Nonconvex proximal splitting: batch and incremental algorithms

2011

Technical Report

ei

Within the unmanageably large class of nonconvex optimization, we consider the rich subclass of nonsmooth problems having composite objectives (this includes the extensively studied convex, composite objective problems as a special case). For this subclass, we introduce a powerful, new framework that permits asymptotically non-vanishing perturbations. In particular, we develop perturbation-based batch and incremental (online like) nonconvex proximal splitting algorithms. To our knowledge, this is the rst time that such perturbation-based nonconvex splitting algorithms are being proposed and analyzed. While the main contribution of the paper is the theoretical framework, we complement our results by presenting some empirical results on matrix factorization.

@techreport{Sra2011,
  title = {Nonconvex proximal splitting: batch and incremental algorithms},
  author = {Sra, S.},
  number = {2},
  institution = {Max Planck Institute for Intelligent Systems, Tübingen, Germany},
  year = {2011},
  doi = {}
}