gemseo.core.mdo_functions.collections.constraints module

A mutable sequence of constraints.

class Constraints(design_space, tolerances)

Bases: Functions

A mutable sequence of constraints.

Parameters:

• design_space (DesignSpace) -- The design space.

• tolerances (ConstraintTolerances) -- The constraint tolerances.

AggregationFunction

alias of EvaluationFunction

aggregate(constraint_index, method=EvaluationFunction.MAX, groups=(), **options)

Aggregate a constraint to generate a reduced dimension constraint.

Parameters:

• constraint_index (int) -- The index of the constraint in constraints.

• method (Callable[[RealArray], float] | AggregationFunction) --

  The aggregation method, e.g. "max", "lower_bound_KS", "upper_bound_KS"``or ``"IKS".

  By default it is set to "MAX".

• groups (Iterable[Sequence[int]]) --

  The groups of components of the constraint to aggregate to produce one aggregation constraint per group of components; if empty, a single aggregation constraint is produced.

  By default it is set to ().

• **options (Any) -- The options of the aggregation method.

Raises:

KeyError -- When the given index is greater or equal to the number of constraints.

Return type:

None

format(function, value=0.0, constraint_type=None, positive=False)

Format a constraint.

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 \(c(x)\leq a\).

Parameters:

• function (MDOFunction) -- The function \(c\).

• value (float) --

  The value \(a\).

  By default it is set to 0.0.

• constraint_type (MDOFunction.ConstraintType | None) -- The type of the constraint. If None, function.f_type must be either MDOFunction.ConstraintType.INEQ or MDOFunction.ConstraintType.EQ.

• positive (bool) --

  Whether the inequality constraint is positive.

  By default it is set to False.

Returns:

A formatted constraint ready to be added to the sequence.

Raises:

• TypeError -- When the constraint of a linear optimization problem is not an MDOLinearFunction.

• ValueError -- When the type of the constraint is missing.

Return type:

MDOFunction

get_active(x_vect, tol=1e-06)

Indicate the active components of the different inequality constraints.

Parameters:

• x_vect (RealArray) -- The vector of design variables.

• tol (float) --

  The tolerance for deciding whether a constraint is active.

  By default it is set to 1e-06.

Returns:

For each constraint, a boolean indicator of activation of its different components.

Return type:

dict[MDOFunction, RealArray]

get_equality_constraints()

Return the equality constraints.

Yields:

The equality constraints.

Return type:

Iterator[MDOFunction]

get_inequality_constraints()

Return the inequality constraints.

Yields:

The inequality constraints.

Return type:

Iterator[MDOFunction]

get_number_of_unsatisfied_constraints(values=mappingproxy({}))

Return the number of scalar constraints not satisfied by design variables.

Parameters:

values (Mapping[str, float | RealArray]) --

The values of the constraints.

By default it is set to {}.

Returns:

The number of unsatisfied scalar constraints.

Return type:

int

is_constraint_satisfied(constraint_type, constraint_value)

Determine if an evaluation satisfies a constraint within a given tolerance.

Parameters:

• constraint_type (MDOFunction.ConstraintType) -- The type of the constraint.

• constraint_value (RealArray) -- The value of the constraint.

Returns:

Whether a value satisfies a constraint.

Return type:

bool

is_point_feasible(point)

Check if a point is feasible.

Note

If the value of a constraint is absent from this point, then this constraint will be considered satisfied.

Parameters:

point (Mapping[str, RealOrComplexArray]) -- An optimization point defined by variable values.

Returns:

The feasibility of the point.

Return type:

bool

property aggregated_constraint_indices: list[int]

The indices of the aggregated constraints.

property tolerances: ConstraintTolerances

The constraint tolerances.