gemseo / mda

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sequential_mda module

A chain of MDAs to build hybrids of MDA algorithms sequentially.

class gemseo.mda.sequential_mda.MDAGSNewton(disciplines, name=None, grammar_type=GrammarType.JSON, tolerance=1e-06, max_mda_iter=10, relax_factor=0.99, linear_solver='DEFAULT', max_mda_iter_gs=3, linear_solver_tolerance=1e-12, warm_start=False, use_lu_fact=False, coupling_structure=None, linear_solver_options=None, log_convergence=False, **newton_mda_options)[source]

Bases: MDASequential

Perform some Gauss-Seidel iterations and then Newton-Raphson iterations.

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

Parameters:

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

  • 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.

  • max_mda_iter (int) –

    The maximum iterations number for the MDA algorithm.

    By default it is set to 10.

  • relax_factor (float) –

    The relaxation factor.

    By default it is set to 0.99.

  • linear_solver (str) –

    The name of the linear solver.

    By default it is set to “DEFAULT”.

  • max_mda_iter_gs (int) –

    The maximum number of iterations of the Gauss-Seidel MDA.

    By default it is set to 3.

  • 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.

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

  • log_convergence (bool) –

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

    By default it is set to False.

  • **newton_mda_options (float | str | None) – The options for the Newton MDA.

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.

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.

warm_start: bool

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

class gemseo.mda.sequential_mda.MDASequential(disciplines, mda_sequence, name=None, grammar_type=GrammarType.JSON, max_mda_iter=10, tolerance=1e-06, linear_solver_tolerance=1e-12, warm_start=False, use_lu_fact=False, coupling_structure=None, linear_solver='DEFAULT', linear_solver_options=None)[source]

Bases: BaseMDA

A sequence of elementary MDAs.

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

Parameters:

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

  • mda_sequence (Sequence[BaseMDA]) – The sequence of MDAs.

  • 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”.

  • max_mda_iter (int) –

    The maximum iterations number for the MDA algorithm.

    By default it is set to 10.

  • 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.

  • 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.

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.

property log_convergence: bool

Whether to log the MDA convergence.

matrix_type: JacobianAssembly.JacobianType

The type of the matrix.

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.

warm_start: bool

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

Examples using MDASequential

Hybrid Jacobi/Newton MDA

Hybrid Jacobi/Newton MDA