FISTA-Condat-Vu: Automatic Differentiation for Hyperparameter Learning in Variational Models
Numerical Analysis
2024-12-16 v1 Numerical Analysis
Optimization and Control
Abstract
Motivated by industrial computed tomography, we propose a memory efficient strategy to estimate the regularization hyperparameter of a non-smooth variational model. The approach is based on a combination of FISTA and Condat-Vu algorithms exploiting the convergence rate of the former and the low per-iteration complexity of the latter. The estimation is cast as a bilevel learning problem where a first-order method is obtained via reduced-memory automatic differentiation to compute the derivatives. The method is validated with experimental industrial tomographic data with the numerical implementation available.
Cite
@article{arxiv.2412.10034,
title = {FISTA-Condat-Vu: Automatic Differentiation for Hyperparameter Learning in Variational Models},
author = {Patricio Guerrero and Simon Bellens and Wim Dewulf},
journal= {arXiv preprint arXiv:2412.10034},
year = {2024}
}