normalize.uv

<p>Rotate U and V using either QR or SVD decompositions.
The QR methods rotate U and V in such a way to obtain an orthogonal U
and a lower triangular V.  The SVD method rotate U and V in such a way
to obtain an orthogonal U and a scaled orthogonal V.</p>

internal

Efficient framework to estimate high-dimensional generalized matrix factorization models using penalized maximum likelihood under a dispersion exponential family specification. Either deterministic and stochastic methods are implemented for the numerical maximization. In particular, the package implements the stochastic gradient descent algorithm with a block-wise mini-batch strategy to speed up the computations and an efficient adaptive learning rate schedule to stabilize the convergence. All the theoretical details can be found in Castiglione et al. (2024, <doi:10.48550/arXiv.2412.20509>). Other methods considered for the optimization are the alternated iterative re-weighted least squares and the quasi-Newton method with diagonal approximation of the Fisher information matrix discussed in Kidzinski et al. (2022, <http://jmlr.org/papers/v23/20-1104.html>).

normalize.uv: Normalize the matrices U and V

Description

Usage

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