> On 21 Oct 2016, at 06:00, Mike meyer <1101...@gmx.net> wrote: > > Let's take a different view of the problem. > Given f=(f_1,...,f_m):R^n -> R^m we want to minimize ||f(x)||. > > What distinguishes this from a general minimization problem is that you know > the structure of the > objective function F(x)=||f(x)||² and have the individual constituents f_j. > Make use of that information as appropriate. > > This is more general than trying to solve the system f(x)=0 or fitting a > model to data. > In this more general setting notions such as underdetermined/overdetermined > system do not apply. > > The restricted view of model fitting serves only to confuse the issue. > For that reason it is (in my view) a bad idea to force the user to set up his > problem in > R-model notation. >
I assume that you have been referring to the R package minpack.lm. I've had a look at the underlying Fortran code (from Minpack and developed by More et.al. made in a distant past) as used by the package. That underlying code returns an error when the condition: number of functions (m) >= the number of independent variables (n) is not satisfied i.e. when m < n. Making that more general would entail a lot of thinking and reworking of the code. As far as I can see it is not possible to just remove the condition m>=n from the underlying Fortran. More (possibly many) changes would be required. Blaming R and/or the package author/maintainer is unfair. If you require a more general version of the algorithm or if you want something else you will have to roll your own package/code. If you don't feel that minpack.lm is appropriate for your application and you want changes you'll have to discuss matters with Moré (http://www.mcs.anl.gov/~more/) if I got the correct link. Berend ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
