learn_connected_graph: Laplacian matrix of a connected graph with Gaussian data...

In fingraph: Learning Graphs for Financial Markets

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

fingraph documentation built on Feb. 16, 2023, 9:50 p.m.