Should this be used instead then in R2eff minimisation? Is it faster than simplex?
Best Troels 2014-08-26 13:04 GMT+02:00 Edward d'Auvergne <edw...@nmr-relax.com>: > Oh, I forgot to mention, but I also converted the > Relax_fit.test_curve_fitting_height and > Relax_fit.test_curve_fitting_volume system tests to use BFGS > optimisation. This is one of the best optimisation techniques when > only the function and gradient are present, as it tries to numerically > approximate the Hessian matrix, updating it as the algorithm moves > along. It is fast and performs incredibly well, so it is a widely > used algorithm. The system tests using BFGS demonstrate that the > gradient works very well for optimisation. It isn't as fast as Newton > optimisation however. > > Regards, > > Edward > > > > > On 26 August 2014 13:00, Edward d'Auvergne <edw...@nmr-relax.com> wrote: >> Hi Troels, >> >> I've now implemented the exponential curve-fitting dfunc() function >> for calculating the gradient. This includes: >> >> - The Python wrapper function >> specific_analyses.relax_fit.optimisation.dfunc_wrapper(), >> - The target_functions/c_chi2.c function dchi2(), >> - The target_functions/exponential.c functions exponential_dI0() and >> exponential_dR(), >> - The target_functions.relax_fit C module dfunc() Python function. >> >> I have tested the gradient using the numerical integration in the >> test_suite/shared_data/curve_fitting/numeric_gradient/integrate.py >> file to determine what the chi-squared gradient should be at different >> parameter combinations. And this has been converted into a few unit >> tests. As this works, that means that the jacobian() function of the >> C module should also be correct and bug-free, hence you should be able >> to use it to obtain the covariance matrix. >> >> This is all I will do for now. All that is left is to do for the >> target_functions.relax_fit C module is simply the same thing, but for >> the Hessian. Feel free to give this a go if you are interested. If I >> have time in the future, I might add this too. >> >> Regards, >> >> Edward >> >> >> >> >> >> On 24 August 2014 17:56, Troels E. Linnet >> <no-reply.invalid-addr...@gna.org> wrote: >>> URL: >>> <http://gna.org/task/?7822> >>> >>> Summary: Implement user function to estimate R2eff and >>> associated errors for exponential curve fitting. >>> Project: relax >>> Submitted by: tlinnet >>> Submitted on: Sun 24 Aug 2014 03:56:36 PM UTC >>> Should Start On: Sun 24 Aug 2014 12:00:00 AM UTC >>> Should be Finished on: Sun 24 Aug 2014 12:00:00 AM UTC >>> Category: relax's source code >>> Priority: 5 - Normal >>> Status: In Progress >>> Percent Complete: 0% >>> Assigned to: tlinnet >>> Open/Closed: Open >>> Discussion Lock: Any >>> Effort: 0.00 >>> >>> _______________________________________________________ >>> >>> Details: >>> >>> A verification script, showed that using scipy.optimize.leastsq reaches the >>> exact same parameters as minfx for exponential curve fitting. >>> >>> The verification script is in: >>> test_suite/shared_data/curve_fitting/profiling/profiling_relax_fit.py >>> test_suite/shared_data/curve_fitting/profiling/verify_error.py >>> >>> The profiling script shows that a 10 X increase in speed can be reached by >>> removing >>> the linear constraints when using minfx. >>> >>> The profiling also shows that scipy.optimize.leastsq is 10X as fast as using >>> minfx, even without linear constraints. >>> >>> scipy.optimize.leastsq is a wrapper around wrapper around MINPACK's lmdif >>> and >>> lmder algorithms. >>> >>> MINPACK is a FORTRAN90 library which solves systems of nonlinear equations, >>> or >>> carries out the least squares minimization of the residual of a set of >>> linear >>> or nonlinear equations. >>> >>> The verification script also shows, that a very heavy and time consuming >>> monte carlo simulation of 2000 steps, reaches the same errors as the errors >>> reported by scipy.optimize.leastsq. >>> >>> The return from scipy.optimize.leastsq, gives the estimated co-variance. >>> Taking the square root of the co-variance corresponds with 2X error reported >>> by minfx after 2000 Monte-Carlo simulations. >>> >>> This could be an extremely time saving step, when performing model fitting >>> in >>> R1rho, where the errors of the R2eff values, are estimated by Monte-Carlo >>> simulations. >>> >>> The following setup illustrates the problem. >>> This was analysed on a: MacBook Pro, 13-inch, Late 2011. >>> With no multi-core setup. >>> >>> Script running is: >>> test_suite/shared_data/dispersion/Kjaergaard_et_al_2013/2_pre_run_r2eff.py >>> >>> This script analyses just the R2eff values for 15 residues. >>> It estimates the errors of R2eff based on 2000 Monte Carlo simulations. >>> For each residues, there is 14 exponential graphs. >>> >>> The script was broken after 35 simulations. >>> This was measured to 20 minutes. >>> So 500 simulations would take about 4.8 Hours. >>> >>> The R2eff values and errors can by scipy.optimize.leastsq can instead be >>> calculated in: 15 residues * 0.02 seconds = 0.3 seconds. >>> >>> >>> >>> >>> _______________________________________________________ >>> >>> Reply to this item at: >>> >>> <http://gna.org/task/?7822> >>> >>> _______________________________________________ >>> Message sent via/by Gna! >>> http://gna.org/ >>> >>> >>> _______________________________________________ >>> relax (http://www.nmr-relax.com) >>> >>> This is the relax-devel mailing list >>> relax-devel@gna.org >>> >>> 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 _______________________________________________ relax (http://www.nmr-relax.com) This is the relax-devel mailing list relax-devel@gna.org 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
