On Regularization of a Source Identification Problem in a Parabolic PDE and its Finite Dimensional Analysis

We consider the inverse problem of identifying a general source term, which is a function of both time variable and the spatial variable, in a parabolic PDE from the knowledge of boundary measurements of the solution on some portion of the lateral boundary. We transform this inverse problem into a problem of solving a compact linear operator equation. For the regularization of the operator equation with noisy data, we employ the standard Tikhonov regularization, and its finite dimensional realization is done using a discretization procedure involving the space $L^2(0,\tau;L^2(Ω))$. For illustrating the specification of an a priori source condition, we have explicitly obtained the range space of the adjoint of the operator involved in the operator equation.     

Regularized collocation method for Fredholm integral equations of the first kind

In this paper we consider a collocation method for solving Fredholm integral equations of the first kind, which is known to be an ill-posed problem. An “unregularized” use of this method can give reliable results in the case when the rate at which smallest singular values of the collocation matrices decrease is known a priori. In this case the number of collocation points plays the role of a regularization parameter. If the a priori information mentioned above is not available, then a combination of collocation with Tikhonov regularization can be the method of choice. We analyze such regularized collocation in a rather general setting, when a solution smoothness is given as a source condition with an operator monotone index function. This setting covers all types of smoothness studied so far in the theory of Tikhonov regularization. One more issue discussed in this paper is an a posteriori choice of the regularization parameter, which allows us to reach an optimal order of accuracy for deterministic noise model without any knowledge of solution smoothness.     

Quadrature based collocation methods for integral equations of the first kind

Problem of solving integral equations of the first kind, ò_a^b k(s, t) x(t) dt = y(s), s ? [a, b]\int_a^b k(s, t) x(t)\, dt = y(s),\, s\in [a, b] arises in many of the inverse problems that appears in applications. The above problem is a prototype of an ill-posed problem. Thus, for obtaining stable approximate solutions based on noisy data, one has to rely on regularization methods. In practice, the noisy data may be based on a finite number of observations of y, say y(τ ₁), y(τ ₂), ..., y(τ _n ) for some τ ₁, ..., τ _n in [a, b]. In this paper, we consider approximations based on a collocation method when the nodes τ ₁, ..., τ _n are associated with a convergent quadrature rule. We shall also consider further regularization of the procedure and show that the derived error estimates are of the same order as in the case of Tikhonov regularization when there is no approximation of the integral operator is involved.     

A modified Newton–Lavrentiev regularization for nonlinear ill-posed Hammerstein-type operator equations

Recently, a new iterative method, called Newton–Lavrentiev regularization (NLR) method, was considered by George (2006) for regularizing a nonlinear ill-posed Hammerstein-type operator equation in Hilbert spaces. In this paper we introduce a modified form of the NLR method and derive order optimal error bounds by choosing the regularization parameter according to the adaptive scheme considered by Pereverzev and Schock (2005).     

A generalization of Arcangeli's method for ill-posed problems leading to optimal rates

Schock (1984) considered a general a posteriori parameter choice strategy for the regularization of ill-posed problems which provide nearly the optimal rate of convergence. We improve the result of Schock and give a class of parameter choice strategies leading to optimal rates As a particular case we prove that the Arcangeli's method do give optimal rate of convergence.     

An a posteriori parameter choice for simplified regularization of ill-posed problems

An a posteriori parameter choice strategy is proposed for the simplified regularization of ill-posed problems where no information about the smoothness of the unknown solution is required. If the smoothness of the solution is known then, as a particular case, the optimal rate is achieved. Our result also includes a recent result of Guacanme (1990).     

The trade-off between regularity and stability in Tikhonov regularization

When deriving rates of convergence for the approximations generated by the application of Tikhonov regularization to ill-posed operator equations, assumptions must be made about the nature of the stabilization (i.e., the choice of the seminorm in the Tikhonov regularization) and the regularity of the least squares solutions which one looks for. In fact, it is clear from works of Hegland, Engl and Neubauer and Natterer that, in terms of the rate of convergence, there is a trade-off between stabilization and regularity. It is this matter which is examined in this paper by means of the best-possible worst-error estimates. The results of this paper provide better estimates than those of Engl and Neubauer, and also include and extend the best possible rate derived by Natterer. The paper concludes with an application of these results to first-kind integral equations with smooth kernels.

     

On uniform convergence of approximation methods for operator equations of the second kind

Schock (1985) has considered the convergence properties of various Galerkin-like methods for the approximate solution of the operator equation of the second kind x - Tx = y, where T is a bounded linear operator on a Banach space X, and x and y belong to X, and proved that the classical Galerkin method and in certain cases, the iterated Galerkin method are arbitrarily slowly convergent whereas the Kantororich method studied by him is uniformly convergent. It is the purpose of this paper to introduce a general class of approximations methods for x - Tx = y which includes the well-known methods of projection and the quadrature methods, and to characterize its uniform convergence, so that an arbitrarily slowly convergent method can be modified to obtain a uniformly convergent method.     

On iterative refinements for spectral sets and spectral subspaces

Stewart (1971) and Demmel (1987) have proposed iterative procedures for refining invariant subspaces of Hilbert space operators and matrices respectively. In this paper, modifications are proposed for these procedures which facilitates their application to bounded Banach space operators. Under regularity conditions (which could include densely defined closed operators) it is shown that the modifications perform as well as or better than the procedures of Stewart and Demmel.     

Optimal order results for a class of regularization methods using unbounded operators

A class of regularization methods using unbounded regularizing operators is considered for obtaining stable approximate solutions for ill-posed operator equations. With an a posteriori as well as an a priori parameter choice strategy, it is shown that the method yields the optimal order. Error estimates have also been obtained under stronger assumptions on the generalized solution. The results of the paper unify and simplify many of the results available in the literature. For example, the optimal results of the paper include, as particular cases for Tikhonov regularization, the main result of Mair (1994) with an a priori parameter choice, and a result of Nair (1999) with an a posteriori parameter choice. Thus the observations of Mair (1994) on Tikhonov regularization of ill-posed problems involving finitely and infinitely smoothing operators is applicable to various other regularization procedures as well. Subsequent results on error estimates include, as special cases, an optimal result of Vainikko (1987) and also some recent results of Tautenhahn (1996) in the setting of Hilbert scales.