I won't add to the quite long discussion about the vagaries of nlminb, but will note that
over a long period of software work in this optimization area I've found a number of
programs and packages that do strange things when the objective is a function of a single
parameter. Some methods quite explicitly throw an error when n<2. It seems nlminb does
not, but that does not mean that the authors ever thought anyone would use their large
package to solve a small 1D problem, or if they did, whether they seriously tested for the
awkward things that happen when one only has one parameter. I've seen similar things go
wrong with matrix packages e.g., eigensolutions of 1 by 1 matrices.
So this msg is to watch carefully when problems are n=1 and put in some checks that
answers make sense.
JN
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