solve_optim1_seq: Solves penalized matrix factorization problem for the range...

In irinagain/SLIDE: Structural Learning and Integrative Decomposition of Multi-View Data

Description

Solves penalized matrix factorization problem for the range of lambda values

Usage

solve_optim1_seq(
  X,
  pvec,
  lambda_seq = NULL,
  n_lambda = 50,
  lambda_max = 1,
  lambda_min = 0.01,
  k_max = 1000,
  eps = 1e-06
)

Arguments

X	A n x p concatenated data matrix of views X_1,...,X_d.
pvec	A vector of values p_1,....,p_d corresponding to the number of measurements within each data view.
lambda_seq	An optional sequence of tuning parameters for the penalized matrix decomposition problem. By default, the algorithm generates its own sequence based on supplied values of n_lambda, lambda_min and lambda_max.
n_lambda	A length of tuning parameter sequence. The default value is 50. It is only used when lambda_seq = NULL.
lambda_max	A maximal value for tuning parameter. The default value is 1. If X is already standardized, it is recommended to set lambda_max to the largest singular value within the view.
lambda_min	A minimal tuning parameter to be considered, the default value is 0.1
k_max	A maximal number of allowed iterations, the default value is 1000.
eps	A convergence tolerance criterion as measured by the differene in objective functions at successive iterations, the default value is 1e-06.

Value

A list with the elements

lambda	A sequence of tuning parameters used.
param	A list with estimates of U and V obtained from solving the penalized matrix factorization problem with corresponding values of tuning parameter.

Examples

n = 50
p1 = 20
p2 = 40
X1 = matrix(rnorm(n*p1), n, p1)
X2 = matrix(rnorm(n*p2), n, p2)
X = cbind(X1, X2)
out = standardizeX(X, pvec = c(p1,p2))
out_solve = solve_optim1_seq(X = out$X, pvec = c(p1,p2), lambda_max = max(out$svec), n_lambda = 30)

irinagain/SLIDE documentation built on Aug. 14, 2021, 2:56 p.m.