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Local convergence of proximal splitting methods for rank constrained problems

Grussler, C and Giselsson, P (2018) Local convergence of proximal splitting methods for rank constrained problems. In: UNSPECIFIED pp. 702-708..

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Abstract

© 2017 IEEE. 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.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Subjects: UNSPECIFIED
Divisions: Div F > Control
Depositing User: Cron Job
Date Deposited: 13 Nov 2018 01:53
Last Modified: 21 Nov 2019 02:08
DOI: 10.1109/CDC.2017.8263743