Global ETD Search

31	Regularizability of ill-posed problems and the modulus of continuity Bot, Radu Ioan, Hofmann, Bernd, Mathe, Peter 17 October 2011 (has links) (PDF) The regularization of linear ill-posed problems is based on their conditional well-posedness when restricting the problem to certain classes of solutions. Given such class one may consider several related real-valued functions, which measure the wellposedness of the problem on such class. Among those functions the modulus of continuity is best studied. For solution classes which enjoy the additional feature of being star-shaped at zero, the authors develop a series of results with focus on continuity properties of the modulus of continuity. In particular it is highlighted that the problem is conditionally well-posed if and only if the modulus of continuity is right-continuous at zero. Those results are then applied to smoothness classes in Hilbert space. This study concludes with a new perspective on a concavity problem for the modulus of continuity, recently addressed by two of the authors in "Some note on the modulus of continuity for ill-posed problems in Hilbert space", 2011. inkorrekt gestellte Probleme Regularisierbarkeit ill-posed problems regularizability modulus of continuity ddc:515 Regularisierung Stetigkeitsmodul
32	On variational methods and gradient flows in image processing Droske, Marc. Unknown Date (has links) (PDF) Essen, University, Diss., 2005--Duisburg.
33	The impact of a curious type of smoothness conditions on convergence rates in l1-regularization Bot, Radu Ioan, Hofmann, Bernd 31 January 2013 (has links) (PDF) Tikhonov-type regularization of linear and nonlinear ill-posed problems in abstract spaces under sparsity constraints gained relevant attention in the past years. Since under some weak assumptions all regularized solutions are sparse if the l1-norm is used as penalty term, the l1-regularization was studied by numerous authors although the non-reflexivity of the Banach space l1 and the fact that such penalty functional is not strictly convex lead to serious difficulties. We consider the case that the sparsity assumption is narrowly missed. This means that the solutions may have an infinite number of nonzero but fast decaying components. For that case we formulate and prove convergence rates results for the l1-regularization of nonlinear operator equations. In this context, we outline the situations of Hölder rates and of an exponential decay of the solution components. l1-Regularisierung Konvergenzraten Quellbedingungen nonlinear ill-posed problems Tikhonov-type regularization l1-regularization sparsity constraints convergence rates solution decay variational inequalities source conditions discrepancy principle ddc:510 ddc:519 Regularisierung Konvergenz
34	About a deficit in low order convergence rates on the example of autoconvolution Bürger, Steven, Hofmann, Bernd 18 December 2013 (has links) (PDF) We revisit in L2-spaces the autoconvolution equation x ∗ x = y with solutions which are real-valued or complex-valued functions x(t) defined on a finite real interval, say t ∈ [0,1]. Such operator equations of quadratic type occur in physics of spectra, in optics and in stochastics, often as part of a more complex task. Because of their weak nonlinearity deautoconvolution problems are not seen as difficult and hence little attention is paid to them wrongly. In this paper, we will indicate on the example of autoconvolution a deficit in low order convergence rates for regularized solutions of nonlinear ill-posed operator equations F(x)=y with solutions x† in a Hilbert space setting. So for the real-valued version of the deautoconvolution problem, which is locally ill-posed everywhere, the classical convergence rate theory developed for the Tikhonov regularization of nonlinear ill-posed problems reaches its limits if standard source conditions using the range of F (x† )∗ fail. On the other hand, convergence rate results based on Hölder source conditions with small Hölder exponent and logarithmic source conditions or on the method of approximate source conditions are not applicable since qualified nonlinearity conditions are required which cannot be shown for the autoconvolution case according to current knowledge. We also discuss the complex-valued version of autoconvolution with full data on [0,2] and see that ill-posedness must be expected if unbounded amplitude functions are admissible. As a new detail, we present situations of local well-posedness if the domain of the autoconvolution operator is restricted to complex L2-functions with a fixed and uniformly bounded modulus function. Selbstfaltung inverse Probleme lokale Korrektheit und Inkorrektheit Tikhonov-Regularisierung Quellbedingungen Konvergenzraten Autoconvolution equation inverse problems local well-posedness and ill-posedness Tikhonov regularization source conditions convergence rates ddc:510 Regularisierung
35	About a deficit in low order convergence rates on the example of autoconvolution Bürger, Steven, Hofmann, Bernd January 2013 (has links) We revisit in L2-spaces the autoconvolution equation x ∗ x = y with solutions which are real-valued or complex-valued functions x(t) defined on a finite real interval, say t ∈ [0,1]. Such operator equations of quadratic type occur in physics of spectra, in optics and in stochastics, often as part of a more complex task. Because of their weak nonlinearity deautoconvolution problems are not seen as difficult and hence little attention is paid to them wrongly. In this paper, we will indicate on the example of autoconvolution a deficit in low order convergence rates for regularized solutions of nonlinear ill-posed operator equations F(x)=y with solutions x† in a Hilbert space setting. So for the real-valued version of the deautoconvolution problem, which is locally ill-posed everywhere, the classical convergence rate theory developed for the Tikhonov regularization of nonlinear ill-posed problems reaches its limits if standard source conditions using the range of F (x† )∗ fail. On the other hand, convergence rate results based on Hölder source conditions with small Hölder exponent and logarithmic source conditions or on the method of approximate source conditions are not applicable since qualified nonlinearity conditions are required which cannot be shown for the autoconvolution case according to current knowledge. We also discuss the complex-valued version of autoconvolution with full data on [0,2] and see that ill-posedness must be expected if unbounded amplitude functions are admissible. As a new detail, we present situations of local well-posedness if the domain of the autoconvolution operator is restricted to complex L2-functions with a fixed and uniformly bounded modulus function. info:eu-repo/classification/ddc/510 ddc:510 Regularisierung
36	Regularization properties of the discrepancy principle for Tikhonov regularization in Banach spaces Anzengruber, Stephan W., Hofmann, Bernd, Mathé, Peter 11 December 2012 (has links) (PDF) The stable solution of ill-posed non-linear operator equations in Banach space requires regularization. One important approach is based on Tikhonov regularization, in which case a one-parameter family of regularized solutions is obtained. It is crucial to choose the parameter appropriately. Here, a variant of the discrepancy principle is analyzed. In many cases such parameter choice exhibits the feature, called regularization property below, that the chosen parameter tends to zero as the noise tends to zero, but slower than the noise level. Here we shall show such regularization property under two natural assumptions. First, exact penalization must be excluded, and secondly, the discrepancy principle must stop after a finite number of iterations. We conclude this study with a discussion of some consequences for convergence rates obtained by the discrepancy principle under the validity of some kind of variational inequality, a recent tool for the analysis of inverse problems. inverse Probleme Tikhonov-Regularisierung discrepancy principle parameter choice properties Inverse problems Tikhonov-type regularization ddc:510 inkorrekt gestelltes Problem
37	Parameter estimation in a generalized bivariate Ornstein-Uhlenbeck model Krämer, Romy, Richter, Matthias, Hofmann, Bernd 07 October 2005 (has links) In this paper, we consider the inverse problem of calibrating a generalization of the bivariate Ornstein-Uhlenbeck model introduced by Lo and Wang. Even though the generalized Black-Scholes option pricing formula still holds, option prices change in comparison to the classical Black-Scholes model. The time-dependent volatility function and the other (real-valued) parameters in the model are calibrated simultaneously from option price data and from some empirical moments of the logarithmic returns. This gives an ill-posed inverse problem, which requires a regularization approach. Applying the theory of Engl, Hanke and Neubauer concerning Tikhonov regularization we show convergence of the regularized solution to the true data and study the form of source conditions which ensure convergence rates. info:eu-repo/classification/ddc/510 ddc:510 Inverses Problem Parameterschätzung Regularisierung financial analysis inverse problem parameter estimation regularization volatility calibration
38	On solving implicitly defined inverse problems by SQP-approaches Hein, Torsten 18 December 2007 (has links) In this paper two basic SQP-approaches for solving implicitly defined inverse problems are presented. Such problems often arises in parameter identification for differential equations. We also include regularization strategies which differ from similar problems in Optimal control. The main focus is on formulating saddle point problems for calculating the next iterate. Conditions for the unique and stable solvability of these problems are presented. The analytical considerations are illustrated by two examples including their discretizations and a numerical case study. info:eu-repo/classification/ddc/510 ddc:510 Elliptische Differentialgleichung Finite-Elemente-Methode Inverses Problem Parameteridentifikation Regularisierung SQP-Methode
39	Regularizability of ill-posed problems and the modulus of continuity Bot, Radu Ioan, Hofmann, Bernd, Mathe, Peter January 2011 (has links) The regularization of linear ill-posed problems is based on their conditional well-posedness when restricting the problem to certain classes of solutions. Given such class one may consider several related real-valued functions, which measure the wellposedness of the problem on such class. Among those functions the modulus of continuity is best studied. For solution classes which enjoy the additional feature of being star-shaped at zero, the authors develop a series of results with focus on continuity properties of the modulus of continuity. In particular it is highlighted that the problem is conditionally well-posed if and only if the modulus of continuity is right-continuous at zero. Those results are then applied to smoothness classes in Hilbert space. This study concludes with a new perspective on a concavity problem for the modulus of continuity, recently addressed by two of the authors in "Some note on the modulus of continuity for ill-posed problems in Hilbert space", 2011. info:eu-repo/classification/ddc/515 ddc:515 Regularisierung Stetigkeitsmodul
40	Beiträge zur Regularisierung inverser Probleme und zur bedingten Stabilität bei partiellen Differentialgleichungen Shao, Yuanyuan 17 January 2013 (has links) (PDF) Wir betrachten die lineare inverse Probleme mit gestörter rechter Seite und gestörtem Operator in Hilberträumen, die inkorrekt sind. Um die Auswirkung der Inkorrektheit zu verringen, müssen spezielle Lösungsmethode angewendet werden, hier nutzen wir die sogenannte Tikhonov Regularisierungsmethode. Die Regularisierungsparameter wählen wir aus das verallgemeinerte Defektprinzip. Eine typische numerische Methode zur Lösen der nichtlinearen äquivalenten Defektgleichung ist Newtonverfahren. Wir schreiben einen Algorithmus, die global und monoton konvergent für beliebige Startwerte garantiert. Um die Stabilität zu garantieren, benutzen wir die Glattheit der Lösung, dann erhalten wir eine sogenannte bedingte Stabilität. Wir demonstrieren die sogenannte Interpolationsmethode zur Herleitung von bedingten Stabilitätsabschätzungen bei inversen Problemen für partielle Differentialgleichungen. Lineare Inverse Probleme Hilberträume gestörte rechte Seite gestörter Operator Tikhonov Regularisierung das verallgemeinerte Defektprinzip Newtonverfahren globale und monotone Konvergenz Mutiparameter-Regularisierung bedingte Stabilitätsabschätzung Interpolationsmethode Ill-posed problems inverse problems noisy hand side noisy operator Tichonov regularization Hilbert scales generalized discrepancy principle Newton's method global convergence monotone convergence multi-parameter regularization conditional stability estimates interpolation source problems ddc:515 Iteration Tichonov-Regularisierung Interpolation Partielle Differentialgleichung

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