In this paper, several boundary element regularization methods, such as iterative, conjugate gradient, Tikhonov regularization and singular value decomposition methods, for solving the Cauchy problem associated to the Helmholtz equation are developed and compared. Regularizing stopping criteria are developed and the convergence, as well as the stability, of the numerical methods proposed are an...

This report develops and studies a new family of NSE-regularizations, Tikhonov Leray Regularization with Time Relaxation Models. This new family of turbulence models is based on a modification (consistent with the large scales) of Tikhonov-Lavrentiev regularization. With this approach, we obtain an approximation of the unfiltered solution by one filtering step. We introduce the modified Tikhono...

| The reconstruction of impedance distribution in electrical impedance tomography (EIT) is a nonlinear ill-posed inverse problem. In order to obtain stable solution the problem has to be regularized. One of the most common methods for this is the generalized Tikhonov reg-ularization. The regularization matrices that are usually used with the Tikhonov method are more or less ad hoc and the assoc...

The estimation of the covariance matrix is a pivotal step in several statistical tasks. In particular, the estimation becomes challenging for high dimensional representations of data when few samples are available. Using the standard Maximum Likelihood estimation (MLE) when the number of samples are lower than the dimension of the data can lead to incorrect estimation e.g. of the covariance mat...

In this paper we present a quasi-Newton projection method for image deblurring. The mathematical problem is a constrained minimization problem, where the objective function is a regularization function and the constraint is the positivity of the solution. The regularization function is a sum of the Kullback-Leibler divergence, used to minimize the error in the presence of Poisson noise, and of ...

Regularization is a general method for solving ill-posed and ill-conditioned problems. Traditionally, ill-conditioning in system identiication problems is usually approached using regularization methods such as ridge regression and principal component regression. In this work it is argued that the Tikhonov regularization method is a powerful alternative for regulariza-tion of non-linear system ...

We study multi-parameter Tikhonov regularization, i.e., with multiple penalties. Such models are useful when the sought-for solution exhibits several distinct features simultaneously. Two choice rules, i.e., discrepancy principle and balancing principle, are studied for choosing an appropriate (vector-valued) regularization parameter, and some theoretical results are presented. In particular, t...

A convergence rate is established for nonstationary iterated Tik-honov regularization, applied to ill-posed problems involving closed, densely deened linear operators, under general conditions on the iteration parameters. It is also shown that an order-optimal accuracy is attained when a certain a posteriori stopping rule is used to determine the iteration number.

It is well known that Tikhonov regularization in standard form may determine approximate solutions that are too smooth, i.e., the approximate solution may lack many details that the desired exact solution might possess. Two different approaches, both referred to as fractional Tikhonov methods have been introduced to remedy this shortcoming. This paper investigates the convergence properties of ...