On 1 September 2014 12:34, Troels Emtekær Linnet <[email protected]> wrote: > Anyway, before minfx can handle constraints in for example BFGS, > this is just a waste of time.
Minfx can do this :) The log-barrier constraint algorithm works with all optimisation techniques in minfx, well, apart from the grid search (https://en.wikipedia.org/wiki/Barrier_function#Logarithmic_barrier_function). And if gradients are supplied, the more powerful Methods-of-Multipliers algorithm can also be used in combination with all optimisation techniques (https://en.wikipedia.org/wiki/Augmented_Lagrangian_method). > I think there will be a 10 x speed up, just for the Jacobian. For the analytic models, you could have a 10x speed up if symbolic gradients and Hessians are implemented. I'm guessing that's what you mean. > And when you have the Jacobian, estimating the errors are trivial. > > std(q) = sqrt ( (dq/dx std(x))*2 + (dq/dz std(z))*2 ) :S I'm not sure about this estimate. It looks rather too linear. I wish errors would be so simple. > where q is the function. x and z are R1 and R1rho_prime. > > So, until then, implementing the Jacobian is only for testing the > error estimation compared to > Monte-Carlo simulations. If you do add the equations, the lib.dispersion.dpl94 module would be the natural place to put them. And the interface as dfunc_DPL94(), d2func_DPL94(), and jacobian_DPL94(). Regards, Edward _______________________________________________ relax (http://www.nmr-relax.com) This is the relax-devel mailing list [email protected] To unsubscribe from this list, get a password reminder, or change your subscription options, visit the list information page at https://mail.gna.org/listinfo/relax-devel
