L2NewtonThr {JSparO} | R Documentation |
L2NewtonThr - Iterative Thresholding Algorithm based on
norm with Newton method
Description
The function aims to solve regularized least squares, where the proximal optimization subproblems will be solved by Newton method.
Usage
L2NewtonThr(A, B, X, s, q, maxIter = 200, innMaxIter = 30, innEps = 1e-06)
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 |
q |
value for |
maxIter |
maximum iteration |
innMaxIter |
maximum iteration in Newton step |
innEps |
criterion to stop inner iteration |
Details
The L2NewtonThr function aims to solve the problem:
to obtain s-joint sparse solution.
Value
The solution of proximal gradient method with regularizer.
Author(s)
Xinlin Hu thompson-xinlin.hu@connect.polyu.hk
Yaohua Hu mayhhu@szu.edu.cn
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_L2q <- L2NewtonThr(A0, B0, X0, s = 10, q = 0.2, maxIter = maxIter0)