gemseo.algos.opt.scipy_local.scipy_local module#

The library of SciPy local optimization algorithms.

class SciPyAlgorithmDescription(algorithm_name, internal_algorithm_name, library_name='SciPy Local', description='', website='', Settings=<class 'gemseo.algos.opt.scipy_local.settings.base_scipy_local_settings.BaseScipyLocalSettings'>, handle_integer_variables=False, handle_equality_constraints=False, handle_inequality_constraints=False, handle_multiobjective=False, positive_constraints=False, for_linear_problems=False, require_gradient=False)[source]#

Bases: OptimizationAlgorithmDescription

The description of the SciPy local optimization library.

Parameters:

  • algorithm_name (str)

  • internal_algorithm_name (str)

  • library_name (str) --

    By default it is set to "SciPy Local".

  • description (str) --

    By default it is set to "".

  • website (str) --

    By default it is set to "".

  • Settings (type[BaseScipyLocalSettings]) --

    By default it is set to <class 'gemseo.algos.opt.scipy_local.settings.base_scipy_local_settings.BaseScipyLocalSettings'>.

  • handle_integer_variables (bool) --

    By default it is set to False.

  • handle_equality_constraints (bool) --

    By default it is set to False.

  • handle_inequality_constraints (bool) --

    By default it is set to False.

  • handle_multiobjective (bool) --

    By default it is set to False.

  • positive_constraints (bool) --

    By default it is set to False.

  • for_linear_problems (bool) --

    By default it is set to False.

  • require_gradient (bool) --

    By default it is set to False.

Settings#

The option validation model for SciPy local optimization library.

alias of BaseScipyLocalSettings

library_name: str = 'SciPy Local'#

The library name.

class ScipyOpt(algo_name)[source]#

Bases: BaseOptimizationLibrary

The library of SciPy optimization algorithms.

Notes

The missing current values of the DesignSpace attached to the OptimizationProblem are automatically initialized with the method DesignSpace.initialize_missing_current_values().

Parameters:

algo_name (str) -- The algorithm name.

Raises:

KeyError -- When the algorithm is not in the library.

ALGORITHM_INFOS: ClassVar[dict[str, SciPyAlgorithmDescription]] = {'L-BFGS-B': SciPyAlgorithmDescription(algorithm_name='L-BFGS-B', internal_algorithm_name='L-BFGS-B', library_name='SciPy Local', description='Limited-memory BFGS algorithm implemented in the SciPy library', website='https://docs.scipy.org/doc/scipy/reference/optimize.minimize-lbfgsb.html', Settings=<class 'gemseo.algos.opt.scipy_local.settings.lbfgsb.L_BFGS_B_Settings'>, handle_integer_variables=False, handle_equality_constraints=False, handle_inequality_constraints=False, handle_multiobjective=False, positive_constraints=False, for_linear_problems=False, require_gradient=True), 'NELDER-MEAD': SciPyAlgorithmDescription(algorithm_name='NELDER-MEAD', internal_algorithm_name='Nelder-Mead', library_name='SciPy Local', description='Nelder-Mead algorithm implemented in the SciPy library', website='https://docs.scipy.org/doc/scipy/reference/optimize.minimize-neldermead.html', Settings=<class 'gemseo.algos.opt.scipy_local.settings.nelder_mead.NELDER_MEAD_Settings'>, handle_integer_variables=False, handle_equality_constraints=False, handle_inequality_constraints=False, handle_multiobjective=False, positive_constraints=False, for_linear_problems=False, require_gradient=False), 'SLSQP': SciPyAlgorithmDescription(algorithm_name='SLSQP', internal_algorithm_name='SLSQP', library_name='SciPy Local', description='Sequential Least-Squares Quadratic Programming (SLSQP) implemented in the SciPy library', website='https://docs.scipy.org/doc/scipy/reference/optimize.minimize-slsqp.html', Settings=<class 'gemseo.algos.opt.scipy_local.settings.slsqp.SLSQP_Settings'>, handle_integer_variables=False, handle_equality_constraints=True, handle_inequality_constraints=True, handle_multiobjective=False, positive_constraints=True, for_linear_problems=False, require_gradient=True), 'TNC': SciPyAlgorithmDescription(algorithm_name='TNC', internal_algorithm_name='TNC', library_name='SciPy Local', description='Truncated Newton (TNC) algorithm implemented in SciPy library', website='https://docs.scipy.org/doc/scipy/reference/optimize.minimize-tnc.html', Settings=<class 'gemseo.algos.opt.scipy_local.settings.tnc.TNC_Settings'>, handle_integer_variables=False, handle_equality_constraints=False, handle_inequality_constraints=False, handle_multiobjective=False, positive_constraints=False, for_linear_problems=False, require_gradient=True)}#

The description of the algorithms contained in the library.