PRISMA: PRoximal Iterative SMoothing Algorithm

Abstract : Motivated by learning problems including max-norm regularized matrix completion and clustering, robust PCA and sparse inverse covariance selection, we propose a novel optimization algorithm for minimizing a convex objective which decomposes into three parts: a smooth part, a simple non-smooth Lipschitz part, and a simple non-smooth non-Lipschitz part. We use a time variant smoothing strategy that allows us to obtain a guarantee that does not depend on knowing in advance the total number of iterations nor a bound on the domain.
Type de document :
Pré-publication, Document de travail
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Contributeur : Puneet Kumar Dokania <>
Soumis le : vendredi 30 août 2013 - 11:57:56
Dernière modification le : vendredi 12 janvier 2018 - 11:23:41


  • HAL Id : hal-00855993, version 1
  • ARXIV : 1206.2372



Francesco Orabona, Andreas Argyriou, Nathan Srebro. PRISMA: PRoximal Iterative SMoothing Algorithm. 2012. 〈hal-00855993〉



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