slope::SolverBase Class Reference

Abstract base class for SLOPE optimization solvers. More...

#include <solver.h>

Inheritance diagram for slope::SolverBase:

Public Member Functions
	SolverBase (JitNormalization jit_normalization, bool intercept)
	Constructs a base solver for SLOPE optimization.
virtual	~SolverBase ()=default
	Default desstructor.
virtual void	run (Eigen::VectorXd &beta0, Eigen::VectorXd &beta, Eigen::MatrixXd &eta, const Eigen::ArrayXd &lambda, const std::unique_ptr< Loss > &loss, const SortedL1Norm &penalty, const Eigen::VectorXd &gradient, const std::vector< int > &working_set, const Eigen::MatrixXd &x, const Eigen::VectorXd &x_centers, const Eigen::VectorXd &x_scales, const Eigen::MatrixXd &y)=0
	Pure virtual function defining the solver's optimization routine.
virtual void	run (Eigen::VectorXd &beta0, Eigen::VectorXd &beta, Eigen::MatrixXd &eta, const Eigen::ArrayXd &lambda, const std::unique_ptr< Loss > &loss, const SortedL1Norm &penalty, const Eigen::VectorXd &gradient, const std::vector< int > &working_set, const Eigen::SparseMatrix< double > &x, const Eigen::VectorXd &x_centers, const Eigen::VectorXd &x_scales, const Eigen::MatrixXd &y)=0
	Pure virtual function defining the solver's optimization routine.

Protected Attributes
JitNormalization	jit_normalization
	JIT feature normalization strategy.
bool	intercept
	If true, fits intercept term.

Detailed Description

Abstract base class for SLOPE optimization solvers.

Provides the interface and common functionality for different SLOPE (Sorted L-One Penalized Estimation) optimization algorithms. Derived classes implement specific optimization strategies like coordinate descent or proximal gradient descent.

See also

slope::PGD

slope::CD

Definition at line 29 of file solver.h.

Constructor & Destructor Documentation

SolverBase()

slope::SolverBase::SolverBase ( JitNormalization  jit_normalization, bool  intercept  )

inline

Constructs a base solver for SLOPE optimization.

Parameters

jit_normalization	Type of just-in-time normalization to apply (None, Center, Scale, or Both)
intercept	Whether to fit an intercept term

Definition at line 39 of file solver.h.

Member Function Documentation

run() [1/2]

virtual void slope::SolverBase::run ( Eigen::VectorXd &  beta0, Eigen::VectorXd &  beta, Eigen::MatrixXd &  eta, const Eigen::ArrayXd &  lambda, const std::unique_ptr< Loss > &  loss, const SortedL1Norm &  penalty, const Eigen::VectorXd &  gradient, const std::vector< int > &  working_set, const Eigen::MatrixXd &  x, const Eigen::VectorXd &  x_centers, const Eigen::VectorXd &  x_scales, const Eigen::MatrixXd &  y  )

pure virtual

Pure virtual function defining the solver's optimization routine.

Parameters

beta0	Intercept terms for each response
beta	Coefficients (size p x m)
eta	Linear predictor matrix (n samples x m responses)
lambda	Vector of regularization parameters
loss	Pointer to loss function object
penalty	Sorted L1 norm object for proximal operations
gradient	Gradient matrix for loss function
working_set	Vector of indices for active predictors
x	Input feature matrix (n samples x p predictors)
x_centers	Vector of feature means for centering
x_scales	Vector of feature scales for normalization
y	Response matrix (n samples x m responses)

Implemented in slope::Hybrid, and slope::PGD.

run() [2/2]

virtual void slope::SolverBase::run ( Eigen::VectorXd &  beta0, Eigen::VectorXd &  beta, Eigen::MatrixXd &  eta, const Eigen::ArrayXd &  lambda, const std::unique_ptr< Loss > &  loss, const SortedL1Norm &  penalty, const Eigen::VectorXd &  gradient, const std::vector< int > &  working_set, const Eigen::SparseMatrix< double > &  x, const Eigen::VectorXd &  x_centers, const Eigen::VectorXd &  x_scales, const Eigen::MatrixXd &  y  )

pure virtual

Pure virtual function defining the solver's optimization routine.

Parameters

beta0	Intercept terms for each response
beta	Coefficient vecttor (size p x m)
eta	Linear predictor matrix (n samples x m responses)
lambda	Vector of regularization parameters
loss	Pointer to loss function object
penalty	Sorted L1 norm object for proximal operations
gradient	Gradient matrix for loss function
working_set	Vector of indices for active predictors
x	Input feature matrix (n samples x p predictors)
x_centers	Vector of feature means for centering
x_scales	Vector of feature scales for normalization
y	Response matrix (n samples x m responses)

Implemented in slope::Hybrid, and slope::PGD.

Member Data Documentation

intercept

bool slope::SolverBase::intercept

protected

If true, fits intercept term.

Definition at line 108 of file solver.h.

jit_normalization

JitNormalization slope::SolverBase::jit_normalization

protected

JIT feature normalization strategy.

Definition at line 107 of file solver.h.

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

• src/slope/solvers/solver.h