rosenbrock module¶
The Rosenbrock analytic problem.
- class gemseo.problems.analytical.rosenbrock.RosenMF(dimension=2)[source]
Bases:
MDODisciplineA multi-fidelity Rosenbrock discipline.
Its expression is \(\mathrm{fidelity} * \mathrm{Rosenbrock}(x)\) where both \(\mathrm{fidelity}\) and \(x\) are provided as input data.
Initialize self. See help(type(self)) for accurate signature.
- Parameters:
dimension (int) –
The dimension of the design space.
By default it is set to 2.
- 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.
- class gemseo.problems.analytical.rosenbrock.Rosenbrock(n_x=2, l_b=-2.0, u_b=2.0, scalar_var=False, initial_guess=None)[source]
Bases:
OptimizationProblemThe Rosenbrock optimization problem.
\[f(x) = \sum_{i=2}^{n_x} 100(x_{i} - x_{i-1}^2)^2 + (1 - x_{i-1})^2\]with the default
DesignSpace\([-0.2,0.2]^{n_x}\).- Parameters:
n_x (int) –
The dimension of the design space.
By default it is set to 2.
l_b (float) –
The lower bound (common value to all variables).
By default it is set to -2.0.
u_b (float) –
The upper bound (common value to all variables).
By default it is set to 2.0.
scalar_var (bool) –
If
True, the design space will contain only scalar variables (as many as the problem dimension); ifFalse, the design space will contain a single multidimensional variable (whose size equals the problem dimension).By default it is set to False.
initial_guess (ndarray | None) – The initial guess for optimal solution.
- get_solution()[source]
Return the theoretical optimal value.
- constraints: list[MDOFunction]
The constraints.
- current_iter: int
The current iteration.
- database: Database
The database to store the optimization problem data.
- design_space: DesignSpace
The design space on which the optimization problem is solved.
- eq_tolerance: float
The tolerance for the equality constraints.
- fd_step: float
The finite differences step.
- ineq_tolerance: float
The tolerance for the inequality constraints.
- max_iter: int
The maximum iteration.
- new_iter_observables: list[MDOFunction]
The observables to be called at each new iterate.
- nonproc_constraints: list[MDOFunction]
The non-processed constraints.
- nonproc_new_iter_observables: list[MDOFunction]
The non-processed observables to be called at each new iterate.
- nonproc_objective: MDOFunction
The non-processed objective function.
- nonproc_observables: list[MDOFunction]
The non-processed observables.
- observables: list[MDOFunction]
The observables.
- pb_type: ProblemType
The type of optimization problem.
- preprocess_options: dict
The options to pre-process the functions.
- solution: OptimizationResult | None
The solution of the optimization problem if solved; otherwise
None.
- stop_if_nan: bool
Whether the optimization stops when a function returns
NaN.
- use_standardized_objective: bool
Whether to use standardized objective for logging and post-processing.
The standardized objective corresponds to the original one expressed as a cost function to minimize. A
DriverLibraryworks with this standardized objective and theDatabasestores its values. However, for convenience, it may be more relevant to log the expression and the values of the original objective.
