gemseo / problems / scalable / parametric / core

scalable_problem module¶

The scalable problem.

class gemseo.problems.scalable.parametric.core.scalable_problem.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 (NDArray[float]) – A design point.
u (NDArray[float] | None) – An uncertain point, if any.

Returns:

The coupling vector associated with the design point \(x\) and the uncertain vector \(U\) if any.

Return type:

NDArray[float]

design_space: _DESIGN_SPACE_CLASS¶: The design space.

disciplines: list[_MAIN_DISCIPLINE_CLASS | _SCALABLE_DISCIPLINE_CLASS]¶: The disciplines.

property main_discipline: _MAIN_DISCIPLINE_CLASS¶: The main discipline.

qp_problem: QuadraticProgrammingProblem¶: The quadratic programming problem.

property scalable_disciplines: list[_SCALABLE_DISCIPLINE_CLASS]¶: The scalable disciplines.

Examples using ScalableProblem¶

Parametric scalable MDO problem - MDF