gemseo / mda

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base_mda_root module

Base module for Newton algorithm variants for solving MDAs.

class gemseo.mda.base_mda_root.BaseMDARoot(disciplines, max_mda_iter=10, name=None, grammar_type=GrammarType.JSON, tolerance=1e-06, linear_solver_tolerance=1e-12, warm_start=False, use_lu_fact=False, coupling_structure=None, log_convergence=False, linear_solver='DEFAULT', linear_solver_options=None, parallel=False, use_threading=True, n_processes=2, acceleration_method=AccelerationMethod.NONE, over_relaxation_factor=1.0)[source]

Bases: BaseMDASolver

Abstract class implementing MDAs based on (Quasi-)Newton methods.

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

Parameters:

  • disciplines (Sequence[MDODiscipline]) – The disciplines from which to compute the MDA.

  • max_mda_iter (int) –

    The maximum iterations number for the MDA algorithm.

    By default it is set to 10.

  • name (str | None) – The name to be given to the MDA. If None, use the name of the class.

  • grammar_type (MDODiscipline.GrammarType) –

    The type of the input and output grammars.

    By default it is set to “JSONGrammar”.

  • tolerance (float) –

    The tolerance of the iterative direct coupling solver; the norm of the current residuals divided by initial residuals norm shall be lower than the tolerance to stop iterating.

    By default it is set to 1e-06.

  • linear_solver_tolerance (float) –

    The tolerance of the linear solver in the adjoint equation.

    By default it is set to 1e-12.

  • warm_start (bool) –

    Whether the second iteration and ongoing start from the previous coupling solution.

    By default it is set to False.

  • use_lu_fact (bool) –

    Whether to store a LU factorization of the matrix when using adjoint/forward differentiation. to solve faster multiple RHS problem.

    By default it is set to False.

  • coupling_structure (MDOCouplingStructure | None) – The coupling structure to be used by the MDA. If None, it is created from disciplines.

  • log_convergence (bool) –

    Whether to log the MDA convergence, expressed in terms of normed residuals.

    By default it is set to False.

  • linear_solver (str) –

    The name of the linear solver.

    By default it is set to “DEFAULT”.

  • linear_solver_options (Mapping[str, Any] | None) – The options passed to the linear solver factory.

  • parallel (bool) –

    Whether to execute and linearize the disciplines in parallel.

    By default it is set to False.

  • use_threading (bool) –

    Whether to use threads instead of processes to parallelize the execution. Processes will copy (serialize) all the disciplines, while threads will share all the memory. If one wants to execute the same discipline multiple times then multiprocessing should be preferred.

    By default it is set to True.

  • n_processes (int) –

    The maximum simultaneous number of threads if use_threading is set to True, otherwise processes, used to parallelize the execution.

    By default it is set to 2.

  • acceleration_method (AccelerationMethod) –

    The acceleration method to be used to improve the convergence rate of the fixed point iteration method.

    By default it is set to “NoTransformation”.

  • over_relaxation_factor (float) –

    The over-relaxation factor.

    By default it is set to 1.0.

execute_all_disciplines(input_local_data, update_local_data=True)[source]

Execute all disciplines.

Parameters:

  • input_local_data (Mapping[str, NDArray]) – The input data of the disciplines.

  • update_local_data (bool) –

    Whether to update the local data from the disciplines.

    By default it is set to True.

Return type:

None

linearize_all_disciplines(input_data, execute=True)[source]

Linearize all disciplines.

Parameters:

  • input_data (Mapping[str, NDArray]) – The input data to be passed to the disciplines.

  • execute (bool) –

    Whether to start by executing the discipline with the input data for which to compute the Jacobian; this allows to ensure that the discipline was executed with the right input data; it can be almost free if the corresponding output data have been stored in the cache.

    By default it is set to True.

Return type:

None

all_couplings: list[str]

The names of all the coupling variables.

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.

n_processes: int

The maximum number of simultaneous threads, if use_threading is True, or processes otherwise, used to parallelize the execution.

name: str

The name of the discipline.

norm0: float | None

The reference residual, if any.

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_history: list[float]

The history of the MDA residuals.

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.

strong_couplings: list[str]

The names of the strong coupling variables.

tolerance: float

The tolerance of the iterative direct coupling solver.

use_lu_fact: bool

Whether to store a LU factorization of the matrix.

use_threading: bool

Whether to use threads instead of processes to parallelize the execution.

warm_start: bool

Whether the second iteration and ongoing start from the previous solution.