mda_jacobi module¶
An MDA using the Jacobi algorithm for the Sobieski’s SSBJ use case.
- class gemseo.problems.sobieski.process.mda_jacobi.SobieskiMDAJacobi(n_processes=1, dtype=DataType.FLOAT, **mda_options)[source]
Bases:
MDAJacobi
An
MDAJacobi
for the Sobieski’s SSBJ use case.Initialize self. See help(type(self)) for accurate signature.
- Parameters:
n_processes (int) –
The maximum simultaneous number of threads, if
use_threading
is True, or processes otherwise, used to parallelize the execution.By default it is set to 1.
dtype (SobieskiBase.DataType) –
The NumPy type for data arrays, either “float64” or “complex128”.
By default it is set to “float64”.
**mda_options (Any) – The options of the MDA.
- assembly: JacobianAssembly
- cache: AbstractCache | None
The cache containing one or several executions of the discipline according to the cache policy.
- coupling_structure: MDOCouplingStructure
The coupling structure to be used by the MDA.
- 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}}
.
- lin_cache_tol_fact: float
The tolerance factor to cache the Jacobian.
- linear_solver: str
The name of the linear solver.
- linear_solver_options: Mapping[str, Any]
The options of the linear solver.
- linear_solver_tolerance: float
The tolerance of the linear solver in the adjoint equation.
- matrix_type: JacobianAssembly.JacobianType
The type of the matrix.
- name: str
The name of the discipline.
- normed_residual: float
The normed residual.
- output_grammar: BaseGrammar
The output grammar.
- re_exec_policy: ReExecutionPolicy
The policy to re-execute the same discipline.
- reset_history_each_run: bool
Whether to reset the history of MDA residuals before each run.
- 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.
- scaling: ResidualScaling
The scaling method applied to MDA residuals for convergence monitoring.
- tolerance: float
The tolerance of the iterative direct coupling solver.
- use_lu_fact: bool
Whether to store a LU factorization of the matrix.
- warm_start: bool
Whether the second iteration and ongoing start from the previous solution.