R help - Apr 2010 - Restricting optimisation algorithm's parameter space

> I have a problem. I am  using the NLME library to fit a non-linear model.
There is a linear  component to the model that has a couple parameter values
that can only  be positive (the coefficients are embedded in a sqrt). When I try
and  fit the model to data the search algorithm tries to see if a negative 
value for one of these parameter values will produce an optimal fit. When  it
does so, it crashes because the equation can not have a negative  value because
its in a sqrt function.
> 
> QUESTION: How do I  restrict the optimisation algorithm's  parameter
space so it does not  search negative values when using GNLM? Are there other
Libraries that Fit Non-linear models and allow for one to control the parameter
space the search algorithm is restricted by?
There are two issues here:

1) Rendering optimization codes resistant to inadmissible parameter vectors
(sqrt of
negatives etc.)

2) Providing constraints, particularly parameter bounds, on parameter inputs to
optimization.

There is quite a lot of activity going on right now with optimization in R. With
Ravi
Varadhan, Kate Mullen and Paul Gilbert, I've been putting some codes on
R-forge in the
OptimizeR project. Doug Bates has done some important kibbitzing. Stefan Theussl
and
others have the R Optimization Infrastructure. There are some differences of
focus in
these projects.

For the present poster, I cannot unfortunately help much on GNLM details, but I
can say
that there are trial versions of methods that will handle bounds (packages
minqa, Rcgmin
and Rvmmin) that accept bounds, along with existing L-BFGS-B in optim(). Rcgmin
and Rvmmin
also accept "masks", that is fixed parameters. These routines are
about to go into CRAN.

It appears GNLM is not a CRAN package. Does it permit substitution of optimizer?
That is
something those of us working in optimization strongly recommend to package
writers i.e.,
make the optimization a black box so any of a number of tools can be slotted in
easily.
That way, advances or even minor fixups to routines can be included in tools
quickly.

However, the "bad parameters" issue, such as trying to take square
root of negatives,
requires the user or the writer of a package that the user calls to code the
objective
function to return a flag -- we are looking at an Inf or an NA, we are not yet
fully
decided on the rules -- AND the optimization package writer to then make it act 
appropriately. Mary Walker-Smith and I had this in BASIC codes in the 1980s, but
we were
maybe a bit ahead of folk and it didn't catch on generally.

If R users have some nice examples of things crashing because of such issues, I
would
really like to hear of them off-list. For us, "nice" means pretty
small and easy to build
into tests. Without such tests -- and users always provide better ones than we
can think
of -- the code is never as robust.

JN