gemseo / formulations

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mdo_formulation module

The base class for all MDO formulations.

class gemseo.formulations.mdo_formulation.MDOFormulation(disciplines, objective_name, design_space, maximize_objective=False, grammar_type=GrammarType.JSON, **options)[source]

Bases: BaseFormulation

A base class for MDO formulations.

Parameters:
  • disciplines (list[MDODiscipline]) – The disciplines.

  • objective_name (str | Sequence[str]) – The name(s) of the discipline output(s) used as objective. If multiple names are passed, the objective will be a vector.

  • design_space (DesignSpace) – The design space.

  • maximize_objective (bool) –

    Whether to maximize the objective.

    By default it is set to False.

  • grammar_type (MDODiscipline.GrammarType) –

    The type of the input and output grammars.

    By default it is set to “JSONGrammar”.

  • **options (Any) – The options of the formulation.

add_constraint(output_name, constraint_type=ConstraintType.EQ, constraint_name='', value=0, positive=False)

Add an equality or inequality constraint to the optimization problem.

An equality constraint is written as \(c(x)=a\), a positive inequality constraint is written as \(c(x)\geq a\) and a negative inequality constraint is written as \(c(x)\leq a\).

This constraint is in addition to those created by the formulation, e.g. consistency constraints in IDF.

The strategy of repartition of the constraints is defined by the formulation.

Parameters:
  • output_name (str | Sequence[str]) – The name(s) of the outputs computed by \(c(x)\). If several names are given, a single discipline must provide all outputs.

  • constraint_type (MDOFunction.ConstraintType) –

    The type of constraint.

    By default it is set to “eq”.

  • constraint_name (str) –

    The name of the constraint to be stored. If empty, the name of the constraint is generated from output_name, constraint_type, value and positive.

    By default it is set to “”.

  • value (float) –

    The value \(a\).

    By default it is set to 0.

  • positive (bool) –

    Whether the inequality constraint is positive.

    By default it is set to False.

Return type:

None

add_observable(output_names, observable_name='', discipline=None)

Add an observable to the optimization problem.

The repartition strategy of the observable is defined in the formulation class.

Parameters:
  • output_names (str | Sequence[str]) – The name(s) of the output(s) to observe.

  • observable_name (str) –

    The name of the observable. If empty, the output name is used by default.

    By default it is set to “”.

  • discipline (MDODiscipline | None) – The discipline computing the observed outputs. If None, the discipline is detected from inner disciplines.

Return type:

None

classmethod get_default_sub_option_values(**options)

Return the default values of the sub-options of the formulation.

When some options of the formulation depend on higher level options, the default values of these sub-options may be obtained here, mainly for use in the API.

Parameters:

**options (str) – The options required to deduce the sub-options grammar.

Returns:

Either None or the sub-options default values.

Return type:

StrKeyMapping

abstract get_expected_dataflow()

Get the expected data exchange sequence.

This method is used for the XDSM representation and can be overloaded by subclasses.

Returns:

The expected sequence of data exchange where the i-th item is described by the starting discipline, the ending discipline and the coupling variables.

Return type:

list[tuple[MDODiscipline, MDODiscipline, list[str]]]

abstract get_expected_workflow()

Get the expected sequence of execution of the disciplines.

This method is used for the XDSM representation and can be overloaded by subclasses.

For instance:

  • [A, B] denotes the execution of A, then the execution of B

  • (A, B) denotes the concurrent execution of A and B

  • [A, (B, C), D] denotes the execution of A, then the concurrent execution of B and C, then the execution of D.

Returns:

A sequence of elements which are either an ExecutionSequence or a tuple of ExecutionSequence for concurrent execution.

Return type:

ExecutionSequence | tuple[ExecutionSequence]

get_optim_variable_names()

Get the optimization unknown names to be provided to the optimizer.

This is different from the design variable names provided by the user, since it depends on the formulation, and can include target values for coupling for instance in IDF.

Returns:

The optimization variable names.

Return type:

list[str]

get_sub_disciplines(recursive=False)

Accessor to the sub-disciplines.

This method lists the sub scenarios’ disciplines. It will list up to one level of disciplines contained inside another one unless the recursive argument is set to True.

Parameters:

recursive (bool) –

If True, the method will look inside any discipline that has other disciplines inside until it reaches a discipline without sub-disciplines, in this case the return value will not include any discipline that has sub-disciplines. If False, the method will list up to one level of disciplines contained inside another one, in this case the return value may include disciplines that contain sub-disciplines.

By default it is set to False.

Returns:

The sub-disciplines.

Return type:

list[MDODiscipline]

classmethod get_sub_options_grammar(**options)

Get the sub-options grammar.

When some options of the formulation depend on higher level options, the schema of the sub-options may be obtained here, mainly for use in the API.

Parameters:

**options (str) – The options required to deduce the sub-options grammar.

Returns:

Either None or the sub-options grammar.

Return type:

JSONGrammar

get_sub_scenarios()

List the disciplines that are actually scenarios.

Returns:

The scenarios.

Return type:

list[Scenario]

get_top_level_disc()

Return the disciplines which inputs are required to run the scenario.

A formulation seeks to compute the objective and constraints from the input variables. It structures the optimization problem into multiple levels of disciplines. The disciplines directly depending on these inputs are called top level disciplines.

By default, this method returns all disciplines. This method can be overloaded by subclasses.

Returns:

The top level disciplines.

Return type:

list[MDODiscipline]

get_x_mask_x_swap_order(masking_data_names, all_data_names=None)

Mask a vector from a subset of names, with respect to a set of names.

This method eventually swaps the order of the values if the order of the data names is inconsistent between these sets.

Parameters:
  • masking_data_names (Iterable[str]) – The names of the kept data.

  • all_data_names (Iterable[str] | None) – The set of all names. If None, use the design variables stored in the design space.

Returns:

The masked version of the input vector.

Raises:

ValueError – If the sizes or the sizes of variables are inconsistent.

Return type:

ndarray

get_x_names_of_disc(discipline)

Get the design variables names of a given discipline.

Parameters:

discipline (MDODiscipline) – The discipline.

Returns:

The names of the design variables.

Return type:

list[str]

mask_x_swap_order(masking_data_names, x_vect, all_data_names=None)

Mask a vector from a subset of names, with respect to a set of names.

This method eventually swaps the order of the values if the order of the data names is inconsistent between these sets.

Parameters:
  • masking_data_names (Iterable[str]) – The names of the kept data.

  • x_vect (ndarray) – The vector to mask.

  • all_data_names (Iterable[str] | None) – The set of all names. If None, use the design variables stored in the design space.

Returns:

The masked version of the input vector.

Raises:

IndexError – when the sizes of variables are inconsistent.

Return type:

ndarray

unmask_x_swap_order(masking_data_names, x_masked, all_data_names=None, x_full=None)

Unmask a vector from a subset of names, with respect to a set of names.

This method eventually swaps the order of the values if the order of the data names is inconsistent between these sets.

Parameters:
  • masking_data_names (Iterable[str]) – The names of the kept data.

  • x_masked (ndarray) – The boolean vector to unmask.

  • all_data_names (Iterable[str] | None) – The set of all names. If None, use the design variables stored in the design space.

  • x_full (ndarray | None) – The default values for the full vector. If None, use the zero vector.

Returns:

The vector related to the input mask.

Raises:

IndexError – when the sizes of variables are inconsistent.

Return type:

ndarray

DEFAULT_SCENARIO_RESULT_CLASS_NAME: ClassVar[str] = 'ScenarioResult'

The name of the ScenarioResult class to be used for post-processing.

NAME: ClassVar[str] = 'MDOFormulation'

The name of the MDO formulation.

property design_space: DesignSpace

The design space on which the formulation is applied.

property disciplines: list[MDODiscipline]

The disciplines of the MDO process.

opt_problem: OptimizationProblem

The optimization problem generated by the formulation from the disciplines.