On the rate of convergence of the proximal alternating linearized minimization algorithm for convex problems
We analyze the proximal alternating linearized minimization algorithm (PALM) for solving non-smooth convex minimization problems where the objective function is a sum of a smooth convex function and block separable non-smooth extended real-valued convex functions. We prove a global non-asymptotic su...
Saved in:
| Main Authors: | Ron Shefi, Marc Teboulle |
|---|---|
| Format: | article |
| Language: | EN |
| Published: |
Elsevier
2016
|
| Subjects: | |
| Online Access: | https://doaj.org/article/24f455963618473180d6a3a1fc22c476 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Uncontrolled inexact information within bundle methods
by: Jérôme Malick, et al.
Published: (2017) -
An exact approach for the multi-constraint graph partitioning problem
by: Diego Recalde, et al.
Published: (2020) -
Dualization and discretization of linear-quadratic control problems with bang–bang solutions
by: Walter Alt, et al.
Published: (2016) -
A globally convergent algorithm for MPCC
by: Abdeslam Kadrani, et al.
Published: (2015) -
A comparison of four approaches from stochastic programming for large-scale unit-commitment
by: Wim van Ackooij
Published: (2017)