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arXiv · 2412.10034

FISTA-Condat-Vu: Automatic Differentiation for Hyperparameter Learning in Variational Models

Patricio Guerrero·Simon Bellens·Wim Dewulf
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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.

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Patricio Guerrero, Simon Bellens, Wim Dewulf. 2024-12-13. FISTA-Condat-Vu: Automatic Differentiation for Hyperparameter Learning in Variational Models. https://arxiv.org/abs/2412.10034

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