saealib.EqualityConstraint#

class saealib.EqualityConstraint(func, tolerance=1e-06)[source]#

Bases: InequalityConstraint

A single equality constraint: h(x) = 0, satisfied within a tolerance.

The violation is max(0, |h(x)| - tolerance), reusing the inequality violation path so that mixed inequality/equality problems aggregate through the same ConstraintHandler. Only violation_from_value() is overridden; violation() and evaluate_with_violation() inherit the equality behavior automatically.

Parameters:

func (callable) – Constraint function h(x). Should return a scalar float.
tolerance (float, optional) – Feasibility tolerance for |h(x)| <= tolerance. Default: 1e-6. Set tolerance=0.0 to defer the feasibility threshold to a handler such as EpsilonConstraintHandler, where the threshold is managed externally rather than baked into the constraint.

Notes

Provide an analytical Jacobian by overriding gradient() to return ∇h(x); this enables gradient-based constraint handlers (e.g. repair).

Methods

`__init__`
`evaluate`	Return the raw constraint value g(x).
`evaluate_with_violation`	Return the raw value and violation with a single function evaluation.
`gradient`	Return the gradient of g(x) with respect to x, if available.
`violation`	Return constraint violation: max(0, g(x) - threshold).
`violation_from_value`	Return equality violation: max(0, abs(h(x)) - tolerance).

Method Details

EqualityConstraint.__init__(func, tolerance=1e-06)[source]#

Parameters:

func (Callable[[...], Any])
tolerance (float)

EqualityConstraint.evaluate(x)#

Return the raw constraint value g(x).

Parameters:: x (ndarray)
Return type:: float

EqualityConstraint.evaluate_with_violation(x)#

Return the raw value and violation with a single function evaluation.

Returns:

g (float) – Raw constraint value g(x).
cv (float) – Constraint violation: max(0, g(x) - threshold).

Parameters:

x (ndarray)

Return type:

tuple[float, float]

EqualityConstraint.gradient(x)#

Return the gradient of g(x) with respect to x, if available.

Override this in a subclass to enable gradient-based constraint handlers (e.g. gradient-based repair). The default returns None, signalling that no analytical Jacobian is provided.

Parameters:: x (np.ndarray) – The solution at which to evaluate the gradient. shape = (dim, )
Returns:: Gradient vector with shape = (dim, ), or None when no gradient is available.
Return type:: np.ndarray or None

EqualityConstraint.violation(x)#

Return constraint violation: max(0, g(x) - threshold).

Parameters:: x (ndarray)
Return type:: float

EqualityConstraint.violation_from_value(g)[source]#

Return equality violation: max(0, abs(h(x)) - tolerance).

Parameters:: g (float)
Return type:: float