Hi!

In my use case, I don't need the exact solutions of an eigenproblem, I

just need ARPACK to give me the N lowest (real part) vectors it can find

after no more than X iterations --- that is, if it finds all N

eigenvectors to the specified tolerance in less than X iterations then

great, but if it doesn't then I still want it to give me the N lowest

vectors it found even if they aren't fully converged.

(The background is that I have an iterative optimization algorithm, so

to make a long story short I just need a cheaply obtainable improved

solution to input to the next step of the iteration, not a perfect

solution, and since the optimization problem is expressed in terms of a

Rayleigh quotient something like the Arnoldi iteration should do a good

job of finding such a solution; in fact, I have good experience using

ARPACK in the past but I am now working with matrices that it is

struggling with.)

The documentation says that after X iterations have passed it gives you

all of the eigenvectors that have converged, which is not what I want as

I want *all* N vectors even if they haven't converged. So my question

is: when the docs say that it only gives you the eigenvectors that

converged, does it mean that it does a transformation which got rid of

the rest and so they are inaccessible? Or does it mean that if I, say,

override the number of converged eigenvectors and set it to the number

that I want before calling the routine that extracts the eigenvectors,

then I can get the full set? Put another way, if I replace the number

of converged eigenvectors with the number I want, then will this result

in garbage, or will it give me what I am looking for?

Thanks a lot in advance!

Greg

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