constraint_aggregation module¶

An MDODiscipline to aggregate constraints.

class gemseo.disciplines.constraint_aggregation.ConstraintAggregation(constraint_names, aggregation_function, name=None, **options)[source]

Bases: MDODiscipline

A discipline that aggregates the constraints computed by other disciplines.

An efficient alternative to constraint aggregation in the optimization problem is to aggregate the constraint in a discipline.

This can be included in a MDO formulation, and in particular in an MDA, so only one adjoint calculation can be performed for the aggregated constraint instead of one adjoint per original constraint dimension.

See [KH15] and [KS83].

Initialize self. See help(type(self)) for accurate signature.

Parameters:

constraint_names (Sequence[str]) – The names of the constraints to aggregate, which must be discipline outputs.
aggregation_function (EvaluationFunction) – The aggregation function or its name, e.g. IKS, KS, POS_SUM and SUM.
name (str | None) – The name of the discipline.
**options (Any) – The options for the aggregation method.

Raises:

ValueError – If the method is not supported.

class EvaluationFunction(value)[source]

Bases: StrEnum

A function to compute an aggregation of constraints.

IKS = 'IKS': The induces exponential function.

KS = 'KS': The Kreisselmeier–Steinhauser function.

MAX = 'MAX': The maximum function.

POS_SUM = 'POS_SUM': The positive sum squared function.

SUM = 'SUM': The sum squared function.

cache: AbstractCache | None: The cache containing one or several executions of the discipline according to the cache policy.

data_processor: DataProcessor: A tool to pre- and post-process discipline data.

exec_for_lin: bool: Whether the last execution was due to a linearization.

input_grammar: BaseGrammar: The input grammar.

jac: dict[str, dict[str, ndarray]]

The Jacobians of the outputs wrt inputs.

The structure is {output: {input: matrix}}.

name: str: The name of the discipline.

output_grammar: BaseGrammar: The output grammar.

re_exec_policy: ReExecutionPolicy: The policy to re-execute the same discipline.

residual_variables: Mapping[str, str]: The output variables mapping to their inputs, to be considered as residuals; they shall be equal to zero.

run_solves_residuals: bool: Whether the run method shall solve the residuals.