103 research outputs found
Density of eigenvalues of random normal matrices
The relation between random normal matrices and conformal mappings discovered
by Wiegmann and Zabrodin is made rigorous by restricting normal matrices to
have spectrum in a bounded set. It is shown that for a suitable class of
potentials the asymptotic density of eigenvalues is uniform with support in the
interior domain of a simple smooth curve.Comment: 17 pages. Corrected versio
Optimal Convergence Rates Results for Linear Inverse Problems in Hilbert Spaces
In this paper, we prove optimal convergence rates results for regularisation
methods for solving linear ill-posed operator equations in Hilbert spaces. The
result generalises existing convergence rates results on optimality to general
source conditions, such as logarithmic source conditions. Moreover, we also
provide optimality results under variational source conditions and show the
connection to approximative source conditions
Density of Eigenvalues of Random Normal Matrices
The relation between random normal matrices and conformal mappings discovered by Wiegmann and Zabrodin is made rigorous by restricting normal matrices to have spectrum in a bounded set. It is shown that for a suitable class of potentials the asymptotic density of eigenvalues is uniform with support in the interior domain of a simple smooth curv
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