Under Ahlfors regularity of exponent β, the minimal energy distance between a measure and its N-point empirical version decays exactly as N to the power -½(1 + q/β) for power kernels with exponent q in (0,2).
Oxford Mathematical Monographs
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A semismooth* Newton method is developed for efficient TV-regularized solution of large-scale linear inverse problems, with locally superlinear convergence and demonstrated use in tomography.
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Sharp Rates of MMD Empirical Estimation with Power Kernels
Under Ahlfors regularity of exponent β, the minimal energy distance between a measure and its N-point empirical version decays exactly as N to the power -½(1 + q/β) for power kernels with exponent q in (0,2).
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Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography
A semismooth* Newton method is developed for efficient TV-regularized solution of large-scale linear inverse problems, with locally superlinear convergence and demonstrated use in tomography.