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PRISMA: PRoximal Iterative SMoothing Algorithm


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.

Dates and versions

hal-00855993 , version 1 (30-08-2013)



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