R help - Jul 2004 - optimisation procedure with flat log-likelihood

Dear R-friends,
I use

optim(par=c(mystartingpoints), fn=myloglikelihoodfunction, gr=NULL,
                    method=c("L-BFGS-B"),  ## I would like to do not
use any
bounds
                    control=list(trace=6, ## just to see what it's going on
                                 maxit=c(20000)), ## to be sure the it
doesn't stop reaching the max iterations
                    data=mydataset)

to optimize some demographic model. I assume that the log-likelihood is
relatively flat because the estimated results are very similar to my
starting values. In addition, I know the "real" parameters as I have
used
simulated data (which have been also found by using GAUSS and replicated by
it).
I already tried various methods and also various starting values but it did
not help. Can maybe anyone give me some suggestion what I could do?

Thanks,
Carlo Giovanni Camarda



+++++
This mail has been sent through the MPI for Demographic Rese...{{dropped}}

Two things to try:

1. Try transforming the parameters.  It may be that one or more
parameters transformed by log or reciprocal, say, will improve the
objective function from the optimizer's viewpoint.

2. specify the gradient explicitly.  If its complicated but not
too complicated you might try a computer algebra package such as 
the free one, yacas, to get the derivative.

Date:   	Fri, 30 Jul 2004 15:41:27 +0200
From:   	Camarda, Carlo Giovanni <Camarda at demogr.mpg.de>
To:   	'r-help at stat.math.ethz.ch' <r-help at stat.math.ethz.ch>
Subject:   	[R] optimisation procedure with flat log-likelihood

Dear R-friends,
I use

optim(par=c(mystartingpoints), fn=myloglikelihoodfunction, gr=NULL,
method=c("L-BFGS-B"), ## I would like to do not use any
bounds
control=list(trace=6, ## just to see what it's going on
maxit=c(20000)), ## to be sure the it
doesn't stop reaching the max iterations
data=mydataset)

to optimize some demographic model. I assume that the log-likelihood is
relatively flat because the estimated results are very similar to my
starting values. In addition, I know the "real" parameters as I have
used
simulated data (which have been also found by using GAUSS and replicated by
it).
I already tried various methods and also various starting values but it did
not help. Can maybe anyone give me some suggestion what I could do?

Thanks,
Carlo Giovanni Camarda