fitbenchmarking.controllers.gsl_controller module

Implements a controller for GSL https://www.gnu.org/software/gsl/ using the pyGSL python interface https://sourceforge.net/projects/pygsl/

class fitbenchmarking.controllers.gsl_controller.GSLController(cost_func)

Bases: fitbenchmarking.controllers.base_controller.Controller

Controller for the GSL fitting software

algorithm_check = {'all': ['lmsder', 'lmder', 'nmsimplex', 'nmsimplex2', 'conjugate_pr', 'conjugate_fr', 'vector_bfgs', 'vector_bfgs2', 'steepest_descent'], 'bfgs': ['vector_bfgs', 'vector_bfgs2'], 'conjugate_gradient': ['conjugate_fr', 'conjugate_pr'], 'deriv_free': ['nmsimplex', 'nmsimplex2'], 'gauss_newton': [], 'general': ['nmsimplex', 'nmsimplex2', 'conjugate_pr', 'conjugate_fr', 'vector_bfgs', 'vector_bfgs2', 'steepest_descent'], 'global_optimization': [], 'levenberg-marquardt': ['lmder', 'lmsder'], 'ls': ['lmsder', 'lmder'], 'simplex': ['nmsimplex', 'nmsimplex2'], 'steepest_descent': ['steepest_descent'], 'trust_region': ['lmder', 'lmsder']}

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.

cleanup()

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

fit()

Run problem with GSL

jacobian_enabled_solvers = ['lmsder', 'lmder', 'conjugate_pr', 'conjugate_fr', 'vector_bfgs', 'vector_bfgs2', 'steepest_descent']

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

setup()

Setup for GSL