@inproceedings{bd11fe0611e74c63a6fd88f9a637fb36,
title = "Local convergence of proximal splitting methods for rank constrained problems",
abstract = "We analyze the local convergence of proximal splitting algorithms to solve optimization problems that are convex besides a rank constraint. For this, we show conditions under which the proximal operator of a function involving the rank constraint is locally identical to the proximal operator of its convex envelope, hence implying local convergence. The conditions imply that the non-convex algorithms locally converge to a solution whenever a convex relaxation involving the convex envelope can be expected to solve the non-convex problem.",
author = "Christian Grussler and Pontus Giselsson",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 56th IEEE Annual Conference on Decision and Control, CDC 2017 ; Conference date: 12-12-2017 Through 15-12-2017",
year = "2017",
month = jun,
day = "28",
doi = "https://doi.org/10.1109/CDC.2017.8263743",
language = "الإنجليزيّة",
series = "2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017",
pages = "702--708",
booktitle = "2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017",
}