Description Usage Arguments Value Author(s) References
View source: R/graphLaplacianEstimation.R
Learn the weighted Laplacian matrix of a graph using the MM method
1 2  learn_laplacian_gle_mm(S, A_mask = NULL, alpha = 0, maxiter = 10000,
reltol = 1e05, record_objective = FALSE, verbose = TRUE)

S 
a pxp sample covariance/correlation matrix 
A_mask 
the binary adjacency matrix of the graph 
alpha 
L1 regularization hyperparameter 
maxiter 
the maximum number of iterations 
reltol 
relative tolerance on the weight vector w 
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 
A list containing possibly the following elements:

the estimated Laplacian Matrix 

the estimated Adjacency Matrix 

boolean flag to indicate whether or not the optimization converged 

values of the objective function at every iteration in case record_objective = TRUE 
Ze Vinicius, Jiaxi Ying, and Daniel Palomar
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. 42314244, Aug. 2019
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