learn_connected_graph {fingraph}

Laplacian matrix of a connected graph with Gaussian data Computes the Laplacian matrix of a graph on the basis of an observed data matrix, where we assume the data to be Gaussian distributed.

Description

Laplacian matrix of a connected graph with Gaussian data

Computes the Laplacian matrix of a graph on the basis of an observed data matrix, where we assume the data to be Gaussian distributed.

Usage

learn_connected_graph(
  S,
  w0 = "naive",
  d = 1,
  rho = 1,
  maxiter = 10000,
  reltol = 1e-05,
  verbose = TRUE
)

Arguments

S

a p x p covariance matrix, where p is the number of nodes in the graph

w0

initial vector of graph weights. Either a vector of length p(p-1)/2 or a string indicating the method to compute an initial value.

d

the nodes' degrees. Either a vector or a single value.

rho

constraint relaxation hyperparameter.

maxiter

maximum number of iterations.

reltol

relative tolerance as a convergence criteria.

verbose

whether or not to show a progress bar during the iterations.

Value

A list containing possibly the following elements:

laplacian

estimated Laplacian matrix

adjacency

estimated adjacency matrix

theta

estimated Laplacian matrix slack variable

maxiter

number of iterations taken to reach convergence

convergence

boolean flag to indicate whether or not the optimization converged