gemseo / problems / scalable / parametric / disciplines

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scalable_discipline module

The scalable discipline.

class gemseo.problems.scalable.parametric.disciplines.scalable_discipline.ScalableDiscipline(index, a_i, D_i0, D_ii, C_ij, **default_input_values)[source]

Bases: BaseDiscipline

A scalable discipline.

It computes the output \(y_i=a_i-D_{i,0}x_0-D_{i,i}x_i+\sum_{j=1\atop j\neq i}^N C_{i,j}y_j\).

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

Parameters:

  • index (int) – The index \(i\) of the scalable discipline.

  • a_i (NDArray) – The offset vector \(a_i\).

  • D_i0 (NDArray) – The coefficient matrix \(D_{i,0}\) to multiply the shared design variable \(x_0\).

  • D_ii (NDArray) – The coefficient matrix \(D_{i,i}\) to multiply the local design variable \(x_i\).

  • C_ij (Mapping[str, NDArray[float]]) – The coefficient matrices \(\left(C_{i,j}\right)_{j=1\atop j\neq i}^N\) where \(C_{i,j}\) is used to multiply the coupling variable \(y_j\).

  • **default_input_values (NDArray[float]) – The default values of the input variables.

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: MutableMapping[str, MutableMapping[str, ndarray | csr_array | JacobianOperator]]

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: dict[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.