Abstract base for sparse least squares functions. More...

#include <vnl_sparse_lst_sqr_function.h>

Public Types
enum	UseGradient { no_gradient, use_gradient }

enum	UseWeights { no_weights, use_weights }

Public Member Functions
	vnl_sparse_lst_sqr_function (unsigned int num_a, unsigned int num_params_per_a, unsigned int num_b, unsigned int num_params_per_b, unsigned int num_params_c, unsigned int num_residuals_per_e, UseGradient g=use_gradient, UseWeights w=no_weights)
	Construct vnl_sparse_lst_sqr_function. More...

	vnl_sparse_lst_sqr_function (unsigned int num_a, unsigned int num_params_per_a, unsigned int num_b, unsigned int num_params_per_b, unsigned int num_params_c, const std::vector< std::vector< bool > > &xmask, unsigned int num_residuals_per_e, UseGradient g=use_gradient, UseWeights w=no_weights)
	Construct vnl_sparse_lst_sqr_function. More...

	vnl_sparse_lst_sqr_function (const std::vector< unsigned int > &a_sizes, const std::vector< unsigned int > &b_sizes, unsigned int num_params_c, const std::vector< unsigned int > &e_sizes, const std::vector< std::vector< bool > > &xmask, UseGradient g=use_gradient, UseWeights w=no_weights)
	Construct vnl_sparse_lst_sqr_function. More...

virtual	~vnl_sparse_lst_sqr_function ()=default

void	throw_failure ()

void	clear_failure ()

virtual void	f (vnl_vector< double > const &a, vnl_vector< double > const &b, vnl_vector< double > const &c, vnl_vector< double > &f)
	Compute all residuals. More...

virtual void	jac_blocks (vnl_vector< double > const &a, vnl_vector< double > const &b, vnl_vector< double > const &c, std::vector< vnl_matrix< double > > &A, std::vector< vnl_matrix< double > > &B, std::vector< vnl_matrix< double > > &C)
	Compute the sparse Jacobian in block form. More...

virtual void	fd_jac_blocks (vnl_vector< double > const &a, vnl_vector< double > const &b, vnl_vector< double > const &c, std::vector< vnl_matrix< double > > &A, std::vector< vnl_matrix< double > > &B, std::vector< vnl_matrix< double > > &C, double stepsize)
	Compute the sparse Jacobian in block form using a finite difference approximation. More...

virtual void	compute_weights (vnl_vector< double > const &a, vnl_vector< double > const &b, vnl_vector< double > const &c, vnl_vector< double > const &f, vnl_vector< double > &weights)
	If using weighted least squares, compute the weights for each i and j. More...

virtual void	apply_weights (vnl_vector< double > const &weights, vnl_vector< double > &f)
	If using weighted least squares, apply the weights to residuals f. More...

virtual void	apply_weights (vnl_vector< double > const &weights, std::vector< vnl_matrix< double > > &A, std::vector< vnl_matrix< double > > &B, std::vector< vnl_matrix< double > > &C)
	If using weighted least squares, apply the weights to residuals A, B, C. More...

virtual void	fij (int i, int j, vnl_vector< double > const &ai, vnl_vector< double > const &bj, vnl_vector< double > const &c, vnl_vector< double > &fij)
	Compute the residuals from the ith component of a, the jth component of b. More...

virtual void	jac_Aij (unsigned int i, unsigned int j, vnl_vector< double > const &ai, vnl_vector< double > const &bj, vnl_vector< double > const &c, vnl_matrix< double > &Aij)
	Calculate the Jacobian A_ij, given the parameter vectors a_i, b_j, and c. More...

virtual void	jac_Bij (unsigned int i, unsigned int j, vnl_vector< double > const &ai, vnl_vector< double > const &bj, vnl_vector< double > const &c, vnl_matrix< double > &Bij)
	Calculate the Jacobian B_ij, given the parameter vectors a_i, b_j, and c. More...

virtual void	jac_Cij (unsigned int i, unsigned int j, vnl_vector< double > const &ai, vnl_vector< double > const &bj, vnl_vector< double > const &c, vnl_matrix< double > &Cij)
	Calculate the Jacobian C_ij, given the parameter vectors a_i, b_j, and c. More...

void	fd_jac_Aij (int i, int j, vnl_vector< double > const &ai, vnl_vector< double > const &bj, vnl_vector< double > const &c, vnl_matrix< double > &Aij, double stepsize)
	Use this to compute a finite-difference Jacobian A_ij. More...

void	fd_jac_Bij (int i, int j, vnl_vector< double > const &ai, vnl_vector< double > const &bj, vnl_vector< double > const &c, vnl_matrix< double > &Bij, double stepsize)
	Use this to compute a finite-difference Jacobian B_ij. More...

void	fd_jac_Cij (int i, int j, vnl_vector< double > const &ai, vnl_vector< double > const &bj, vnl_vector< double > const &c, vnl_matrix< double > &Cij, double stepsize)
	Use this to compute a finite-difference Jacobian C_ij. More...

virtual void	compute_weight_ij (int i, int j, vnl_vector< double > const &ai, vnl_vector< double > const &bj, vnl_vector< double > const &c, vnl_vector< double > const &fij, double &weight)
	If using weighted least squares, compute the weight. More...

virtual void	apply_weight_ij (int i, int j, double const &weight, vnl_vector< double > &fij)
	If using weighted least squares, apply the weight to fij. More...

virtual void	apply_weight_ij (int i, int j, double const &weight, vnl_matrix< double > &Aij, vnl_matrix< double > &Bij, vnl_matrix< double > &Cij)
	If using weighted least squares, apply the weight to Aij, Bij, Cij. More...

virtual void	trace (int iteration, vnl_vector< double > const &a, vnl_vector< double > const &b, vnl_vector< double > const &c, vnl_vector< double > const &e)
	Called after each LM iteration to print debugging etc. More...

unsigned int	number_of_params_a (int i) const
	Return the number of parameters of a_j. More...

unsigned int	number_of_params_b (int j) const
	Return the number of parameters of b_i. More...

unsigned int	number_of_params_c () const
	Return the number of parameters of c. More...

unsigned int	number_of_residuals (int k) const
	Return the number of residuals in the kth residual vector. More...

unsigned int	number_of_residuals (int i, int j) const
	Return the number of residuals for x_ij. More...

unsigned int	index_a (int i) const
	return the index of aj in a. More...

unsigned int	index_b (int j) const
	return the index of bj in b. More...

unsigned int	index_e (int k) const
	return the index of ek in e. More...

unsigned int	number_of_a () const
	Return the number of subsets in `a`. More...

unsigned int	number_of_b () const
	Return the number of subsets in `b`. More...

unsigned int	number_of_e () const
	Return the number of residual vectors. More...

bool	has_gradient () const
	Return true if the derived class has indicated that gradf has been implemented. More...

bool	has_weights () const
	Return true if the derived class has indicated that. More...

const vnl_crs_index &	residual_indices () const
	Return a const reference to the residual indexer. More...

Public Attributes
bool	failure

Protected Attributes
vnl_crs_index	residual_indices_

std::vector< unsigned int >	indices_a_

std::vector< unsigned int >	indices_b_

unsigned int	num_params_c_

std::vector< unsigned int >	indices_e_

bool	use_gradient_

bool	use_weights_

Private Member Functions
void	dim_warning (unsigned int n_unknowns, unsigned int n_residuals)

Detailed Description

Abstract base for sparse least squares functions.

vnl_sparse_lst_sqr_function is an abstract base for functions to be minimized by an optimizer. To define your own function to be minimized, subclass from vnl_sparse_lst_sqr_function, and implement the pure virtual f (and optionally grad_f).

This differs from a vnl_least_squares_function in that many entries in the Jacobian are known to be zero, and we don't want to compute them. The particular sparse structure is that described in Hartley and Zisserman section A4.3. It is assumed that the parameter vector can be partitioned into sets A and B. These are further partitioned into subsets {a_1, a_2, ... a_m} and {b_1, b_2, ... b_n}. Likewise, the residual vector X is partitioned into {x_11, x_12, ... x_mn} (not all x_ij are required). We further assume that dx_ij/da_k = 0 for all i != k and dx_ij/db_k = 0 for all j != k.

This implementation further generalizes the concept by allowing for a third set of parameters C that are non-sparse. That is, dx_ij/dC != 0 for all i and j (in general).

An example use case is bundle adjustment where each a_i is the parameters for one of m cameras, each b_j is the parameters of a 3D point, and x_ij is the projection error of the jth point by the ith camera. If type C parameters are used, they might represent the unknown intrinic camera parameters that are assumed to be fixed over all images.

Definition at line 45 of file vnl_sparse_lst_sqr_function.h.

Member Enumeration Documentation

◆ UseGradient

enum vnl_sparse_lst_sqr_function::UseGradient

Enumerator
no_gradient
use_gradient

Definition at line 48 of file vnl_sparse_lst_sqr_function.h.

◆ UseWeights

enum vnl_sparse_lst_sqr_function::UseWeights

Enumerator
no_weights
use_weights

Definition at line 52 of file vnl_sparse_lst_sqr_function.h.

Constructor & Destructor Documentation

◆ vnl_sparse_lst_sqr_function() [1/3]

vnl_sparse_lst_sqr_function::vnl_sparse_lst_sqr_function	(	unsigned int	num_a,
		unsigned int	num_params_per_a,
		unsigned int	num_b,
		unsigned int	num_params_per_b,
		unsigned int	num_params_c,
		unsigned int	num_residuals_per_e,
		UseGradient	g = `use_gradient`,
		UseWeights	w = `no_weights`
	)

Construct vnl_sparse_lst_sqr_function.

Assumes A consists of num_a parameters each of size num_params_per_a Assumes B consists of num_b parameters each of size num_params_per_b Assumes C consists of num_params_c parameters Assumes there is a residual x_ij for all i and j, each of size num_residuals_per_e The optional argument should be no_gradient if the gradf function has not been implemented. Default is use_gradient.

Definition at line 29 of file vnl_sparse_lst_sqr_function.cxx.

◆ vnl_sparse_lst_sqr_function() [2/3]

vnl_sparse_lst_sqr_function::vnl_sparse_lst_sqr_function	(	unsigned int	num_a,
		unsigned int	num_params_per_a,
		unsigned int	num_b,
		unsigned int	num_params_per_b,
		unsigned int	num_params_c,
		const std::vector< std::vector< bool > > &	xmask,
		unsigned int	num_residuals_per_e,
		UseGradient	g = `use_gradient`,
		UseWeights	w = `no_weights`
	)

Construct vnl_sparse_lst_sqr_function.

Assumes A consists of num_a parameters each of size num_params_per_a Assumes B consists of num_b parameters each of size num_params_per_b Assumes C consists of num_params_c parameters xmask is a mask for residual availability. residual e_ij exists only if mask[i][j]==true Assumes each available residual has size num_residuals_per_e The optional argument should be no_gradient if the gradf function has not been implemented. Default is use_gradient.

Definition at line 68 of file vnl_sparse_lst_sqr_function.cxx.

◆ vnl_sparse_lst_sqr_function() [3/3]

vnl_sparse_lst_sqr_function::vnl_sparse_lst_sqr_function	(	const std::vector< unsigned int > &	a_sizes,
		const std::vector< unsigned int > &	b_sizes,
		unsigned int	num_params_c,
		const std::vector< unsigned int > &	e_sizes,
		const std::vector< std::vector< bool > > &	xmask,
		UseGradient	g = `use_gradient`,
		UseWeights	w = `no_weights`
	)

Construct vnl_sparse_lst_sqr_function.

This constructor is the most general

Parameters

a_sizes	is a vector describing the number of parameters for each a_i
b_sizes	is a vector describing the number of parameters for each b_j
num_params_c	is the number of C parameters
e_sizes	is a vector describing the number of parameters for each residual e_ij
xmask	is a mask for residual availability. residual e_ij exists only if mask[i][j]==true xmask must be a_sizes.size() by b_sizes.size() and contain e_sizes.size() true entries The optional argument should be no_gradient if the gradf function has not been implemented. Default is use_gradient.

Definition at line 113 of file vnl_sparse_lst_sqr_function.cxx.

◆ ~vnl_sparse_lst_sqr_function()

virtual vnl_sparse_lst_sqr_function::~vnl_sparse_lst_sqr_function ( )

virtualdefault

Member Function Documentation

◆ apply_weight_ij() [1/2]

void vnl_sparse_lst_sqr_function::apply_weight_ij	(	int	i,
		int	j,
		double const &	weight,
		vnl_vector< double > &	fij
	)

virtual

If using weighted least squares, apply the weight to fij.

The default implementation multiplies fij by weight.

Definition at line 531 of file vnl_sparse_lst_sqr_function.cxx.

◆ apply_weight_ij() [2/2]

void vnl_sparse_lst_sqr_function::apply_weight_ij	(	int	i,
		int	j,
		double const &	weight,
		vnl_matrix< double > &	Aij,
		vnl_matrix< double > &	Bij,
		vnl_matrix< double > &	Cij
	)

virtual

If using weighted least squares, apply the weight to Aij, Bij, Cij.

The default implementation multiplies each matrix by weight.

Definition at line 542 of file vnl_sparse_lst_sqr_function.cxx.

◆ apply_weights() [1/2]

void vnl_sparse_lst_sqr_function::apply_weights	(	vnl_vector< double > const &	weights,
		vnl_vector< double > &	e
	)

virtual

If using weighted least squares, apply the weights to residuals f.

The default implementation applies weights by calling apply_weight_ij for each valid pair of i and j. You do not need to overload this method unless you want to provide a more specialized implementation for your problem.

Definition at line 301 of file vnl_sparse_lst_sqr_function.cxx.

◆ apply_weights() [2/2]

void vnl_sparse_lst_sqr_function::apply_weights	(	vnl_vector< double > const &	weights,
		std::vector< vnl_matrix< double > > &	A,
		std::vector< vnl_matrix< double > > &	B,
		std::vector< vnl_matrix< double > > &	C
	)

virtual

If using weighted least squares, apply the weights to residuals A, B, C.

The default implementation applies weights by calling apply_weight_ij for each valid pair of i and j. You do not need to overload this method unless you want to provide a more specialized implementation for your problem.

Definition at line 324 of file vnl_sparse_lst_sqr_function.cxx.

◆ clear_failure()

void vnl_sparse_lst_sqr_function::clear_failure ( )

inline

Definition at line 114 of file vnl_sparse_lst_sqr_function.h.

◆ compute_weight_ij()

void vnl_sparse_lst_sqr_function::compute_weight_ij	(	int	i,
		int	j,
		vnl_vector< double > const &	ai,
		vnl_vector< double > const &	bj,
		vnl_vector< double > const &	c,
		vnl_vector< double > const &	fij,
		double &	weight
	)

virtual

If using weighted least squares, compute the weight.

Return the weight in weight. The default implementation sets weight = 1

Definition at line 517 of file vnl_sparse_lst_sqr_function.cxx.

◆ compute_weights()

void vnl_sparse_lst_sqr_function::compute_weights	(	vnl_vector< double > const &	a,
		vnl_vector< double > const &	b,
		vnl_vector< double > const &	c,
		vnl_vector< double > const &	e,
		vnl_vector< double > &	weights
	)

virtual

If using weighted least squares, compute the weights for each i and j.

Return the weights in weights. The default implementation computes weights by calling compute_weight_ij for each valid pair of i and j. You do not need to overload this method unless you want to provide a more specialized implementation for your problem.

Definition at line 267 of file vnl_sparse_lst_sqr_function.cxx.

◆ dim_warning()

void vnl_sparse_lst_sqr_function::dim_warning	(	unsigned int	n_unknowns,
		unsigned int	n_residuals
	)

private

Definition at line 13 of file vnl_sparse_lst_sqr_function.cxx.

◆ f()

void vnl_sparse_lst_sqr_function::f	(	vnl_vector< double > const &	a,
		vnl_vector< double > const &	b,
		vnl_vector< double > const &	c,
		vnl_vector< double > &	e
	)

virtual

Compute all residuals.

Given the parameter vectors a, b, and c, compute the vector of residuals f. f has been sized appropriately before the call. The default implementation computes f by calling fij for each valid pair of i and j. You do not need to overload this method unless you want to provide a more efficient implementation for your problem.

Definition at line 155 of file vnl_sparse_lst_sqr_function.cxx.

◆ fd_jac_Aij()

void vnl_sparse_lst_sqr_function::fd_jac_Aij	(	int	i,
		int	j,
		vnl_vector< double > const &	ai,
		vnl_vector< double > const &	bj,
		vnl_vector< double > const &	c,
		vnl_matrix< double > &	Aij,
		double	stepsize
	)

Use this to compute a finite-difference Jacobian A_ij.

Definition at line 391 of file vnl_sparse_lst_sqr_function.cxx.

◆ fd_jac_Bij()

void vnl_sparse_lst_sqr_function::fd_jac_Bij	(	int	i,
		int	j,
		vnl_vector< double > const &	ai,
		vnl_vector< double > const &	bj,
		vnl_vector< double > const &	c,
		vnl_matrix< double > &	Bij,
		double	stepsize
	)

Use this to compute a finite-difference Jacobian B_ij.

Definition at line 431 of file vnl_sparse_lst_sqr_function.cxx.

◆ fd_jac_blocks()

void vnl_sparse_lst_sqr_function::fd_jac_blocks	(	vnl_vector< double > const &	a,
		vnl_vector< double > const &	b,
		vnl_vector< double > const &	c,
		std::vector< vnl_matrix< double > > &	A,
		std::vector< vnl_matrix< double > > &	B,
		std::vector< vnl_matrix< double > > &	C,
		double	stepsize
	)

virtual

Compute the sparse Jacobian in block form using a finite difference approximation.

Given the parameter vectors a, b and c, compute the set of block Jacobians Aij, Bij, and Cij. The finite difference approximation is done independently at each block. All Aij, Bij, and Cij have been sized appropriately before the call. The default implementation computes A, B, and C by calling jac_Aij, jac_Bij, and jac_Cij for each valid pair of i and j. You do not need to overload this method unless you want to provide a more efficient implementation for your problem.

Definition at line 229 of file vnl_sparse_lst_sqr_function.cxx.

◆ fd_jac_Cij()

void vnl_sparse_lst_sqr_function::fd_jac_Cij	(	int	i,
		int	j,
		vnl_vector< double > const &	ai,
		vnl_vector< double > const &	bj,
		vnl_vector< double > const &	c,
		vnl_matrix< double > &	Cij,
		double	stepsize
	)

Use this to compute a finite-difference Jacobian C_ij.

Definition at line 471 of file vnl_sparse_lst_sqr_function.cxx.

◆ fij()

void vnl_sparse_lst_sqr_function::fij	(	int	i,
		int	j,
		vnl_vector< double > const &	ai,
		vnl_vector< double > const &	bj,
		vnl_vector< double > const &	c,
		vnl_vector< double > &	fij
	)

virtual

Compute the residuals from the ith component of a, the jth component of b.

Given the parameter vectors ai, bj, and c, compute the vector of residuals fij. fij has been sized appropriately before the call.

Definition at line 346 of file vnl_sparse_lst_sqr_function.cxx.

◆ has_gradient()

bool vnl_sparse_lst_sqr_function::has_gradient ( ) const

inline

Return true if the derived class has indicated that gradf has been implemented.

Definition at line 312 of file vnl_sparse_lst_sqr_function.h.

◆ has_weights()

bool vnl_sparse_lst_sqr_function::has_weights ( ) const

inline

Return true if the derived class has indicated that.

apply_weights or apply_weight_ij have been implemented

Definition at line 316 of file vnl_sparse_lst_sqr_function.h.

◆ index_a()

unsigned int vnl_sparse_lst_sqr_function::index_a ( int i ) const

inline

return the index of aj in a.

Definition at line 294 of file vnl_sparse_lst_sqr_function.h.

◆ index_b()

unsigned int vnl_sparse_lst_sqr_function::index_b ( int j ) const

inline

return the index of bj in b.

Definition at line 297 of file vnl_sparse_lst_sqr_function.h.

◆ index_e()

unsigned int vnl_sparse_lst_sqr_function::index_e ( int k ) const

inline

return the index of ek in e.

Definition at line 300 of file vnl_sparse_lst_sqr_function.h.

◆ jac_Aij()

void vnl_sparse_lst_sqr_function::jac_Aij	(	unsigned int	i,
		unsigned int	j,
		vnl_vector< double > const &	ai,
		vnl_vector< double > const &	bj,
		vnl_vector< double > const &	c,
		vnl_matrix< double > &	Aij
	)

virtual

Calculate the Jacobian A_ij, given the parameter vectors a_i, b_j, and c.

Definition at line 357 of file vnl_sparse_lst_sqr_function.cxx.

◆ jac_Bij()

void vnl_sparse_lst_sqr_function::jac_Bij	(	unsigned int	i,
		unsigned int	j,
		vnl_vector< double > const &	ai,
		vnl_vector< double > const &	bj,
		vnl_vector< double > const &	c,
		vnl_matrix< double > &	Bij
	)

virtual

Calculate the Jacobian B_ij, given the parameter vectors a_i, b_j, and c.

Definition at line 368 of file vnl_sparse_lst_sqr_function.cxx.

◆ jac_blocks()

void vnl_sparse_lst_sqr_function::jac_blocks	(	vnl_vector< double > const &	a,
		vnl_vector< double > const &	b,
		vnl_vector< double > const &	c,
		std::vector< vnl_matrix< double > > &	A,
		std::vector< vnl_matrix< double > > &	B,
		std::vector< vnl_matrix< double > > &	C
	)

virtual

Compute the sparse Jacobian in block form.

Given the parameter vectors a, b, and c, compute the set of block Jacobians Aij, Bij, and Cij. All Aij, Bij, and Cij have been sized appropriately before the call. The default implementation computes A, B, and C by calling jac_Aij, jac_Bij, and jac_Cij for each valid pair of i and j. You do not need to overload this method unless you want to provide a more efficient implementation for your problem.

Definition at line 190 of file vnl_sparse_lst_sqr_function.cxx.

◆ jac_Cij()

void vnl_sparse_lst_sqr_function::jac_Cij	(	unsigned int	i,
		unsigned int	j,
		vnl_vector< double > const &	ai,
		vnl_vector< double > const &	bj,
		vnl_vector< double > const &	c,
		vnl_matrix< double > &	Cij
	)

virtual

Calculate the Jacobian C_ij, given the parameter vectors a_i, b_j, and c.

Definition at line 379 of file vnl_sparse_lst_sqr_function.cxx.

◆ number_of_a()

unsigned int vnl_sparse_lst_sqr_function::number_of_a ( ) const

inline

Return the number of subsets in a.

Definition at line 303 of file vnl_sparse_lst_sqr_function.h.

◆ number_of_b()

unsigned int vnl_sparse_lst_sqr_function::number_of_b ( ) const

inline

Return the number of subsets in b.

Definition at line 306 of file vnl_sparse_lst_sqr_function.h.

◆ number_of_e()

unsigned int vnl_sparse_lst_sqr_function::number_of_e ( ) const

inline

Return the number of residual vectors.

Definition at line 309 of file vnl_sparse_lst_sqr_function.h.

◆ number_of_params_a()

unsigned int vnl_sparse_lst_sqr_function::number_of_params_a ( int i ) const

inline

Return the number of parameters of a_j.

Definition at line 274 of file vnl_sparse_lst_sqr_function.h.

◆ number_of_params_b()

unsigned int vnl_sparse_lst_sqr_function::number_of_params_b ( int j ) const

inline

Return the number of parameters of b_i.

Definition at line 277 of file vnl_sparse_lst_sqr_function.h.

◆ number_of_params_c()

unsigned int vnl_sparse_lst_sqr_function::number_of_params_c ( ) const

inline

Return the number of parameters of c.

Definition at line 280 of file vnl_sparse_lst_sqr_function.h.

◆ number_of_residuals() [1/2]

unsigned int vnl_sparse_lst_sqr_function::number_of_residuals ( int k ) const

inline

Return the number of residuals in the kth residual vector.

Definition at line 283 of file vnl_sparse_lst_sqr_function.h.

◆ number_of_residuals() [2/2]

unsigned int vnl_sparse_lst_sqr_function::number_of_residuals	(	int	i,
		int	j
	)		const

inline

Return the number of residuals for x_ij.

Definition at line 286 of file vnl_sparse_lst_sqr_function.h.

◆ residual_indices()

const vnl_crs_index& vnl_sparse_lst_sqr_function::residual_indices ( ) const

inline

Return a const reference to the residual indexer.

Definition at line 319 of file vnl_sparse_lst_sqr_function.h.

◆ throw_failure()

void vnl_sparse_lst_sqr_function::throw_failure ( )

inline

Definition at line 113 of file vnl_sparse_lst_sqr_function.h.

◆ trace()

void vnl_sparse_lst_sqr_function::trace	(	int	iteration,
		vnl_vector< double > const &	a,
		vnl_vector< double > const &	b,
		vnl_vector< double > const &	c,
		vnl_vector< double > const &	e
	)

virtual

Called after each LM iteration to print debugging etc.

Definition at line 554 of file vnl_sparse_lst_sqr_function.cxx.

Member Data Documentation

◆ failure

bool vnl_sparse_lst_sqr_function::failure

Definition at line 56 of file vnl_sparse_lst_sqr_function.h.

◆ indices_a_

std::vector<unsigned int> vnl_sparse_lst_sqr_function::indices_a_

protected

Definition at line 323 of file vnl_sparse_lst_sqr_function.h.

◆ indices_b_

std::vector<unsigned int> vnl_sparse_lst_sqr_function::indices_b_

protected

Definition at line 324 of file vnl_sparse_lst_sqr_function.h.

◆ indices_e_

std::vector<unsigned int> vnl_sparse_lst_sqr_function::indices_e_

protected

Definition at line 326 of file vnl_sparse_lst_sqr_function.h.

◆ num_params_c_

unsigned int vnl_sparse_lst_sqr_function::num_params_c_

protected

Definition at line 325 of file vnl_sparse_lst_sqr_function.h.

◆ residual_indices_

vnl_crs_index vnl_sparse_lst_sqr_function::residual_indices_

protected

Definition at line 322 of file vnl_sparse_lst_sqr_function.h.

◆ use_gradient_

bool vnl_sparse_lst_sqr_function::use_gradient_

protected

Definition at line 328 of file vnl_sparse_lst_sqr_function.h.

◆ use_weights_

bool vnl_sparse_lst_sqr_function::use_weights_

protected

Definition at line 329 of file vnl_sparse_lst_sqr_function.h.

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

core/vnl/vnl_sparse_lst_sqr_function.h
core/vnl/vnl_sparse_lst_sqr_function.cxx

Public Types

Public Member Functions

Public Attributes

Protected Attributes

Private Member Functions

Detailed Description

Member Enumeration Documentation

◆ UseGradient

◆ UseWeights

Constructor & Destructor Documentation

◆ vnl_sparse_lst_sqr_function() [1/3]

◆ vnl_sparse_lst_sqr_function() [2/3]

◆ vnl_sparse_lst_sqr_function() [3/3]

◆ ~vnl_sparse_lst_sqr_function()

Member Function Documentation

◆ apply_weight_ij() [1/2]

◆ apply_weight_ij() [2/2]

◆ apply_weights() [1/2]

◆ apply_weights() [2/2]

◆ clear_failure()

◆ compute_weight_ij()

◆ compute_weights()

◆ dim_warning()

◆ f()

◆ fd_jac_Aij()

◆ fd_jac_Bij()

◆ fd_jac_blocks()

◆ fd_jac_Cij()

◆ fij()

◆ has_gradient()

◆ has_weights()

◆ index_a()

◆ index_b()

◆ index_e()

◆ jac_Aij()

◆ jac_Bij()

◆ jac_blocks()

◆ jac_Cij()

◆ number_of_a()

◆ number_of_b()

◆ number_of_e()

◆ number_of_params_a()

◆ number_of_params_b()

◆ number_of_params_c()

◆ number_of_residuals() [1/2]

◆ number_of_residuals() [2/2]

◆ residual_indices()

◆ throw_failure()

◆ trace()

Member Data Documentation

◆ failure

◆ indices_a_

◆ indices_b_

◆ indices_e_

◆ num_params_c_

◆ residual_indices_

◆ use_gradient_

◆ use_weights_