svds.undirected_factor_model: Compute the singular value decomposition of the expected adjacency matrix of an undirected factor model

Description

Compute the singular value decomposition of the expected adjacency matrix of an undirected factor model

# S3 method for undirected_factor_model
svds(A, k = A$k, nu = k, nv = k, opts = list(), ...)

A: An undirected_factor_model().
k: Desired rank of decomposition.
nu: Number of left singular vectors to be computed. This must be between 0 and k.
nv: Number of right singular vectors to be computed. This must be between 0 and k.
opts: Control parameters related to the computing algorithm. See Details below.
...: Unused, included only for consistency with generic signature.

The opts argument is a list that can supply any of the following parameters:

ncv: Number of Lanzcos basis vectors to use. More vectors will result in faster convergence, but with greater memory use. ncv must be satisfy \(k < ncv \le p\) where p = min(m, n). Default is min(p, max(2*k+1, 20)).
tol: Precision parameter. Default is 1e-10.
maxitr: Maximum number of iterations. Default is 1000.
center: Either a logical value (TRUE/FALSE), or a numeric vector of length \(n\). If a vector \(c\) is supplied, then SVD is computed on the matrix \(A - 1c'\), in an implicit way without actually forming this matrix. center = TRUE has the same effect as center = colMeans(A). Default is FALSE.
scale: Either a logical value (TRUE/FALSE), or a numeric vector of length \(n\). If a vector \(s\) is supplied, then SVD is computed on the matrix \((A - 1c')S\), where \(c\) is the centering vector and \(S = diag(1/s)\). If scale = TRUE, then the vector \(s\) is computed as the column norm of \(A - 1c'\). Default is FALSE.