learn_laplacian_gle_admm: Learn the weighted Laplacian matrix of a graph using the ADMM...

In spectralGraphTopology: Learning Graphs from Data via Spectral Constraints

Learn the weighted Laplacian matrix of a graph using the ADMM method

Description

Learn the weighted Laplacian matrix of a graph using the ADMM method

Usage

learn_laplacian_gle_admm(
  S,
  A_mask = NULL,
  alpha = 0,
  rho = 1,
  maxiter = 10000,
  reltol = 1e-05,
  record_objective = FALSE,
  verbose = TRUE
)

Arguments

S	a pxp sample covariance/correlation matrix
A_mask	the binary adjacency matrix of the graph
alpha	L1 regularization hyperparameter
rho	ADMM convergence rate hyperparameter
maxiter	the maximum number of iterations
reltol	relative tolerance on the Laplacian matrix estimation
record_objective	whether or not to record the objective function. Default is FALSE
verbose	if TRUE, then a progress bar will be displayed in the console. Default is TRUE

Value

A list containing possibly the following elements:

Laplacian	the estimated Laplacian Matrix
Adjacency	the estimated Adjacency Matrix
convergence	boolean flag to indicate whether or not the optimization converged
obj_fun	values of the objective function at every iteration in case record_objective = TRUE

Author(s)

Ze Vinicius, Jiaxi Ying, and Daniel Palomar

References

Licheng Zhao, Yiwei Wang, Sandeep Kumar, and Daniel P. Palomar. Optimization Algorithms for Graph Laplacian Estimation via ADMM and MM. IEEE Trans. on Signal Processing, vol. 67, no. 16, pp. 4231-4244, Aug. 2019

spectralGraphTopology documentation built on March 18, 2022, 7:35 p.m.