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math.OC2024
Efficient gradient-based methods for bilevel learning via recycling Krylov subspaces
Matthias J. Ehrhardt, Silvia Gazzola, Sebastian J. Scott
Many optimization problems require hyperparameters, i.e., parameters that must be pre-specified in advance, such as regularization parameters and parametric regularizers in variati…
math.OC2023
On Optimal Regularization Parameters via Bilevel Learning
Matthias J. Ehrhardt, Silvia Gazzola, Sebastian J. Scott
Variational regularization is commonly used to solve linear inverse problems, and involves augmenting a data fidelity by a regularizer. The regularizer is used to promote a priori…