Sparse Levenberg Marquardt nonlinear least squares. More...

#include <vnl_sparse_lm.h>

Inheritance diagram for vnl_sparse_lm:

Public Types
enum	ReturnCodes { ERROR_FAILURE =-1, ERROR_DODGY_INPUT = 0, CONVERGED_FTOL = 1, CONVERGED_XTOL = 2, CONVERGED_XFTOL = 3, CONVERGED_GTOL = 4, FAILED_TOO_MANY_ITERATIONS = 5, TOO_MANY_ITERATIONS = FAILED_TOO_MANY_ITERATIONS, FAILED_FTOL_TOO_SMALL = 6, FAILED_XTOL_TOO_SMALL = 7, FAILED_GTOL_TOO_SMALL = 8, FAILED_USER_REQUEST = 9 }
	Some generic return codes that apply to all minimizers. More...

Public Member Functions
	vnl_sparse_lm (vnl_sparse_lst_sqr_function &f)
	Initialize with the function object that is to be minimized. More...

	~vnl_sparse_lm () override
	Destructor. More...

bool	minimize (vnl_vector< double > &a, vnl_vector< double > &b, vnl_vector< double > &c, bool use_gradient=true, bool use_weights=true)
	Minimize the function supplied in the constructor until convergence or failure. More...

void	diagnose_outcome () const
	Provide an ASCII diagnosis of the last minimization on std::ostream. More...

void	diagnose_outcome (std::ostream &) const

vnl_matrix< double > const &	get_JtJ ()
	Return J'*J computed at last minimum. More...

const vnl_vector< double > &	get_weights () const
	Access the final weights after optimization. More...

void	set_f_tolerance (double v)
	Set the convergence tolerance on F (sum of squared residuals). More...

double	get_f_tolerance () const

void	set_x_tolerance (double v)
	Set the convergence tolerance on X. More...

double	get_x_tolerance () const

void	set_g_tolerance (double v)
	Set the convergence tolerance on Grad(F)' * F. More...

double	get_g_tolerance () const

void	set_max_function_evals (int v)
	Set the termination maximum number of iterations. More...

int	get_max_function_evals () const

void	set_epsilon_function (double v)
	Set the step length for FD Jacobian. More...

double	get_epsilon_function () const

void	set_trace (bool on)
	Turn on per-iteration printouts. More...

bool	get_trace () const

void	set_verbose (bool verb)
	Set verbose flag. More...

bool	get_verbose () const

void	set_check_derivatives (int cd)
	Set check_derivatives flag. Negative values may mean fewer checks. More...

int	get_check_derivatives () const

double	get_start_error () const
	Return the error of the function when it was evaluated at the start point of the last minimization. More...

double	get_end_error () const
	Return the best error that was achieved by the last minimization, corresponding to the returned x. More...

int	get_num_evaluations () const
	Return the total number of times the function was evaluated by the last minimization. More...

int	get_num_iterations () const
	Return the number of {iterations} in the last minimization. More...

bool	obj_value_reduced ()
	Whether the error reduced in the last minimization. More...

virtual vnl_matrix< double > const &	get_covariance ()
	Return the covariance of the estimate at the end. More...

virtual std::string	is_a () const
	Return the name of the class. More...

virtual bool	is_class (std::string const &s) const
	Return true if the name of the class matches the argument. More...

ReturnCodes	get_failure_code () const
	Return the failure code of the last minimization. More...

Protected Member Functions
void	init (vnl_sparse_lst_sqr_function *f)

void	reset ()

void	report_eval (double f)
	Called by derived classes after each function evaluation. More...

virtual bool	report_iter ()
	Called by derived classes after each iteration. More...

Protected Attributes
double	tau_
	used to compute the initial damping. More...

vnl_sparse_lst_sqr_function *	f_
	the function to minimize. More...

vnl_matrix< double >	inv_covar_

bool	set_covariance_

double	xtol
	Termination tolerance on X (solution vector) More...

long	maxfev
	Termination maximum number of iterations. More...

double	ftol
	Termination tolerance on F (sum of squared residuals) More...

double	gtol
	Termination tolerance on Grad(F)' * F = 0. More...

double	epsfcn
	Step length for FD Jacobian. More...

unsigned	num_iterations_

long	num_evaluations_

double	start_error_

double	end_error_

bool	trace

bool	verbose_

int	check_derivatives_

ReturnCodes	failure_code_

Private Member Functions
void	allocate_matrices ()
	allocate matrix memory by setting all the matrix sizes. More...

bool	check_vector_sizes (vnl_vector< double > const &a, vnl_vector< double > const &b, vnl_vector< double > const &c)
	check vector sizes and verify that they match the problem size. More...

void	compute_normal_equations ()
	compute the blocks making up the the normal equations: Jt J d = Jt e. More...

vnl_vector< double >	extract_diagonal () const
	extract the vector on the diagonal of Jt J. More...

void	set_diagonal (const vnl_vector< double > &diag)
	set the vector on the diagonal of Jt J. More...

void	compute_invV_Y ()
	compute all inv(Vi) and Yij. More...

void	compute_Z_Sa (vnl_matrix< double > &Sa)
	compute Z and Sa. More...

void	compute_Ma (const vnl_matrix< double > &H)
	compute Ma. More...

void	compute_Mb ()
	compute Mb. More...

void	solve_dc (vnl_vector< double > &dc)
	solve for dc. More...

void	compute_sea (vnl_vector< double > const &dc, vnl_vector< double > &sea)
	compute sea using ea, Z, dc, Y, and eb. More...

void	compute_Sa_sea (vnl_matrix< double > &Sa, vnl_vector< double > &sea)
	compute Sa and sea. More...

void	backsolve_db (vnl_vector< double > const &da, vnl_vector< double > const &dc, vnl_vector< double > &db)
	back solve to find db using da and dc. More...

Private Attributes
const int	num_a_

const int	num_b_

const int	num_e_

const int	num_nz_

const int	size_a_

const int	size_b_

const int	size_c_

const int	size_e_

std::vector< vnl_matrix< double > >	A_
	Storage for each of the Jacobians A_ij, B_ij, and C_ij. More...

std::vector< vnl_matrix< double > >	B_

std::vector< vnl_matrix< double > >	C_

std::vector< vnl_matrix< double > >	U_
	Storage for normal equation blocks. More...

std::vector< vnl_matrix< double > >	V_

vnl_matrix< double >	T_

std::vector< vnl_matrix< double > >	W_

std::vector< vnl_matrix< double > >	R_

std::vector< vnl_matrix< double > >	Q_

vnl_vector< double >	ea_

vnl_vector< double >	eb_

vnl_vector< double >	ec_

vnl_vector< double >	e_

vnl_vector< double >	weights_

std::vector< vnl_matrix< double > >	inv_V_

std::vector< vnl_matrix< double > >	Y_

std::vector< vnl_matrix< double > >	Z_

std::vector< vnl_matrix< double > >	Ma_

std::vector< vnl_matrix< double > >	Mb_

Detailed Description

Sparse Levenberg Marquardt nonlinear least squares.

Unlike vnl_levenberg_marquardt this does not use the MINPACK routines. This class implements sparse Levenberg Marquardt as described in the Hartley and Zisserman "Multiple View Geometry" book and further described in a technical report on sparse bundle adjustment available at http://www.ics.forth.gr/~lourakis/sba

Definition at line 35 of file vnl_sparse_lm.h.

Member Enumeration Documentation

◆ ReturnCodes

enum vnl_nonlinear_minimizer::ReturnCodes

inherited

Some generic return codes that apply to all minimizers.

Enumerator
ERROR_FAILURE
ERROR_DODGY_INPUT
CONVERGED_FTOL
CONVERGED_XTOL
CONVERGED_XFTOL
CONVERGED_GTOL
FAILED_TOO_MANY_ITERATIONS
TOO_MANY_ITERATIONS
FAILED_FTOL_TOO_SMALL
FAILED_XTOL_TOO_SMALL
FAILED_GTOL_TOO_SMALL
FAILED_USER_REQUEST

Definition at line 102 of file vnl_nonlinear_minimizer.h.

Constructor & Destructor Documentation

◆ vnl_sparse_lm()

vnl_sparse_lm::vnl_sparse_lm ( vnl_sparse_lst_sqr_function & f )

Initialize with the function object that is to be minimized.

Definition at line 22 of file vnl_sparse_lm.cxx.

◆ ~vnl_sparse_lm()

vnl_sparse_lm::~vnl_sparse_lm ( )

overridedefault

Destructor.

Member Function Documentation

◆ allocate_matrices()

void vnl_sparse_lm::allocate_matrices ( )

private

allocate matrix memory by setting all the matrix sizes.

Definition at line 352 of file vnl_sparse_lm.cxx.

◆ backsolve_db()

void vnl_sparse_lm::backsolve_db	(	vnl_vector< double > const &	da,
		vnl_vector< double > const &	dc,
		vnl_vector< double > &	db
	)

private

back solve to find db using da and dc.

Definition at line 751 of file vnl_sparse_lm.cxx.

◆ check_vector_sizes()

bool vnl_sparse_lm::check_vector_sizes	(	vnl_vector< double > const &	a,
		vnl_vector< double > const &	b,
		vnl_vector< double > const &	c
	)

private

check vector sizes and verify that they match the problem size.

Definition at line 315 of file vnl_sparse_lm.cxx.

◆ compute_invV_Y()

void vnl_sparse_lm::compute_invV_Y ( )

private

compute all inv(Vi) and Yij.

Definition at line 493 of file vnl_sparse_lm.cxx.

◆ compute_Ma()

void vnl_sparse_lm::compute_Ma ( const vnl_matrix< double > & H )

private

compute Ma.

Definition at line 580 of file vnl_sparse_lm.cxx.

◆ compute_Mb()

void vnl_sparse_lm::compute_Mb ( )

private

compute Mb.

Definition at line 600 of file vnl_sparse_lm.cxx.

◆ compute_normal_equations()

void vnl_sparse_lm::compute_normal_equations ( )

private

compute the blocks making up the the normal equations: Jt J d = Jt e.

Definition at line 392 of file vnl_sparse_lm.cxx.

◆ compute_Sa_sea()

void vnl_sparse_lm::compute_Sa_sea	(	vnl_matrix< double > &	Sa,
		vnl_vector< double > &	sea
	)

private

compute Sa and sea.

only used when size_c_ == 0

Definition at line 695 of file vnl_sparse_lm.cxx.

◆ compute_sea()

void vnl_sparse_lm::compute_sea	(	vnl_vector< double > const &	dc,
		vnl_vector< double > &	sea
	)

private

compute sea using ea, Z, dc, Y, and eb.

Definition at line 667 of file vnl_sparse_lm.cxx.

◆ compute_Z_Sa()

void vnl_sparse_lm::compute_Z_Sa ( vnl_matrix< double > & Sa )

private

compute Z and Sa.

Definition at line 521 of file vnl_sparse_lm.cxx.

◆ diagnose_outcome() [1/2]

void vnl_sparse_lm::diagnose_outcome ( ) const

Provide an ASCII diagnosis of the last minimization on std::ostream.

Definition at line 782 of file vnl_sparse_lm.cxx.

◆ diagnose_outcome() [2/2]

void vnl_sparse_lm::diagnose_outcome ( std::ostream & s ) const

Definition at line 789 of file vnl_sparse_lm.cxx.

◆ extract_diagonal()

vnl_vector< double > vnl_sparse_lm::extract_diagonal ( ) const

private

extract the vector on the diagonal of Jt J.

Definition at line 451 of file vnl_sparse_lm.cxx.

◆ get_check_derivatives()

int vnl_nonlinear_minimizer::get_check_derivatives ( ) const

inlineinherited

Definition at line 82 of file vnl_nonlinear_minimizer.h.

◆ get_covariance()

vnl_matrix< double > const & vnl_nonlinear_minimizer::get_covariance ( )

virtualinherited

Return the covariance of the estimate at the end.

Definition at line 30 of file vnl_nonlinear_minimizer.cxx.

◆ get_end_error()

double vnl_nonlinear_minimizer::get_end_error ( ) const

inlineinherited

Return the best error that was achieved by the last minimization, corresponding to the returned x.

Definition at line 92 of file vnl_nonlinear_minimizer.h.

◆ get_epsilon_function()

double vnl_nonlinear_minimizer::get_epsilon_function ( ) const

inlineinherited

Definition at line 70 of file vnl_nonlinear_minimizer.h.

◆ get_f_tolerance()

double vnl_nonlinear_minimizer::get_f_tolerance ( ) const

inlineinherited

Definition at line 46 of file vnl_nonlinear_minimizer.h.

◆ get_failure_code()

ReturnCodes vnl_nonlinear_minimizer::get_failure_code ( ) const

inlineinherited

Return the failure code of the last minimization.

Definition at line 132 of file vnl_nonlinear_minimizer.h.

◆ get_g_tolerance()

double vnl_nonlinear_minimizer::get_g_tolerance ( ) const

inlineinherited

Definition at line 60 of file vnl_nonlinear_minimizer.h.

◆ get_JtJ()

vnl_matrix< double > const & vnl_sparse_lm::get_JtJ ( )

Return J'*J computed at last minimum.

it is an approximation of inverse of covariance

Definition at line 837 of file vnl_sparse_lm.cxx.

◆ get_max_function_evals()

int vnl_nonlinear_minimizer::get_max_function_evals ( ) const

inlineinherited

Definition at line 64 of file vnl_nonlinear_minimizer.h.

◆ get_num_evaluations()

int vnl_nonlinear_minimizer::get_num_evaluations ( ) const

inlineinherited

Return the total number of times the function was evaluated by the last minimization.

Definition at line 95 of file vnl_nonlinear_minimizer.h.

◆ get_num_iterations()

int vnl_nonlinear_minimizer::get_num_iterations ( ) const

inlineinherited

Return the number of {iterations} in the last minimization.

Each iteration may have comprised several function evaluations.

Definition at line 99 of file vnl_nonlinear_minimizer.h.

◆ get_start_error()

double vnl_nonlinear_minimizer::get_start_error ( ) const

inlineinherited

Return the error of the function when it was evaluated at the start point of the last minimization.

For minimizers driven by a vnl_least_squares_function (Levenberg-Marquardt) this is usually the RMS error. For those driven by a vnl_cost_function (CG, LBFGS, Amoeba) it is simply the value of the vnl_cost_function at the start (usually the sum of squared residuals).

Definition at line 89 of file vnl_nonlinear_minimizer.h.

◆ get_trace()

bool vnl_nonlinear_minimizer::get_trace ( ) const

inlineinherited

Definition at line 74 of file vnl_nonlinear_minimizer.h.

◆ get_verbose()

bool vnl_nonlinear_minimizer::get_verbose ( ) const

inlineinherited

Definition at line 78 of file vnl_nonlinear_minimizer.h.

◆ get_weights()

const vnl_vector<double>& vnl_sparse_lm::get_weights ( ) const

inline

Access the final weights after optimization.

Definition at line 69 of file vnl_sparse_lm.h.

◆ get_x_tolerance()

double vnl_nonlinear_minimizer::get_x_tolerance ( ) const

inlineinherited

Definition at line 56 of file vnl_nonlinear_minimizer.h.

◆ init()

void vnl_sparse_lm::init ( vnl_sparse_lst_sqr_function * f )

protected

Definition at line 56 of file vnl_sparse_lm.cxx.

◆ is_a()

std::string vnl_nonlinear_minimizer::is_a ( ) const

virtualinherited

Return the name of the class.

Used by polymorphic IO

Definition at line 70 of file vnl_nonlinear_minimizer.cxx.

◆ is_class()

bool vnl_nonlinear_minimizer::is_class ( std::string const & s ) const

virtualinherited

Return true if the name of the class matches the argument.

Used by polymorphic IO

Definition at line 78 of file vnl_nonlinear_minimizer.cxx.

◆ minimize()

bool vnl_sparse_lm::minimize	(	vnl_vector< double > &	a,
		vnl_vector< double > &	b,
		vnl_vector< double > &	c,
		bool	use_gradient = `true`,
		bool	use_weights = `true`
	)

Minimize the function supplied in the constructor until convergence or failure.

On return, a, b, and c are such that f(a,b,c) is the lowest value achieved. Returns true for convergence, false for failure. If use_gradient is set to false, a finite difference approximation will be used, even if the Jacobian functions have been provided. If use_weights is set to false, weights will not be computed even if a weighting function has been provided.

Systems to solve will be Sc*dc=sec and Sa*da=sea.

Definition at line 83 of file vnl_sparse_lm.cxx.

◆ obj_value_reduced()

bool vnl_nonlinear_minimizer::obj_value_reduced ( )

inlineinherited

Whether the error reduced in the last minimization.

Definition at line 118 of file vnl_nonlinear_minimizer.h.

◆ report_eval()

void vnl_nonlinear_minimizer::report_eval ( double f )

protectedinherited

Called by derived classes after each function evaluation.

Definition at line 45 of file vnl_nonlinear_minimizer.cxx.

◆ report_iter()

bool vnl_nonlinear_minimizer::report_iter ( )

protectedvirtualinherited

Called by derived classes after each iteration.

When true is returned, minimizer should stop with code FAILED_USER_REQUEST. Derived classes can redefine this function to make the optimizer stop when a condition is satisfied.

Definition at line 58 of file vnl_nonlinear_minimizer.cxx.

◆ reset()

void vnl_nonlinear_minimizer::reset ( )

protectedinherited

Definition at line 36 of file vnl_nonlinear_minimizer.cxx.

◆ set_check_derivatives()

void vnl_nonlinear_minimizer::set_check_derivatives ( int cd )

inlineinherited

Set check_derivatives flag. Negative values may mean fewer checks.

Definition at line 81 of file vnl_nonlinear_minimizer.h.

◆ set_diagonal()

void vnl_sparse_lm::set_diagonal ( const vnl_vector< double > & diag )

private

set the vector on the diagonal of Jt J.

Definition at line 474 of file vnl_sparse_lm.cxx.

◆ set_epsilon_function()

void vnl_nonlinear_minimizer::set_epsilon_function ( double v )

inlineinherited

Set the step length for FD Jacobian.

Be aware that set_x_tolerance will reset this to xtol * 0.001. The default is 1e-11.

Definition at line 69 of file vnl_nonlinear_minimizer.h.

◆ set_f_tolerance()

void vnl_nonlinear_minimizer::set_f_tolerance ( double v )

inlineinherited

Set the convergence tolerance on F (sum of squared residuals).

When the differences in successive RMS errors is less than this, the routine terminates. So this is effectively the desired precision of your minimization. Setting it too low wastes time, too high might cause early convergence. The default of 1e-9 is on the safe side, but if speed is an issue, you can try raising it.

Definition at line 45 of file vnl_nonlinear_minimizer.h.

◆ set_g_tolerance()

void vnl_nonlinear_minimizer::set_g_tolerance ( double v )

inlineinherited

Set the convergence tolerance on Grad(F)' * F.

Definition at line 59 of file vnl_nonlinear_minimizer.h.

◆ set_max_function_evals()

void vnl_nonlinear_minimizer::set_max_function_evals ( int v )

inlineinherited

Set the termination maximum number of iterations.

Definition at line 63 of file vnl_nonlinear_minimizer.h.

◆ set_trace()

void vnl_nonlinear_minimizer::set_trace ( bool on )

inlineinherited

Turn on per-iteration printouts.

Definition at line 73 of file vnl_nonlinear_minimizer.h.

◆ set_verbose()

void vnl_nonlinear_minimizer::set_verbose ( bool verb )

inlineinherited

Set verbose flag.

Definition at line 77 of file vnl_nonlinear_minimizer.h.

◆ set_x_tolerance()

void vnl_nonlinear_minimizer::set_x_tolerance ( double v )

inlineinherited

Set the convergence tolerance on X.

When the length of the steps taken in X are about this long, the routine terminates. The default is 1e-8, which should work for many problems, but if you can get away with 1e-4, say, minimizations will be much quicker.

Definition at line 52 of file vnl_nonlinear_minimizer.h.

◆ solve_dc()

void vnl_sparse_lm::solve_dc ( vnl_vector< double > & dc )

private

solve for dc.

Definition at line 626 of file vnl_sparse_lm.cxx.

Member Data Documentation

◆ A_

std::vector<vnl_matrix<double> > vnl_sparse_lm::A_

private

Storage for each of the Jacobians A_ij, B_ij, and C_ij.

Definition at line 141 of file vnl_sparse_lm.h.

◆ B_

std::vector<vnl_matrix<double> > vnl_sparse_lm::B_

private

Definition at line 142 of file vnl_sparse_lm.h.

◆ C_

std::vector<vnl_matrix<double> > vnl_sparse_lm::C_

private

Definition at line 143 of file vnl_sparse_lm.h.

◆ check_derivatives_

int vnl_nonlinear_minimizer::check_derivatives_

protectedinherited

Definition at line 153 of file vnl_nonlinear_minimizer.h.

◆ e_

vnl_vector<double> vnl_sparse_lm::e_

private

Definition at line 160 of file vnl_sparse_lm.h.

◆ ea_

vnl_vector<double> vnl_sparse_lm::ea_

private

Definition at line 155 of file vnl_sparse_lm.h.

◆ eb_

vnl_vector<double> vnl_sparse_lm::eb_

private

Definition at line 156 of file vnl_sparse_lm.h.

◆ ec_

vnl_vector<double> vnl_sparse_lm::ec_

private

Definition at line 157 of file vnl_sparse_lm.h.

◆ end_error_

double vnl_nonlinear_minimizer::end_error_

protectedinherited

Definition at line 147 of file vnl_nonlinear_minimizer.h.

◆ epsfcn

double vnl_nonlinear_minimizer::epsfcn

protectedinherited

Step length for FD Jacobian.

Definition at line 141 of file vnl_nonlinear_minimizer.h.

◆ f_

vnl_sparse_lst_sqr_function* vnl_sparse_lm::f_

protected

the function to minimize.

Definition at line 76 of file vnl_sparse_lm.h.

◆ failure_code_

ReturnCodes vnl_nonlinear_minimizer::failure_code_

protectedinherited

Definition at line 154 of file vnl_nonlinear_minimizer.h.

◆ ftol

double vnl_nonlinear_minimizer::ftol

protectedinherited

Termination tolerance on F (sum of squared residuals)

Definition at line 139 of file vnl_nonlinear_minimizer.h.

◆ gtol

double vnl_nonlinear_minimizer::gtol

protectedinherited

Termination tolerance on Grad(F)' * F = 0.

Definition at line 140 of file vnl_nonlinear_minimizer.h.

◆ inv_covar_

vnl_matrix<double> vnl_sparse_lm::inv_covar_

protected

Definition at line 78 of file vnl_sparse_lm.h.

◆ inv_V_

std::vector<vnl_matrix<double> > vnl_sparse_lm::inv_V_

private

Definition at line 166 of file vnl_sparse_lm.h.

◆ Ma_

std::vector<vnl_matrix<double> > vnl_sparse_lm::Ma_

private

Definition at line 169 of file vnl_sparse_lm.h.

◆ maxfev

long vnl_nonlinear_minimizer::maxfev

protectedinherited

Termination maximum number of iterations.

Definition at line 138 of file vnl_nonlinear_minimizer.h.

◆ Mb_

std::vector<vnl_matrix<double> > vnl_sparse_lm::Mb_

private

Definition at line 170 of file vnl_sparse_lm.h.

◆ num_a_

const int vnl_sparse_lm::num_a_

private

Definition at line 130 of file vnl_sparse_lm.h.

◆ num_b_

const int vnl_sparse_lm::num_b_

private

Definition at line 131 of file vnl_sparse_lm.h.

◆ num_e_

const int vnl_sparse_lm::num_e_

private

Definition at line 132 of file vnl_sparse_lm.h.

◆ num_evaluations_

long vnl_nonlinear_minimizer::num_evaluations_

protectedinherited

Definition at line 145 of file vnl_nonlinear_minimizer.h.

◆ num_iterations_

unsigned vnl_nonlinear_minimizer::num_iterations_

protectedinherited

Definition at line 144 of file vnl_nonlinear_minimizer.h.

◆ num_nz_

const int vnl_sparse_lm::num_nz_

private

Definition at line 133 of file vnl_sparse_lm.h.

◆ Q_

std::vector<vnl_matrix<double> > vnl_sparse_lm::Q_

private

Definition at line 153 of file vnl_sparse_lm.h.

◆ R_

std::vector<vnl_matrix<double> > vnl_sparse_lm::R_

private

Definition at line 152 of file vnl_sparse_lm.h.

◆ set_covariance_

bool vnl_sparse_lm::set_covariance_

protected

Definition at line 79 of file vnl_sparse_lm.h.

◆ size_a_

const int vnl_sparse_lm::size_a_

private

Definition at line 135 of file vnl_sparse_lm.h.

◆ size_b_

const int vnl_sparse_lm::size_b_

private

Definition at line 136 of file vnl_sparse_lm.h.

◆ size_c_

const int vnl_sparse_lm::size_c_

private

Definition at line 137 of file vnl_sparse_lm.h.

◆ size_e_

const int vnl_sparse_lm::size_e_

private

Definition at line 138 of file vnl_sparse_lm.h.

◆ start_error_

double vnl_nonlinear_minimizer::start_error_

protectedinherited

Definition at line 146 of file vnl_nonlinear_minimizer.h.

◆ T_

vnl_matrix<double> vnl_sparse_lm::T_

private

Definition at line 149 of file vnl_sparse_lm.h.

◆ tau_

double vnl_sparse_lm::tau_

protected

used to compute the initial damping.

Definition at line 74 of file vnl_sparse_lm.h.

◆ trace

bool vnl_nonlinear_minimizer::trace

protectedinherited

Definition at line 149 of file vnl_nonlinear_minimizer.h.

◆ U_

std::vector<vnl_matrix<double> > vnl_sparse_lm::U_

private

Storage for normal equation blocks.

diagonals of JtJ

Definition at line 147 of file vnl_sparse_lm.h.

◆ V_

std::vector<vnl_matrix<double> > vnl_sparse_lm::V_

private

Definition at line 148 of file vnl_sparse_lm.h.

◆ verbose_

bool vnl_nonlinear_minimizer::verbose_

protectedinherited

Definition at line 152 of file vnl_nonlinear_minimizer.h.

◆ W_

std::vector<vnl_matrix<double> > vnl_sparse_lm::W_

private

Definition at line 151 of file vnl_sparse_lm.h.

◆ weights_

vnl_vector<double> vnl_sparse_lm::weights_

private

Definition at line 163 of file vnl_sparse_lm.h.

◆ xtol

double vnl_nonlinear_minimizer::xtol

protectedinherited

Termination tolerance on X (solution vector)

Definition at line 137 of file vnl_nonlinear_minimizer.h.

◆ Y_

std::vector<vnl_matrix<double> > vnl_sparse_lm::Y_

private

Definition at line 167 of file vnl_sparse_lm.h.

◆ Z_

std::vector<vnl_matrix<double> > vnl_sparse_lm::Z_

private

Definition at line 168 of file vnl_sparse_lm.h.

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

core/vnl/algo/vnl_sparse_lm.h
core/vnl/algo/vnl_sparse_lm.cxx

Public Types

Public Member Functions

Protected Member Functions

Protected Attributes

Private Member Functions

Private Attributes

Detailed Description

Member Enumeration Documentation

◆ ReturnCodes

Constructor & Destructor Documentation

◆ vnl_sparse_lm()

◆ ~vnl_sparse_lm()

Member Function Documentation

◆ allocate_matrices()

◆ backsolve_db()

◆ check_vector_sizes()

◆ compute_invV_Y()

◆ compute_Ma()

◆ compute_Mb()

◆ compute_normal_equations()

◆ compute_Sa_sea()

◆ compute_sea()

◆ compute_Z_Sa()

◆ diagnose_outcome() [1/2]

◆ diagnose_outcome() [2/2]

◆ extract_diagonal()

◆ get_check_derivatives()

◆ get_covariance()

◆ get_end_error()

◆ get_epsilon_function()

◆ get_f_tolerance()

◆ get_failure_code()

◆ get_g_tolerance()

◆ get_JtJ()

◆ get_max_function_evals()

◆ get_num_evaluations()

◆ get_num_iterations()

◆ get_start_error()

◆ get_trace()

◆ get_verbose()

◆ get_weights()

◆ get_x_tolerance()

◆ init()

◆ is_a()

◆ is_class()

◆ minimize()

◆ obj_value_reduced()

◆ report_eval()

◆ report_iter()

◆ reset()

◆ set_check_derivatives()

◆ set_diagonal()

◆ set_epsilon_function()

◆ set_f_tolerance()

◆ set_g_tolerance()

◆ set_max_function_evals()

◆ set_trace()

◆ set_verbose()

◆ set_x_tolerance()

◆ solve_dc()

Member Data Documentation

◆ A_

◆ B_

◆ C_

◆ check_derivatives_

◆ e_

◆ ea_

◆ eb_

◆ ec_

◆ end_error_

◆ epsfcn

◆ f_

◆ failure_code_

◆ ftol

◆ gtol

◆ inv_covar_

◆ inv_V_

◆ Ma_

◆ maxfev

◆ Mb_