qiskit.optimization.algorithms.MinimumEigenOptimizationResult¶

class MinimumEigenOptimizationResult(x, fval, variables, status, samples=None, min_eigen_solver_result=None, raw_samples=None)[source]¶

Minimum Eigen Optimizer Result.

Parameters

x (Union[List[float], ndarray]) – the optimal value found by MinimumEigensolver.
fval (float) – the optimal function value.
variables (List[Variable]) – the list of variables of the optimization problem.
status (OptimizationResultStatus) – the termination status of the optimization algorithm.
min_eigen_solver_result (Optional[MinimumEigensolverResult]) – the result obtained from the underlying algorithm.
samples (Optional[List[SolutionSample]]) – the x value, the objective function value of the original problem, the probability, and the status of sampling.
raw_samples (Optional[List[SolutionSample]]) – the x values of the QUBO, the objective function value of the QUBO, and the probability of sampling.

__init__(x, fval, variables, status, samples=None, min_eigen_solver_result=None, raw_samples=None)[source]¶

Parameters

x (Union[List[float], ndarray]) – the optimal value found by MinimumEigensolver.
fval (float) – the optimal function value.
variables (List[Variable]) – the list of variables of the optimization problem.
status (OptimizationResultStatus) – the termination status of the optimization algorithm.
min_eigen_solver_result (Optional[MinimumEigensolverResult]) – the result obtained from the underlying algorithm.
samples (Optional[List[SolutionSample]]) – the x value, the objective function value of the original problem, the probability, and the status of sampling.
raw_samples (Optional[List[SolutionSample]]) – the x values of the QUBO, the objective function value of the QUBO, and the probability of sampling.

Methods

__init__(x, fval, variables, status[, …])

type x: Union[List[float], ndarray]

get_correlations()

Get <Zi x Zj> correlation matrix from samples.

Attributes

`fval`	Returns the optimal function value.
`min_eigen_solver_result`	Returns a result object obtained from the instance of `MinimumEigensolver`.
`raw_results`	Return the original results object from the optimization algorithm.
`raw_samples`	Returns the list of raw solution samples of `MinimumEigensolver`.
`samples`	Returns the list of solution samples
`status`	Returns the termination status of the optimization algorithm.
`variable_names`	Returns the list of variable names of the optimization problem.
`variables`	Returns the list of variables of the optimization problem.
`variables_dict`	Returns the optimal value as a dictionary of the variable name and corresponding value.
`x`	Returns the optimal value found in the optimization or None in case of FAILURE.

property fval¶

Returns the optimal function value.

Return type: float
Returns: The function value corresponding to the optimal value found in the optimization.

get_correlations()[source]¶

Get <Zi x Zj> correlation matrix from samples.

Return type: ndarray

property min_eigen_solver_result¶

Returns a result object obtained from the instance of MinimumEigensolver.

Return type: MinimumEigensolverResult

property raw_results¶

Return the original results object from the optimization algorithm.

Currently a dump for any leftovers.

Return type: Any
Returns: Additional result information of the optimization algorithm.

property raw_samples¶

Returns the list of raw solution samples of MinimumEigensolver.

Return type: Optional[List[SolutionSample]]
Returns: The list of raw solution samples of MinimumEigensolver.

property samples¶

Returns the list of solution samples

Return type: List[SolutionSample]
Returns: The list of solution samples.

property status¶

Returns the termination status of the optimization algorithm.

Return type: OptimizationResultStatus
Returns: The termination status of the algorithm.

property variable_names¶

Returns the list of variable names of the optimization problem.

Return type: List[str]
Returns: The list of variable names of the optimization problem.

property variables¶

Returns the list of variables of the optimization problem.

Return type: List[Variable]
Returns: The list of variables.

property variables_dict¶

Returns the optimal value as a dictionary of the variable name and corresponding value.

Return type: Dict[str, float]
Returns: The optimal value as a dictionary of the variable name and corresponding value.

property x¶

Returns the optimal value found in the optimization or None in case of FAILURE.

Return type: Optional[ndarray]
Returns: The optimal value found in the optimization.