Dear Edward.
I am working on a systematic investigations of dynamic parameters for hundreds
of datasets.
For one example, a CPMG analysis is setup for:
17 variations of tau_cpmg
The number of MC simulations is 50.
82 spins which are all clustered.
There is no grid search, and only TSMFK01 is used.
I do one grid search in the start, minimise this, copy over the
parameters and take median, make a clustering analysis, and then
repeat the last step 60 times.
This would again would be needed to repeat 5-8 times for other
datasets with variations.
And then for other proteins. (Sigh..)
I have setup relax to use 20 processors on our server, and a
dispersion analysis takes
between 2-6 Hours.
That is a reasonable timeframe for an normal analysis of this type.
But I have to squeeze hundreds of these analysis through relax, to get
variation of the dynamic parameters.
Our old Igor Pro scripts, could do a global fitting in 10 minutes.
That does not include MC simulations.
But I wonder if I could speed up relax by changing function tolerance
and maximum number of iterations:
minimise(min_algor='simplex', line_search=None, hessian_mod=None,
hessian_type=None, func_tol=OPT_FUNC_TOL, grad_tol=None,
max_iter=OPT_MAX_ITERATIONS, constraints=True, scaling=True,
verbosity=1)
where standard values of:
OPT_FUNC_TOL = 1e-25
OPT_MAX_ITERATIONS = 10000000
Could you advise if this strategy is possible?
What I hope for, is that an analysis come down to 10-20 minutes?
Maybe I could cut away the MC simulations, since I am mostly
interested in the fitted dynamic parameters, and not so much about
their error?
Thank you in advance!
Best
Troels
The need for speeding things up in dispersion analysis