slope::Hybrid Class Reference

Hybrid CD-PGD solver for SLOPE. More...

#include <hybrid.h>

Inheritance diagram for slope::Hybrid:

Collaboration diagram for slope::Hybrid:

Public Member Functions
	Hybrid (JitNormalization jit_normalization, bool intercept, bool update_clusters, int cd_iterations)
	Constructs Hybrid solver for SLOPE optimization.
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) override
	Pure virtual function defining the solver's optimization routine.
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) override
	Pure virtual function defining the solver's optimization routine.
Public Member Functions inherited from slope::SolverBase
	SolverBase (JitNormalization jit_normalization, bool intercept)
	Constructs a base solver for SLOPE optimization.
virtual	~SolverBase ()=default
	Default desstructor.

Additional Inherited Members
Protected Attributes inherited from slope::SolverBase
JitNormalization	jit_normalization
	JIT feature normalization strategy.
bool	intercept
	If true, fits intercept term.

Detailed Description

Hybrid CD-PGD solver for SLOPE.

This solver alternates between coordinate descent (CD) and proximal gradient descent (PGD) steps to solve the SLOPE optimization problem. Because the SLOPE problem is non-separable, CD steps do not work out-of-the-box. In particular, they cannot be used to split he clusters (or would need to be augmented with additional steps). Instead, we use a hybrid method:

• PGD: Splits (or merges) clusters
• CD: Coordinate descent over the clusters, with good performance but can only merge, not split, clusters. cases

The switching between methods is controlled by the cd_iterations parameter, which determines how often PGD steps are taken versus CD steps.

Definition at line 35 of file hybrid.h.

Constructor & Destructor Documentation

Hybrid()

slope::Hybrid::Hybrid ( JitNormalization  jit_normalization, bool  intercept, bool  update_clusters, int  cd_iterations  )

inline

Constructs Hybrid solver for SLOPE optimization.

Parameters

jit_normalization	Feature normalization strategy
intercept	If true, fits intercept term
update_clusters	If true, updates clusters during optimization
cd_iterations	Frequency of proximal gradient descent updates

Definition at line 45 of file hybrid.h.

Member Function Documentation

run() [1/2]

void slope::Hybrid::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  )

overridevirtual

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)

Implements slope::SolverBase.

Definition at line 17 of file hybrid.cpp.

run() [2/2]

void slope::Hybrid::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  )

overridevirtual

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)

Implements slope::SolverBase.

Definition at line 46 of file hybrid.cpp.

---

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

• src/slope/solvers/hybrid.h
• src/slope/solvers/hybrid.cpp