learn_laplacian_gle_mm {spectralGraphTopology} | R Documentation |
Learn the weighted Laplacian matrix of a graph using the MM method
Description
Learn the weighted Laplacian matrix of a graph using the MM method
Usage
learn_laplacian_gle_mm(
S,
A_mask = NULL,
alpha = 0,
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 |
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 |
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