Re: Constraints in full_analysis.py

Hi Seb, That's an interesting one. That message is a debugging statement that should only be triggered if someone goes into the relax source code and tries to reprogram the constraints algorithm or tries to implement a completely new algorithm. It shouldn't appear in the 1.2 stable releases

Re: Constraints in full_analysis.py

opt, GMW Hessian mod, More and Thuente line search {S2=0.970, te=2048, Rex=0.149} [ Failed ] == Moreover, the --debug option wouldn't give more details concerning the incorrect supply of the constraints... I'll try to dig further... Cheers Séb Edward d'Auvergne

Re: Constraints

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

Re: Constraints

like to have these constraints, as well as the diagonal scaling values, more accessible to the user, but unfortunately I have never managed to design a flexible system for this. The constraint values for model-free analysis are documented in my model-free model elimination paper (http://www.nmr

Re: Constraints

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

Constraints

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

The constraints have been incorrectly supplied.

='final', number=200) relax monte_carlo.create_data(run='final', method='back_calc') relax monte_carlo.initial_values(run='final') relax minimise(*args=('newton',), run='final', func_tol=1e-25, max_iterations=1000, constraints=1, scaling=1, print_flag=1) Simulation 1 The constraints have been