An efficient semismooth* Newton method is presented for minimizing Tikhonov functionals with total variation regularization, offering superlinear convergence for large-scale tomographic imaging problems.
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Proximal stochastic spectral preconditioning converges for nonconvex constrained objectives under heavy-tailed noise, with a variance-reduced version achieving faster rates and a refined analysis of Muon iterations.
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Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography
An efficient semismooth* Newton method is presented for minimizing Tikhonov functionals with total variation regularization, offering superlinear convergence for large-scale tomographic imaging problems.
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Constrained Stochastic Spectral Preconditioning Converges for Nonconvex Objectives
Proximal stochastic spectral preconditioning converges for nonconvex constrained objectives under heavy-tailed noise, with a variance-reduced version achieving faster rates and a refined analysis of Muon iterations.