Finding continuous distributions ( ) s  , or more precisely quasi-continuous renderings, that fit multicomponent relaxation data is an ill-posed problem-there is no unique solution that is stable to the noise. Regularization, which constrains the fitted distributions in ways intended to suppress unphysical features in the displayed results, must be imposed. Regularization protocols have long been essential to widely used quasi-continuous analysis protocols . Many protocols are available, including imposition of non-negativity and the use of Tikhonov regularization. Developing and applying more advanced regularization methods that apply in specific situations, for example when the underlying distributions have both narrow and broad components ) , remains an active aspect of research in multi-component exponential relaxation data analysis.