Classes
struct	fac_options_t
	options for matrix factorisations More...

class	TLU
	Computes LU factorisation \( A = LU \). More...

class	TLDU
	Computes LDU factorisation \( A = LDU \). More...

class	TLL
	computes Cholesky factorisation \( A = LL^T \) or \( A=LL^H \) More...

class	TLDL
	computes LDL factorisation \( A = LDL^T \) or \( A = LDL^H \) More...

struct	inv_options_t
	options for matrix inversion More...

struct	solve_option_t
	options for how to solve with given matrix More...

struct	eval_option_t
	options for how to evaluate given matrix More...

class	TLowRankApx< T_value >
	base class for all low rank approximation techniques More...

class	TZeroLRApx< T_value >
	Approximate all low-rank blocks by zero, e.g. for nearfield only. More...

class	TDenseLRApx< T_value >
	Computes dense matrix block without approximation. More...

class	TSVDLRApx< T_value >
	Uses exact SVD to compute low rank approximation (WARNING: O(n³) complexity) More...

class	TRandSVDLRApx< T_value >
	Uses randomized SVD to compute low rank approximation (WARNING: O(n²) complexity) More...

class	TRRQRLRApx< T_value >
	Uses rank-revealing QR to compute low rank approximation. More...

class	TACA< T_value >
	Defines interface for all ACA algorithms and implements classical ACA. More...

class	TACAPlus< T_value >
	Implements ACA+, which corrects some of the deficits of the original ACA algorithm. More...

class	TACAFull< T_value >
	ACA with full pivot search (complexity: O(n²)) More...

class	THCA< T_value >
	uses hybrid cross approximation (HCA) for computing low rank approximation More...

class	TPermHCAGeneratorFn< T_value >
	base class for HCA generator functions using row/column permutations More...

Functions
template<typename value_t >
void	eval_diag (const TMatrix< value_t > A, TVector< value_t > v, const matop_t op, const eval_option_t &options)
	evaluate D·x=y with (block) diagonal D

template<typename value_t >
void	eval (const TMatrix< value_t > A, TVector< value_t > v, const matop_t op, const eval_option_t &options)
	evaluate L·U·x = y with lower triangular L and upper triangular U

template<typename value_t >
void	eval_lower (const TMatrix< value_t > A, TVector< value_t > v, const matop_t op, const eval_option_t &options)
	evaluate A·x = y with lower triangular A

template<typename value_t >
void	eval_upper (const TMatrix< value_t > A, TVector< value_t > v, const matop_t op, const eval_option_t &options)
	evaluate A·x = y with upper triangular A

template<typename value_t >
void	invert (TMatrix< value_t > *A, const TTruncAcc &acc, const inv_options_t &opts=inv_options_t())
	compute \( A^{-1} \)

template<typename value_t >
DAG::Graph	gen_dag_invert (TMatrix< value_t > *A, const inv_options_t &opts=inv_options_t())
	generate DAG for computing \( A^{-1} \)

template<typename value_t >
void	invert_ll (TMatrix< value_t > *A, const TTruncAcc &acc, const inv_options_t &opts=inv_options_t())
	compute \( A^{-1} \)

template<typename value_t >
DAG::Graph	gen_dag_invert_ll (TMatrix< value_t > *A, const inv_options_t &opts=inv_options_t())
	generate DAG for computing \( A^{-1} \) for lower-left part of \(A\)

template<typename value_t >
void	invert_ll (TMatrix< value_t > L, TMatrix< value_t > X, const TTruncAcc &acc, const inv_options_t &opts=inv_options_t())
	compute \( X = L^{-1} \)

template<typename value_t >
void	invert_ur (TMatrix< value_t > *A, const TTruncAcc &acc, const inv_options_t &opts=inv_options_t())
	compute \( A^{-1} \)

template<typename value_t >
DAG::Graph	gen_dag_invert_ur (TMatrix< value_t > *A, const inv_options_t &opts=inv_options_t())
	generate DAG for computing \( A^{-1} \) for upper-right part of \(A\)

template<typename value_t >
void	invert_diag (TMatrix< value_t > *A, const TTruncAcc &acc, const inv_options_t &opts=inv_options_t())
	compute \( A^{-1} \)

template<typename value_t >
std::unique_ptr< TVector< value_t > >	inverse_diag (TMatrix< value_t > *A, const TTruncAcc &acc, const inv_options_t &opts=inv_options_t())
	Compute diagonal of \( A^{-1} \).

template<typename value_t >
void	gauss_elim (TMatrix< value_t > A, TMatrix< value_t > C, const TTruncAcc &acc, const inv_options_t &opts=inv_options_t())
	Compute \(A^{-1} \) via Gaussian elimination.

template<typename value_t >
void	gauss_elim (TMatrix< value_t > *A, const TTruncAcc &acc, const inv_options_t &opts=inv_options_t())
	Compute \(A^{-1} \) via Gaussian elimination.

template<typename value_t >
void	invert (TDenseMatrix< value_t > *A)
	compute \( A^{-1} \)

template<typename value_t >
std::unique_ptr< TDenseMatrix< value_t > >	inverse (const TDenseMatrix< value_t > *A)
	compute and return \( A^{-1} \)

template<typename value_t >
void	multiply_diag (const value_t alpha, const matop_t op_A, const TMatrix< value_t > A, const matop_t op_D, const TMatrix< value_t > D, const matop_t op_B, const TMatrix< value_t > B, const value_t beta, TMatrix< value_t > C, const TTruncAcc &acc, TProgressBar *progress=NULL)
	compute C ≔ β·C + α·op(A)·op(D)·op(B) with (block) diagonal matrix D

template<typename value_t >
void	multiply_diag_accu (const value_t alpha, const matop_t op_A, const TMatrix< value_t > A, const matop_t op_D, const TMatrix< value_t > D, const matop_t op_B, const TMatrix< value_t > B, TMatrix< value_t > C, const TTruncAcc &acc)
	compute C ≔ C + α·op(A)·op(D)·op(B) with (block) diagonal matrix D using accumulators

template<typename value_t >
std::unique_ptr< TMatrix< value_t > >	multiply_diag (const value_t alpha, const matop_t op_A, const TMatrix< value_t > A, const matop_t op_D, const TMatrix< value_t > D, const matop_t op_B, const TMatrix< value_t > *B)
	compute C ≔ α·op(A)·op(D)·op(B) with (block) diagonal matrix D

template<typename value_t >
size_t	multiply_diag_steps (const matop_t op_A, const TMatrix< value_t > A, const matop_t op_D, const TMatrix< value_t > D, const matop_t op_B, const TMatrix< value_t > B, const TMatrix< value_t > C)
	return number of steps for computing C ≔ C + op(A)·op(D)·op(B)

template<typename value_t >
void	mul_diag_left (const TScalarVector< value_t > &v, TMatrix< value_t > *A)
	compute B = diag(v)·A and overwrite A

template<typename value_t >
void	mul_diag_right (TMatrix< value_t > *A, const TScalarVector< value_t > &v)
	compute B = A·diag(v) and overwrite A

Fourier Transformation
Functions for the forward and backward Fourier transformation of vectors.
template<typename real_t >
void	fft (TVector< std::complex< real_t > > *v)
	perform FFT for vector v (inplace)

template<typename real_t >
void	ifft (TVector< std::complex< real_t > > *v)
	perform inverse FFT for vector v (inplace)

Matrix Addition
Functions for matrix addition
template<typename value_t >
void	add (const value_t alpha, const TMatrix< value_t > A, const value_t beta, TMatrix< value_t > C, const TTruncAcc &acc)
	C ≔ α·A + β·C.

template<typename value_t >
void	add_identity (TMatrix< value_t > *A, const value_t lambda)
	compute A ≔ A + λ·I

template<typename value_t >
std::unique_ptr< TMatrix< value_t > >	add (std::list< const TMatrix< value_t > * > &matrices, const TTruncAcc &acc)
	compute and return Σ_i A_i

Matrix Factorisation
Functions related to matrix factorisation, e.g. LU or Cholesky factorisation.
template<typename value_t >
std::unique_ptr< TFacMatrix< value_t > >	factorise (TMatrix< value_t > *A, const TTruncAcc &acc, const fac_options_t &options=fac_options_t())
	compute factorisation of A

template<typename value_t >
std::unique_ptr< TFacInvMatrix< value_t > >	factorise_inv (TMatrix< value_t > *A, const TTruncAcc &acc, const fac_options_t &options=fac_options_t())
	compute factorisation of A and return inverse operator

template<typename value_t >
void	lu (TMatrix< value_t > *A, const TTruncAcc &acc, const fac_options_t &options=fac_options_t())
	compute LU factorisation

template<typename value_t >
std::unique_ptr< TFacInvMatrix< value_t > >	lu_inv (TMatrix< value_t > *A, const TTruncAcc &acc, const fac_options_t &options=fac_options_t())
	compute LU factorisation and return inverse operator

template<typename value_t >
void	ldu (TMatrix< value_t > *A, const TTruncAcc &acc, const fac_options_t &options=fac_options_t())
	compute LDU factorisation

template<typename value_t >
std::unique_ptr< TFacInvMatrix< value_t > >	ldu_inv (TMatrix< value_t > *A, const TTruncAcc &acc, const fac_options_t &options=fac_options_t())
	compute LDU factorisation and return inverse operator

template<typename value_t >
void	ll (TMatrix< value_t > *A, const TTruncAcc &acc, const fac_options_t &options=fac_options_t())
	compute Cholesky factorisation

template<typename value_t >
std::unique_ptr< TFacInvMatrix< value_t > >	ll_inv (TMatrix< value_t > *A, const TTruncAcc &acc, const fac_options_t &options=fac_options_t())
	compute Cholesky factorisation and return inverse operator

template<typename value_t >
void	ldl (TMatrix< value_t > *A, const TTruncAcc &acc, const fac_options_t &options=fac_options_t())
	compute LDL^H factorisation

template<typename value_t >
std::unique_ptr< TFacInvMatrix< value_t > >	ldl_inv (TMatrix< value_t > *A, const TTruncAcc &acc, const fac_options_t &options=fac_options_t())
	compute LDL factorisation and return inverse operator

template<typename value_t >
void	test_convert_dense (TBlockMatrix< value_t > *B, const uint i, const uint j)
	test, if given matrix B_ij is lowrank with too large rank and convert to dense

Matrix Multiplication
Functions for matrix multiplication.
template<typename value_t >
void	multiply (const value_t alpha, const matop_t op_A, const TMatrix< value_t > A, const matop_t op_B, const TMatrix< value_t > B, const value_t beta, TMatrix< value_t > C, const TTruncAcc &acc, TProgressBar progress=nullptr)
	compute C ≔ β·C + α·op(A)·op(B)

template<typename value_t >
void	multiply (const value_t alpha, const TMatrix< value_t > A, const TMatrix< value_t > B, const value_t beta, TMatrix< value_t > C, const TTruncAcc &acc, TProgressBar progress=nullptr)
	compute C = β·C + α·A·B

template<typename value_t >
void	multiply_accu (const value_t alpha, const matop_t op_A, const TMatrix< value_t > A, const matop_t op_B, const TMatrix< value_t > B, const value_t beta, TMatrix< value_t > *C, const TTruncAcc &acc)
	compute C ≔ C + α·op(A)·op(B) using accumulators

template<typename value_t >
std::unique_ptr< TMatrix< value_t > >	multiply (const value_t alpha, const matop_t op_A, const TMatrix< value_t > A, const matop_t op_B, const TMatrix< value_t > B)
	compute C ≔ β·C + α·op(A)·op(B)

template<typename value_t >
void	multiply_ll_left (const value_t alpha, const TMatrix< value_t > L, TMatrix< value_t > A, const TTruncAcc &acc, const eval_option_t &opts)
	compute A ≔ α·L·A with lower left tridiagonal L

template<typename value_t >
void	multiply_ll_right (const value_t alpha, TMatrix< value_t > A, const TMatrix< value_t > L, const TTruncAcc &acc, const eval_option_t &opts)
	compute A ≔ α·A·L with lower left tridiagonal L

template<typename value_t >
void	multiply_ll_right (const value_t alpha, const TMatrix< value_t > A, const TMatrix< value_t > B, TMatrix< value_t > *C, const TTruncAcc &acc, const eval_option_t &opts)
	compute C ≔ C + α·A·B with lower left tridiagonal B

template<typename value_t >
void	multiply_ur_left (const value_t alpha, const TMatrix< value_t > U, TMatrix< value_t > A, const TTruncAcc &acc, const eval_option_t &opts)
	compute A ≔ α·U·A with upper right tridiagonal U

template<typename value_t >
void	multiply_ur_right (const value_t alpha, TMatrix< value_t > A, const TMatrix< value_t > U, const TTruncAcc &acc, const eval_option_t &opts)
	compute A ≔ α·A·U with upper right tridiagonal U

template<typename value_t >
void	multiply_ur_ll (const TMatrix< value_t > U, const TMatrix< value_t > L, TMatrix< value_t > *A, const TTruncAcc &acc, const eval_option_t &opts_U, const eval_option_t &opts_L)
	Compute A ≔ A + U·L.

template<typename value_t >
void	multiply_llt_d_ll (const TMatrix< value_t > L, const TMatrix< value_t > D, TMatrix< value_t > *C, const matop_t op_L, const TTruncAcc &acc)
	Compute C ≔ C + L^T·D·L.

template<typename value_t >
void	multiply_llt_d_left (const value_t alpha, const TMatrix< value_t > L, const TMatrix< value_t > D, TMatrix< value_t > *B, const matop_t op_L, const TTruncAcc &acc)
	Compute B ≔ op(L)·D·B inplace, overwriting B.

template<typename value_t >
void	multiply_llt_d_left (const value_t alpha, const TMatrix< value_t > L, const TMatrix< value_t > D, const TMatrix< value_t > B, TMatrix< value_t > C, const matop_t op_L, const TTruncAcc &acc)
	Compute C ≔ C + op(L)·D·B.

template<typename value_t >
void	add_product (const value_t alpha, const matop_t op_A, const TMatrix< value_t > A, const matop_t op_B, const TMatrix< value_t > B, TMatrix< value_t > *C, const TTruncAcc &acc, const bool lazy=CFG::Arith::lazy_eval)

Matrix-Vector Multiplication
Functions for (parallel) matrix-vector multiplication
template<typename value_t >
void	mul_vec (const TProcSet &ps, const value_t alpha, const TMatrix< value_t > A, const TVector< value_t > x, const value_t beta, TVector< value_t > *y, const matop_t op)
	compute y ≔ α·A·x + β·y on (possibly) distributed matrix A on all processors in ps

template<typename value_t >
void	mul_vec_diag (const value_t alpha, const matop_t op_A, const TMatrix< value_t > A, const matop_t op_D, const TMatrix< value_t > D, const TVector< value_t > x, const value_t beta, TVector< value_t > y)
	compute y ≔ α·A·D·x + β·y with diagonal matrix D

Detailed Description

This module provides most higher level algebra functions, e.g. matrix multiplication, inversion and factorisation. See also Basic Matrix Algebra and Matrix Factorisation for an introduction into H-arithmetic.

To include all algebra functions and classes add

#include <hlib-alg.hh>

to your source files.

Function Documentation

◆ add()

template<typename value_t >

void add	(	const value_t	alpha,
		const TMatrix< value_t > *	A,
		const value_t	beta,
		TMatrix< value_t > *	C,
		const TTruncAcc &	acc
	)

     The function computes the sum \f$ C := \alpha A + \beta C \f$ with
     a predefined accuracy \a acc. Thread parallel execution is supported.

Parameters

alpha	scaling factor for A
A	update matrix for C
beta	scaling factor for C
C	matrix to update
acc	accuracy of summation

◆ add_product()

template<typename value_t >

void add_product	(	const value_t	alpha,
		const matop_t	op_A,
		const TMatrix< value_t > *	A,
		const matop_t	op_B,
		const TMatrix< value_t > *	B,
		TMatrix< value_t > *	C,
		const TTruncAcc &	acc,
		const bool	lazy = `CFG::Arith::lazy_eval`
	)

start accumulator based matrix multiplication C = C + α·A·B

◆ eval()

template<typename value_t >

void eval	(	const TMatrix< value_t > *	A,
		TVector< value_t > *	v,
		const matop_t	op,
		const eval_option_t &	options
	)

L and U are both stored in A
on entry: v = x
on exit : v = y

◆ eval_diag()

template<typename value_t >

void eval_diag	(	const TMatrix< value_t > *	A,
		TVector< value_t > *	v,
		const matop_t	op,
		const eval_option_t &	options
	)

on entry: v = x
on exit : v = y

◆ eval_lower()

template<typename value_t >

void eval_lower	(	const TMatrix< value_t > *	A,
		TVector< value_t > *	v,
		const matop_t	op,
		const eval_option_t &	options
	)

on entry: v = x
on exit : v = y

◆ eval_upper()

template<typename value_t >

void eval_upper	(	const TMatrix< value_t > *	A,
		TVector< value_t > *	v,
		const matop_t	op,
		const eval_option_t &	options
	)

on entry: v = x
on exit : v = y

◆ factorise()

template<typename value_t >

std::unique_ptr< TFacMatrix< value_t > > factorise	(	TMatrix< value_t > *	A,
		const TTruncAcc &	acc,
		const fac_options_t &	options = `fac_options_t()`
	)

     Compute triangular factorisation of \a A while choosing
     appropriate factorisation method depending on the format
     of \a A, e.g. if unsymmetric, symmetric or hermitian.

     The return value is an operator object representing the
     factorised form and suitable for evaluation, e.g.
     matrix-vector multiplication (see TFacMatrix).
     \a A will be overwritten with the actual factorisation data.

◆ factorise_inv()

template<typename value_t >

std::unique_ptr< TFacInvMatrix< value_t > > factorise_inv	(	TMatrix< value_t > *	A,
		const TTruncAcc &	acc,
		const fac_options_t &	options = `fac_options_t()`
	)

     Compute triangular factorisation of \a A as in factorise
     but instead of an operator for evaluation of the factorised
     \a A, an operator for evaluation of the inverse of \a A is
     returned (see TFacInvMatrix).

◆ gauss_elim() [1/2]

template<typename value_t >

void gauss_elim	(	TMatrix< value_t > *	A,
		const TTruncAcc &	acc,
		const inv_options_t &	opts = `inv_options_t()`
	)

     \a A is overwritten by \f$A^{-1} \f$. The elimination
     is performed in-place, although local copy operations
     are needed. (ATTENTION: experimental)

◆ gauss_elim() [2/2]

template<typename value_t >

void gauss_elim	(	TMatrix< value_t > *	A,
		TMatrix< value_t > *	C,
		const TTruncAcc &	acc,
		const inv_options_t &	opts = `inv_options_t()`
	)

     \a A is overwritten by \f$A^{-1} \f$. \a C is optional
     and may be used during computation as temporary space.
     If present, it should have same format as \a A.

◆ inverse()

template<typename value_t >

std::unique_ptr< TDenseMatrix< value_t > > inverse ( const TDenseMatrix< value_t > * A )

     Compute inverse of \f$ A \f$ (dense matrix version)
     but do not modify \f$ A \f$.

◆ inverse_diag()

template<typename value_t >

std::unique_ptr< TVector< value_t > > inverse_diag	(	TMatrix< value_t > *	A,
		const TTruncAcc &	acc,
		const inv_options_t &	opts = `inv_options_t()`
	)

     Compute only the diagonal of \a A and return the resulting
     vector. \a A is modified during computation.

◆ invert() [1/2]

template<typename value_t >

void invert ( TDenseMatrix< value_t > * A )

     Compute inverse of \f$ A \f$ (dense matrix version).
     \a A is overwritten by \f$ A^{-1} \f$ during inversion.

◆ invert() [2/2]

template<typename value_t >

void invert	(	TMatrix< value_t > *	A,
		const TTruncAcc &	acc,
		const inv_options_t &	opts = `inv_options_t()`
	)

     Compute inverse of \f$ A \f$ with block-wise accuracy \a acc.
     \a A is overwritten by \f$ A^{-1} \f$ during inversion.

◆ invert_diag()

template<typename value_t >

void invert_diag	(	TMatrix< value_t > *	A,
		const TTruncAcc &	acc,
		const inv_options_t &	opts = `inv_options_t()`
	)

     Compute inverse of diagonal matrix \f$ A \f$ with block-wise
     accuracy \a acc. \a A is overwritten by \f$ A^{-1} \f$
     during inversion.

◆ invert_ll() [1/2]

template<typename value_t >

void invert_ll	(	TMatrix< value_t > *	A,
		const TTruncAcc &	acc,
		const inv_options_t &	opts = `inv_options_t()`
	)

     Compute inverse of lower-left triangular matrix \f$ A \f$ with 
     block-wise accuracy \a acc. \a A is overwritten by \f$ A^{-1} \f$
     during inversion.

◆ invert_ll() [2/2]

template<typename value_t >

void invert_ll	(	TMatrix< value_t > *	L,
		TMatrix< value_t > *	X,
		const TTruncAcc &	acc,
		const inv_options_t &	opts = `inv_options_t()`
	)

     Compute inverse of lower-left triangular matrix \f$ L \f$ with 
     block-wise accuracy \a acc. \a L may be modified during inversion.

◆ invert_ur()

template<typename value_t >

void invert_ur	(	TMatrix< value_t > *	A,
		const TTruncAcc &	acc,
		const inv_options_t &	opts = `inv_options_t()`
	)

     Compute inverse of upper-right triangular matrix \f$ A \f$ with 
     block-wise accuracy \a acc. \a A is overwritten by \f$ A^{-1} \f$
     during inversion.

◆ mul_vec()

template<typename value_t >

void mul_vec	(	const TProcSet &	ps,
		const value_t	alpha,
		const TMatrix< value_t > *	A,
		const TVector< value_t > *	x,
		const value_t	beta,
		TVector< value_t > *	y,
		const matop_t	op
	)

Parameters

ps	processor set defining processors involved in computation
alpha	scaling factor for multiplication
A	matrix to multiply with
x	vector to multiply with
beta	scaling factor of updated vector
y	vector to update with multiplication result
op	defines transformation of matrix, e.g. transposed, adjoint (

See also: matop_t)

◆ mul_vec_diag()

template<typename value_t >

void mul_vec_diag	(	const value_t	alpha,
		const matop_t	op_A,
		const TMatrix< value_t > *	A,
		const matop_t	op_D,
		const TMatrix< value_t > *	D,
		const TVector< value_t > *	x,
		const value_t	beta,
		TVector< value_t > *	y
	)

Parameters

alpha	scaling factor for update
op_A	transformation of matrix A, e.g. transposed, adjoint (

See also: matop_t)

Parameters

A	arbitrary matrix
op_D	transformation of matrix D
D	diagonal matrix
x	source vector
beta	scaling factor for destination
y	destination vector

◆ multiply() [1/3]

template<typename value_t >

std::unique_ptr< TMatrix< value_t > > multiply	(	const value_t	alpha,
		const matop_t	op_A,
		const TMatrix< value_t > *	A,
		const matop_t	op_B,
		const TMatrix< value_t > *	B
	)

     The function computes to matrix product \f$\alpha \tilde A \tilde B\f$
     where \f$\tilde A\f$ and \f$\tilde B\f$ may be the non-modified, transposed or adjoint
     matrices \f$A\f$ and \f$B\f$ respectively.

     The output format is based on the format of \f$A\f$ or \f$B\f$, which assumes that at least
     one of those is a non-blocked matrix. Otherwise, an exception is thrown!

Parameters

alpha	scaling factor of product
op_A	matrix modifier for A
A	first matrix factor
op_B	matrix modifier for B
B	second matrix factor

◆ multiply() [2/3]

template<typename value_t >

void multiply	(	const value_t	alpha,
		const matop_t	op_A,
		const TMatrix< value_t > *	A,
		const matop_t	op_B,
		const TMatrix< value_t > *	B,
		const value_t	beta,
		TMatrix< value_t > *	C,
		const TTruncAcc &	acc,
		TProgressBar *	progress = `nullptr`
	)

     The function computes to matrix product \f$C := \beta C + \alpha \tilde A \tilde B\f$
     where \f$\tilde A\f$ and \f$\tilde B\f$ may be the non-modified, transposed or adjoint
     matrices \f$A\f$ and \f$B\f$ respectively.

     The result of the multiplication is written to \f$C\f$, whereby the block structure
     of \a C is not changed, e.g. the resulting block structure of the product is defined
     by \a C.

     Thread-parallel execution is supported by the matrix multiplication.

     This function is available in various versions without corresponding parameters, e.g.
     without \a op_A, \a op_B.

Parameters

alpha	scaling factor of product
op_A	matrix modifier for A
A	first matrix factor
op_B	matrix modifier for B
B	second matrix factor
beta	scaling factor for C
C	matrix to update
acc	accuracy of multiplication
progress	optional progress bar

◆ multiply() [3/3]

template<typename value_t >

void multiply	(	const value_t	alpha,
		const TMatrix< value_t > *	A,
		const TMatrix< value_t > *	B,
		const value_t	beta,
		TMatrix< value_t > *	C,
		const TTruncAcc &	acc,
		TProgressBar *	progress = `nullptr`
	)

inline

     matrices are provided as matrix view (or in combination with matrix)

◆ multiply_diag()

template<typename value_t >

std::unique_ptr< TMatrix< value_t > > multiply_diag	(	const value_t	alpha,
		const matop_t	op_A,
		const TMatrix< value_t > *	A,
		const matop_t	op_D,
		const TMatrix< value_t > *	D,
		const matop_t	op_B,
		const TMatrix< value_t > *	B
	)

not all matrices must be blocked!

◆ multiply_llt_d_left() [1/2]

template<typename value_t >

void multiply_llt_d_left	(	const value_t	alpha,
		const TMatrix< value_t > *	L,
		const TMatrix< value_t > *	D,
		const TMatrix< value_t > *	B,
		TMatrix< value_t > *	C,
		const matop_t	op_L,
		const TTruncAcc &	acc
	)

     Compute C ≔ C + op(L)·D·B with lower left tridiagonal L,
     diagonal D and general B

◆ multiply_llt_d_left() [2/2]

template<typename value_t >

void multiply_llt_d_left	(	const value_t	alpha,
		const TMatrix< value_t > *	L,
		const TMatrix< value_t > *	D,
		TMatrix< value_t > *	B,
		const matop_t	op_L,
		const TTruncAcc &	acc
	)

     Compute B ≔ op(L)·D·B with lower left tridiagonal L and
     diagonal D.

◆ multiply_llt_d_ll()

template<typename value_t >

void multiply_llt_d_ll	(	const TMatrix< value_t > *	L,
		const TMatrix< value_t > *	D,
		TMatrix< value_t > *	C,
		const matop_t	op_L,
		const TTruncAcc &	acc
	)

     Compute C ≔ C + L^T·D·L with lower left tridiagonal L and
     diagonal D.

◆ multiply_ur_ll()

template<typename value_t >

void multiply_ur_ll	(	const TMatrix< value_t > *	U,
		const TMatrix< value_t > *	L,
		TMatrix< value_t > *	A,
		const TTruncAcc &	acc,
		const eval_option_t &	opts_U,
		const eval_option_t &	opts_L
	)

     Compute A ≔ A + U·L with lower left tridiagonal L and
     upper right tridiagonal U.

Classes

Functions

Fourier Transformation

Matrix Addition

Matrix Factorisation

Matrix Multiplication

Matrix-Vector Multiplication

Detailed Description

Function Documentation

◆ add()

◆ add_product()

◆ eval()

◆ eval_diag()

◆ eval_lower()

◆ eval_upper()

◆ factorise()

◆ factorise_inv()

◆ gauss_elim() [1/2]

◆ gauss_elim() [2/2]

◆ inverse()

◆ inverse_diag()

◆ invert() [1/2]

◆ invert() [2/2]

◆ invert_diag()

◆ invert_ll() [1/2]

◆ invert_ll() [2/2]

◆ invert_ur()

◆ mul_vec()

◆ mul_vec_diag()

◆ multiply() [1/3]

◆ multiply() [2/3]

◆ multiply() [3/3]

◆ multiply_diag()

◆ multiply_llt_d_left() [1/2]

◆ multiply_llt_d_left() [2/2]

◆ multiply_llt_d_ll()

◆ multiply_ur_ll()