cirq_google.line.placement.anneal_minimize

Minimize solution using Simulated Annealing meta-heuristic.

cirq_google.line.placement.anneal_minimize(
    initial: cirq_google.line.placement.optimization.T,
    cost_func: Callable[[T], float],
    move_func: Callable[[T], T],
    random_sample: Callable[[], float],
    temp_initial: float = 0.01,
    temp_final: float = 1e-06,
    cooling_factor: float = 0.99,
    repeat: int = 100,
    trace_func: Optional[Callable[[T, float, float, float, bool], None]] = None
) -> cirq_google.line.placement.optimization.T

initial Initial solution of type T to the problem.
cost_func Callable which takes current solution of type T, evaluates it and returns float with the cost estimate. The better solution is, the lower resulting value should be; negative values are allowed.
move_func Callable which takes current solution of type T and returns a new solution candidate of type T which is random iteration over input solution. The input solution, which is argument to this callback should not be mutated.
random_sample Callable which gives uniformly sampled random value from the [0, 1) interval on each call.
temp_initial Optional initial temperature for simulated annealing optimization. Scale of this value is cost_func-dependent.
temp_final Optional final temperature for simulated annealing optimization, where search should be stopped. Scale of this value is cost_func-dependent.
cooling_factor Optional factor to be applied to the current temperature and give the new temperature, this must be strictly greater than 0 and strictly lower than 1.
repeat Optional number of iterations to perform at each given temperature.
trace_func Optional callback for tracing simulated annealing progress. This is going to be called at each algorithm step for the arguments: solution candidate (T), current temperature (float), candidate cost (float), probability of accepting candidate (float), and acceptance decision (boolean).

The best solution found.

ValueError When supplied arguments are invalid.