In this paper we deal with the degree of ill-posedness of linear operator equations
Ax = y, x ∈ X, y ∈ Y, in the Hilbert space X = Y = L2(0, 1), where A = M о J is a compact operator that may be decomposed into the simple integration operator J with a well-known decay rate of singular values and a multiplication operator M determined by the multiplier function m. This case occurs for example for nonlinear operator equations F(x) = y with a forward operator F = N о J where N is a Nemytskii operator. Then the local degree of ill-posedness of the nonlinear equation at a point
x0 of the domain of F is investigated via the Fréchet derivative of F which has the form
F' (x0) = M о J. We show the restricted influence of such multiplication operators M mapping in L2(0,1).
If the multiplier function m has got zeros, the determination of the degree of ill-posedness is not trivial. We are going to investigate this situation and provide analytical tools as well as their limitations. For power and exponential type multiplier functions with essential zeros we will show by using several numerical approaches that the unbounded inverse of the injective multiplication operator does not influence the local degree of ill-posedness. We provide a conjecture, verified by several numerical studies, how these multiplication operators influence the singular values of
A = M о J.
Finally we analyze the influence of those multiplication operators M on the possibilities of Tikhonov regularization and corresponding convergence rates. We investigate the role of approximate source conditions in the method of Tikhonov regularization for linear and nonlinear ill-posed operator equations. Based on the studies on approximate source conditions we indicate that only integrals of m and not the decay of multiplier functions near zero determines the convergence behavior of the regularized solution.
We investigate the applicability of the method of maximum entropy regularization (MER) to a specific nonlinear ill-posed inverse problem (SIP) in a purely time-dependent model of option pricing, introduced and analyzed for an L2 -setting in . In order to include the identification of volatility functions with a weak pole, we extend the results of [12, 13], concerning convergence and convergence rates of regularized solutions in L1 , in some details. Numerical case studies illustrate the chances and limitations of (MER) versus Tikhonov regularization (TR) for smooth solutions and solutions with a sharp peak. A particular paragraph is devoted to the singular case of at-the-money options, where derivatives of the forward operator degenerate.