qiskit.optimization.converters.LinearEqualityToPenalty
class LinearEqualityToPenalty(penalty=None)
Convert a problem with only equality constraints to unconstrained with penalty terms.
Parameters
penalty (Optional[float]) – Penalty factor to scale equality constraints that are added to objective. If None is passed, penalty factor will be automatically calculated.
__init__
__init__(penalty=None)
Parameters
penalty (Optional[float]) – Penalty factor to scale equality constraints that are added to objective. If None is passed, penalty factor will be automatically calculated.
Methods
__init__([penalty]) | type penaltyOptional[float] |
convert(problem) | Convert a problem with equality constraints into an unconstrained problem. |
interpret(x) | Convert the result of the converted problem back to that of the original problem |
Attributes
penalty | Returns the penalty factor used in conversion. |
convert
convert(problem)
Convert a problem with equality constraints into an unconstrained problem.
Parameters
problem (QuadraticProgram) – The problem to be solved, that does not contain inequality constraints.
Return type
QuadraticProgram
Returns
The converted problem, that is an unconstrained problem.
Raises
QiskitOptimizationError – If an inequality constraint exists.
interpret
interpret(x)
Convert the result of the converted problem back to that of the original problem
Parameters
x (Union[ndarray, List[float]]) – The result of the converted problem or the given result in case of FAILURE.
Return type
ndarray
Returns
The result of the original problem.
Raises
QiskitOptimizationError – if the number of variables in the result differs from that of the original problem.
penalty
Returns the penalty factor used in conversion.
Return type
Optional[float]
Returns
The penalty factor used in conversion.