L1SoftThr: L1SoftThr - Iterative Soft Thresholding Algorithm based on...

In JSparO: Joint Sparse Optimization via Proximal Gradient Method for Cell Fate Conversion

L1SoftThr - Iterative Soft Thresholding Algorithm based on l_{1,1} norm

Description

The function aims to solve l_{1,1} regularized least squares.

Usage

L1SoftThr(A, B, X, s, maxIter = 200)

Arguments

A	Gene expression data of transcriptome factors (i.e. feature matrix in machine learning). The dimension of A is m * n.
B	Gene expression data of target genes (i.e. observation matrix in machine learning). The dimension of B is m * t.
X	Gene expression data of Chromatin immunoprecipitation or other matrix (i.e. initial iterative point in machine learning). The dimension of X is n * t.
s	joint sparsity level
maxIter	maximum iteration

Details

The L1SoftThr function aims to solve the problem:

\min \|AX-B\|_F^2 + λ \|X\|_{1,1}

to obtain s-joint sparse solution.

Value

The solution of proximal gradient method with l_{1,1} regularizer.

Author(s)

Xinlin Hu [e-mail]

Yaohua Hu [e-mail]

Examples

m <- 256; n <- 1024; t <- 5; maxIter0 <- 50
A0 <- matrix(rnorm(m * n), nrow = m, ncol = n)
B0 <- matrix(rnorm(m * t), nrow = m, ncol = t)
X0 <- matrix(0, nrow = n, ncol = t)
NoA <- norm(A0, '2'); A0 <- A0/NoA; B0 <- B0/NoA
res_L11 <- L1SoftThr(A0, B0, X0, s = 10, maxIter = maxIter0)

JSparO documentation built on Aug. 18, 2022, 9:06 a.m.