gemseo / algos / opt

# opt_lib module¶

Optimization library wrappers base class.

class gemseo.algos.opt.opt_lib.OptimizationAlgorithmDescription(algorithm_name, internal_algorithm_name, library_name='', description='', website='', handle_integer_variables=False, require_gradient=False, handle_equality_constraints=False, handle_inequality_constraints=False, handle_multiobjective=False, positive_constraints=False, problem_type='non-linear')[source]

The description of an optimization algorithm.

Parameters:
• algorithm_name (str) –

• internal_algorithm_name (str) –

• library_name (str) –

By default it is set to “”.

• description (str) –

By default it is set to “”.

• website (str) –

By default it is set to “”.

• handle_integer_variables (bool) –

By default it is set to False.

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.

• problem_type (str) –

By default it is set to “non-linear”.

algorithm_name: str

The name of the algorithm in GEMSEO.

description: str = ''

A description of the algorithm.

handle_equality_constraints: bool = False

Whether the optimization algorithm handles equality constraints.

handle_inequality_constraints: bool = False

Whether the optimization algorithm handles inequality constraints.

handle_integer_variables: bool = False

Whether the optimization algorithm handles integer variables.

handle_multiobjective: bool = False

Whether the optimization algorithm handles multiple objectives.

internal_algorithm_name: str

The name of the algorithm in the wrapped library.

library_name: str = ''

The name of the wrapped library.

positive_constraints: bool = False

Whether the optimization algorithm requires positive constraints.

problem_type: str = 'non-linear'

The type of problem (see OptimizationProblem.AVAILABLE_PB_TYPES).

Whether the optimization algorithm requires the gradient.

website: str = ''

The website of the wrapped library or algorithm.

class gemseo.algos.opt.opt_lib.OptimizationLibrary[source]

Bases: DriverLib

Base optimization library defining a collection of optimization algorithms.

Typically used as:

1. Instantiate an OptimizationLibrary.

2. Select the algorithm with algo_name.

3. Solve an OptimizationProblem with execute().

Note

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

algorithm_handles_eqcstr(algo_name)[source]

Check if an algorithm handles equality constraints.

Parameters:

algo_name (str) – The name of the algorithm.

Returns:

Whether the algorithm handles equality constraints.

Return type:

bool

algorithm_handles_ineqcstr(algo_name)[source]

Check if an algorithm handles inequality constraints.

Parameters:

algo_name (str) – The name of the algorithm.

Returns:

Whether the algorithm handles inequality constraints.

Return type:

bool

deactivate_progress_bar()

Deactivate the progress bar.

Return type:

None

driver_has_option(option_name)

Check the existence of an option.

Parameters:

option_name (str) – The name of the option.

Returns:

Whether the option exists.

Return type:

bool

ensure_bounds(orig_func, normalize=True)

Project the design vector onto the design space before execution.

Parameters:
• orig_func – The original function.

• normalize

Whether to use the normalized design space.

By default it is set to True.

Returns:

A function calling the original function with the input data projected onto the design space.

execute(problem, algo_name=None, eval_obs_jac=False, skip_int_check=False, **options)

Execute the driver.

Parameters:
• problem (OptimizationProblem) – The problem to be solved.

• algo_name (str | None) – The name of the algorithm. If None, use the algo_name attribute which may have been set by the factory.

• eval_obs_jac (bool) –

Whether to evaluate the Jacobian of the observables.

By default it is set to False.

• skip_int_check (bool) –

Whether to skip the integer variable handling check of the selected algorithm.

By default it is set to False.

• **options (DriverLibOptionType) – The options for the algorithm.

Returns:

The optimization result.

Raises:

ValueError – If algo_name was not either set by the factory or given as an argument.

Return type:

OptimizationResult

Filter the algorithms capable of solving the problem.

Parameters:

problem (Any) – The problem to be solved.

Returns:

The names of the algorithms adapted to this problem.

Return type:

list[str]

finalize_iter_observer()

Finalize the iteration observer.

Return type:

None

get_optimum_from_database(message=None, status=None)

Retrieves the optimum from the database and builds an optimization result object from it.

get_right_sign_constraints()[source]

Transforms the problem constraints into their opposite sign counterpart if the algorithm requires positive constraints.

get_x0_and_bounds_vects(normalize_ds)

Gets x0, bounds, normalized or not depending on algo options, all as numpy arrays.

Parameters:

normalize_ds – Whether to normalize the input variables that are not integers, according to the normalization policy of the design space.

Returns:

The current value, the lower bounds and the upper bounds.

init_iter_observer(max_iter, message='...')

Initialize the iteration observer.

It will handle the stopping criterion and the logging of the progress bar.

Parameters:
• max_iter (int) – The maximum number of iterations.

• message (str) –

The message to display at the beginning.

By default it is set to “…”.

Raises:

ValueError – If the max_iter is not greater than or equal to one.

Return type:

None

init_options_grammar(algo_name)

Initialize the options’ grammar.

Parameters:

algo_name (str) – The name of the algorithm.

Return type:

JSONGrammar

Returns True if the algorithm requires a gradient evaluation.

Parameters:

algo_name – The name of the algorithm.

is_algo_requires_positive_cstr(algo_name)[source]

Check if an algorithm requires positive constraints.

Parameters:

algo_name (str) – The name of the algorithm.

Returns:

Whether the algorithm requires positive constraints.

Return type:

bool

classmethod is_algorithm_suited(algorithm_description, problem)

Check if an algorithm is suited to a problem according to its description.

Parameters:
• algorithm_description (AlgorithmDescription) – The description of the algorithm.

• problem (Any) – The problem to be solved.

Returns:

Whether the algorithm is suited to the problem.

Return type:

bool

new_iteration_callback(x_vect=None)[source]

Verify the design variable and objective value stopping criteria.

Parameters:

x_vect (ndarray | None) – The design variables values. If None, use the values of the last iteration.

Raises:
• FtolReached – If the defined relative or absolute function tolerance is reached.

• XtolReached – If the defined relative or absolute x tolerance is reached.

Return type:

None

COMPLEX_STEP_METHOD = 'complex_step'
DIFFERENTIATION_METHODS = ['user', 'complex_step', 'finite_differences']
EQ_TOLERANCE = 'eq_tolerance'
EVAL_OBS_JAC_OPTION = 'eval_obs_jac'
FINITE_DIFF_METHOD = 'finite_differences'
F_TOL_ABS = 'ftol_abs'
F_TOL_REL = 'ftol_rel'
INEQ_TOLERANCE = 'ineq_tolerance'
LIBRARY_NAME: ClassVar[str | None] = None

The name of the interfaced library.

LS_STEP_NB_MAX = 'max_ls_step_nb'
LS_STEP_SIZE_MAX = 'max_ls_step_size'
MAX_DS_SIZE_PRINT = 40
MAX_FUN_EVAL = 'max_fun_eval'
MAX_ITER = 'max_iter'
MAX_TIME = 'max_time'
NORMALIZE_DESIGN_SPACE_OPTION = 'normalize_design_space'
OPTIONS_DIR: Final[str] = 'options'

The name of the directory containing the files of the grammars of the options.

OPTIONS_MAP: dict[str, str] = {}

The names of the options in GEMSEO mapping to those in the wrapped library.

PG_TOL = 'pg_tol'
ROUND_INTS_OPTION = 'round_ints'
STOP_CRIT_NX = 'stop_crit_n_x'
USE_DATABASE_OPTION = 'use_database'
VERBOSE = 'verbose'
X_TOL_ABS = 'xtol_abs'
X_TOL_REL = 'xtol_rel'
activate_progress_bar: ClassVar[bool] = True

Whether to activate the progress bar in the optimization log.

algo_name: str | None

The name of the algorithm used currently.

property algorithms: list[str]

The available algorithms.

descriptions: dict[str, AlgorithmDescription]

The description of the algorithms contained in the library.

internal_algo_name: str | None

The internal name of the algorithm used currently.

It typically corresponds to the name of the algorithm in the wrapped library if any.

opt_grammar: JSONGrammar | None

The grammar defining the options of the current algorithm.

problem: Any | None

The problem to be solved.