cpp.fit.newton: Fit a GMF model using the diagonal quasi-Newton algorithm

In sgdGMF: Estimation of Generalized Matrix Factorization Models via Stochastic Gradient Descent

Fit a GMF model using the diagonal quasi-Newton algorithm

Description

Fit a GMF model using the diagonal quasi-Newton algorithm

Usage

cpp.fit.newton(
  Y,
  X,
  B,
  A,
  Z,
  U,
  V,
  O,
  W,
  familyname,
  linkname,
  varfname,
  ncomp,
  lambda,
  maxiter = 500L,
  stepsize = 0.1,
  eps = 1e-08,
  nafill = 1L,
  tol = 1e-05,
  damping = 0.001,
  verbose = TRUE,
  frequency = 10L,
  parallel = FALSE,
  nthreads = 1L
)

Arguments

Y	matrix of responses (n \times m)
X	matrix of row fixed effects (n \times p)
B	initial row-effect matrix (n \times p)
A	initial column-effect matrix (n \times q)
Z	matrix of column fixed effects (m \times q)
U	initial factor matrix (n \times d)
V	initial loading matrix (m \times d)
O	matrix of constant offset (n \times m)
W	matrix of constant weights (n \times m)
familyname	a glm model family name
linkname	a glm link function name
varfname	variance function name
ncomp	rank of the latent matrix factorization
lambda	penalization parameters
maxiter	maximum number of iterations
stepsize	stepsize of the quasi-Newton update
eps	shrinkage factor for extreme predictions
nafill	how often the missing values are updated
tol	tolerance threshold for the stopping criterion
damping	diagonal dumping factor for the Hessian matrix
verbose	if TRUE, print the optimization status
frequency	how often the optimization status is printed
parallel	if TRUE, allows for parallel computing
nthreads	number of cores to be used in parallel

sgdGMF documentation built on April 3, 2025, 7:37 p.m.