public class SimulatedAnealingMinimizer
extends java.lang.Object
A cost minimizer which will fit a MovAvgModel to the data.
This optimizer uses naive simulated annealing. Random solutions in the problem space
are generated, compared against the last period of data, and the least absolute deviation
is recorded as a cost.
If the new cost is better than the old cost, the new coefficients are chosen. If the new
solution is worse, there is a temperature-dependent probability it will be randomly selected
anyway. This allows the algo to sample the problem space widely. As iterations progress,
the temperature decreases and the algorithm rejects poor solutions more regularly,
theoretically honing in on a global minimum.