learn_laplacian_gle_admm {spectralGraphTopology}

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