[ADMB Users] matrix inverse

[Mollie Brooks]

Thanks Dave.
The original question was about multivariate normally distributed  
data. Most entries of my variance-covariance matrix are 0. Will your  
code for finding the inverse of a sparse Hessian work in this case?

my original question:
"I need to find the maximum likelihood estimates of a nonlinear model  
with 10 parameters. The data is multivariate normal. I've written some  
of the code, but then realized that I wrote it terribly inefficiently.  
I'm wondering, will ADMB have trouble working with a vector of length  
7374 and a sparse variance covariance matrix that is 7374x7374? To get  
the likelihhod, it will have to invert the matrix. Is there a special  
function to do this?"

thanks,
Mollie

Mollie Brooks
Ph.D. Candidate
NSF IGERT Fellow
Biology Department
University of Florida
mbrooks at ufl.edu
www.zoology.ufl.edu/mbrooks



On Sep 2, 2010, at 10:31 AM, dave fournier wrote:

> Not quite sure what the original question was, but I
> assume it was about an RE model with a big sparse Hessian.
>
> For the spatial model example the covariance matrix is 10,000x10000
> which is why the current code needs something like 8GB memory.
> My new code reduces this considerably by not forming the inverse of  
> the
> sparse Hessian. Hopefully it can be included into the new code base.
> As I recall it needed something like 1.5GB memory for a sparse
> 250^2 x 250^2 Hessian.
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