Hi Troels, This "NameError: The logarithmic barrier gradient is not implemented yet." problem should no longer bother you:
http://article.gmane.org/gmane.science.mathematics.minfx.scm/52 This is in the minfx trunk. Regards, Edward On 1 September 2014 13:53, Edward d'Auvergne <[email protected]> wrote: > Oh, right, that's another 'feature' I didn't implement as I was too > busy finishing things off to publish our dispersion analysis in relax > paper :S It also would have served no purpose - if there are > gradients implemented for a model, then we can use the > Methods-of-Multipliers as a better alternative algorithm anyway. > > Cheers, > > Edward > > > > > On 1 September 2014 13:08, Troels Emtekær Linnet <[email protected]> > wrote: >> Hi Edward. >> >> If I in systemtest: >> test_estimate_r2eff_err >> >> change: >> self.interpreter.minimise.execute(min_algor='Newton', >> constraints=False, verbosity=1) >> to >> self.interpreter.minimise.execute(min_algor='Newton', >> constraints=True, verbosity=1) >> >> Then I get: >> >> ############################################## >> >> relax> minimise.grid_search(lower=None, upper=None, inc=11, >> verbosity=1, constraints=True, skip_preset=True) >> >> >> Grid search setup: the spin block [':52@N'] >> -------------------------------------------- >> >> ...... >> >> relax> minimise.execute(min_algor='Newton', line_search=None, >> hessian_mod=None, hessian_type=None, func_tol=1e-25, grad_tol=None, >> max_iter=10000000, constraints=True, scaling=True, verbosity=1) >> Resetting the minimisation statistics. >> >> >> Fitting to spin :52@N, frequency 799777399.1 and dispersion point 431.0 >> ----------------------------------------------------------------------- >> >> >> >> Logarithmic barrier function >> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ >> k: 0 xk: [ 8.8, 200000.0001] fk: 37.0428718161 >> Entering sub-algorithm. >> >> Newton minimisation >> ~~~~~~~~~~~~~~~~~~~ >> Line search: Backtracking line search. >> Hessian modification: The Gill, Murray, and Wright modified >> Cholesky algorithm. >> E >> ====================================================================== >> ERROR: test_estimate_r2eff_err >> (test_suite.system_tests.relax_disp.Relax_disp) >> Test the user function for estimating R2eff errors from exponential >> curve fitting. >> ---------------------------------------------------------------------- >> Traceback (most recent call last): >> File >> "/sbinlab2/tlinnet/software/NMR-relax/relax_trunk/test_suite/system_tests/relax_disp.py", >> line 2990, in test_estimate_r2eff_err >> self.interpreter.minimise.execute(min_algor='Newton', >> constraints=True, verbosity=1) >> File >> "/sbinlab2/tlinnet/software/NMR-relax/relax_trunk/prompt/uf_objects.py", >> line 223, in __call__ >> self._backend(*new_args, **uf_kargs) >> File >> "/sbinlab2/tlinnet/software/NMR-relax/relax_trunk/pipe_control/minimise.py", >> line 527, in minimise >> api.minimise(min_algor=min_algor, min_options=min_options, >> func_tol=func_tol, grad_tol=grad_tol, max_iterations=max_iter, >> constraints=constraints, scaling_matrix=scaling_matrix, >> verbosity=verbosity) >> File >> "/sbinlab2/tlinnet/software/NMR-relax/relax_trunk/specific_analyses/relax_disp/api.py", >> line 668, in minimise >> minimise_r2eff(spins=spins, spin_ids=spin_ids, >> min_algor=min_algor, min_options=min_options, func_tol=func_tol, >> grad_tol=grad_tol, max_iterations=max_iterations, >> constraints=constraints, scaling_matrix=scaling_matrix[model_index], >> verbosity=verbosity, sim_index=sim_index, lower=lower_i, >> upper=upper_i, inc=inc_i) >> File >> "/sbinlab2/tlinnet/software/NMR-relax/relax_trunk/specific_analyses/relax_disp/optimisation.py", >> line 424, in minimise_r2eff >> results = generic_minimise(func=func, dfunc=dfunc, d2func=d2func, >> args=(), x0=param_vector, min_algor=min_algor, >> min_options=min_options, func_tol=func_tol, grad_tol=grad_tol, >> maxiter=max_iterations, A=A, b=b, full_output=True, >> print_flag=verbosity) >> File >> "/sbinlab2/software/python-enthought-dis/canopy-1.4.0-full-rh5-64/Canopy_64bit/User/lib/python2.7/site-packages/minfx/generic.py", >> line 399, in generic_minimise >> results = log_barrier_function(func=func, dfunc=dfunc, >> d2func=d2func, args=args, x0=x0, min_options=min_options, A=A, b=b, >> func_tol=func_tol, grad_tol=grad_tol, maxiter=maxiter, >> full_output=full_output, print_flag=print_flag) >> File >> "/sbinlab2/software/python-enthought-dis/canopy-1.4.0-full-rh5-64/Canopy_64bit/User/lib/python2.7/site-packages/minfx/log_barrier_function.py", >> line 96, in log_barrier_function >> results = min.minimise() >> File >> "/sbinlab2/software/python-enthought-dis/canopy-1.4.0-full-rh5-64/Canopy_64bit/User/lib/python2.7/site-packages/minfx/log_barrier_function.py", >> line 264, in minimise >> results = self.generic_minimise(func=self.func_log, >> dfunc=self.func_dlog, d2func=self.func_d2log, args=self.args, >> x0=self.xk, min_algor=self.min_algor, min_options=self.min_options, >> func_tol=self.func_tol, grad_tol=self.grad_tol, maxiter=maxiter, >> full_output=1, print_flag=self.print_flag, print_prefix="\t") >> File >> "/sbinlab2/software/python-enthought-dis/canopy-1.4.0-full-rh5-64/Canopy_64bit/User/lib/python2.7/site-packages/minfx/generic.py", >> line 326, in generic_minimise >> results = newton(func=func, dfunc=dfunc, d2func=d2func, args=args, >> x0=x0, min_options=min_options, func_tol=func_tol, grad_tol=grad_tol, >> maxiter=maxiter, full_output=full_output, print_flag=print_flag, >> print_prefix=print_prefix) >> File >> "/sbinlab2/software/python-enthought-dis/canopy-1.4.0-full-rh5-64/Canopy_64bit/User/lib/python2.7/site-packages/minfx/newton.py", >> line 47, in newton >> min = Newton(func, dfunc, d2func, args, x0, min_options, func_tol, >> grad_tol, maxiter, a0, mu, eta, mach_acc, full_output, print_flag, >> print_prefix) >> File >> "/sbinlab2/software/python-enthought-dis/canopy-1.4.0-full-rh5-64/Canopy_64bit/User/lib/python2.7/site-packages/minfx/newton.py", >> line 156, in __init__ >> self.setup_newton() >> File >> "/sbinlab2/software/python-enthought-dis/canopy-1.4.0-full-rh5-64/Canopy_64bit/User/lib/python2.7/site-packages/minfx/newton.py", >> line 211, in setup_newton >> self.dfk, self.g_count = self.dfunc(*(self.xk,)+self.args), self.g_count >> + 1 >> File >> "/sbinlab2/software/python-enthought-dis/canopy-1.4.0-full-rh5-64/Canopy_64bit/User/lib/python2.7/site-packages/minfx/log_barrier_function.py", >> line 211, in func_dlog >> raise NameError("The logarithmic barrier gradient is not implemented >> yet.") >> NameError: The logarithmic barrier gradient is not implemented yet. >> >> ---------------------------------------------------------------------- >> >> >> >> 2014-09-01 12:42 GMT+02:00 Edward d'Auvergne <[email protected]>: >>> 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
