qiskit.optimization.algorithms.RecursiveMinimumEigenOptimizationResult¶

class RecursiveMinimumEigenOptimizationResult(x, fval, variables, status, replacements, history)[Quellcode]¶

Recursive Eigen Optimizer Result.

Constructs an instance of the result class.

Parameter

x (Union[List[float], ndarray]) – the optimal value found in the optimization.
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.
replacements (Dict[str, Tuple[str, int]]) – a dictionary of substituted variables. Key is a variable being substituted, value is a tuple of substituting variable and a weight, either 1 or -1.
history (Tuple[List[MinimumEigenOptimizationResult], OptimizationResult]) – a tuple containing intermediate results. The first element is a list of MinimumEigenOptimizerResult obtained by invoking MinimumEigenOptimizer iteratively, the second element is an instance of OptimizationResult obtained at the last step via min_num_vars_optimizer.

__init__(x, fval, variables, status, replacements, history)[Quellcode]¶

Constructs an instance of the result class.

Parameter

x (Union[List[float], ndarray]) – the optimal value found in the optimization.
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.
replacements (Dict[str, Tuple[str, int]]) – a dictionary of substituted variables. Key is a variable being substituted, value is a tuple of substituting variable and a weight, either 1 or -1.
history (Tuple[List[MinimumEigenOptimizationResult], OptimizationResult]) – a tuple containing intermediate results. The first element is a list of MinimumEigenOptimizerResult obtained by invoking MinimumEigenOptimizer iteratively, the second element is an instance of OptimizationResult obtained at the last step via min_num_vars_optimizer.

Methods

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

Constructs an instance of the result class.

Attributes

`fval`	Returns the optimal function value.
`history`	Returns intermediate results.
`raw_results`	Return the original results object from the optimization algorithm.
`replacements`	Returns a dictionary of substituted variables.
`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.

Rückgabetyp: float
Rückgabe: The function value corresponding to the optimal value found in the optimization.

property history¶

Returns intermediate results. The first element is a list of MinimumEigenOptimizerResult obtained by invoking MinimumEigenOptimizer iteratively, the second element is an instance of OptimizationResult obtained at the last step via min_num_vars_optimizer.

Rückgabetyp: Tuple[List[MinimumEigenOptimizationResult], OptimizationResult]

property raw_results¶

Return the original results object from the optimization algorithm.

Currently a dump for any leftovers.

Rückgabetyp: Any
Rückgabe: Additional result information of the optimization algorithm.

property replacements¶

Returns a dictionary of substituted variables. Key is a variable being substituted, value is a tuple of substituting variable and a weight, either 1 or -1.

Rückgabetyp: Dict[str, Tuple[str, int]]

property status¶

Returns the termination status of the optimization algorithm.

Rückgabetyp: OptimizationResultStatus
Rückgabe: The termination status of the algorithm.

property variable_names¶

Returns the list of variable names of the optimization problem.

Rückgabetyp: List[str]
Rückgabe: The list of variable names of the optimization problem.

property variables¶

Returns the list of variables of the optimization problem.

Rückgabetyp: List[Variable]
Rückgabe: The list of variables.

property variables_dict¶

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

Rückgabetyp: Dict[str, float]
Rückgabe: 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.

Rückgabetyp: Optional[ndarray]
Rückgabe: The optimal value found in the optimization.