The apparently different approaches of least-squares parameter-matching Podé model reduction methods are shown to be related via a unifying theory. From the formulation it is possible to show several interesting features of the least-squares approach which lead to a fuller understanding of exactly how the reduced model approximates the system. An error index is derived for the general case and it is shown that a range of system parameter preservation options are available to the user. A numerical example illustrates the main points of the paper.
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