We treat the estimation of a sparse set of sources emitting plane waves observed by a sensor array as a complex-valued LASSO (c–LASSO) problem where the usual l1-norm constraint is replaced by the l1-norm of a matrix D times the solution vector. When the sparsity order is given, algorithmically selecting a suitable value for the c–LASSO regularization parameter remains a challenging task. The c...

The L-curve is a log-log plot of the norm of a regularized solution versus the norm of the corresponding residual norm. It is a convenient graphical tool for displaying the trade-off between the size of a regularized solution and its fit to the given data, as the regularization parameter varies. The L-curve thus gives insight into the regularizing properties of the underlying regularization met...

Current methods for computing vortex sheet separation use a regularization parameter which is discontinuous from the body to the vortex sheet. We propose two methods for reducing the errors associated with the discontinuity and improving convergence with respect to the regularization parameter. The ‘‘velocity smoothing” method is the simpler of the two, and removes the discontinuity in regulari...

In the article, Tikhonov regularization of control-constrained optimal control problems is investigated. Typically the solutions of such problems exhibit a so-called bang-bang structure. We develop a parameter choice rule that adaptively selects the Tikhonov regularization parameter depending on a-posteriori computable quantities. We prove that this choice leads to optimal convergence rates wit...

In this paper a class of semilinear elliptic optimal control problem with pointwise state and control constraints is studied. We show that sufficient second order optimality conditions for regularized problems with small regularization parameter can be obtained from a second order sufficient condition assumed for the unregularized problem. Moreover, error estimates with respect to the regulariz...

A new parameter choice rule for inverse problems is introduced. This parameter choice rule was developed for total variation regularization in electron tomography and might in general be useful for L regularization of inverse problems with high levels of noise in the data.

Since the classical iterative methods for solving nonlinear ill-posed problems are locally convergent, this paper constructs a robust and widely convergent method for identifying parameter based on homotopy algorithm, and investigates this method’s convergence in the light of Lyapunov theory. Furthermore, we consider 1-D elliptic type equation to testify that the homotopy regularization can ide...

One of the two existing strategies of resolving singularities of multifold Mellin–Barnes integrals in the dimensional regularization parameter, or a parameter of the analytic regularization, is formulated in a modified form. The corresponding algorithm is implemented as a Mathematica code MBresolve.m E-mail: [email protected] E-mail: [email protected]

We discuss adaptive strategies for choosing regularization parameters in TikhonovPhillips regularization of discretized linear operator equations. Two rules turn out to be entirely based on the underlying regularization scheme. Among them only the discrepancy principle allows to search for the optimal regularization parameter from the easiest problem. This possible advantage cannot be used with...