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  "Title": "Estimation of Generalized Matrix Factorization Models via\nStochastic Gradient Descent",
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  "Description": "Efficient framework to estimate high-dimensional\ngeneralized matrix factorization models using penalized maximum\nlikelihood under a dispersion exponential family specification.\nEither deterministic and stochastic methods are implemented for\nthe numerical maximization. In particular, the package\nimplements the stochastic gradient descent algorithm with a\nblock-wise mini-batch strategy to speed up the computations and\nan efficient adaptive learning rate schedule to stabilize the\nconvergence. All the theoretical details can be found in\nCastiglione et al. (2024, <doi:10.48550/arXiv.2412.20509>).\nOther methods considered for the optimization are the\nalternated iterative re-weighted least squares and the\nquasi-Newton method with diagonal approximation of the Fisher\ninformation matrix discussed in Kidzinski et al. (2022,\n<http://jmlr.org/papers/v23/20-1104.html>).",
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