Test it and see ;) And if you are interested in the speed up in this part of the analysis, you can take it one more step and implement the Hessian in the C modules and then time the Newton algorithm!
Regards, Edward On 26 August 2014 13:26, Troels Emtekær Linnet <[email protected]> wrote: > 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 <[email protected]>: >> 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 <[email protected]> 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 >>> <[email protected]> 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 >>>> [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 _______________________________________________ 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
