first of all, I love the new release - thank you so much!
However, when playing with the mxConstraint function, I encountered the following peculiar problem. When imposing the constraint
mxConstraint(varF1 == varF/(1-beta2))
I get the error message:
In model 'FAwithin' NPSOL returned a non-zero status code 6. The model does not satisfy the first-order optimality conditions to the required accuracy, and no improved point for the merit function could be found during the final linesearch (Mx status RED).
However, when turning the equation around and imposing the exact same constraint that way:
mxConstraint(varF1-(beta2)*(varF1)-varF == 0)
the model runs smoothly, the constraint works and I do not get any error message (but the SE seem to be incorrect). Are there some restrictions on how I have to impose a constraint?
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