[Rd] bounds violations, infinite loops in optim/L-BFGS-B (PR#671)
ripley@stats.ox.ac.uk
ripley@stats.ox.ac.uk
Thu, 28 Sep 2000 19:12:44 +0200 (MET DST)
> From: bolker@zoo.ufl.edu
> Date: Tue, 26 Sep 2000 16:14:13 +0200 (MET DST)
>
> I'm having some trouble with optim(method="L-BFGS-B"),
> and I'm not sure I have the ability to track down and fix
> what seem to be bugs within optim().
> I'm bootstrapping an original data set and fitting a model
> to each bootstrapped data set. For some bootstrapped samples,
> optim() sets negative parameter values (despite the fact that
> I have explicitly set non-zero lower bounds on the parameters)
> and chokes. For other samples, it appears to get into an infinite
> loop (ignoring the finite value of maxit).
>
> Functions and samples that provoke the problem are below: I
> was able to reproduce the problems running this with --vanilla.
>
> Not exactly a bug but: there is no tracing information built
> into L-BFGS-B (setting trace=TRUE has no effect).
It's a feature, on the TODO list to be added one day.
> Also, a general question: are both nlm() and optim() going to
> be around indefinitely? Should I be using one or the other?
There are no plans to remove either. `indefinitely' is not part
of the non-warranty.
> For the moment I've managed to (sort of) get around the problem
> with try(), but the infinite loops are a real nuisance ...
On Solaris, I get neither negative values nor an infinite loop
on your examples.
On Linux RH6.2/gcc 2.95.2 I get
NA 1 NA 1 1 1 1 0.6831783 1 0.6831783 1 NA
NA -Inf NA -Inf -Inf -Inf -Inf -160.2173 -Inf -107.0805 -Inf NA
Error in optim(c(min(boot.total) - 1, 100, 1), nllfun2g.boot, lower = rep(fuzz,
:
L-BFGS-B needs finite values of fn
for the first, and probably a loop for the second. I think this is
the usual problem with inconsistent internal precision on Linux
compilers, so try compiling optim.c with -ffloat-store to make gcc
IEEE-compliant. (At least, that's what the Linux gcc man page says.)
Can you provide some evidence for negative parameter values? That's
not supposed to happen, but as I rarely fail to supply derivatives,
I have not tested it much.
Finally, I think you have omitted to supply scaling consts for your problem,
and optim will work a lot better if you do, as it will also do if
you can supply analytical derivatives.
--
Brian D. Ripley, ripley@stats.ox.ac.uk
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
1 South Parks Road, +44 1865 272860 (secr)
Oxford OX1 3TG, UK Fax: +44 1865 272595
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