[R] box-constrained

[Ravi Varadhan]

Hi,

Let me make the following points in response to your questions:

1.  Your call to optim() with "L-BFGS-B" as the method is correct.  Just
make sure that your function "f" is defined as negative log-likelihood,
since optim is by default a minimizer.  The other option is to define
log-likelihood as usual, but set control=list(fnscale=-1).

2.  You can add derivative (or gradient to be more precise) by defining that
function and then using the "gr" argument in optim.  Specifying exact
gradient almost always improves the convergence of the iterative schemes,
especially for ill-conditioned problems (flat region around the local
minima). So, if it is not too much trouble, and you are confident of your
differentiation skills, you should do that.  However, in most cases the
approximate finite-difference gradient used by optim() should be good
enough.

3.  Regardless of whether it is easy to compute the exact gradient or not,
it is generally a bad idea to maximize the likelihood that involves the
product of a large number of very small numbers.  It is almost always better
to maximize the log-likelihood.  Since the objective function is additive
rather than multiplicative, it has better numerical conditioning.

Ravi.


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Ravi Varadhan, Ph.D.

Assistant Professor, The Center on Aging and Health

Division of Geriatric Medicine and Gerontology 

Johns Hopkins University

Ph: (410) 502-2619

Fax: (410) 614-9625

Email: rvaradhan at jhmi.edu

Webpage:  http://www.jhsph.edu/agingandhealth/People/Faculty/Varadhan.html

 

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-----Original Message-----
From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On
Behalf Of Gustave Lefou
Sent: Wednesday, March 05, 2008 1:34 PM
To: r-help at r-project.org
Subject: [R] box-constrained

Hello everybody,

I have a question about box-constrained optimization. I've done some
research and I found that optim could do that. Are there other ways in R ?

Is the following correct if I have a function f of two parameters belonging
for example to [0,1] and [0,Infinity] ?
optim(par=param, fn=f, method="L-BFGS-B", lower=c(0,0), upper=c(1,Inf))

My other question is whether it is possible to add the derivatives of my
function (like in nlm) and whether it is better to add them ?

If there is no need to add the derivatives, then I guess I could wish to
optimize the likelihood directly rather than to optimize the
log-likelihood... Indeed one aspect of the log-likelihood is to make the
derivatives tractable (in iid cases). Do you agree ?

Thank you !
Gustave

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