Linear algebra routines. More...

#include <LinAlgd.h>

Public Types
enum	SVD { eSVD1, eSVD2, eSVD1, eSVD2 }

enum	SVD { eSVD1, eSVD2, eSVD1, eSVD2 }

Public Member Functions
void	compress ()

void	free_memory ()

MATRIXN &	pseudo_invert (MATRIXN &A, REAL tol=(REAL)-1.0)

void	update_QR_rank1 (MATRIXN &Q, MATRIXN &R, const VECTORN &u, const VECTORN &v)

void	update_QR_delete_cols (MATRIXN &Q, MATRIXN &R, unsigned k, unsigned p)

void	update_QR_insert_cols (MATRIXN &Q, MATRIXN &R, MATRIXN &U, unsigned k)

void	update_QR_insert_rows (MATRIXN &Q, MATRIXN &R, MATRIXN &U, unsigned k)

void	update_QR_delete_rows (MATRIXN &Q, MATRIXN &R, unsigned k, unsigned p)

template<class X >
X &	inverse_LU (X &M, const std::vector< int > &pivwork)
	Computes a matrix inverse using the factorization determined via factor_LU() More...

template<class X >
unsigned	calc_rank (X &A, REAL tol=(REAL)-1.0)
	Calculates the rank of a matrix.

template<class Y >
MATRIXN &	nullspace (Y &A, MATRIXN &nullspace, REAL tol=-1.0)
	Computes the nullspace of a matrix. More...

template<class X >
REAL	cond (X &A)
	Computes the condition number of a matrix.

template<class X >
bool	is_SPSD (X &m, REAL tol)
	Determines whether a symmetric matrix is positive semi-definite.

template<class X >
bool	is_SPD (X &m, REAL tol)
	Determines whether a matrix is positive-definite.

template<class X , class Y >
void	eig_symm (X &A, Y &evals)
	Computes the eigenvalues of the matrix A. More...

template<class X , class Y >
void	eig_symm_plus (X &A_evecs, Y &evals)
	Computes the eigenvalues and eigenvectors of the matrix A. More...

template<class X , class MatU , class VecS , class MatV >
void	svd (X &A, MatU &U, VecS &S, MatV &V)

template<class X , class MatU , class VecS , class MatV >
void	svd1 (X &A, MatU &U, VecS &S, MatV &V)
	Does an 'in place' SVD (destroying A) More...

template<class X , class MatU , class VecS , class MatV >
void	svd2 (X &A, MatU &U, VecS &S, MatV &V)
	Does an 'in place' SVD (destroying A), using divide and conquer algorithm. More...

template<class X >
X &	solve_symmetric_fast (MATRIXN &A, X &XB)
	Solves a symmetric, indefinite square matrix. More...

template<class X >
X &	inverse_symmetric (X &A)
	Inverts the symmetric, indefinite matrix A. More...

template<class X >
X &	invert (X &A)
	Inverts the matrix A using LU factorization. More...

template<class X , class Y , class Vec , class Z >
X &	solve_LS_fast (const Y &U, const Vec &S, const Z &V, X &XB, REAL tol=(REAL)-1.0)
	Most robust system of linear equations solver (solves AX = B) More...

template<class X , class Y >
X &	solve_LS_fast (Y &A, X &XB, SVD svd_algo, REAL tol)
	Most robust system of linear equations solver (solves AX = B) More...

template<class X , class Y >
Y &	solve_fast (X &A, Y &XB)
	Solves the general system AX = B. More...

template<class X , class Y >
X &	solve_LS_fast1 (Y &A, X &XB, REAL tol=(REAL)-1.0)

template<class X , class Y >
X &	solve_LS_fast2 (Y &A, X &XB, REAL tol=(REAL)-1.0)

template<class ARMat , class QMat >
void	factor_QR (ARMat &AR, QMat &Q, std::vector< int > &PI)
	Performs the QR factorization of a matrix with column pivoting. More...

template<class ARMat , class QMat >
void	factor_QR (ARMat &AR, QMat &Q)
	Performs the QR factorization of a matrix. More...

void	compress ()

void	free_memory ()

MATRIXN &	pseudo_invert (MATRIXN &A, REAL tol=(REAL)-1.0)

void	update_QR_rank1 (MATRIXN &Q, MATRIXN &R, const VECTORN &u, const VECTORN &v)

void	update_QR_delete_cols (MATRIXN &Q, MATRIXN &R, unsigned k, unsigned p)

void	update_QR_insert_cols (MATRIXN &Q, MATRIXN &R, MATRIXN &U, unsigned k)

void	update_QR_insert_rows (MATRIXN &Q, MATRIXN &R, MATRIXN &U, unsigned k)

void	update_QR_delete_rows (MATRIXN &Q, MATRIXN &R, unsigned k, unsigned p)

template<class X >
X &	inverse_LU (X &M, const std::vector< int > &pivwork)
	Computes a matrix inverse using the factorization determined via factor_LU() More...

template<class X >
unsigned	calc_rank (X &A, REAL tol=(REAL)-1.0)
	Calculates the rank of a matrix.

template<class Y >
MATRIXN &	nullspace (Y &A, MATRIXN &nullspace, REAL tol=-1.0)
	Computes the nullspace of a matrix. More...

template<class X >
REAL	cond (X &A)
	Computes the condition number of a matrix.

template<class X >
bool	is_SPSD (X &m, REAL tol)
	Determines whether a symmetric matrix is positive semi-definite.

template<class X >
bool	is_SPD (X &m, REAL tol)
	Determines whether a matrix is positive-definite.

template<class X , class Y >
void	eig_symm (X &A, Y &evals)
	Computes the eigenvalues of the matrix A. More...

template<class X , class Y >
void	eig_symm_plus (X &A_evecs, Y &evals)
	Computes the eigenvalues and eigenvectors of the matrix A. More...

template<class X , class MatU , class VecS , class MatV >
void	svd (X &A, MatU &U, VecS &S, MatV &V)

template<class X , class MatU , class VecS , class MatV >
void	svd1 (X &A, MatU &U, VecS &S, MatV &V)
	Does an 'in place' SVD (destroying A) More...

template<class X , class MatU , class VecS , class MatV >
void	svd2 (X &A, MatU &U, VecS &S, MatV &V)
	Does an 'in place' SVD (destroying A), using divide and conquer algorithm. More...

template<class X >
X &	solve_symmetric_fast (MATRIXN &A, X &XB)
	Solves a symmetric, indefinite square matrix. More...

template<class X >
X &	inverse_symmetric (X &A)
	Inverts the symmetric, indefinite matrix A. More...

template<class X >
X &	invert (X &A)
	Inverts the matrix A using LU factorization. More...

template<class X , class Y , class Vec , class Z >
X &	solve_LS_fast (const Y &U, const Vec &S, const Z &V, X &XB, REAL tol=(REAL)-1.0)
	Most robust system of linear equations solver (solves AX = B) More...

template<class X , class Y >
X &	solve_LS_fast (Y &A, X &XB, SVD svd_algo, REAL tol)
	Most robust system of linear equations solver (solves AX = B) More...

template<class X , class Y >
Y &	solve_fast (X &A, Y &XB)
	Solves the general system AX = B. More...

template<class X , class Y >
X &	solve_LS_fast1 (Y &A, X &XB, REAL tol=(REAL)-1.0)

template<class X , class Y >
X &	solve_LS_fast2 (Y &A, X &XB, REAL tol=(REAL)-1.0)

template<class ARMat , class QMat >
void	factor_QR (ARMat &AR, QMat &Q, std::vector< int > &PI)
	Performs the QR factorization of a matrix with column pivoting. More...

template<class ARMat , class QMat >
void	factor_QR (ARMat &AR, QMat &Q)
	Performs the QR factorization of a matrix. More...

Static Public Member Functions
static void	factor_LDL (MATRIXN &M, std::vector< int > &IPIV)

static void	givens (REAL a, REAL b, REAL &c, REAL &s)

static MATRIX2	givens (REAL c, REAL s)

static void	householder (REAL alpha, const VECTORN &x, REAL &tau, VECTORN &v)

static VECTORN &	solve_sparse_direct (const SPARSEMATRIXN &A, const VECTORN &b, Transposition trans, VECTORN &x)

static MATRIXN &	solve_sparse_direct (const SPARSEMATRIXN &A, const MATRIXN &B, Transposition trans, MATRIXN &X)

template<class X >
static X &	solve_tridiagonal_fast (VECTORN &dl, VECTORN &d, VECTORN &du, X &XB)
	Solves a tridiagonal system. More...

template<class X >
static bool	factor_chol (X &A)
	Performs the Cholesky factorization of a matrix. More...

template<class X >
static bool	factor_LU (X &A, std::vector< int > &pivwork)
	Performs the LU factorization of a matrix. More...

template<class X , class Y >
static X &	solve_tri_fast (Y &A, bool utri, bool transpose_A, X &XB)
	Solves a triangular system of linear equations. More...

template<class X >
static X &	solve_LDL_fast (const MATRIXN &M, const std::vector< int > &pivwork, X &XB)
	Solves systems of linear equations using the factorization determined via factor_LDL() More...

template<class X , class Y >
static X &	solve_chol_fast (const Y &M, X &XB)
	Solves a system of linear equations using the factorization determined via factor_chol() More...

template<class Y , class X >
static X &	solve_LU_fast (const Y &M, bool transpose, const std::vector< int > &pivwork, X &XB)
	Solves a system of linear equations using the factorization determined via factor_LU() More...

template<class Y , class X >
static X &	solve_SPD_fast (Y &A, X &XB)
	Solves a system of equations A*X = B using a symmetric, positive-definite square matrix. More...

template<class X >
static X &	inverse_chol (X &A)
	Inverts the symmetric, positive-definite matrix A using Cholesky factorization. More...

template<class X >
static X &	inverse_SPD (X &A)
	Inverts the symmetric, positive-definite matrix A using Cholesky factorization. More...

static void	factor_LDL (MATRIXN &M, std::vector< int > &IPIV)

static void	givens (REAL a, REAL b, REAL &c, REAL &s)

static MATRIX2	givens (REAL c, REAL s)

static void	householder (REAL alpha, const VECTORN &x, REAL &tau, VECTORN &v)

static VECTORN &	solve_sparse_direct (const SPARSEMATRIXN &A, const VECTORN &b, Transposition trans, VECTORN &x)

static MATRIXN &	solve_sparse_direct (const SPARSEMATRIXN &A, const MATRIXN &B, Transposition trans, MATRIXN &X)

template<class X >
static X &	solve_tridiagonal_fast (VECTORN &dl, VECTORN &d, VECTORN &du, X &XB)
	Solves a tridiagonal system. More...

template<class X >
static bool	factor_chol (X &A)
	Performs the Cholesky factorization of a matrix. More...

template<class X >
static bool	factor_LU (X &A, std::vector< int > &pivwork)
	Performs the LU factorization of a matrix. More...

template<class X , class Y >
static X &	solve_tri_fast (Y &A, bool utri, bool transpose_A, X &XB)
	Solves a triangular system of linear equations. More...

template<class X >
static X &	solve_LDL_fast (const MATRIXN &M, const std::vector< int > &pivwork, X &XB)
	Solves systems of linear equations using the factorization determined via factor_LDL() More...

template<class X , class Y >
static X &	solve_chol_fast (const Y &M, X &XB)
	Solves a system of linear equations using the factorization determined via factor_chol() More...

template<class Y , class X >
static X &	solve_LU_fast (const Y &M, bool transpose, const std::vector< int > &pivwork, X &XB)
	Solves a system of linear equations using the factorization determined via factor_LU() More...

template<class Y , class X >
static X &	solve_SPD_fast (Y &A, X &XB)
	Solves a system of equations A*X = B using a symmetric, positive-definite square matrix. More...

template<class X >
static X &	inverse_chol (X &A)
	Inverts the symmetric, positive-definite matrix A using Cholesky factorization. More...

template<class X >
static X &	inverse_SPD (X &A)
	Inverts the symmetric, positive-definite matrix A using Cholesky factorization. More...

Public Attributes
FastThreadable< MATRIXN >	workM
	work matrices

FastThreadable< MATRIXN >	workM2

FastThreadable< MATRIXN >	U
	work matrix (for SVD)

FastThreadable< std::vector < INTEGER > >	pivwork
	work STL integer vector (pivoting)

FastThreadable< MATRIXN >	V
	work matrix (for SVD)

FastThreadable< VECTORN >	S
	work vector (for SVD/eigenvalues)

FastThreadable< VECTORN >	workv
	work vectors (LAPACK routines)

FastThreadable< VECTORN >	workv2

FastThreadable< std::vector < INTEGER > >	iworkv
	work STL integer vector (LAPACK routines)

Detailed Description

Linear algebra routines.

LinAlg is a set of static routines that interface to LAPACK. I have included only very few routines here, however they should be some of the most utilized: SVD, (SVD-based) pseudo-inverse, linear equation solving, and matrix inverse.

Member Function Documentation

template<class X , class Y >

void Ravelin::LINALG::eig_symm	(	X &	A,
		Y &	evals
	)

inline

Computes the eigenvalues of the matrix A.

Parameters

A	a matrix
evals	on return, the eigenvalues will be stored here in ascending order

template<class X , class Y >

void Ravelin::LINALG::eig_symm	(	X &	A,
		Y &	evals
	)

inline

Computes the eigenvalues of the matrix A.

Parameters

A	a matrix
evals	on return, the eigenvalues will be stored here in ascending order

template<class X , class Y >

void Ravelin::LINALG::eig_symm_plus	(	X &	A_evecs,
		Y &	evals
	)

inline

Computes the eigenvalues and eigenvectors of the matrix A.

Parameters

A	a square symmetric matrix on input, eigenvectors corresponding to eigenvalues on return
evals	on return, the eigenvalues will be stored here in ascending order

template<class X , class Y >

void Ravelin::LINALG::eig_symm_plus	(	X &	A_evecs,
		Y &	evals
	)

inline

Computes the eigenvalues and eigenvectors of the matrix A.

Parameters

A	a square symmetric matrix on input, eigenvectors corresponding to eigenvalues on return
evals	on return, the eigenvalues will be stored here in ascending order

template<class X >

static bool Ravelin::LINALG::factor_chol ( X & A )

inlinestatic

Performs the Cholesky factorization of a matrix.

Parameters

A	the matrix A on input; the factorized (upper triangular) matrix on output

Returns: true if matrix factored successfully, false otherwise

template<class X >

static bool Ravelin::LINALG::factor_chol ( X & A )

inlinestatic

Performs the Cholesky factorization of a matrix.

Parameters

A	the matrix A on input; the factorized (upper triangular) matrix on output

Returns: true if matrix factored successfully, false otherwise

template<class X >

static bool Ravelin::LINALG::factor_LU	(	X &	A,
		std::vector< int > &	pivwork
	)

inlinestatic

Performs the LU factorization of a matrix.

Parameters

A	the m x n matrix to be factored; on exit, the factors L and U from the factorization M = PLU (unit diagonal elements of L are not stored)
pivwork	on output, contains the pivot indices (for 1 <= i <= min(m,n), row i of A was interchanged with row pivwork[i])

Returns: false if A is singular, true otherwise

template<class X >

static bool Ravelin::LINALG::factor_LU	(	X &	A,
		std::vector< int > &	pivwork
	)

inlinestatic

Performs the LU factorization of a matrix.

Parameters

A	the m x n matrix to be factored; on exit, the factors L and U from the factorization M = PLU (unit diagonal elements of L are not stored)
pivwork	on output, contains the pivot indices (for 1 <= i <= min(m,n), row i of A was interchanged with row pivwork[i])

Returns: false if A is singular, true otherwise

template<class ARMat , class QMat >

void Ravelin::LINALG::factor_QR	(	ARMat &	AR,
		QMat &	Q,
		std::vector< int > &	PI
	)

inline

Performs the QR factorization of a matrix with column pivoting.

Factorizes A*P = Q*R

Parameters

AQ	the matrix A on input; the matrix R on output
Q	the matrix Q on output
PI	the column pivots on output

template<class ARMat , class QMat >

void Ravelin::LINALG::factor_QR	(	ARMat &	AR,
		QMat &	Q,
		std::vector< int > &	PI
	)

inline

Performs the QR factorization of a matrix with column pivoting.

Factorizes A*P = Q*R

Parameters

AQ	the matrix A on input; the matrix R on output
Q	the matrix Q on output
PI	the column pivots on output

template<class ARMat , class QMat >

void Ravelin::LINALG::factor_QR	(	ARMat &	AR,
		QMat &	Q
	)

inline

Performs the QR factorization of a matrix.

Parameters

AQ	the m x n matrix A on input; the matrix min(m,n) x n R on output
Q	the m x min(m,n) matrix Q on output

template<class ARMat , class QMat >

void Ravelin::LINALG::factor_QR	(	ARMat &	AR,
		QMat &	Q
	)

inline

Performs the QR factorization of a matrix.

Parameters

AQ	the m x n matrix A on input; the matrix min(m,n) x n R on output
Q	the m x min(m,n) matrix Q on output

template<class X >

static X& Ravelin::LINALG::inverse_chol ( X & A )

inlinestatic

Inverts the symmetric, positive-definite matrix A using Cholesky factorization.

Parameters

A	the Cholesky factorization of a matrix; contains the inverse on return

template<class X >

static X& Ravelin::LINALG::inverse_chol ( X & A )

inlinestatic

Inverts the symmetric, positive-definite matrix A using Cholesky factorization.

Parameters

A	the Cholesky factorization of a matrix; contains the inverse on return

template<class X >

X& Ravelin::LINALG::inverse_LU	(	X &	M,
		const std::vector< int > &	pivwork
	)

inline

Computes a matrix inverse using the factorization determined via factor_LU()

Parameters

M	the LU-factored matrix
pivwork	the pivots determined by factor_LU()

Note: throws SingularException if matrix is singular

template<class X >

X& Ravelin::LINALG::inverse_LU	(	X &	M,
		const std::vector< int > &	pivwork
	)

inline

Computes a matrix inverse using the factorization determined via factor_LU()

Parameters

M	the LU-factored matrix
pivwork	the pivots determined by factor_LU()

Note: throws SingularException if matrix is singular

template<class X >

static X& Ravelin::LINALG::inverse_SPD ( X & A )

inlinestatic

Inverts the symmetric, positive-definite matrix A using Cholesky factorization.

Parameters

A	a square, symmetric positive-definite matrix; contains the inverse on return

template<class X >

static X& Ravelin::LINALG::inverse_SPD ( X & A )

inlinestatic

Inverts the symmetric, positive-definite matrix A using Cholesky factorization.

Parameters

A	a square, symmetric positive-definite matrix; contains the inverse on return

template<class X >

X& Ravelin::LINALG::inverse_symmetric ( X & A )

inline

Inverts the symmetric, indefinite matrix A.

Parameters

A	a square, symmetric indefinite matrix; inverse will be contained here on return

template<class X >

X& Ravelin::LINALG::inverse_symmetric ( X & A )

inline

Inverts the symmetric, indefinite matrix A.

Parameters

A	a square, symmetric indefinite matrix; inverse will be contained here on return

template<class X >

X& Ravelin::LINALG::invert ( X & A )

inline

Inverts the matrix A using LU factorization.

Parameters

A	a square matrix; contains the inverse on return

template<class X >

X& Ravelin::LINALG::invert ( X & A )

inline

Inverts the matrix A using LU factorization.

Parameters

A	a square matrix; contains the inverse on return

template<class Y >

MATRIXN& Ravelin::LINALG::nullspace	(	Y &	A,
		MATRIXN &	nullspace,
		REAL	tol = `-1.0`
	)

inline

Computes the nullspace of a matrix.

Note: A is destroyed on return

template<class Y >

MATRIXN& Ravelin::LINALG::nullspace	(	Y &	A,
		MATRIXN &	nullspace,
		REAL	tol = `-1.0`
	)

inline

Computes the nullspace of a matrix.

Note: A is destroyed on return

template<class X , class Y >

static X& Ravelin::LINALG::solve_chol_fast	(	const Y &	M,
		X &	XB
	)

inlinestatic

Solves a system of linear equations using the factorization determined via factor_chol()

Parameters

M	the Cholesky decomposition performed by factor_chol()
XB	the right hand sides on input, the vectors X on return

template<class X , class Y >

static X& Ravelin::LINALG::solve_chol_fast	(	const Y &	M,
		X &	XB
	)

inlinestatic

Solves a system of linear equations using the factorization determined via factor_chol()

Parameters

M	the Cholesky decomposition performed by factor_chol()
XB	the right hand sides on input, the vectors X on return

template<class X , class Y >

Y& Ravelin::LINALG::solve_fast	(	X &	A,
		Y &	XB
	)

inline

Solves the general system AX = B.

Parameters

A	a square matrix (destroyed on return)
XB	the matrix B on input, the matrix X on return

template<class X , class Y >

Y& Ravelin::LINALG::solve_fast	(	X &	A,
		Y &	XB
	)

inline

Solves the general system AX = B.

Parameters

A	a square matrix (destroyed on return)
XB	the matrix B on input, the matrix X on return

template<class X >

static X& Ravelin::LINALG::solve_LDL_fast	(	const MATRIXN &	M,
		const std::vector< int > &	pivwork,
		X &	XB
	)

inlinestatic

Solves systems of linear equations using the factorization determined via factor_LDL()

Parameters

M	the factorization performed by factor_LDL()
XB	the right hand sides on input, the vectors X on return

template<class X >

static X& Ravelin::LINALG::solve_LDL_fast	(	const MATRIXN &	M,
		const std::vector< int > &	pivwork,
		X &	XB
	)

inlinestatic

Solves systems of linear equations using the factorization determined via factor_LDL()

Parameters

M	the factorization performed by factor_LDL()
XB	the right hand sides on input, the vectors X on return

template<class X , class Y , class Vec , class Z >

X& Ravelin::LINALG::solve_LS_fast	(	const Y &	U,
		const Vec &	S,
		const Z &	V,
		X &	XB,
		REAL	tol = `(REAL) -1.0`
	)

inline

Most robust system of linear equations solver (solves AX = B)

Solves rank-deficient and underdetermined (minimum norm solution) systems. Computes least-squares solution to overdetermined systems.

Parameters

A	the coefficient matrix (destroyed on return)
XB	the matrix B on input, the matrix X on return
tol	the tolerance for determining the rank of A; if tol < 0.0, tol is computed using machine epsilon

template<class X , class Y , class Vec , class Z >

X& Ravelin::LINALG::solve_LS_fast	(	const Y &	U,
		const Vec &	S,
		const Z &	V,
		X &	XB,
		REAL	tol = `(REAL) -1.0`
	)

inline

Most robust system of linear equations solver (solves AX = B)

Solves rank-deficient and underdetermined (minimum norm solution) systems. Computes least-squares solution to overdetermined systems.

Parameters

A	the coefficient matrix (destroyed on return)
XB	the matrix B on input, the matrix X on return
tol	the tolerance for determining the rank of A; if tol < 0.0, tol is computed using machine epsilon

template<class X , class Y >

X& Ravelin::LINALG::solve_LS_fast	(	Y &	A,
		X &	XB,
		SVD	svd_algo,
		REAL	tol
	)

inline

Most robust system of linear equations solver (solves AX = B)

Solves rank-deficient and underdetermined (minimum norm solution) systems. Computes least-squares solution to overdetermined systems.

Parameters

A	the coefficient matrix (destroyed on return)
XB	the matrix B on input, the matrix X on return
tol	the tolerance for determining the rank of A; if tol < 0.0, tol is computed using machine epsilon

template<class X , class Y >

X& Ravelin::LINALG::solve_LS_fast	(	Y &	A,
		X &	XB,
		SVD	svd_algo,
		REAL	tol
	)

inline

Most robust system of linear equations solver (solves AX = B)

Solves rank-deficient and underdetermined (minimum norm solution) systems. Computes least-squares solution to overdetermined systems.

Parameters

A	the coefficient matrix (destroyed on return)
XB	the matrix B on input, the matrix X on return
tol	the tolerance for determining the rank of A; if tol < 0.0, tol is computed using machine epsilon

template<class Y , class X >

static X& Ravelin::LINALG::solve_LU_fast	(	const Y &	M,
		bool	transpose,
		const std::vector< int > &	pivwork,
		X &	XB
	)

inlinestatic

Solves a system of linear equations using the factorization determined via factor_LU()

Parameters

M	the LU factorization performed by factor_LU()
XB	the right hand side on input, the matrix X on return
transpose	if true, solves M'X = B
pivwork	pivots computed by factor_LU()

template<class Y , class X >

static X& Ravelin::LINALG::solve_LU_fast	(	const Y &	M,
		bool	transpose,
		const std::vector< int > &	pivwork,
		X &	XB
	)

inlinestatic

Solves a system of linear equations using the factorization determined via factor_LU()

Parameters

M	the LU factorization performed by factor_LU()
XB	the right hand side on input, the matrix X on return
transpose	if true, solves M'X = B
pivwork	pivots computed by factor_LU()

template<class Y , class X >

static X& Ravelin::LINALG::solve_SPD_fast	(	Y &	A,
		X &	XB
	)

inlinestatic

Solves a system of equations A*X = B using a symmetric, positive-definite square matrix.

Parameters

A	the matrix coefficients; this matrix will be destroyed on return
XB	on input B, on output X

template<class Y , class X >

static X& Ravelin::LINALG::solve_SPD_fast	(	Y &	A,
		X &	XB
	)

inlinestatic

Solves a system of equations A*X = B using a symmetric, positive-definite square matrix.

Parameters

A	the matrix coefficients; this matrix will be destroyed on return
XB	on input B, on output X

template<class X >

X& Ravelin::LINALG::solve_symmetric_fast	(	MATRIXN &	A,
		X &	XB
	)

inline

Solves a symmetric, indefinite square matrix.

Parameters

A	the matrix to be solved; the matrix is destroyed on return
XB	the RHS B (A*X = B) on input; the solution, X, on return

template<class X >

X& Ravelin::LINALG::solve_symmetric_fast	(	MATRIXN &	A,
		X &	XB
	)

inline

Solves a symmetric, indefinite square matrix.

Parameters

A	the matrix to be solved; the matrix is destroyed on return
XB	the RHS B (A*X = B) on input; the solution, X, on return

template<class X , class Y >

static X& Ravelin::LINALG::solve_tri_fast	(	Y &	A,
		bool	utri,
		bool	transpose_A,
		X &	XB
	)

inlinestatic

Solves a triangular system of linear equations.

Parameters

A	the matrix
utri	if true A is upper triangular (lower triangular otherwise)
transpose_A	if true, solves A'*x = b
XB	contains b on entry, x on return

Returns: reference to XB

template<class X , class Y >

static X& Ravelin::LINALG::solve_tri_fast	(	Y &	A,
		bool	utri,
		bool	transpose_A,
		X &	XB
	)

inlinestatic

Solves a triangular system of linear equations.

Parameters

A	the matrix
utri	if true A is upper triangular (lower triangular otherwise)
transpose_A	if true, solves A'*x = b
XB	contains b on entry, x on return

Returns: reference to XB

template<class X >

static X& Ravelin::LINALG::solve_tridiagonal_fast	(	VECTORN &	dl,
		VECTORN &	d,
		VECTORN &	du,
		X &	XB
	)

inlinestatic

Solves a tridiagonal system.

Parameters

dl	the (n-1) elements on the subdiagonal (destroyed on return)
d	the n elements on the diagonal (destroyed on return)
du	the n elements on the superdiagonal (destroyed on return)
XB	the right hand side on input, the solution on return

Returns: the solution throws SingularException if the matrix is singular

template<class X >

static X& Ravelin::LINALG::solve_tridiagonal_fast	(	VECTORN &	dl,
		VECTORN &	d,
		VECTORN &	du,
		X &	XB
	)

inlinestatic

Solves a tridiagonal system.

Parameters

dl	the (n-1) elements on the subdiagonal (destroyed on return)
d	the n elements on the diagonal (destroyed on return)
du	the n elements on the superdiagonal (destroyed on return)
XB	the right hand side on input, the solution on return

Returns: the solution throws SingularException if the matrix is singular

template<class X , class MatU , class VecS , class MatV >

void Ravelin::LINALG::svd1	(	X &	A,
		MatU &	U,
		VecS &	S,
		MatV &	V
	)

inline

Does an 'in place' SVD (destroying A)

The singular value decomposition of A is U*S*V' (' is the transpose operator); to recompose A, it will be necessary to transpose V before multiplication (i.e., V is returned by the algorithm, not V'). Note: passed matrices and vectors U, S, and V are resized as necessary.

Parameters

A	the matrix on which the SVD will be performed (destroyed on return)
U	on output, a A.rows() x A.rows() orthogonal matrix
S	on output, a min(A.rows(), A.columns()) length vector of singular values
V	on output, a A.columns() x A.columns() orthogonal matrix

template<class X , class MatU , class VecS , class MatV >

void Ravelin::LINALG::svd1	(	X &	A,
		MatU &	U,
		VecS &	S,
		MatV &	V
	)

inline

Does an 'in place' SVD (destroying A)

The singular value decomposition of A is U*S*V' (' is the transpose operator); to recompose A, it will be necessary to transpose V before multiplication (i.e., V is returned by the algorithm, not V'). Note: passed matrices and vectors U, S, and V are resized as necessary.

Parameters

A	the matrix on which the SVD will be performed (destroyed on return)
U	on output, a A.rows() x A.rows() orthogonal matrix
S	on output, a min(A.rows(), A.columns()) length vector of singular values
V	on output, a A.columns() x A.columns() orthogonal matrix

template<class X , class MatU , class VecS , class MatV >

void Ravelin::LINALG::svd2	(	X &	A,
		MatU &	U,
		VecS &	S,
		MatV &	V
	)

inline

Does an 'in place' SVD (destroying A), using divide and conquer algorithm.

The singular value decomposition of A is U*S*V' (' is the transpose operator); to recompose A, it will be necessary to transpose V before multiplication (i.e., V is returned by the algorithm, not V'). Note: passed matrices and vectors U, S, and V are resized as necessary.

Parameters

A	the matrix on which the SVD will be performed (destroyed on return)
U	on output, a A.rows() x A.rows() orthogonal matrix
S	on output, a min(A.rows(), A.columns()) length vector of singular values
V	on output, a A.columns() x A.columns() orthogonal matrix

template<class X , class MatU , class VecS , class MatV >

void Ravelin::LINALG::svd2	(	X &	A,
		MatU &	U,
		VecS &	S,
		MatV &	V
	)

inline

Does an 'in place' SVD (destroying A), using divide and conquer algorithm.

The singular value decomposition of A is U*S*V' (' is the transpose operator); to recompose A, it will be necessary to transpose V before multiplication (i.e., V is returned by the algorithm, not V'). Note: passed matrices and vectors U, S, and V are resized as necessary.

Parameters

A	the matrix on which the SVD will be performed (destroyed on return)
U	on output, a A.rows() x A.rows() orthogonal matrix
S	on output, a min(A.rows(), A.columns()) length vector of singular values
V	on output, a A.columns() x A.columns() orthogonal matrix

The documentation for this class was generated from the following files:

/home/drum/Ravelin.new/include/Ravelin/LinAlgd.h
/home/drum/Ravelin.new/include/Ravelin/LinAlgf.h

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