Work

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

Year: 2024

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2412.10034

Authors: Guerrero, Patricio, Bellens, Simon, Dewulf, Wim

Institution: KU Leuven

Language: English

Topic: Numerical methods in inverse problems

Subfield: Mathematical Physics

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funder: Fonds Wetenschappelijk Onderzoek

Award: Development of a hybrid data-driven / model-based regularization method for X-ray CT-metrology

OpenAlex ID: W4405433970

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