Classic Kirkpatrick et al. 1983 SA: hill climber that also accepts
worse moves with probability `exp(-Δ/T)` where T anneals geometrically
from `initial_temperature` to `final_temperature` over the iteration
count.
Single-objective only. Generic over decision type — works on real
vectors, bool vectors, permutations, anything. Tracks the best-seen
incumbent across the run (not just the last accepted move) so the
result reflects the actual best ever visited, not where the random
walk happened to end.
Tests cover: convergence on Sphere1D under reasonable hyperparameters,
deterministic reruns, panic on multi-objective, panic on
non-positive temperatures.
