Proves sharp rates E_q(μ_N, ω) ≍ N^{-(1/2)(1 + q/β)} for empirical energy distance approximation under Ahlfors regularity of exponent β.
Oxford Mathematical Monographs
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
An efficient semismooth* Newton method is presented for minimizing Tikhonov functionals with total variation regularization, offering superlinear convergence for large-scale tomographic imaging problems.
citing papers explorer
-
Sharp Rates of MMD Empirical Estimation with Power Kernels
Proves sharp rates E_q(μ_N, ω) ≍ N^{-(1/2)(1 + q/β)} for empirical energy distance approximation under Ahlfors regularity of exponent β.
-
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.