On Wed, Nov 7, 2012 at 5:10 PM, CdeMills
<address@hidden> wrote:
Hello,
I'm busy with one "classical" engineering topic, i.e. modelling current in
micro-electronics devices. The main issue is that I have to deal with
variables which may span 8 orders of magnitude, like f.i. current going from
nano-amps to amps.
The computation succeeds, but at the end I have to invert a matrix whose
eigenvalues also span many orders of magnitude. I use a qr() decomposition,
followed by a call to chol2inv. Yet the result of chol2inv is sometimes not
full rank, while the result of qr() is.
Is there some way to get more resolution ? I.e. being able to compute the
inverse of matrices whose the ratio between the greatest and the smallest
eigenvalues module span 12 decades ?
I doubt that higher resolution is the answer. I experienced a similar issue with corrosion current densities ranging over just a few orders of magnitude less. I implemented a weighting scheme so that 1 +/- 0.001 mA would be treated the same as 1 +/- 0.001 nA during the analysis. Increasing the resolution just lets the noise in the larger values dominate the model. If you provide more details, perhaps I can be more specific. I had other problems, including missing data, and never achieved completely satisfactory results.
Chris