Solution Class¶

It is a class that is used to store all the information about the solution of the problem.

Solution Attributes¶

The class has the following attributes:

energies: list[float], i.e. the list of energies of the solution obtained in each run
best_energy, i.e. the lowest energy obtained in all the runs
best_solution: dict[str, Any], i.e. the best solution obtained in all the runs in binary variables
best_solution_original_var: dict[str, Any], i.e. the best solution obtained in all the runs in variables originally declared
solutions_original_var: list[dict[str, Any]], i.e. the list of solutions obtained in each run in variables originally declared
solutions: list[dict[str, Any]], i.e. the list of solutions obtained in each run in binary variables
time: float: the time taken to solve the problem
solver_info: dict[str, Any], i.e. the information about the solver setting to solve the problem

Solution Methods¶

The class has the following methods for helping users in analyzing the solution:

optimal_solution_cost_functions_values(): This method is used to get the cost functions values of the best solution obtained in all the runs
check_constraint_optimal_solution(): This method is used to check if the best solution obtained in all the runs satisfies the constraints
check_constraint_all_solutions(): This method is used to check if all the solutions obtained in all the runs satisfy the constraints
show_cumulative(save: bool = False, show: bool = True, filename: str = “”, label: str = “”, latex: bool = False)This method is used to show the cumulative plot of the energies obtained in all the runs. The parameters are:
- save: bool, i.e. whether to save the plot or not in a file
- show: bool, i.e. whether to show the plot or not
- filename: str, i.e. the name of the file where the plot will be saved
- label: str, i.e. the label of the plot
- latex: bool, i.e. whether to show the plot in latex format or not
valid_solutions(weak: bool = True): This method is used to get the rate of valid solutions obtained in all the runs. The parameters are:
- weak: bool, i.e. whether to consider in the evaluation the weak constraints
p_range(ref_value: float | None = None): This method is used to get the p-range of the best solution obtained in all the runs, which is the probability of obtaining a final energy lower than a certain value. The parameters are:
- ref_value: float | None, i.e. the reference value to calculate the p-value
tts(ref_value: float | None = None, target_probability: float = 0.99): This method is used to get the time-to-solution of the best solution obtained in all the runs, which is the time required to obtain a solution with a certain probability. The parameters are:
- ref_value: float | None, i.e. the reference value to calculate the p-range
- target_probability: float, i.e. the target probability to calculate the time-to-solution
wring_json_reports(filename: str = “report”, weak: bool = False, ref_value: float | None = None, target_probability: float = 0.99, problem_features: bool = False)This method is used to write the reports in json format. The parameters are:
- filename: str, i.e. the name of the file where the report will be saved
- weak: bool, i.e. whether to consider in the evaluation the weak constraints
- ref_value: float | None, i.e. the reference value to calculate the p-range
- target_probability: float, i.e. the target probability to calculate the time-to-solution
- problem_features: bool, i.e. whether to show the problem features in the report or not

Examples:¶

from mqt.qao.constraints import Constraints
from mqt.qao.variables import Variables
from mqt.qao.objectivefunction import ObjectiveFunction
from mqt.qao.problem import Problem
from mqt.qao.solver import Solver

variables = Variables()
m1 = variables.add_continuous_variables_array(
    "M1", [1, 2], -1, 2, -1, "uniform", "logarithmic 2"
)
m2 = variables.add_continuous_variables_array(
    "M2", [2, 1], -1, 2, -1, "uniform", "logarithmic 2"
)
objective_function = ObjectiveFunction()
objective_function.add_objective_function(np.matmul(m1, m2).item(0, 0))
constraint = Constraints()
constraint.add_constraint(constraint_expr, variable_precision=True)
problem = Problem()
problem.create_problem(variables, constraint, objective_function)
solver = Solver()
solution = solver.solve_simulated_annealing(
    problem,
    max_lambda_update=max_lambda_update,
    lambda_update_mechanism=lambda_update,
    lambda_strategy=lambda_strategy,
)

print(solution.optimal_solution_cost_functions_values())
print(solution.check_constraint_optimal_solution())
print(solution.check_constraint_all_solutions())
solution.show_cumulative()
print(solution.valid_solutions())
print(solution.p_range())
print(solution.tts())
solution.wring_json_reports()