Yes clearly a bug... there are numerous variations ... problem seems to be for a linear function whenever the first function valuation is 1.
e.g. two more examples: nlminb( obj = function(x) x+1, start=0, lower=-Inf, upper=Inf ) nlminb( obj = function(x) x+2, start=-1, lower=-Inf, upper=Inf ) (I wasn't sure where best to report a bug, so emailed the help list) On 9 July 2010 22:10, Peter Ehlers <ehl...@ucalgary.ca> wrote: > Actually, it looks like any value other than 1.0 > (and in (lower, upper)) for start will work. > > -Peter Ehlers > > > On 2010-07-09 14:45, Ravi Varadhan wrote: > >> Setting abs.tol = 0 works! This turns-off the absolute function >> convergence >> criterion. >> >> >> nlminb( objective=function(x) x, start=1, lower=-2, upper=2, >>> >> control=list(abs.tol=0)) >> $par >> [1] -2 >> >> $objective >> [1] -2 >> >> $convergence >> [1] 0 >> >> $message >> [1] "both X-convergence and relative convergence (5)" >> >> $iterations >> [1] 3 >> >> $evaluations >> function gradient >> 3 3 >> >> >> This is clearly a bug. >> >> >> Ravi. >> >> -----Original Message----- >> From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] >> On >> Behalf Of Ravi Varadhan >> Sent: Friday, July 09, 2010 4:42 PM >> To: 'Duncan Murdoch'; 'Matthew Killeya' >> Cc: r-help@r-project.org; ba...@stat.wisc.edu >> Subject: Re: [R] Not nice behaviour of nlminb (windows 32 bit, version >> 2.11.1) >> >> Duncan, `nlminb' is not intended for non-negative functions only. There >> is >> indeed something strange happening in the algorithm! >> >> start<- 1.0 # converges to wrong minimum >> >> startp<- 1.0 + .Machine$double.eps # correct >> >> startm<- 1.0 - .Machine$double.eps # correct >> >> nlminb( objective=obj, start=start, lower=-2, upper=2) >>> >> $par >> [1] 0 >> >> $objective >> [1] 0 >> >> $convergence >> [1] 0 >> >> $message >> [1] "absolute function convergence (6)" >> >> $iterations >> [1] 1 >> >> $evaluations >> function gradient >> 2 2 >> >> >>> nlminb( objective=obj, start=startp, lower=-2, upper=2) >>> >> $par >> [1] -2 >> >> $objective >> [1] -2 >> >> $convergence >> [1] 0 >> >> $message >> [1] "both X-convergence and relative convergence (5)" >> >> $iterations >> [1] 3 >> >> $evaluations >> function gradient >> 3 3 >> >> >>> nlminb( objective=obj, start=startm, lower=-2, upper=2) >>> >> $par >> [1] -2 >> >> $objective >> [1] -2 >> >> $convergence >> [1] 0 >> >> $message >> [1] "both X-convergence and relative convergence (5)" >> >> $iterations >> [1] 3 >> >> $evaluations >> function gradient >> 3 3 >> >> >> From the convergence message the `absolute function convergence' seems to >>> >> be >> the culprit, although I do not understand why that stopping criterion is >> becoming effective, when the algorithm is started at x=1, but not at any >> other values. The documentation in IPORT makes it clear that this >> criterion >> is effective only for functions where f(x*) = 0, where x* is a local >> minimum. In this example, x=0 is not a local minimum for f(x), so that >> criterion should not apply. >> >> >> Ravi. >> >> >> -----Original Message----- >> From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] >> On >> Behalf Of Duncan Murdoch >> Sent: Friday, July 09, 2010 3:45 PM >> To: Matthew Killeya >> Cc: r-help@r-project.org; ba...@stat.wisc.edu >> Subject: Re: [R] Not nice behaviour of nlminb (windows 32 bit, version >> 2.11.1) >> >> On 09/07/2010 10:37 AM, Matthew Killeya wrote: >> >>> nlminb( obj = function(x) x, start=1, lower=-Inf, upper=Inf ) >>> >>> >> If you read the PORT documentation carefully, you'll see that their >> convergence criteria are aimed at minimizing positive functions. (They >> never state this explicitly, as far as I can see.) So one stopping >> criterion is that |f(x)|< abs.tol, and that's what it found for you. I >> don't know if there's a way to turn this off. >> >> Doug or Deepayan, do you know if nlminb can be made to work on functions >> that go negative? >> >> Duncan Murdoch >> >> $par >>> [1] 0 >>> >>> $objective >>> [1] 0 >>> >>> $convergence >>> [1] 0 >>> >>> $message >>> [1] "absolute function convergence (6)" >>> >>> $iterations >>> [1] 1 >>> >>> $evaluations >>> function gradient >>> 2 2 >>> >>> [[alternative HTML version deleted]] >>> >>> [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list 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.
