acmtf_fg: Function value and gradient calculation for ACMTF

In CMTFtoolbox: Create (Advanced) Coupled Matrix and Tensor Factorization Models

Function value and gradient calculation for ACMTF

Description

Function value and gradient calculation for ACMTF

Usage

acmtf_fg(x, Z, alpha = 1, beta = rep(0.001, length(Z$object)), epsilon = 1e-08)

Arguments

x	Vectorized parameters of the CMTF model.
Z	Z object as generated by setupCMTFdata().
alpha	Alpha value of the loss function as specified by Acar et al., 2014
beta	Beta value of the loss function as specified by Acar et al., 2014
epsilon	Epsilon value of the loss function as specified by Acar et al., 2014

Value

A list containing the function ("fn") and the gradient ("gr").

Examples

A = array(rnorm(108*2), c(108, 2))
B = array(rnorm(100*2), c(100, 2))
C = array(rnorm(10*2), c(10, 2))
D = array(rnorm(100*2), c(100,2))
E = array(rnorm(10*2), c(10,2))

df1 = reinflateTensor(A, B, C)
df2 = reinflateTensor(A, D, E)
datasets = list(df1, df2)
modes = list(c(1,2,3), c(1,4,5))
Z = setupCMTFdata(datasets, modes, normalize=FALSE)

init = initializeACMTF(Z, 2, output="vect")
outcome = acmtf_fg(init, Z)
f = outcome$fn
g = outcome$gr

CMTFtoolbox documentation built on Aug. 29, 2026, 1:07 a.m.