saealib.Problem¶

class saealib.Problem(func, dim, n_obj, direction, lb, ub, eps=None, comparator=None, constraints=None, *, eps_cv=1e-06, eps_obj=1e-06, handler=None)[source]¶

Bases: object

Definition of optimization problem.

Parameters:

func (callable)
dim (int)
n_obj (int)
direction (np.ndarray)
lb (list[float])
ub (list[float])
eps (float | None)
comparator (Comparator | None)
constraints (list[InequalityConstraint] | None)
eps_cv (float)
eps_obj (float)
handler (ConstraintHandler | None)

dim¶

Dimension of the design variables.

Type:: int

n_obj¶

Number of objectives.

Type:: int

direction¶

Optimization direction per objective. shape = (n_obj, ) Each element must be +1 (maximize) or -1 (minimize).

Type:: np.ndarray

lb¶

Lower bounds for design variables. shape = (dim, )

Type:: np.ndarray

ub¶

Upper bounds for design variables. shape = (dim, )

Type:: np.ndarray

comparator¶

Comparator instance to compare solutions.

Type:: Comparator

eps_cv¶

Epsilon for constraint violation feasibility threshold.

Type:: float

eps_obj¶

Epsilon for objective value equality comparison.

Type:: float

func¶

Objective function to evaluate solutions.

Type:: callable -> float

constraints¶

List of inequality constraint definitions.

Type:: list[InequalityConstraint]

handler¶

Constraint-handling strategy used to aggregate violations and augment objectives.

Type:: ConstraintHandler

Methods

`__init__`	Initialize Problem instance.
`evaluate`	Evaluate the objective function at given solution x.
`evaluate_constraints`	Evaluate all constraint functions at x.

Method Details

Problem.__init__(func, dim, n_obj, direction, lb, ub, eps=None, comparator=None, constraints=None, *, eps_cv=1e-06, eps_obj=1e-06, handler=None)[source]¶

Initialize Problem instance.

Parameters:

func (callable -> float) – Objective function to evaluate solutions.
dim (int) – Dimension of the design variables.
n_obj (int) – Number of objectives.
direction (np.ndarray) – Optimization direction per objective. shape = (n_obj, ) Each element must be +1 (maximize) or -1 (minimize).
lb (list[float]) – Lower bounds for design variables. length = dim
ub (list[float]) – Upper bounds for design variables. length = dim
eps (float, optional) – Deprecated. Use eps_cv and eps_obj. Will be removed in 0.1.0.
comparator (Comparator, optional) – Comparator instance to use. If None, auto-selected based on n_obj: n_obj == 1 -> SingleObjectiveComparator, n_obj > 1 -> NSGA2Comparator.
constraints (list[InequalityConstraint], optional) – List of inequality constraint definitions. Default: empty list.
eps_cv (float, optional) – Epsilon for constraint violation feasibility threshold. Default: 1e-6.
eps_obj (float, optional) – Epsilon for objective value equality comparison. Default: 1e-6.
handler (ConstraintHandler, optional) – Constraint-handling strategy. If None, a StaticToleranceHandler (sum-of-violations, fixed eps_cv) is used, reproducing the default behavior.

Problem.evaluate(x, g=None)[source]¶

Evaluate the objective function at given solution x.

After computing the raw objective, handler.augment_objective is applied so that penalty-based or augmented-Lagrangian handlers can transform the objective using constraint information. The default StaticToleranceHandler leaves the objective unchanged.

Parameters:

x (np.ndarray) – The solution to evaluate.
g (np.ndarray, optional) – Pre-computed raw constraint values g(x), shape = (n_constraints, ). When None, constraints are evaluated internally if any are defined. Pass this to avoid re-evaluating constraints when g is already available (e.g. from evaluate_constraints()).

Returns:

The objective value(s) at solution x. shape = (n_obj, )

Return type:

np.ndarray

Problem.evaluate_constraints(x)[source]¶

Evaluate all constraint functions at x.

Parameters:

x (np.ndarray) – The solution to evaluate. shape = (dim, )

Returns:

g (np.ndarray) – Raw constraint values. shape = (n_constraints, ) Empty array when no constraints are defined.
cv (float) – Aggregate constraint violation as computed by handler.compute_cv. 0.0 when no constraints are defined.

Return type:

tuple[ndarray, float]