fitbenchmarking.controllers.theseus_controller module

Implements a controller for the Theseus fitting software.

class fitbenchmarking.controllers.theseus_controller.TheseusController(cost_func)

Bases: Controller

Controller for Theseus

algorithm_check = {'MCMC': [], 'all': ['Levenberg_Marquardt', 'Gauss-Newton'], 'bfgs': [], 'conjugate_gradient': [], 'deriv_free': [], 'gauss_newton': ['Gauss-Newton'], 'general': [], 'global_optimization': [], 'levenberg-marquardt': ['Levenberg_Marquardt'], 'ls': ['Levenberg_Marquardt', 'Gauss-Newton'], '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

  • 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.


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


Run problem with Theseus

jacobian_enabled_solvers = ['Levenberg_Marquardt', 'Gauss-Newton']

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 problem ready to be run with Theseus

class fitbenchmarking.controllers.theseus_controller.TheseusCostFunction(*args: Any, **kwargs: Any)

Bases: CostFunction

Cost function for Theseus ai

dim() int

Lenght of x data

error() Tuple[List[torch.Tensor], List[float]]

Resdiuals in pytorch tensor form for Theseus ai

jacobians() Tuple[List[torch.Tensor], torch.Tensor]

Jacobians in pytorch tensor form for Theseus ai