Type: Package
Title: Parametric Simplex Method for Sparse Learning
Version: 1.0.2
Date: 2020-01-21
Author: Zichong Li, Qianli Shen
Maintainer: Zichong Li <zichongli5@gmail.com>
LinkingTo: Rcpp, RcppEigen
Description: Implements a unified framework of parametric simplex method for a variety of sparse learning problems (e.g., Dantzig selector (for linear regression), sparse quantile regression, sparse support vector machines, and compressive sensing) combined with efficient hyper-parameter selection strategies. The core algorithm is implemented in C++ with Eigen3 support for portable high performance linear algebra. For more details about parametric simplex method, see Haotian Pang (2017) https://papers.nips.cc/paper/6623-parametric-simplex-method-for-sparse-learning.pdf.
Imports: Matrix
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Packaged: 2020-01-22 09:22:31 UTC; lizichong
RoxygenNote: 6.1.1
Repository: CRAN
Date/Publication: 2020-01-22 11:10:02 UTC

Parametric Simplex Method for Sparse Learning

Description

A package for parametric simplex method for sparse learning

Details

Package: PRIMAL
Type: Package
Version: 1.0.0
Date: 2019-08-15

The package "PRIMAL" provides 5 main functions:
(1) The dantzig selector solver applying simplex method. Please refer to Dantzig_solver.
(2) The sparse SVM solver applying simplex method. Please refer to SparseSVM_solver.
(3) The compressed sensing solver. Please refer to CompressedSensing_solver.
(4) The quantile regression solver. Please refer to QuantileRegression_solver.
(5) The solver for standard formulation of parametric simplex method. Please refer to PSM_solver.

Author(s)

Qianli Shen, Zichong Li

See Also

plot.primal, print.primal, coef.primal

Solve given compressed sensing problem in parametric simplex method

Description

Solve given compressed sensing problem in parametric simplex method

Usage

CompressedSensing_solver(X, y, max_it = 50, lambda_threshold = 0.01)

Arguments

X

x is an n by d data matrix

y

y is a length n response vector

max_it

This is the number of the maximum path length one would like to achieve. The default length is 50.

lambda_threshold

The parametric simplex method will stop when the calculated parameter is smaller than lambda. The default value is 0.01.

Value

An object with S3 class "primal" is returned:

data

The n by d data matrix from the input

response

The length n response vector from the input

beta

A matrix of regression estimates whose columns correspond to regularization parameters for parametric simplex method.

df

The degree of freedom (number of nonzero coefficients) along the solution path.

value

The sequence of optimal value of the object function corresponded to the sequence of lambda.

iterN

The number of iteration in the program.

lambda

The sequence of regularization parameters lambda obtained in the program.

type

The type of the problem, such as Dantzig and SparseSVM.

See Also

primal-package, Dantzig_solver

Examples

## Compressed Sensing
## We set X to be standard normal random matrix and generate Y using gaussian noise.
## Generate the design matrix and coefficient vector
n = 100 # sample number
d = 250 # sample dimension
c = 0.5 # correlation parameter
s = 20  # support size of coefficient
set.seed(1024)
X = scale(matrix(rnorm(n*d),n,d)+c*rnorm(n))/sqrt(n-1)*sqrt(n)
beta = c(rnorm(s), rep(0, d-s))
## Generate response using Gaussian noise, and solve the solution path
noise = rnorm(n)
Y = X%*%beta + noise
## Compressed Sensing solved with parametric simplex method
fit.compressed = CompressedSensing_solver(X, Y, max_it = 100, lambda_threshold = 0.01)
###lambdas used
print(fit.compressed$lambda)
## number of nonzero coefficients for each lambda
print(fit.compressed$df)
## Visualize the solution path
plot(fit.compressed)

Solve given Dantzig selector problem in parametric simplex method

Description

Solve given Dantzig selector problem in parametric simplex method

Usage

Dantzig_solver(X, y, max_it = 50, lambda_threshold = 0.01)

Arguments

X

x is an n by d data matrix

y

y is a length n response vector

max_it

This is the number of the maximum path length one would like to achieve. The default length is 50.

lambda_threshold

The parametric simplex method will stop when the calculated parameter is smaller than lambda. The default value is 0.01.

Value

An object with S3 class "primal" is returned:

data

The n by d data matrix from the input

response

The length n response vector from the input

beta

A matrix of regression estimates whose columns correspond to regularization parameters for parametric simplex method.

df

The degree of freedom (number of nonzero coefficients) along the solution path.

value

The sequence of optimal value of the object function corresponded to the sequence of lambda.

iterN

The number of iteration in the program.

lambda

The sequence of regularization parameters lambda obtained in the program.

type

The type of the problem, such as Dantzig and SparseSVM.

See Also

primal-package

Examples

## Dantzig
## We set X to be standard normal random matrix and generate Y using gaussian noise.
## Generate the design matrix and coefficient vector
n = 100 # sample number
d = 250 # sample dimension
c = 0.5 # correlation parameter
s = 20  # support size of coefficient
set.seed(1024)
X = scale(matrix(rnorm(n*d),n,d)+c*rnorm(n))/sqrt(n-1)*sqrt(n)
beta = c(rnorm(s), rep(0, d-s))
## Generate response using Gaussian noise, and solve the solution path
noise = rnorm(n)
Y = X%*%beta + noise
## Dantzig selection solved with parametric simplex method
fit.dantzig = Dantzig_solver(X, Y, max_it = 100, lambda_threshold = 0.01)
###lambdas used
print(fit.dantzig$lambda)
## number of nonzero coefficients for each lambda
print(fit.dantzig$df)
## Visualize the solution path
plot(fit.dantzig)

Solve given problem in parametric simplex method

Description

Solve given problem in parametric simplex method

Usage

PSM_solver(A, b, b_bar, c, c_bar, B_init = NULL, max_it = 50,
  lambda_threshold = 0.01)

Arguments

A

A is an n by d data matrix

b

b is a length n response vector

b_bar

b_bar is a length n vector time to parameter in constraints.

c

c is a length d vector in target function.

c_bar

c_bar is a length d vector time to parameter in target function

B_init

B_init is the index of initial basic colume.

max_it

This is the number of the maximum path length one would like to achieve. The default length is 50.

lambda_threshold

The parametric simplex method will stop when the calculated parameter is smaller than lambda. The default value is 0.01.

Value

An object with S3 class "primal" is returned:

data

The n by d data matrix from the input

response

The length n response vector from the input

beta

A matrix of regression estimates whose columns correspond to regularization parameters for parametric simplex method.

beta0

A vector of regression estimates whose index correspond to regularization parameters for parametric simplex method.

df

The degree of freecom (number of nonzero coefficients) along the solution path.

value

The sequence of optimal value of the object function corresponded to the sequence of lambda.

iterN

The number of iteration in the program.

lambda

The sequence of regularization parameters lambda obtained in the program.

type

The type of the problem, such as Dantzig and SparseSVM.

See Also

primal-package

Examples

## This example show how to use PSM_solver() to solve dantzig problem.
## Generate the design matrix and coefficient vector
n = 100 # sample number
d = 250 # sample dimension
c = 0.5 # correlation parameter
s = 20  # support size of coefficient
set.seed(1024)
X = scale(matrix(rnorm(n*d),n,d)+c*rnorm(n))/sqrt(n-1)*sqrt(n)
beta = c(rnorm(s), rep(0, d-s))
## Generate response using Gaussian noise, and solve the solution path
noise = rnorm(n)
Y = X%*%beta + noise
## Define parameters for dantzig problem
XtX = t(X)%*%X
A = cbind(cbind(rbind(XtX,-XtX),-rbind(XtX,-XtX)),diag(rep(1,2*d)))
b = rbind(t(X)%*%Y,-t(X)%*%Y)
c = c(rep(-1,2*d),rep(0,2*d))
c_bar = rep(0,4*d)
b_bar = rep(1,2*d)
B_init = seq(2*d,4*d-1)
## Dantzig selection solved with parametric simplex method
fit.dantzig = PSM_solver(A, b, b_bar, c, c_bar, B_init, max_it = 50, lambda_threshold = 0.01)
###lambdas used
print(fit.dantzig$lambda)
## number of nonzero coefficients for each lambda
print(fit.dantzig$df)
## Visualize the solution path
plot(fit.dantzig)

Solve given quantile regression problem in parametric simplex method

Description

Solve given quantile regression problem in parametric simplex method

Usage

QuantileRegression_solver(X, y, max_it = 50, lambda_threshold = 0.01,
  tau = 0.5)

Arguments

X

x is an n by d data matrix

y

y is a length n response vector

max_it

This is the number of the maximum path length one would like to achieve. The default length is 50.

lambda_threshold

The parametric simplex method will stop when the calculated parameter is smaller than lambda. The default value is 0.01.

tau

The quantile number you want. The default quantile is 0.5

Value

An object with S3 class "primal" is returned:

data

The n by d data matrix from the input

response

The length n response vector from the input

beta

A matrix of regression estimates whose columns correspond to regularization parameters for parametric simplex method.

df

The degree of freedom (number of nonzero coefficients) along the solution path.

value

The sequence of optimal value of the object function corresponded to the sequence of lambda.

iterN

The number of iteration in the program.

lambda

The sequence of regularization parameters lambda obtained in the program.

type

The type of the problem, such as Dantzig and SparseSVM.

See Also

primal-package, Dantzig_solver

Examples

## Quantile Regression
## We set X to be standard normal random matrix and generate Y using gaussian noise
## with default quantile number to be 0.5.
## Generate the design matrix and coefficient vector
n = 100 # sample number
d = 250 # sample dimension
c = 0.5 # correlation parameter
s = 20  # support size of coefficient
set.seed(1024)
X = scale(matrix(rnorm(n*d),n,d)+c*rnorm(n))/sqrt(n-1)*sqrt(n)
beta = c(rnorm(s), rep(0, d-s))
## Generate response using Gaussian noise, and solve the solution path
noise = rnorm(n)
Y = X%*%beta + noise
## Quantile Regression problem solved with parametric simplex method
fit.quantile = QuantileRegression_solver(X, Y, max_it = 100, lambda_threshold = 0.01)
###lambdas used
print(fit.quantile$lambda)
## number of nonzero coefficients for each lambda
print(fit.quantile$df)
## Visualize the solution path
plot(fit.quantile)

Solve given Sparse SVM problem in parametric simplex method

Description

Solve given Sparse SVM problem in parametric simplex method

Usage

SparseSVM_solver(X, y, max_it = 50, lambda_threshold = 0.01)

Arguments

X

x is an n by d data matrix

y

y is a length n response vector

max_it

This is the number of the maximum path length one would like to achieve. The default length is 50.

lambda_threshold

The parametric simplex method will stop when the calculated parameter is smaller than lambda. The default value is 0.01.

Value

An object with S3 class "primal" is returned:

data

The n by d data matrix from the input

response

The length n response vector from the input

beta

A matrix of regression estimates whose columns correspond to regularization parameters for parametric simplex method.

beta0

A vector of regression estimates whose index correspond to regularization parameters for parametric simplex method.

df

The degree of freecom (number of nonzero coefficients) along the solution path.

value

The sequence of optimal value of the object function corresponded to the sequence of lambda.

iterN

The number of iteration in the program.

lambda

The sequence of regularization parameters lambda obtained in the program.

type

The type of the problem, such as Dantzig and SparseSVM.

See Also

primal-package

Examples

## SparseSVM
## We set the X matrix to be normal random matrix and Y is a vector consists of -1 and 1
## with the number of iteration to be 1000.
## Generate the design matrix and coefficient vector
n = 200 # sample number
d = 100 # sample dimension
c = 0.5 # correlation parameter
s = 20  # support size of coefficient
set.seed(1024)
X = matrix(rnorm(n*d),n,d)+c*rnorm(n)
## Generate response and solve the solution path
Y <- sample(c(-1,1),n,replace = TRUE)
## Sparse SVM solved with parametric simplex method
fit.SVM = SparseSVM_solver(X, Y, max_it = 1000, lambda_threshold = 0.01)
## lambdas used
print(fit.SVM$lambda)
## Visualize the solution path
plot(fit.SVM)

Coef function for S3 class "primal"

Description

Print the estimated solution correspond to a specific parameter.

Usage

## S3 method for class 'primal'
coef(object, n = NULL, ...)

Arguments

object

An object with S3 class "primal".

n

The index of the wanted parameter.

...

System reserved (No specific usage)

See Also

Dantzig_solver, SparseSVM_solver

Plot function for S3 class "primal"

Description

Plot regularization path and parameter obtained from the algorithm.

Usage

## S3 method for class 'primal'
plot(x, n = NULL, ...)

Arguments

x

An object with S3 class "primal"

n

If n = NULL, three graph will be shown together. If n is a number, then the corresponding graph will be shown.

...

System reserved (No specific usage)

See Also

Dantzig_solver, SparseSVM_solver

Print function for S3 class "primal"

Description

Print the information about the model usage, the parameter path, degree of freedom of the solution path.

Usage

## S3 method for class 'primal'
print(x, ...)

Arguments

x

An object with S3 class "primal".

...

System reserved (No specific usage)

See Also

Dantzig_solver, SparseSVM_solver

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