM_HOST_DEVICE void	CalcLeftInverse< 3, 2 > (const real_t d, real_t left_inv)

template<>
MFEM_HOST_DEVICE void	CalcEigenvalues< 2 > (const real_t data, real_t lambda, real_t *vec)

template<>
MFEM_HOST_DEVICE void	CalcEigenvalues< 3 > (const real_t data, real_t lambda, real_t *vec)

template<>
MFEM_HOST_DEVICE real_t	CalcSingularvalue< 2 > (const real_t *data, const int i)
	Return the i'th singular value of the matrix of size 2 with given data.

template<>
MFEM_HOST_DEVICE real_t	CalcSingularvalue< 3 > (const real_t *data, const int i)
	Return the i'th singular value of the matrix of size 3 with given data.

MFEM_HOST_DEVICE void	LSolve (const real_t data, const int m, const int ipiv, real_t *x)
	Assuming L.U = P.A factored matrix of size (m x m), compute X <- L^{-1} P X, for a vector X of length m.

MFEM_HOST_DEVICE void	USolve (const real_t data, const int m, real_t x)
	Assuming L.U = P.A factored matrix of size (m x m), compute X <- U^{-1} X, for a vector X of length m.

MFEM_HOST_DEVICE void	LUSolve (const real_t data, const int m, const int ipiv, real_t *x)
	Assuming L.U = P.A for a factored matrix (m x m),.

MFEM_HOST_DEVICE void	SubMult (const int m, const int n, const int r, const real_t A21, const real_t X1, real_t *X2)
	Given an (n x m) matrix A21, compute X2 <- X2 - A21 X1, for matrices X1, and X2 of size (m x r) and (n x r), respectively.

MFEM_HOST_DEVICE void	BlockFactor (const real_t data, int m, const int ipiv, int n, real_t A12, real_t A21, real_t *A22)

MFEM_HOST_DEVICE bool	LUFactor (real_t A, const int m, int ipiv, const real_t tol=0.0)
	Compute the LU factorization of the m x m matrix A.

Function Documentation

◆ Add() [1/4]

template<typename TA , typename TB >

MFEM_HOST_DEVICE void mfem::kernels::Add	(	const int	height,
		const int	width,
		const real_t	alpha,
		const TA *	Adata,
		TB *	Bdata )

inline

Compute B +=alpha*A, where the matrices A and B are of size height x width with data Adata and Bdata.

Definition at line 312 of file kernels.hpp.

◆ Add() [2/4]

template<typename TA , typename TB >

MFEM_HOST_DEVICE void mfem::kernels::Add	(	const int	height,
		const int	width,
		const TA *	Adata,
		TB *	Bdata )

inline

Compute B += A, where the matrices A and B are of size height x width with data Adata and Bdata.

Definition at line 299 of file kernels.hpp.

◆ Add() [3/4]

template<typename TALPHA , typename TA , typename TB , typename TC >

MFEM_HOST_DEVICE void mfem::kernels::Add	(	const int	height,
		const int	width,
		const TALPHA	alpha,
		const TA *	Adata,
		const TB *	Bdata,
		TC *	Cdata )

inline

Compute C = A + alpha*B, where the matrices A, B and C are of size height x width with data Adata, Bdata and Cdata.

Definition at line 266 of file kernels.hpp.

◆ Add() [4/4]

template<typename TALPHA , typename TBETA , typename TA , typename TB , typename TC >

MFEM_HOST_DEVICE void mfem::kernels::Add	(	const int	height,
		const int	width,
		const TALPHA	alpha,
		const TA *	Adata,
		const TBETA	beta,
		const TB *	Bdata,
		TC *	Cdata )

inline

Compute C = alpha*A + beta*B, where the matrices A, B and C are of size height x width with data Adata, Bdata and Cdata.

Definition at line 283 of file kernels.hpp.

◆ AddMult()

template<typename TA , typename TB , typename TC >

MFEM_HOST_DEVICE void mfem::kernels::AddMult	(	const int	Aheight,
		const int	Awidth,
		const int	Bwidth,
		const TB *	Bdata,
		const TC *	Cdata,
		TA *	Adata,
		const TB	alpha,
		const TA	beta )

inline

Matrix-matrix multiplication: A = alpha * B * C + beta * A, where the matrices A, B and C are of sizes Aheight x Awidth, Aheight x Bwidth and Bwidth x Awidth, respectively.

Definition at line 341 of file kernels.hpp.

◆ AddMultAtB() [1/2]

template<typename TA , typename TB , typename TC >

MFEM_HOST_DEVICE void mfem::kernels::AddMultAtB	(	const int	Aheight,
		const int	Awidth,
		const int	Bwidth,
		const TA *	Adata,
		const TB *	Bdata,
		TC *	AtBdata )

inline

Multiply the transpose of a matrix of size Aheight x Awidth and data Adata with a matrix of size Aheight x Bwidth and data Bdata: At * B. Add the result to the matrix with data AtBdata.

Definition at line 458 of file kernels.hpp.

◆ AddMultAtB() [2/2]

template<typename TA , typename TB , typename TC >

MFEM_HOST_DEVICE void mfem::kernels::AddMultAtB	(	const int	Aheight,
		const int	Awidth,
		const int	Bwidth,
		const TA *	Adata,
		const TB *	Bdata,
		TC *	Cdata,
		const TB	alpha,
		const TA	beta )

inline

Compute C = alpha*At*B + beta*C.

Multiply the transpose of a matrix of size Aheight x Awidth and data Adata with a matrix of size Aheight x Bwidth and data Bdata.

Definition at line 411 of file kernels.hpp.

◆ AddMultVWt()

template<int dim>

MFEM_HOST_DEVICE void mfem::kernels::AddMultVWt	(	const real_t *	v,
		const real_t *	w,
		real_t *	VWt )

inline

Dense matrix operation: VWt += v w^t.

Definition at line 67 of file kernels.hpp.

◆ BlockFactor()

MFEM_HOST_DEVICE void mfem::kernels::BlockFactor	(	const real_t *	data,
		int	m,
		const int *	ipiv,
		int	n,
		real_t *	A12,
		real_t *	A21,
		real_t *	A22 )

inline

Assuming P.A = L.U factored data of size (m x m), compute the 2x2 block decomposition: | P 0 | | A A12 | = | L 0 | | U U12 | | 0 I | | A21 A22 | | L21 I | | 0 S22 | where A12, A21, and A22 are matrices of size (m x n), (n x m), and (n x n), respectively. The blocks are overwritten as follows: A12 <- U12 = L^{-1} P A12 A21 <- L21 = A21 U^{-1} A22 <- S22 = A22 - L21 U12. The block S22 is the Schur complement.

Definition at line 1783 of file kernels.hpp.

◆ CalcAdjugate()

template<int dim, typename T >

MFEM_HOST_DEVICE void mfem::kernels::CalcAdjugate	(	const T *	data,
		T *	adj_data )

inline

Return the adjugate of a matrix.

Definition at line 256 of file kernels.hpp.

◆ CalcEigenvalues()

template<int dim>

MFEM_HOST_DEVICE void mfem::kernels::CalcEigenvalues	(	const real_t *	data,
		real_t *	lambda,
		real_t *	vec )

Compute the spectrum of the matrix of size dim with given data, returning the eigenvalues in the array lambda and the eigenvectors in the array vec (listed consecutively).

◆ CalcEigenvalues< 2 >()

template<>

MFEM_HOST_DEVICE void mfem::kernels::CalcEigenvalues< 2 >	(	const real_t *	data,
		real_t *	lambda,
		real_t *	vec )

inline

Compute the spectrum of the matrix of size 2 with given data, returning the eigenvalues in the array lambda and the eigenvectors in the array vec (listed consecutively).

Definition at line 1179 of file kernels.hpp.

◆ CalcEigenvalues< 3 >()

template<>

MFEM_HOST_DEVICE void mfem::kernels::CalcEigenvalues< 3 >	(	const real_t *	data,
		real_t *	lambda,
		real_t *	vec )

inline

Compute the spectrum of the matrix of size 3 with given data, returning the eigenvalues in the array lambda and the eigenvectors in the array vec (listed consecutively).

Definition at line 1210 of file kernels.hpp.

◆ CalcInverse()

template<int dim, typename T >

MFEM_HOST_DEVICE void mfem::kernels::CalcInverse	(	const T *	data,
		T *	inv_data )

inline

Return the inverse of a matrix with given size and data into the matrix with data inv_data.

Definition at line 246 of file kernels.hpp.

◆ CalcLeftInverse()

template<int HEIGHT, int WIDTH>

MFEM_HOST_DEVICE void mfem::kernels::CalcLeftInverse	(	const real_t *	data,
		real_t *	left_inv )

Given a matrix of size 2x1, 3x1, or 3x2, compute the left inverse.

◆ CalcLeftInverse< 2, 1 >()

template<>

MFEM_HOST_DEVICE void mfem::kernels::CalcLeftInverse< 2, 1 >	(	const real_t *	d,
		real_t *	left_inv )

inline

Definition at line 1140 of file kernels.hpp.

◆ CalcLeftInverse< 3, 1 >()

template<>

MFEM_HOST_DEVICE void mfem::kernels::CalcLeftInverse< 3, 1 >	(	const real_t *	d,
		real_t *	left_inv )

inline

Definition at line 1148 of file kernels.hpp.

◆ CalcLeftInverse< 3, 2 >()

template<>

MFEM_HOST_DEVICE void mfem::kernels::CalcLeftInverse< 3, 2 >	(	const real_t *	d,
		real_t *	left_inv )

inline

Definition at line 1157 of file kernels.hpp.

◆ CalcSingularvalue()

template<int dim>

MFEM_HOST_DEVICE real_t mfem::kernels::CalcSingularvalue	(	const real_t *	data,
		const int	i )

Return the i'th singular value of the matrix of size dim with given data.

◆ CalcSingularvalue< 2 >()

template<>

MFEM_HOST_DEVICE real_t mfem::kernels::CalcSingularvalue< 2 >	(	const real_t *	data,
		const int	i )

inline

Return the i'th singular value of the matrix of size 2 with given data.

Definition at line 1412 of file kernels.hpp.

◆ CalcSingularvalue< 3 >()

template<>

MFEM_HOST_DEVICE real_t mfem::kernels::CalcSingularvalue< 3 >	(	const real_t *	data,
		const int	i )

inline

Return the i'th singular value of the matrix of size 3 with given data.

Definition at line 1460 of file kernels.hpp.

◆ Det()

template<int dim, typename T >

MFEM_HOST_DEVICE T mfem::kernels::Det ( const T * data )

inline

Compute the determinant of a square matrix of size dim with given data.

Definition at line 237 of file kernels.hpp.

◆ Diag()

template<int dim>

MFEM_HOST_DEVICE void mfem::kernels::Diag	(	const real_t	c,
		real_t *	data )

inline

Creates n x n diagonal matrix with diagonal elements c.

Definition at line 49 of file kernels.hpp.

◆ DistanceSquared()

template<int dim>

MFEM_HOST_DEVICE real_t mfem::kernels::DistanceSquared	(	const real_t *	x,
		const real_t *	y )

inline

Compute the square of the Euclidean distance to another vector.

Definition at line 40 of file kernels.hpp.

◆ FNorm() [1/2]

template<int H, int W, typename T >

MFEM_HOST_DEVICE real_t mfem::kernels::FNorm ( const T * data )

inline

Compute the Frobenius norm of the matrix.

Definition at line 113 of file kernels.hpp.

◆ FNorm() [2/2]

template<int H, int W, typename T >

MFEM_HOST_DEVICE void mfem::kernels::FNorm	(	real_t &	scale_factor,
		real_t &	scaled_fnorm2,
		const T *	data )

inline

Definition at line 79 of file kernels.hpp.

◆ FNorm2()

template<int H, int W, typename T >

MFEM_HOST_DEVICE real_t mfem::kernels::FNorm2 ( const T * data )

inline

Compute the square of the Frobenius norm of the matrix.

Definition at line 123 of file kernels.hpp.

◆ LSolve()

MFEM_HOST_DEVICE void mfem::kernels::LSolve	(	const real_t *	data,
		const int	m,
		const int *	ipiv,
		real_t *	x )

inline

Assuming L.U = P.A factored matrix of size (m x m), compute X <- L^{-1} P X, for a vector X of length m.

Definition at line 1700 of file kernels.hpp.

◆ LUFactor()

MFEM_HOST_DEVICE bool mfem::kernels::LUFactor	(	real_t *	A,
		const int	m,
		int *	ipiv,
		const real_t	tol = 0.0 )

inline

Compute the LU factorization of the m x m matrix A.

Factorize the matrix of size (m x m) overwriting it with the LU factors. The factorization is such that L.U = P.A, where A is the original matrix and P is a permutation matrix represented by ipiv.

Parameters

[in,out]	A	matrix
[in]	m	size of the square matrix
[out]	ipiv	array of pivots (length m)
[in]	tol	optional fuzzy comparison tolerance. Defaults to 0.0.

Returns: true if the factorization succeeds, false otherwise (zero pivot).

Definition at line 1825 of file kernels.hpp.

◆ LUSolve()

MFEM_HOST_DEVICE void mfem::kernels::LUSolve	(	const real_t *	data,
		const int	m,
		const int *	ipiv,
		real_t *	x )

inline

Assuming L.U = P.A for a factored matrix (m x m),.

Definition at line 1745 of file kernels.hpp.

◆ Mult() [1/2]

template<typename TA , typename TB , typename TC >

MFEM_HOST_DEVICE void mfem::kernels::Mult	(	const int	Aheight,
		const int	Awidth,
		const int	Bwidth,
		const TB *	Bdata,
		const TC *	Cdata,
		TA *	Adata )

inline

Matrix-matrix multiplication: A = B * C, where the matrices A, B and C are of sizes Aheight x Awidth, Aheight x Bwidth and Bwidth x Awidth, respectively.

Definition at line 372 of file kernels.hpp.

◆ Mult() [2/2]

template<typename TA , typename TX , typename TY >

MFEM_HOST_DEVICE void mfem::kernels::Mult	(	const int	height,
		const int	width,
		const TA *	data,
		const TX *	x,
		TY *	y )

inline

Matrix vector multiplication: y = A x, where the matrix A is of size height x width with given data, while x and y specify the data of the input and output vectors.

Definition at line 163 of file kernels.hpp.

◆ MultABt()

template<typename TA , typename TB , typename TC >

MFEM_HOST_DEVICE void mfem::kernels::MultABt	(	const int	Aheight,
		const int	Awidth,
		const int	Bheight,
		const TA *	Adata,
		const TB *	Bdata,
		TC *	ABtdata )

inline

Multiply a matrix of size Aheight x Awidth and data Adata with the transpose of a matrix of size Bheight x Awidth and data Bdata: A * Bt. Return the result in a matrix with data ABtdata.

Definition at line 383 of file kernels.hpp.

◆ MultAtB()

template<typename TA , typename TB , typename TC >

MFEM_HOST_DEVICE void mfem::kernels::MultAtB	(	const int	Aheight,
		const int	Awidth,
		const int	Bwidth,
		const TA *	Adata,
		const TB *	Bdata,
		TC *	AtBdata )

inline

Multiply the transpose of a matrix of size Aheight x Awidth and data Adata with a matrix of size Aheight x Bwidth and data Bdata: At * B. Return the result in a matrix with data AtBdata.

Definition at line 447 of file kernels.hpp.

◆ MultTranspose()

template<typename TA , typename TX , typename TY >

MFEM_HOST_DEVICE void mfem::kernels::MultTranspose	(	const int	height,
		const int	width,
		const TA *	data,
		const TX *	x,
		TY *	y )

inline

Matrix transpose vector multiplication: y = At x, where the matrix A is of size height x width with given data, while x and y specify the data of the input and output vectors.

Definition at line 196 of file kernels.hpp.

◆ Norml2()

template<typename T >

MFEM_HOST_DEVICE real_t mfem::kernels::Norml2	(	const int	size,
		const T *	data )

inline

Returns the l2 norm of the Vector with given size and data.

Definition at line 133 of file kernels.hpp.

◆ Set()

template<typename TA , typename TB >

MFEM_HOST_DEVICE void mfem::kernels::Set	(	const int	height,
		const int	width,
		const real_t	alpha,
		const TA *	Adata,
		TB *	Bdata )

inline

Compute B = alpha*A, where the matrices A and B are of size height x width with data Adata and Bdata.

Definition at line 326 of file kernels.hpp.

◆ SubMult()

MFEM_HOST_DEVICE void mfem::kernels::SubMult	(	const int	m,
		const int	n,
		const int	r,
		const real_t *	A21,
		const real_t *	X1,
		real_t *	X2 )

inline

Given an (n x m) matrix A21, compute X2 <- X2 - A21 X1, for matrices X1, and X2 of size (m x r) and (n x r), respectively.

Definition at line 1755 of file kernels.hpp.

◆ Subtract()

template<int dim>

MFEM_HOST_DEVICE void mfem::kernels::Subtract	(	const real_t	a,
		const real_t *	x,
		const real_t *	y,
		real_t *	z )

inline

Vector subtraction operation: z = a * (x - y)

Definition at line 58 of file kernels.hpp.

◆ Symmetrize()

template<typename T >

MFEM_HOST_DEVICE void mfem::kernels::Symmetrize	(	const int	size,
		T *	data )

inline

Symmetrize a square matrix with given size and data: A -> (A+A^T)/2.

Definition at line 223 of file kernels.hpp.

◆ USolve()

MFEM_HOST_DEVICE void mfem::kernels::USolve	(	const real_t *	data,
		const int	m,
		real_t *	x )

inline

Assuming L.U = P.A factored matrix of size (m x m), compute X <- U^{-1} X, for a vector X of length m.

Definition at line 1725 of file kernels.hpp.

Classes

Functions

Function Documentation

◆ Add() [1/4]

◆ Add() [2/4]

◆ Add() [3/4]

◆ Add() [4/4]

◆ AddMult()

◆ AddMultAtB() [1/2]

◆ AddMultAtB() [2/2]

◆ AddMultVWt()

◆ BlockFactor()

◆ CalcAdjugate()

◆ CalcEigenvalues()

◆ CalcEigenvalues< 2 >()

◆ CalcEigenvalues< 3 >()

◆ CalcInverse()

◆ CalcLeftInverse()

◆ CalcLeftInverse< 2, 1 >()

◆ CalcLeftInverse< 3, 1 >()

◆ CalcLeftInverse< 3, 2 >()

◆ CalcSingularvalue()

◆ CalcSingularvalue< 2 >()

◆ CalcSingularvalue< 3 >()

◆ Det()

◆ Diag()

◆ DistanceSquared()

◆ FNorm() [1/2]

◆ FNorm() [2/2]

◆ FNorm2()

◆ LSolve()

◆ LUFactor()

◆ LUSolve()

◆ Mult() [1/2]

◆ Mult() [2/2]

◆ MultABt()

◆ MultAtB()

◆ MultTranspose()

◆ Norml2()

◆ Set()

◆ SubMult()

◆ Subtract()

◆ Symmetrize()

◆ USolve()