prox.l1

Calculates the proximal L1 mapping for the given input matrix

Alternating Manifold Proximal Gradient Method for Sparse PCA uses the Alternating Manifold Proximal
Gradient (AManPG) method to find sparse principal components from a data or covariance matrix. Provides
a novel algorithm for solving the sparse principal component analysis problem which provides
advantages over existing methods in terms of efficiency and convergence guarantees.
Chen, S., Ma, S., Xue, L., & Zou, H. (2020) <doi:10.1287/ijoo.2019.0032>.
Zou, H., Hastie, T., & Tibshirani, R. (2006) <doi:10.1198/106186006X113430>.
Zou, H., & Xue, L. (2018) <doi:10.1109/JPROC.2018.2846588>.

Zhong Zheng

amanpg

Alternating Manifold Proximal Gradient Method for Sparse PCA

Shixiang Chen

Justin Huang

Benjamin Jochem

Shiqian Ma

Haichuan Xu

Lingzhou Xue

Hui Zou

prox.l1 function

<dl> <dt>z</dt>
<dd>input matrix</dd> <dt>lambda</dt>
<dd>parameters for calculating proximal L1 mapping</dd> <dt>r</dt>
<dd>number of columns used in matrix</dd></dl>

Arguments

Shixiang Chen, Justin Huang, Benjamin Jochem, Shiqian Ma, Lingzhou Xue and Hui Zou

Author

Proximal L1 Mapping — prox.l1

<dl>

 <dt>z</dt>
<dd>input matrix</dd>

 <dt>lambda</dt>
<dd>parameters for calculating proximal L1 mapping</dd>

 <dt>r</dt>
<dd>number of columns used in matrix</dd>

</dl>

prox.l1: Proximal L1 Mapping

Description

Usage

Value

Arguments

Author

References