fitbenchmarking.controllers.gofit_controller module

Implements a controller for global optimization algorithms.

class fitbenchmarking.controllers.gofit_controller.GOFitController(cost_func)

Bases: Controller

Controller for GOFit multistart global optimisation algorithms

algorithm_check = {'MCMC': [], 'all': ['alternating', 'multistart', 'regularisation'], 'bfgs': [], 'conjugate_gradient': [], 'deriv_free': [], 'gauss_newton': ['regularisation'], 'general': [], 'global_optimization': ['alternating', 'multistart'], 'levenberg-marquardt': ['regularisation'], 'ls': ['alternating', 'multistart', 'regularisation'], '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 employ 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

  • MCMC - Markov Chain Monte Carlo 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.

The algorithm_check dictionary is used to determine which minimizers to run given the algorithm_type selected in Fitting Options. For guidance on how to categorise minimizers, see the Optimization Algorithms section of the FitBenchmarking docs.

cleanup()

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

fit()

Run problem with GoFit

jacobian_enabled_solvers = ['multistart', 'regularisation']

Within the controller class, you must define the list jacobian_enabled_solvers if any of the minimizers for the specific software are able to use jacobian information.

  • jacobian_enabled_solvers: a list of minimizers in a specific software that allow Jacobian information to be passed into the fitting algorithm

no_bounds_minimizers = ['regularisation']

Used to check whether the selected minimizers is compatible with problems that have parameter bounds

setup()

Setup problem ready to be run with GoFit

support_for_bounds = True

Used to check whether the fitting software has support for bounded problems, set as True if at least some minimizers in the fitting software have support for bounds