An efficient semismooth* Newton method is presented for minimizing Tikhonov functionals with total variation regularization, offering superlinear convergence for large-scale tomographic imaging problems.
Convergence rates in constrained Tikhonov regulariza- tion: equivalence of projected source conditions and variational inequalities.Inverse Problems, 27(8):085001, 11
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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.