Numerical issues related to the modelling of electrochemical impedance data by non-linear least-squares

Non-linear least-squares (NLS) fitting is the typical approach to the modelling of electrochemical impedance spectroscopy (EIS) data. In general the application of NLS to EIS models can give rise to ill-posed problems. On the one side, with ill-posed problems it is not possible to prove a priori that a unique solution exists. On the other side, the relevant numerical approximations cannot ensure that a unique solution exists even a posteriori. It is therefore basically pointless to endeavour to achieve one absolute minimum of any objective function for an EIS model in an NLS problem. A lack of awareness of the above-mentioned factors might render numerical approaches tending to locate the absolute minimum questionable.