Dave,
I looked at this as much as I could. There is nothing wrong with XMIN
settings. The problem seems to be that as you indicated sqm gradients
are not accurate. It bothers lbfgs less, but it kills tncg. The reason
being that tncg relies on numerous Hess x some_vector products and
these are computed via finite difference using gradients.
Unfortunately, if gradients are off (it is probably very difficult to
quantify how much off is significant) the product won't be just
inaccurate, it would just not be the product of the Hessian with
something. Therefore, the conjugate gradient part of tncg runs amok
and will never converge. I guess, at this point we are stuck with
lbfgs for sqm. I tried larger lbfgs memory, but it didn't change
things too much. What is the reason for the inaccurate gradients? Can
it be fixed. Even if it is way too slow, just to check that
minimization works better? I'd be glad to look into this some more,
but right now I don't have any suggestion.
Istvan
Quoting case <case.biomaps.rutgers.edu>:
> Hi Istvan:
>
> About minimization methods in sqm, look at the test case in
> amber11/test/antechamber/sustiva. (I've attached the sqm input; you
> can just type 'sqm -i sqm.in -o output-file').
>
> With LBFGS, this input runs for 3650 cycles, reaching a final energy
> of -120.503799 and a gradient of 0.000498. The energy gets to within 0.1
> kcal/mol of its final value after 2270 cycles.
>
> With TCNG, the minimization exits after 10,000 cycles, with a energy of
> -120.3472 and a gradient of 0.001713. It gets to within 0.1 kcal/mol
> of its final energy after 860 cycles, but this is still 0.17 kcal/mol higher
> than the state found by LBFGS.
>
> So: this might be the result of gradients that are slightly wrong,
> or it might
> indicate that one of the parameters defining the TCNG scheme is not set
> correctly in sqm. (Or, something else....)
>
> ...regards...dave
>
>
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Received on Mon Dec 14 2009 - 17:30:03 PST
