Back to Search
Start Over
Convergence Rate Comparison of Proximal Algorithms for Non-Smooth Convex Optimization With an Application to Texture Segmentation.
- Source :
- IEEE Signal Processing Letters; Jun2022, Vol. 29, p1337-1341, 5p
- Publication Year :
- 2022
-
Abstract
- In this paper we provide a theoretical and numerical comparison of convergence rates of forward-backward, Douglas-Rachford, and Peaceman-Rachford algorithms for minimizing the sum of a convex proper lower semicontinuous function and a strongly convex differentiable function with Lipschitz continuous gradient. Our results extend the comparison made in [1], when both functions are smooth, to the context where only one is assumed differentiable. Optimal step-sizes and rates of the three algorithms are compared theoretically and numerically in the context of texture segmentation problem, obtaining very sharp estimations and illustrating the high efficiency of Peaceman-Rachford splitting. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10709908
- Volume :
- 29
- Database :
- Complementary Index
- Journal :
- IEEE Signal Processing Letters
- Publication Type :
- Academic Journal
- Accession number :
- 158517134
- Full Text :
- https://doi.org/10.1109/LSP.2022.3179169