bluemira.optimisation._nlopt.optimiser

Attributes

NLOPT_ALG_MAPPING

Classes

NloptOptimiser

Optimiser implementation using NLOpt as the backend.

Functions

`_check_algorithm`(...)	Validate, and convert, the given algorithm.
`_process_nlopt_result`(→ None)	Communicate to the user the NLopt optimisation result.

Module Contents

bluemira.optimisation._nlopt.optimiser.NLOPT_ALG_MAPPING

class bluemira.optimisation._nlopt.optimiser.NloptOptimiser(algorithm: bluemira.optimisation._algorithm.AlgorithmType, n_variables: int, f_objective: bluemira.optimisation.typed.ObjectiveCallable, df_objective: bluemira.optimisation.typed.OptimiserCallable | None = None, opt_conditions: collections.abc.Mapping[str, int | float] | None = None, opt_parameters: collections.abc.Mapping[str, Any] | None = None, *, keep_history: bool = False)

Bases: bluemira.optimisation._optimiser.Optimiser

Optimiser implementation using NLOpt as the backend.

Parameters:

algorithm (bluemira.optimisation._algorithm.AlgorithmType) –
The optimisation algorithm to use. Available algorithms are:
- SLSQP
- COBYLA
- SBPLX
- MMA
- BFGS
- DIRECT
- DIRECT_L
- CRS
- ISRES
n_variables (int) – The number of optimisation parameters.
f_objective (bluemira.optimisation.typed.ObjectiveCallable) – The objective function to minimise. This function must take one argument (a numpy array), and return a numpy array or float.
df_objective (bluemira.optimisation.typed.OptimiserCallable | None) – The derivative of the objective function. This must take the form: \(f(x) \rightarrow y\) where \(x\) is a numpy array containing the optimisation parameters, and \(y\) is a numpy array where each element \(i\) is the partial derivative \(\frac{\partial f(x)}{\partial x_{i}}\). If not given, and a gradient based algorithm is used, a numerical approximation of the gradient will be made.
opt_conditions (collections.abc.Mapping[str, int | float] | None) –
The stopping conditions for the optimiser. At least one stopping condition is required. Supported conditions are:
- ftol_abs: float
- ftol_rel: float
- xtol_abs: float
- xtol_rel: float
- max_eval: int
- max_time: float
- stop_val: float
opt_parameters (collections.abc.Mapping[str, Any] | None) – Parameters specific to the algorithm being used. Consult the NLopt documentation for these.
keep_history (bool) – Whether to record the history of each step of the optimisation. (default: False)

_keep_history = False

_opt

_eq_constraints: list[bluemira.optimisation._nlopt.functions.Constraint] = []

_ineq_constraints: list[bluemira.optimisation._nlopt.functions.Constraint] = []

property algorithm: bluemira.optimisation._algorithm.Algorithm

returns: the optimiser’s algorithm.

Return type:: bluemira.optimisation._algorithm.Algorithm

property opt_conditions: dict[str, float]

returns: the optimiser’s stopping conditions.

Return type:: dict[str, float]

property opt_parameters: collections.abc.Mapping[str, int | float]

returns: the optimiser algorithms’s parameters.

Return type:: collections.abc.Mapping[str, int | float]

property lower_bounds: numpy.ndarray

returns: the lower bounds for the optimisation parameters.

Return type:: numpy.ndarray

property upper_bounds: numpy.ndarray

returns: the upper bounds for the optimisation parameters.

Return type:: numpy.ndarray

add_eq_constraint(f_constraint: bluemira.optimisation.typed.OptimiserCallable, tolerance: numpy.ndarray, df_constraint: bluemira.optimisation.typed.OptimiserCallable | None = None) → None

Add an equality constraint.

See add_eq_constraint().

Raises:

OptimisationError – Algorithm does not support equality constraints

Parameters:

f_constraint (bluemira.optimisation.typed.OptimiserCallable)
tolerance (numpy.ndarray)
df_constraint (bluemira.optimisation.typed.OptimiserCallable | None)

Return type:

None

add_ineq_constraint(f_constraint: bluemira.optimisation.typed.OptimiserCallable, tolerance: numpy.ndarray, df_constraint: bluemira.optimisation.typed.OptimiserCallable | None = None) → None

Add an inequality constraint.

See add_ineq_constraint().

Raises:

OptimisationError – Algorithm does not support inequality constraints

Parameters:

f_constraint (bluemira.optimisation.typed.OptimiserCallable)
tolerance (numpy.ndarray)
df_constraint (bluemira.optimisation.typed.OptimiserCallable | None)

Return type:

None

optimise(x0: numpy.ndarray | None = None) → bluemira.optimisation._optimiser.OptimiserResult

Run the optimisation.

See optimise().

Returns:

The result of optimisation

Raises:

KeyboardInterrupt – Optimisation halted by user
OptimisationError – low level optimisation error

Parameters:

x0 (numpy.ndarray | None)

Return type:

bluemira.optimisation._optimiser.OptimiserResult

_get_constraint_history()

set_lower_bounds(bounds: numpy.typing.ArrayLike) → None

Set the lower bound for each optimisation parameter.

See set_lower_bounds().

Parameters:: bounds (numpy.typing.ArrayLike)
Return type:: None

set_upper_bounds(bounds: numpy.typing.ArrayLike) → None

Set the upper bound for each optimisation parameter.

See set_upper_bounds().

Parameters:: bounds (numpy.typing.ArrayLike)
Return type:: None

_get_previous_iter_result() → tuple[numpy.ndarray, float]

Returns:: the parameterisation and result from the previous iteration.
Return type:: tuple[numpy.ndarray, float]

_handle_round_off_error() → tuple[numpy.ndarray, float]

Handle a round-off error occurring in an optimisation.

It’s likely the last call was a decent solution, so return that (with a warning).

Returns:: the parameterisation and result from the previous iteration with a warning
Return type:: tuple[numpy.ndarray, float]

_set_algorithm(alg: bluemira.optimisation._algorithm.AlgorithmType) → None

Set the optimiser’s algorithm.

Parameters:: alg (bluemira.optimisation._algorithm.AlgorithmType)
Return type:: None

_set_objective_function(func: bluemira.optimisation.typed.ObjectiveCallable, df: bluemira.optimisation.typed.OptimiserCallable | None, n_variables: int) → None

Wrap and set the objective function.

Parameters:

func (bluemira.optimisation.typed.ObjectiveCallable)
df (bluemira.optimisation.typed.OptimiserCallable | None)
n_variables (int)

Return type:

None

_set_termination_conditions(opt_conditions: collections.abc.Mapping[str, int | float]) → None

Validate and set the termination conditions.

Parameters:: opt_conditions (collections.abc.Mapping[str, int | float])
Return type:: None

_set_algorithm_parameters(opt_parameters: collections.abc.Mapping) → None

Parameters:: opt_parameters (collections.abc.Mapping)
Return type:: None

bluemira.optimisation._nlopt.optimiser._check_algorithm(algorithm: bluemira.optimisation._algorithm.AlgorithmType) → bluemira.optimisation._algorithm.Algorithm

Validate, and convert, the given algorithm.

Returns:: validated and converted algorithm
Parameters:: algorithm (bluemira.optimisation._algorithm.AlgorithmType)
Return type:: bluemira.optimisation._algorithm.Algorithm

bluemira.optimisation._nlopt.optimiser._process_nlopt_result(opt: nlopt.opt, algorithm: bluemira.optimisation._algorithm.Algorithm) → None

Communicate to the user the NLopt optimisation result.

Usually this would be called when dealing with an error in an optimisation loop.

Parameters:

opt (nlopt.opt) – The optimiser to check
algorithm (bluemira.optimisation._algorithm.Algorithm) – The optimisation algorithm

Return type:

None