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We investigate the convergence theory of several known as well as new heuristic parameter choice rules for convex Tikhonov regularization. The success of such methods is dependent on whether certain restrictions on the noise are satisfied. In the linear theory, such conditions are well understood and hold for typically irregular noise. In this paper, we extend the convergence analysis of heuristic rules using noise restrictions to the convex setting and prove convergence of the aforementioned methods therewith. The convergence theory is exemplified for the case of an ill-posed problem with a diagonal forward operator in 'q spaces. Numerical examples also provide further insight. [ABSTRACT FROM AUTHOR]

This paper proves convergence of variations of the unsymmetric kernel-based collocation method introduced by Kansa in 1986. Since then, this method has been very successfully used in many applications, though it may theoretically fail in special situations, and though it had no error bound or convergence proof up to now. Thus it is necessary to add assumptions or to make modifications. Our modifications prevent numerical failure by dropping strict collocation and allow a rigorous mathematical analysis proving error bounds and convergence rates. These rates improve with the smoothness of the solution, the domain, and the kernel providing the trial spaces, but they are currently not yet optimal and deserve refinement. They are based on rates of approximation to the residuals by nonstationary meshless kernel-based trial spaces, and they are independent of the type of differential operator. The results are applicable to large classes of linear problems in strong form, provided that there is a smooth solution and the test and trial discretizations are chosen with some care. Our analysis does not require assumptions like ellipticity, and it can be extended to ill-posed problems. [ABSTRACT FROM AUTHOR]

We introduce a class of stabilizing Newton-Kaczmarz methods for nonlinear ill-posed problems and analyze their convergence and regularization behavior. As usual for iterative methods for solving nonlinear ill-posed problems, conditions on the nonlinearity (or the derivatives) have to be imposed in order to obtain convergence. As we shall discuss in general and in some specific examples, the nonlinearity conditions obtained for the Newton-Kaczmarz methods are less restrictive than those for previously existing iteration methods and can be verified for several practical applications. We also discuss the discretization and efficient numerical solution of the linear problems arising in each step of a Newton-Kaczmarz method, and we carry out numerical experiments for two model problems. [ABSTRACT FROM AUTHOR]

We consider a regularization concept for the solution of ill-posed operator equations, where the operator is composed of a continuous and a discontinuous operator. A particular application is level set regularization, where we develop a novel concept of minimizers. The proposed level set regularization is capable of handling changing topologies. A functional analytic framework explaining the splitting of topologies is given. The asymptotic limit of the level set regularization method is an evolution process, which is implemented numerically, and the quality of the proposed algorithm is demonstrated by solving an inverse source problem. [ABSTRACT FROM AUTHOR]

We study the efficiency of the approximate solution of ill-posed problems, based on discretized noisy observations, which we assume to be given beforehand. A basic purpose of the paper is the consideration of stochastic noise, but deterministic noise is also briefly discussed. We restrict ourselves to problems which can be formulated in Hilbert scales. Within this framework we shall quantify the degree of ill-posedness, provide general conditions on projection schemes to achieve the best possible order of accuracy. We pay particular attention on the problem of self-regularization vs. Tikhonov regularization. Moreover, we study the information complexity. Asymptotically, any method which achieves the best possible order of accuracy must use at least such amount of noisy observations. [ABSTRACT FROM AUTHOR]