smt_optim package

smt_optim package#

Subpackages#

Submodules#

smt_optim.frameworks module#

minimize(objective: list[Callable], design_space: DesignSpace | ndarray, method: str | None = None, costs: list = [1], max_iter: int = 100, max_budget: float = inf, constraints: list = [], driver_kwargs: dict = {}, strategy_kwargs: dict = {}, verbose: bool = True) → State[source]#

Minimize the objective function with respect to the problem properties.

This function provides a unified interface to perform optimization using different acquisition-based strategies (e.g., EGO, SEGO, MFSEGO, VFPI). It supports mono-fidelity and multi-fidelity optimization, with optional constraints and budget control.

Parameters:

  • objective (list[Callable]) – List of objective function callables ordered by increasing fidelity. For mono-fidelity problems, the function callable must still be provided as a single-element list.

  • design_space (ds.DesignSpace | np.ndarray) – Problem design space. If a np.ndarray is provided, the problem will be treated as fully continuous.

  • method (str {"ego", "sego", "mfsego", "vfpi"} or None, optional) – Optimization framework to use. If None, the method is selected automatically based on the problem characteristics between {“ego”, “sego”, “mfsego”}.

  • costs (list[float], optional) – Evaluation cost associated with each fidelity level, ordered from lowest to highest fidelity. Required for multi-fidelity optimization. Defaults to [1] for mono-fidelity problems.

  • max_iter (int, default=100) – Maximum number of optimization iteration.

  • max_budget (float, default=np.inf) – Maximum total evaluation budget.The optimization stops when this budget is exhausted.

  • constraints (list[dict], optional) – List of the constraint definitions.

  • driver_kwargs (dict, optional) – Additional keyword arguments passed to the optimization driver.

  • strategy_kwargs (dict, optional) – Additional keyword arguments passed to the acquisition strategy.

  • verbose (bool) – If True, prints progress information during optimization.

Returns:

Final optimization state.

Return type:

State

Module contents#

minimize(objective: list[Callable], design_space: DesignSpace | ndarray, method: str | None = None, costs: list = [1], max_iter: int = 100, max_budget: float = inf, constraints: list = [], driver_kwargs: dict = {}, strategy_kwargs: dict = {}, verbose: bool = True) → State[source]#

Minimize the objective function with respect to the problem properties.

This function provides a unified interface to perform optimization using different acquisition-based strategies (e.g., EGO, SEGO, MFSEGO, VFPI). It supports mono-fidelity and multi-fidelity optimization, with optional constraints and budget control.

Parameters:

  • objective (list[Callable]) – List of objective function callables ordered by increasing fidelity. For mono-fidelity problems, the function callable must still be provided as a single-element list.

  • design_space (ds.DesignSpace | np.ndarray) – Problem design space. If a np.ndarray is provided, the problem will be treated as fully continuous.

  • method (str {"ego", "sego", "mfsego", "vfpi"} or None, optional) – Optimization framework to use. If None, the method is selected automatically based on the problem characteristics between {“ego”, “sego”, “mfsego”}.

  • costs (list[float], optional) – Evaluation cost associated with each fidelity level, ordered from lowest to highest fidelity. Required for multi-fidelity optimization. Defaults to [1] for mono-fidelity problems.

  • max_iter (int, default=100) – Maximum number of optimization iteration.

  • max_budget (float, default=np.inf) – Maximum total evaluation budget.The optimization stops when this budget is exhausted.

  • constraints (list[dict], optional) – List of the constraint definitions.

  • driver_kwargs (dict, optional) – Additional keyword arguments passed to the optimization driver.

  • strategy_kwargs (dict, optional) – Additional keyword arguments passed to the acquisition strategy.

  • verbose (bool) – If True, prints progress information during optimization.

Returns:

Final optimization state.

Return type:

State