fitbenchmarking.controllers.horace_controller module

Implements a controller to the lm implementation in Herbert/Horace

class fitbenchmarking.controllers.horace_controller.HoraceController(cost_func)

Bases: fitbenchmarking.controllers.matlab_mixin.MatlabMixin, fitbenchmarking.controllers.base_controller.Controller

Controller for fit in Herbert

algorithm_check = {'all': ['lm-lsqr'], 'bfgs': [], 'conjugate_gradient': [], 'deriv_free': ['lm-lsqr'], 'gauss_newton': [], 'general': [], 'global_optimization': [], 'levenberg-marquardt': ['lm-lsqr'], 'ls': ['lm-lsqr'], 'simplex': [], 'steepest_descent': [], 'trust_region': []}

Within the controller class, you must initialize a dictionary, algorithm_check, such that the keys are given by:

  • all - all minimizers

  • ls - least-squares fitting algorithms

  • deriv_free - derivative free algorithms (these are algorithms that cannot use information about derivatives – e.g., the Simplex method in Mantid)

  • general - minimizers which solve a generic min f(x)

  • simplex - derivative free simplex based algorithms e.g. Nelder-Mead

  • trust_region - algorithms which emply a trust region approach

  • levenberg-marquardt - minimizers that use the Levenberg-Marquardt algorithm

  • gauss_newton - minimizers that use the Gauss Newton algorithm

  • bfgs - minimizers that use the BFGS algorithm

  • conjugate_gradient - Conjugate Gradient algorithms

  • steepest_descent - Steepest Descent algorithms

  • global_optimization - Global Optimization algorithms

The values of the dictionary are given as a list of minimizers for that specific controller that fit into each of the above categories. See for example the GSL controller.


Convert the result to a numpy array and populate the variables results will be read from.


Run problem with Horace

incompatible_problems = ['mantid']

A list of incompatible problem formats for this controller.


Setup for Matlab fitting