A mollification method for ill-posed problems

Abstract

A mollification method for a class of ill-posed problems is suggested. The idea of the method is very simple and natural: if the data are given inexactly then we try to find a sequence of ``mollification operators" which map the improper data into well-posedness classes of the problem (mollify the improper data). Within these mollified data our problem becomes well-posed. And when these facts are in hand we try to obtain error estimates and optimal or ``quasi-optimal" mollification parameters. The method is working not only for problems in Hilbert spaces, but also for problems in Banach spaces. Applications of the method to concrete problems, like numerical differentiation, parabolic equations backwards in time, the Cauchy problem for the Laplace equation, one- and multidimensional non-characteristic Cauchy problems for parabolic equations (in infinite or finite domains),... give us very sharp stability estimates of H\"older continuous type. In these cases the method is optimal in the sense that it gives the same order of H\"older continuous dependence on the data as for the regularized problems. Furthermore, the method may be implemented numerically using fast Fourier transforms. For the first time a uniform stability estimate of H\"older continuous type of the solution of the heat equation backwards in time in the space \(L_{p}({\Bbb R}),\) for all \(p~\in ~(1, \infty]\) could be established by our mollification method. A new simple sharp pointwise estimate of H\"older type for the weak solution of a non-characteristic Cauchy problem for parabolic equations in a finite domain is established.