gemseo.problems.mdo.scalable.parametric.core.scalable_problem module#
The scalable problem.
- class ScalableProblem(discipline_settings=((1, 1), (1, 1)), d_0=1, add_random_variables=False, alpha=0.5, seed=0)[source]#
Bases:
object
The scalable problem.
It builds a set of strongly coupled scalable disciplines completed by a system discipline computing the objective function and the constraints.
These disciplines are defined on a unit design space, i.e. design variables belongs to \([0, 1]\).
- Parameters:
discipline_settings (Sequence[ScalableDisciplineSettings]) --
The configurations of the different scalable disciplines.
By default it is set to (ScalableDisciplineSettings(d_i=1, p_i=1), ScalableDisciplineSettings(d_i=1, p_i=1)).
d_0 (int) --
The size of the shared design variable \(x_0\).
By default it is set to 1.
add_random_variables (bool) --
Whether to add a centered random variable \(u_i\) on the output of the \(i\)-th scalable discipline.
By default it is set to False.
alpha (float) --
The proportion of feasible design points.
By default it is set to 0.5.
seed (int) --
The seed for reproducibility.
By default it is set to 0.
- compute_y(x, u=None)[source]#
Compute the coupling vector \(y\).
- Parameters:
x (RealArray) -- The design point at which to compute \(y\).
u (RealArray | None) -- The uncertain point at which to compute \(y\), if any.
- Returns:
The coupling vector \(y\) at the design point \(x\) and the uncertain point \(u\).
- Return type:
RealArray
- differentiate_y(x, u=None)[source]#
Compute the derivatives of the coupling output \(y\).
- Parameters:
x (RealArray) -- The design point at which to compute \(y\).
u (RealArray | None) -- The uncertain point at which to compute \(y\), if any.
- Returns:
The derivatives of the coupling output \(y\) at the design point \(x\) and the uncertain point \(u\).
- Return type:
RealArray
- design_space: _DESIGN_SPACE_CLASS#
The design space.
- property main_discipline: _MAIN_DISCIPLINE_CLASS#
The main discipline.
- qp_problem: QuadraticProgrammingProblem#
The quadratic programming problem.