qiskit.optimization.algorithms.RecursiveMinimumEigenOptimizationResult
class RecursiveMinimumEigenOptimizationResult(x, fval, variables, status, replacements, history)
Recursive Eigen Optimizer Result.
Constructs an instance of the result class.
Parameters
- 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 ofMinimumEigenOptimizerResultobtained by invokingMinimumEigenOptimizeriteratively, the second element is an instance ofOptimizationResultobtained at the last step via min_num_vars_optimizer.
__init__
__init__(x, fval, variables, status, replacements, history)
Constructs an instance of the result class.
Parameters
- 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 ofMinimumEigenOptimizerResultobtained by invokingMinimumEigenOptimizeriteratively, the second element is an instance ofOptimizationResultobtained 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. |
fval
Returns the optimal function value.
Return type
float
Returns
The function value corresponding to the optimal value found in the optimization.
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.
Return type
Tuple[List[MinimumEigenOptimizationResult], OptimizationResult]
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.
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.
Return type
Dict[str, Tuple[str, int]]
status
Returns the termination status of the optimization algorithm.
Return type
OptimizationResultStatus
Returns
The termination status of the algorithm.
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.
variables
Returns the list of variables of the optimization problem.
Return type
List[Variable]
Returns
The list of variables.
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.
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.