Optimization

Run an optimization through vamos.optimize. For a first execution rather than a signature lookup, start with the Quickstart.

Stable public API. The 1.x compatibility policy defines the supported surface; import from the public facade shown below.

from vamos import optimize

Quickstart · Results · Algorithm configuration

max_evaluations is a hard evaluation budget subject to documented algorithm cardinality requirements. An explicit integer seed controls the built-in stochastic path in the same materially relevant environment; cross-backend bitwise equality is not promised. The generated reference below defines the arguments and their defaults.

optimize(problem, *, algorithm='auto', max_evaluations=None, termination=None, pop_size=None, engine=None, seed=DEFAULT_SEED, verbose=False, n_var=None, n_obj=None, problem_kwargs=None, algorithm_config=None, eval_strategy=None, live_viz=None, checkpoint=None)

optimize(problem: str | ProblemProtocol, *, algorithm: AlgorithmName | str = 'auto', max_evaluations: int | None = None, termination: TerminationSpec | None = None, pop_size: int | None = None, engine: EngineName | str | None = None, seed: int | None = 42, verbose: bool = False, n_var: int | None = None, n_obj: int | None = None, problem_kwargs: Mapping[str, object] | None = None, algorithm_config: AlgorithmConfigProtocol | None = None, eval_strategy: EvaluationBackend | str | None = None, live_viz: LiveVisualization | None = None, checkpoint: CheckpointPayload | None = None) -> OptimizationResult

optimize(problem: str | ProblemProtocol, *, algorithm: AlgorithmName | str = 'auto', max_evaluations: int | None = None, termination: TerminationSpec | None = None, pop_size: int | None = None, engine: EngineName | str | None = None, seed: list[int] | tuple[int, ...], verbose: bool = False, n_var: int | None = None, n_obj: int | None = None, problem_kwargs: Mapping[str, object] | None = None, algorithm_config: AlgorithmConfigProtocol | None = None, eval_strategy: EvaluationBackend | str | None = None, live_viz: LiveVisualization | None = None, checkpoint: CheckpointPayload | None = None) -> StudyResult

Unified entry point for VAMOS optimization.

This function consolidates multiple APIs into a single powerful interface: - Accepts problem names (strings) or instances - Supports AutoML with algorithm="auto" - Handles multi-run studies with seed=[0,1,2,...] - Prefer optimize(...) for all runs (explicit options are available).

Parameters:

Name	Type	Description	Default
problem	str | ProblemProtocol	Problem name (for registered problems) or a problem instance.	required
algorithm	AlgorithmName | str	Algorithm name or "auto" for automatic selection.	"auto"
max_evaluations	int | None	Maximum function evaluations. Auto-determined when omitted.	None
termination	TerminationSpec | None	Explicit termination pair for advanced runs that also pass algorithm_config. For example ("max_evaluations", 10000).	None
pop_size	int | None	Population size. Auto-determined when omitted.	None
engine	EngineName | str | None	Backend engine (for example "numpy", "numba", "moocore", or "auto").	None
seed	int | None | list[int] | tuple[int, ...]	Random seed for one run, None to generate and record a seed before execution, or a sequence of explicit seeds for multi-run studies.	``42``
verbose	bool	Enable VAMOS logging for the run.	``False``
n_var	int | None	Override problem dimensions when using a registered string problem key.	None
n_obj	int | None	Override problem dimensions when using a registered string problem key.	None
problem_kwargs	Mapping[str, object] | None	Extra keyword arguments forwarded to problem instantiation.	None
algorithm_config	AlgorithmConfigProtocol | None	Explicit algorithm config object.	None
eval_strategy	EvaluationBackend | str | None	Evaluation backend name or backend instance.	None
live_viz	LiveVisualization | None	Live visualization callback.	None
checkpoint	CheckpointPayload | None	Warm-start checkpoint for compatible algorithms. Multi-seed runs do not accept checkpoints.	None

Returns:

Type	Description
OptimizationResult | StudyResult	A single-run result for scalar seed input, or a sequence-compatible StudyResult when seed is a list/tuple.

Raises:

Type	Description
ConfigurationError	If inputs are invalid or the algorithm/engine combination is not supported.
Examples:	AutoML mode - zero config result = vamos.optimize("zdt1") Specify algorithm result = vamos.optimize("zdt1", algorithm="moead", max_evaluations=5000) Multi-seed study study = vamos.optimize("zdt1", seed=[0, 1, 2, 3, 4]) study.mean("evaluations")