[Octave-bug-tracker] [bug #38063] optim: expfit inaccurate (incorrect)

[Muhali]

Follow-up Comment #6, bug #38063 (project octave):

This leaves the most frequently encountered, hence most important case of a
simple exponential fit, with expfit utterly failing.

So if I were a natural or life scientist, reading in the expfit help that
'This method is known to be more stable than 'brute-force' non-linear least
squares fitting', I would probably be somewhat clueless about what octave is
offering.

To avoid this, what is wrong with renaming 'expfit' to 'prony' (plus
correcting the help text) and have 'expfit' reserved for the case above and
use a simple nonlinear regression, or just not have any 'expfit' at all?

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