qiskit.optimization.algorithms.OptimizationResult
class OptimizationResult(x, fval, variables, status, raw_results=None, samples=None)
A base class for optimization results.
The optimization algorithms return an object of the type OptimizationResult with the information about the solution obtained.
OptimizationResult allows users to get the value of a variable by specifying an index or a name as follows.
Examples
>>> from qiskit.optimization import QuadraticProgram
>>> from qiskit.optimization.algorithms import CplexOptimizer
>>> problem = QuadraticProgram()
>>> _ = problem.binary_var('x1')
>>> _ = problem.binary_var('x2')
>>> _ = problem.binary_var('x3')
>>> problem.minimize(linear={'x1': 1, 'x2': -2, 'x3': 3})
>>> print([var.name for var in problem.variables])
['x1', 'x2', 'x3']
>>> optimizer = CplexOptimizer()
>>> result = optimizer.solve(problem)
>>> print(result.variable_names)
['x1', 'x2', 'x3']
>>> print(result.x)
[0. 1. 0.]
>>> print(result[1])
1.0
>>> print(result['x1'])
0.0
>>> print(result.fval)
-2.0
>>> print(result.variables_dict)
{'x1': 0.0, 'x2': 1.0, 'x3': 0.0}The order of variables should be equal to that of the problem solved by optimization algorithms. Optimization algorithms and converters of QuadraticProgram should maintain the order when generating a new OptimizationResult object.
Parameters
- x (
Union[List[float],ndarray,None]) – the optimal value found in the optimization, or possibly None in case of FAILURE. - fval (
float) – the optimal function value. - variables (
List[Variable]) – the list of variables of the optimization problem. - raw_results (
Optional[Any]) – the original results object from the optimization algorithm. - status (
OptimizationResultStatus) – the termination status of the optimization algorithm. - samples (
Optional[List[SolutionSample]]) – the solution samples.
Raises
QiskitOptimizationError – if sizes of x and variables do not match.
__init__
__init__(x, fval, variables, status, raw_results=None, samples=None)
Parameters
- x (
Union[List[float],ndarray,None]) – the optimal value found in the optimization, or possibly None in case of FAILURE. - fval (
float) – the optimal function value. - variables (
List[Variable]) – the list of variables of the optimization problem. - raw_results (
Optional[Any]) – the original results object from the optimization algorithm. - status (
OptimizationResultStatus) – the termination status of the optimization algorithm. - samples (
Optional[List[SolutionSample]]) – the solution samples.
Raises
QiskitOptimizationError – if sizes of x and variables do not match.
Methods
__init__(x, fval, variables, status[, …]) | type xUnion[List[float], ndarray, None] |
Attributes
fval | Returns the optimal function value. |
raw_results | Return the original results object from the optimization algorithm. |
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. |
fval
Returns the optimal function value.
Return type
float
Returns
The function value corresponding to the optimal value found in the optimization.
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.
samples
Returns the list of solution samples
Return type
List[SolutionSample]
Returns
The list of solution samples.
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.