Applied Math Seminar Announcement

Regularized approximations to the solutions of ill-posed problems typically vary from over-smoothed, inaccurate reconstructions to under-smoothed and unstable solutions as the regularization parameter varies about its optimal value.

It thus makes sense to compare two (or more) regularized approximations, and seek the parameter values where the distance between the approximations is a minimum.

This work advances the theory and practice of this methodology. The method appears to work very well and be very stable, particularly in the presence of extreme error levels.