MDA algorithms

Warning

Some algorithms may require the installation of GEMSEO with all its features and some others may depend on plugins.

Note

All the features of the wrapped optimization libraries may not be exposed through GEMSEO.

GSNewtonMDA

Module: gemseo.mda.sequential_mda

Required parameters
  • disciplines : Sequence[MDODiscipline]

    The disciplines from which to compute the MDA.

Optional parameters
  • coupling_structure : MDOCouplingStructure | None, optional

    The coupling structure to be used by the MDA. If None, it is created from disciplines.

    By default it is set to None.

  • grammar_type : str, optional

    The type of the input and output grammars, either MDODiscipline.JSON_GRAMMAR_TYPE or MDODiscipline.SIMPLE_GRAMMAR_TYPE.

    By default it is set to JSONGrammar.

  • linear_solver : str, optional

    The type of linear solver to be used to solve the Newton problem.

    By default it is set to DEFAULT.

  • linear_solver_options : Mapping[str, Any], optional

    The options passed to the linear solver factory.

    By default it is set to None.

  • linear_solver_tolerance : float, optional

    The tolerance of the linear solver in the adjoint equation.

    By default it is set to 1e-12.

  • log_convergence : bool, optional

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

    By default it is set to False.

  • max_mda_iter : int, optional

    The maximum iterations number for the MDA algorithm.

    By default it is set to 10.

  • max_mda_iter_gs : int, optional

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

    By default it is set to 3.

  • name : str | None, optional

    The name to be given to the MDA. If None, use the name of the class.

    By default it is set to None.

  • relax_factor : float, optional

    The relaxation factor.

    By default it is set to 0.99.

  • tolerance : float, optional

    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.

  • use_lu_fact : bool, optional

    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.

  • warm_start : bool, optional

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

    By default it is set to False.

  • **newton_mda_options : float

    The options passed to MDANewtonRaphson.

MDAChain

Module: gemseo.mda.mda_chain

Required parameters
  • disciplines : Sequence[MDODiscipline]

    The disciplines from which to compute the MDA.

Optional parameters
  • chain_linearize : bool, optional

    Whether to linearize the chain of execution. Otherwise, linearize the overall MDA with base class method. This last option is preferred to minimize computations in adjoint mode, while in direct mode, linearizing the chain may be cheaper.

    By default it is set to False.

  • coupling_structure : MDOCouplingStructure | None, optional

    The coupling structure to be used by the MDA. If None, it is created from disciplines.

    By default it is set to None.

  • grammar_type : str, optional

    The type of the input and output grammars, either MDODiscipline.JSON_GRAMMAR_TYPE or MDODiscipline.SIMPLE_GRAMMAR_TYPE.

    By default it is set to JSONGrammar.

  • inner_mda_name : str, optional

    The class name of the inner-MDA.

    By default it is set to MDAJacobi.

  • linear_solver : str, optional

    The name of the linear solver.

    By default it is set to DEFAULT.

  • linear_solver_options : Mapping[str, Any], optional

    The options passed to the linear solver factory.

    By default it is set to None.

  • linear_solver_tolerance : float, optional

    The tolerance of the linear solver in the adjoint equation.

    By default it is set to 1e-12.

  • log_convergence : bool, optional

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

    By default it is set to False.

  • max_mda_iter : int, optional

    The maximum iterations number for the MDA algorithm.

    By default it is set to 20.

  • n_processes : int, optional

    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 2.

  • name : str | None, optional

    The name to be given to the MDA. If None, use the name of the class.

    By default it is set to None.

  • sub_coupling_structures : Iterable[MDOCouplingStructure] | None, optional

    The coupling structures to be used by the inner-MDAs. If None, they are created from the sub-disciplines.

    By default it is set to None.

  • tolerance : float, optional

    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.

  • use_lu_fact : bool, optional

    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.

  • **inner_mda_options : float | int | bool | str | None

    The options of the inner-MDAs.

MDAGaussSeidel

Module: gemseo.mda.gauss_seidel

Required parameters
  • disciplines : Sequence[MDODiscipline]

    The disciplines from which to compute the MDA.

Optional parameters
  • coupling_structure : MDOCouplingStructure | None, optional

    The coupling structure to be used by the MDA. If None, it is created from disciplines.

    By default it is set to None.

  • grammar_type : str, optional

    The type of the input and output grammars, either MDODiscipline.JSON_GRAMMAR_TYPE or MDODiscipline.SIMPLE_GRAMMAR_TYPE.

    By default it is set to JSONGrammar.

  • linear_solver : str, optional

    The name of the linear solver.

    By default it is set to DEFAULT.

  • linear_solver_options : Mapping[str, Any], optional

    The options passed to the linear solver factory.

    By default it is set to None.

  • linear_solver_tolerance : float, optional

    The tolerance of the linear solver in the adjoint equation.

    By default it is set to 1e-12.

  • log_convergence : bool, optional

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

    By default it is set to False.

  • max_mda_iter : int, optional

    The maximum iterations number for the MDA algorithm.

    By default it is set to 10.

  • name : str | None, optional

    The name to be given to the MDA. If None, use the name of the class.

    By default it is set to None.

  • over_relax_factor : float, optional

    The relaxation coefficient, used to make the method more robust, if 0<over_relax_factor<1 or faster if 1<over_relax_factor<=2. If over_relax_factor =1., it is deactivated.

    By default it is set to 1.0.

  • tolerance : float, optional

    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.

  • use_lu_fact : bool, optional

    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.

  • warm_start : bool, optional

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

    By default it is set to False.

MDAJacobi

Module: gemseo.mda.jacobi

Required parameters
  • disciplines : Sequence[MDODiscipline]

    The disciplines from which to compute the MDA.

Optional parameters
  • acceleration : str, optional

    The type of acceleration to be used to extrapolate the residuals and save CPU time by reusing the information from the last iterations, either None, "m2d", or "secant", "m2d" is faster but uses the 2 last iterations.

    By default it is set to m2d.

  • coupling_structure : MDOCouplingStructure | None, optional

    The coupling structure to be used by the MDA. If None, it is created from disciplines.

    By default it is set to None.

  • grammar_type : str, optional

    The type of the input and output grammars, either MDODiscipline.JSON_GRAMMAR_TYPE or MDODiscipline.SIMPLE_GRAMMAR_TYPE.

    By default it is set to JSONGrammar.

  • linear_solver : str, optional

    The name of the linear solver.

    By default it is set to DEFAULT.

  • linear_solver_options : Mapping[str, Any], optional

    The options passed to the linear solver factory.

    By default it is set to None.

  • linear_solver_tolerance : float, optional

    The tolerance of the linear solver in the adjoint equation.

    By default it is set to 1e-12.

  • log_convergence : bool, optional

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

    By default it is set to False.

  • max_mda_iter : int, optional

    The maximum iterations number for the MDA algorithm.

    By default it is set to 10.

  • n_processes : int, optional

    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 2.

  • name : str | None, optional

    The name to be given to the MDA. If None, use the name of the class.

    By default it is set to None.

  • tolerance : float, optional

    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.

  • use_lu_fact : bool, optional

    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.

  • use_threading : bool, optional

    Whether to use threads instead of processes to parallelize the execution; multiprocessing will copy (serialize) all the disciplines, while threading will share all the memory. This is important to note if you want to execute the same discipline multiple times, you shall use multiprocessing.

    By default it is set to True.

  • warm_start : bool, optional

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

    By default it is set to False.

MDANewtonRaphson

Module: gemseo.mda.newton

Required parameters
  • disciplines : Sequence[MDODiscipline]

    The disciplines from which to compute the MDA.

Optional parameters
  • coupling_structure : MDOCouplingStructure | None, optional

    The coupling structure to be used by the MDA. If None, it is created from disciplines.

    By default it is set to None.

  • grammar_type : str, optional

    The type of the input and output grammars, either MDODiscipline.JSON_GRAMMAR_TYPE or MDODiscipline.SIMPLE_GRAMMAR_TYPE.

    By default it is set to JSONGrammar.

  • linear_solver : str, optional

    The name of the linear solver.

    By default it is set to DEFAULT.

  • linear_solver_options : Mapping[str, Any], optional

    The options passed to the linear solver factory.

    By default it is set to None.

  • linear_solver_tolerance : float, optional

    The tolerance of the linear solver in the adjoint equation.

    By default it is set to 1e-12.

  • log_convergence : bool, optional

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

    By default it is set to False.

  • max_mda_iter : int, optional

    The maximum iterations number for the MDA algorithm.

    By default it is set to 10.

  • name : str | None, optional

    The name to be given to the MDA. If None, use the name of the class.

    By default it is set to None.

  • relax_factor : float, optional

    The relaxation factor in the Newton step.

    By default it is set to 0.99.

  • tolerance : float, optional

    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.

  • use_lu_fact : bool, optional

    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.

  • warm_start : bool, optional

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

    By default it is set to False.

MDAQuasiNewton

Module: gemseo.mda.newton

Required parameters
  • disciplines : Sequence[MDODiscipline]

    The disciplines from which to compute the MDA.

Optional parameters
  • coupling_structure : MDOCouplingStructure | None, optional

    The coupling structure to be used by the MDA. If None, it is created from disciplines.

    By default it is set to None.

  • grammar_type : str, optional

    The type of the input and output grammars, either MDODiscipline.JSON_GRAMMAR_TYPE or MDODiscipline.SIMPLE_GRAMMAR_TYPE.

    By default it is set to JSONGrammar.

  • linear_solver : str, optional

    The name of the linear solver.

    By default it is set to DEFAULT.

  • linear_solver_options : Mapping[str, Any], optional

    The options passed to the linear solver factory.

    By default it is set to None.

  • linear_solver_tolerance : float, optional

    The tolerance of the linear solver in the adjoint equation.

    By default it is set to 1e-12.

  • max_mda_iter : int, optional

    The maximum iterations number for the MDA algorithm.

    By default it is set to 10.

  • method : str, optional

    The name of the method in scipy root finding, among QUASI_NEWTON_METHODS.

    By default it is set to hybr.

  • name : str | None, optional

    The name to be given to the MDA. If None, use the name of the class.

    By default it is set to None.

  • tolerance : float, optional

    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.

  • use_gradient : bool, optional

    Whether to use the analytic gradient of the discipline.

    By default it is set to False.

  • use_lu_fact : bool, optional

    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.

  • warm_start : bool, optional

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

    By default it is set to False.

MDARoot

Module: gemseo.mda.newton

Required parameters
  • disciplines : Sequence[MDODiscipline]

    The disciplines from which to compute the MDA.

Optional parameters
  • coupling_structure : MDOCouplingStructure | None, optional

    The coupling structure to be used by the MDA. If None, it is created from disciplines.

    By default it is set to None.

  • grammar_type : str, optional

    The type of the input and output grammars, either MDODiscipline.JSON_GRAMMAR_TYPE or MDODiscipline.SIMPLE_GRAMMAR_TYPE.

    By default it is set to JSONGrammar.

  • linear_solver : str, optional

    The name of the linear solver.

    By default it is set to DEFAULT.

  • linear_solver_options : Mapping[str, Any], optional

    The options passed to the linear solver factory.

    By default it is set to None.

  • linear_solver_tolerance : float, optional

    The tolerance of the linear solver in the adjoint equation.

    By default it is set to 1e-12.

  • log_convergence : bool, optional

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

    By default it is set to False.

  • max_mda_iter : int, optional

    The maximum iterations number for the MDA algorithm.

    By default it is set to 10.

  • name : str | None, optional

    The name to be given to the MDA. If None, use the name of the class.

    By default it is set to None.

  • tolerance : float, optional

    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.

  • use_lu_fact : bool, optional

    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.

  • warm_start : bool, optional

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

    By default it is set to False.

MDASequential

Module: gemseo.mda.sequential_mda

Required parameters
  • disciplines : Sequence[MDODiscipline]

    The disciplines from which to compute the MDA.

  • mda_sequence : Sequence[MDA]

    The sequence of MDAs.

Optional parameters
  • coupling_structure : MDOCouplingStructure | None, optional

    The coupling structure to be used by the MDA. If None, it is created from disciplines.

    By default it is set to None.

  • grammar_type : str, optional

    The type of the input and output grammars, either MDODiscipline.JSON_GRAMMAR_TYPE or MDODiscipline.SIMPLE_GRAMMAR_TYPE.

    By default it is set to JSONGrammar.

  • linear_solver : str, optional

    The name of the linear solver.

    By default it is set to DEFAULT.

  • linear_solver_options : Mapping[str, Any], optional

    The options passed to the linear solver factory.

    By default it is set to None.

  • linear_solver_tolerance : float, optional

    The tolerance of the linear solver in the adjoint equation.

    By default it is set to 1e-12.

  • max_mda_iter : int, optional

    The maximum iterations number for the MDA algorithm.

    By default it is set to 10.

  • name : str | None, optional

    The name to be given to the MDA. If None, use the name of the class.

    By default it is set to None.

  • tolerance : float, optional

    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.

  • use_lu_fact : bool, optional

    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.

  • warm_start : bool, optional

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

    By default it is set to False.