neurop.Problem.TSPProblem¶

class neurop.Problem.TSPProblem(graph: Graph, initializer=None, penalty=10)¶

Traveling Salesperson Problems

__init__(graph: Graph, initializer=None, penalty=10)¶

Initializes the TSP problem.

Parameters:

problem (OptProblem) – the optlang problem
initializer (_type_, optional) – function to initialize the parameters of the problem. Defaults to None, which means that parameters will be randomly initialized.

Methods

`__init__`(graph[, initializer, penalty])	Initializes the TSP problem.
`convert_to_model`(model_type[, backend])	Converts the problem to a model of given type, if supported.
`evaluate_constraints`(params)	Evaluates the constraints of the problem for a given set of (binary) parameter values.
`evaluate_objective`(params)	Evaluates the objective function of the problem for a given set of (binary) parameter values.
`supports_model`(model_type)
`to_qubo`([backend])

convert_to_model(model_type, backend=None) → BaseModel¶

Converts the problem to a model of given type, if supported.

Parameters:: backend (Backend, optional) – The backend for which to derive the QUBO form. Defaults to None, which means no special requirements are imposed on the Q matrix.
Raises:: ValueError – May return a ValueError if the problem cannot be converted to the required model type.
Returns:: returns the QUBO matrix
Return type:: np.ndarray

evaluate_constraints(params: ndarray) → Iterable[bool]¶: Evaluates the constraints of the problem for a given set of (binary) parameter values.

evaluate_objective(params: ndarray) → float¶: Evaluates the objective function of the problem for a given set of (binary) parameter values.

NeuroP