bluemira.geometry.optimisation._optimise

Classes

GeomOptimiserResult

Container for the result of a geometry optimisation.

Functions

optimise_geometry(, algorithm, opt_conditions, ...)

Minimise the given objective function for a geometry parameterisation.

Module Contents

class bluemira.geometry.optimisation._optimise.GeomOptimiserResult

Bases: Generic[_GeomT]

Inheritance diagram of bluemira.geometry.optimisation._optimise.GeomOptimiserResult

Container for the result of a geometry optimisation.

geom: _GeomT

The geometry parameterisation with optimised parameters.

f_x: float

The evaluation of the optimised parameterisation.

n_evals: int

The number of evaluations of the objective function in the optimisation.

history: list[tuple[numpy.ndarray, float]]

The history of the parameterisation at each iteration.

constraint_history: list[tuple[numpy.ndarray, Ellipsis]]

The history of the constraints at each iteration.

constraints_satisfied: bool | None = None

Whether all constraints have been satisfied to within the required tolerance.

Is None if constraints have not been checked.

bluemira.geometry.optimisation._optimise.optimise_geometry(geom: _GeomT, f_objective: bluemira.geometry.optimisation.typed.GeomOptimiserObjective, df_objective: bluemira.geometry.optimisation.typed.GeomOptimiserCallable | None = None, *, keep_out_zones: collections.abc.Iterable[bluemira.geometry.wire.BluemiraWire | KeepOutZoneDict | bluemira.geometry.optimisation._tools.KeepOutZone] = (), algorithm: bluemira.optimisation._algorithm.AlgorithmType = Algorithm.SLSQP, opt_conditions: collections.abc.Mapping[str, int | float] | None = None, opt_parameters: collections.abc.Mapping[str, Any] | None = None, eq_constraints: collections.abc.Iterable[bluemira.geometry.optimisation.typed.GeomConstraintT] = (), ineq_constraints: collections.abc.Iterable[bluemira.geometry.optimisation.typed.GeomConstraintT] = (), keep_history: bool = False, check_constraints: bool = True, check_constraints_warn: bool = True) → GeomOptimiserResult[_GeomT]

Minimise the given objective function for a geometry parameterisation.

Parameters:

  • geom (_GeomT) – The geometry to optimise the parameters of. The existing parameterisation is used as the initial guess in the optimisation.

  • f_objective (bluemira.geometry.optimisation.typed.GeomOptimiserObjective) – The objective function to minimise. Must take as an argument a GeometryParameterisation and return a float.

  • df_objective (bluemira.geometry.optimisation.typed.GeomOptimiserCallable | None) – The derivative of the objective function, by default None. If not given, an approximation of the derivative is made using the ‘central differences’ method. This argument is ignored if a non-gradient based algorithm is used.

  • keep_out_zones (collections.abc.Iterable[bluemira.geometry.wire.BluemiraWire | KeepOutZoneDict | bluemira.geometry.optimisation._tools.KeepOutZone]) – An iterable of keep-out zones: closed wires that the geometry must not intersect. Each item can be given as a KeepOutZone, or a dictionary with keys the same as the properties of the class KeepOutZone, or just a BluemiraWire.

  • algorithm (bluemira.optimisation._algorithm.AlgorithmType) – The optimisation algorithm to use, by default Algorithm.SLSQP.

  • opt_conditions (collections.abc.Mapping[str, int | float] | None) –

    The stopping conditions for the optimiser. Supported conditions are:

    • ftol_abs: float

    • ftol_rel: float

    • xtol_abs: float

    • xtol_rel: float

    • max_eval: int

    • max_time: float

    • stop_val: float

    for defaults see AlgorithmDefaultConditions.

  • opt_parameters (collections.abc.Mapping[str, Any] | None) – The algorithm-specific optimisation parameters.

  • eq_constraints (collections.abc.Iterable[bluemira.geometry.optimisation.typed.GeomConstraintT]) –

    The equality constraints for the optimiser.

    A dict with keys:

    • f_constraint: the constraint function.

    • tolerance: the tolerances in each constraint dimension.

    • df_constraint (optional): the derivative of the constraint

      function. If not given, a numerical approximation of the gradient is made (if a gradient is required).

    The constraint is a vector-valued, non-linear, equality constraint of the form \(f_{c}(g) = 0\).

    The constraint function should have the form \(f(g) \rightarrow y\), where:

    • \(g\) is a geometry parameterisation.

    • \(y\) is a numpy array containing the values of the constraint at \(g\), with size \(m\), where \(m\) is the dimensionality of the constraint.

    The tolerance array must have the same dimensionality as the constraint.

    The gradient function should have the same form as the constraint function, however its output should have size \(n \times m\) where, again, \(m\) is the dimensionality of the constraint and \(n\) is the number of parameters in the geometry parameterisation.

    Equality constraints are only supported by algorithms:

    • SLSQP

    • COBYLA

    • ISRES

  • ineq_constraints (collections.abc.Iterable[bluemira.geometry.optimisation.typed.GeomConstraintT])

  • keep_history (bool)

  • check_constraints (bool)

  • check_constraints_warn (bool)

Return type:

GeomOptimiserResult[_GeomT]

ineq_constraints:

The geometric inequality constraints for the optimiser. This argument has the same form as the eq_constraint argument, but each constraint is of the form \(f_{c}(g) \le 0\).

Inequality constraints are only supported by algorithms:

  • SLSQP

  • COBYLA

  • ISRES

keep_history:

Whether or not to record the history of the optimisation parameters at each iteration. Note that this can significantly impact the performance of the optimisation. (default: False)

check_constraints:

Whether to check all constraints have been satisfied at the end of the optimisation, and warn if they have not. Note that, if this is set to False, the result’s constraints_satisfied attribute will be set to None.

check_constraints_warn:

Whether to print a warning that constraints have not been satisfied at the end of an optimisation. This argument has no effect if check_constraints is False.

Return type:

The result of the optimisation.

Parameters: