Hi Ed,
Thanks for these informations... I was afraid that you could have
limited relax to small proteins in the first drafts of the program with
forgetting to change these limits afterwards... thus creating artefacts
when used with bigger systems.
However, as I see, the limits are all general and the size of the
protein (up to the huge correlation time of 200 ns !) does not influence
the results...
As usual, relax is extremely professional...
Thanks Ed !
Cheers !
Séb
Edward d'Auvergne wrote:
Hi,
Unless turned off, the constraints are used all of the time. In the
minimisation this turns on the Method of Multipliers (also known as
the Augmented Lagrangian) algorithm. In the grid search, any points
outside of the limits are dropped. Unfortunately these constraints
are hard coded as I couldn't, at the time, come up with a flexible way
for the user to modify the default values. There are a number of
methods for applying constraints, but currently only linear
constraints are supported. These are in the form:
A.x >= b,
where A is an matrix of coefficients, x is an array of parameter
values, and b is a vector of scalars. These translate into
constraints such as:
S2 >= 0,
-S2 >= -1,
etc.
The full list of constraints can be seen in the documentation string
for the linear_constraints() method in the specific_fns/model_free.py
file (relax-1.2). The diffusion tensor parameter constraints aren't
yet documented, but can be seen in the comments in the code of
linear_constraints(). I hope this info helps.
Regards,
Edward
On Fri, Feb 22, 2008 at 5:38 PM, Sébastien Morin
<[EMAIL PROTECTED]> wrote:
Hi,
I have a question about constraints in relax...
I would like to know what are the built-in constraints in relax,
especially for what concerns 'tau' (tau_m, tau_e, tau_s, tau_f) as well
as 'r' and 'csa' (for models m1x and m2x).
What I'd like to know is if those parameters are contrained during grid
search, optimization, elimination and selection, except for
'tau_(e,f,s)' values which should not exceed '1.5 x tau_m'...
I understand that constraint are used by default within the method of
multipliers algorithm, but don't really get what are those constraint
and on which variables they act...
Thanks for help !
Cheers,
Séb :)
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