grasp_ils_vnd_pr

givp.grasp_ils_vnd_pr

grasp_ils_vnd_pr(func: ObjectiveFn, bounds: BoundsLike, *, num_vars: int | None = None, minimize: bool | None = None, direction: str | None = None, config: GraspIlsVndConfig | None = None, initial_guess: Sequence[float] | None = None, iteration_callback: IterationCallback | None = None, seed: int | None = None, verbose: bool = False) -> OptimizeResult

Minimize (or maximize) a scalar function with GRASP-ILS-VND-PR.

Parameters:

Name	Type	Description	Default
func	ObjectiveFn	Objective callable mapping a 1-D np.ndarray to a scalar.	required
bounds	BoundsLike	Either a sequence of (low, high) pairs (SciPy style) or a (lower, upper) tuple of two equally-sized sequences.	required
num_vars	int | None	Optional explicit number of variables. Inferred from bounds when omitted.	None
minimize	bool | None	Boolean flag for the optimization sense. True minimizes, False maximizes. Preferred over direction for new code.	None
direction	str | None	'minimize' or 'maximize' (SciPy/Optuna style). Kept for backward compatibility. Ignored when minimize is given. Defaults to 'minimize' when neither flag is supplied.	None
config	GraspIlsVndConfig | None	Algorithm hyper-parameters. GraspIlsVndConfig() is used when omitted. Any sense field on config is overridden by the explicit minimize/direction kwargs when provided.	None
initial_guess	Sequence[float] | None	Optional warm-start vector, evaluated and inserted in the elite pool before the first iteration.	None
iteration_callback	IterationCallback | None	Optional callable invoked once per outer iteration with (iteration, best_cost_in_core_sign, best_solution).	None
seed	int | None	Optional integer seed for full reproducibility. When given, every internal RNG is derived deterministically from this seed, so two calls with the same inputs return the same result.	None
verbose	bool	If True, prints progress information to stdout.	False

Returns:

Name	Type	Description
OptimizeResult	OptimizeResult	Dataclass with x (best solution), fun (best
	OptimizeResult	objective value in the user's original sign) and metadata.

Raises:

Type	Description
ValueError	If both minimize and direction are passed with conflicting values, or if direction is not one of 'minimize' / 'maximize'.