No screening rule - uses all variables. More...

#include <screening.h>

Inheritance diagram for slope::NoScreening:

Collaboration diagram for slope::NoScreening:

Public Member Functions
std::vector< int >	initialize (int feature_count, int alpha_max_ind) override
	Initialize the screening rule at the start of the path algorithm.
void	screen (std::vector< int > &working_set, Eigen::VectorXd &gradient, const Eigen::ArrayXd &lambda_curr, const Eigen::ArrayXd &lambda_prev, const Eigen::VectorXd &beta) override
	Screen for the next path step.
bool	checkKktViolations (Eigen::VectorXd &gradient, const Eigen::VectorXd &beta, const Eigen::ArrayXd &lambda_curr, std::vector< int > &working_set, const Eigen::MatrixXd &x, const Eigen::MatrixXd &residual, const Eigen::VectorXd &x_centers, const Eigen::VectorXd &x_scales, JitNormalization jit_normalization) override
	Check for KKT violations and update working set if necessary.
bool	checkKktViolations (Eigen::VectorXd &gradient, const Eigen::VectorXd &beta, const Eigen::ArrayXd &lambda_curr, std::vector< int > &working_set, const Eigen::SparseMatrix< double > &x, const Eigen::MatrixXd &residual, const Eigen::VectorXd &x_centers, const Eigen::VectorXd &x_scales, JitNormalization jit_normalization) override
	Check for KKT violations with sparse matrix input.
bool	checkKktViolations (Eigen::VectorXd &gradient, const Eigen::VectorXd &beta, const Eigen::ArrayXd &lambda_curr, std::vector< int > &working_set, const Eigen::Map< Eigen::MatrixXd > &x, const Eigen::MatrixXd &residual, const Eigen::VectorXd &x_centers, const Eigen::VectorXd &x_scales, JitNormalization jit_normalization) override
	Check for KKT violations with sparse matrix input.
bool	checkKktViolations (Eigen::VectorXd &gradient, const Eigen::VectorXd &beta, const Eigen::ArrayXd &lambda_curr, std::vector< int > &working_set, const Eigen::Map< Eigen::SparseMatrix< double > > &x, const Eigen::MatrixXd &residual, const Eigen::VectorXd &x_centers, const Eigen::VectorXd &x_scales, JitNormalization jit_normalization) override
	Check for KKT violations with sparse matrix input.
std::string	toString () const override
	Get string representation of the screening rule.
Public Member Functions inherited from slope::ScreeningRule
virtual	~ScreeningRule ()=default
	Virtual destructor.

Additional Inherited Members
Protected Attributes inherited from slope::ScreeningRule
std::vector< int >	strong_set
	Strong set of variables.

Detailed Description

No screening rule - uses all variables.

Definition at line 192 of file screening.h.

Member Function Documentation

◆ checkKktViolations() [1/4]

bool slope::NoScreening::checkKktViolations	(	Eigen::VectorXd &	gradient,
		const Eigen::VectorXd &	beta,
		const Eigen::ArrayXd &	lambda_curr,
		std::vector< int > &	working_set,
		const Eigen::Map< Eigen::MatrixXd > &	x,
		const Eigen::MatrixXd &	residual,
		const Eigen::VectorXd &	x_centers,
		const Eigen::VectorXd &	x_scales,
		JitNormalization	jit_normalization )

overridevirtual

Check for KKT violations with sparse matrix input.

Parameters

gradient	The gradient vector
beta	Current beta coefficients
lambda_curr	Current lambda values
working_set	Current working set (will be updated if violations found)
x	Design matrix (sparse format)
residual	Current residuals
x_centers	Centers for normalization
x_scales	Scales for normalization
jit_normalization	Whether to use JIT normalization

Returns: True if no violations found, false otherwise

Implements slope::ScreeningRule.

◆ checkKktViolations() [2/4]

bool slope::NoScreening::checkKktViolations	(	Eigen::VectorXd &	gradient,
		const Eigen::VectorXd &	beta,
		const Eigen::ArrayXd &	lambda_curr,
		std::vector< int > &	working_set,
		const Eigen::Map< Eigen::SparseMatrix< double > > &	x,
		const Eigen::MatrixXd &	residual,
		const Eigen::VectorXd &	x_centers,
		const Eigen::VectorXd &	x_scales,
		JitNormalization	jit_normalization )

overridevirtual

Check for KKT violations with sparse matrix input.

Parameters

gradient	The gradient vector
beta	Current beta coefficients
lambda_curr	Current lambda values
working_set	Current working set (will be updated if violations found)
x	Design matrix (sparse format)
residual	Current residuals
x_centers	Centers for normalization
x_scales	Scales for normalization
jit_normalization	Whether to use JIT normalization

Returns: True if no violations found, false otherwise

Implements slope::ScreeningRule.

◆ checkKktViolations() [3/4]

bool slope::NoScreening::checkKktViolations	(	Eigen::VectorXd &	gradient,
		const Eigen::VectorXd &	beta,
		const Eigen::ArrayXd &	lambda_curr,
		std::vector< int > &	working_set,
		const Eigen::MatrixXd &	x,
		const Eigen::MatrixXd &	residual,
		const Eigen::VectorXd &	x_centers,
		const Eigen::VectorXd &	x_scales,
		JitNormalization	jit_normalization )

overridevirtual

Check for KKT violations and update working set if necessary.

Parameters

gradient	The gradient vector
beta	Current beta coefficients
lambda_curr	Current lambda values
working_set	Current working set (will be updated if violations found)
x	Design matrix
residual	Current residuals
x_centers	Centers for normalization
x_scales	Scales for normalization
jit_normalization	Whether to use JIT normalization

Returns: True if no violations found, false otherwise

Implements slope::ScreeningRule.

◆ checkKktViolations() [4/4]

bool slope::NoScreening::checkKktViolations	(	Eigen::VectorXd &	gradient,
		const Eigen::VectorXd &	beta,
		const Eigen::ArrayXd &	lambda_curr,
		std::vector< int > &	working_set,
		const Eigen::SparseMatrix< double > &	x,
		const Eigen::MatrixXd &	residual,
		const Eigen::VectorXd &	x_centers,
		const Eigen::VectorXd &	x_scales,
		JitNormalization	jit_normalization )

overridevirtual

Check for KKT violations with sparse matrix input.

Parameters

gradient	The gradient vector
beta	Current beta coefficients
lambda_curr	Current lambda values
working_set	Current working set (will be updated if violations found)
x	Design matrix (sparse format)
residual	Current residuals
x_centers	Centers for normalization
x_scales	Scales for normalization
jit_normalization	Whether to use JIT normalization

Returns: True if no violations found, false otherwise

Implements slope::ScreeningRule.

◆ initialize()

std::vector< int > slope::NoScreening::initialize	(	int	feature_count,
		int	alpha_max_ind )

overridevirtual

Initialize the screening rule at the start of the path algorithm.

Parameters

feature_count	The total number of coefficients
alpha_max_ind	The index of the feature with maximum absolute gradient

Returns: The initial working set

Implements slope::ScreeningRule.

◆ screen()

void slope::NoScreening::screen	(	std::vector< int > &	working_set,
		Eigen::VectorXd &	gradient,
		const Eigen::ArrayXd &	lambda_curr,
		const Eigen::ArrayXd &	lambda_prev,
		const Eigen::VectorXd &	beta )

overridevirtual

Screen for the next path step.

Parameters

gradient	The gradient vector
lambda_curr	Current lambda values
lambda_prev	Previous lambda values
beta	Current beta coefficients
working_set	Working set to update for the current path step

Implements slope::ScreeningRule.

◆ toString()

std::string slope::NoScreening::toString ( ) const

overridevirtual

Get string representation of the screening rule.

Returns: Name of the screening rule

Implements slope::ScreeningRule.

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

include/slope/screening.h

Public Member Functions

Additional Inherited Members

Detailed Description

Member Function Documentation

◆ checkKktViolations() [1/4]

◆ checkKktViolations() [2/4]

◆ checkKktViolations() [3/4]

◆ checkKktViolations() [4/4]

◆ initialize()

◆ screen()

◆ toString()