JSparO (version 1.5.0)

demo_JSparO: demo_JSparO - The demo of JSparO package

Description

This is the main function of JSparO aimed to solve the low-order regularization models with \(l_{p,q}\) norm.

Usage

demo_JSparO(A, B, X, s, p, q, maxIter = 200)

Value

The solution of proximal gradient method with \(l_{p,q}\) regularizer.

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

p

value for \(l_{p,q}\) norm (i.e., p = 1 or 2)

q

value for \(l_{p,q}\) norm (i.e., 0 <= q <= 1)

maxIter

maximum iteration

Author

Xinlin Hu

Yaohua Hu

Details

The demo_JSparO function is used to solve joint sparse optimization problem via different algorithms. Based on \(l_{p,q}\) norm, functions with different p and q are implemented to solve the problem: $$\min \|AX-B\|_F^2 + \lambda \|X\|_{p,q}$$ to obtain s-joint sparse solution.

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)
res_JSparO <- demo_JSparO(A0, B0, X0, s = 10, p = 2, q = 'half', maxIter = maxIter0)