Exact Optimization by Means of Sequentially Adaptive Bayesian Learning

Simulated annealing is a well-established approach to optimization that is ro-
bust for irregular objective functions. Recently it has been improved using se-
quential Monte Carlo. This paper presents further improvements that yield the
global optimum with accuracy constrained only by the limitations of ‡floating point
arithmetic. Performance is illustrated using a standard set of six test problems in
which simulated annealing has had mixed success. Our approach reliably fi…nds the
exact global optimum in all six cases, and with fewer function evaluations than
competing simulated annealing algorithms. This approach is a specifi…c case of the
sequentially adaptive Bayesian learning algorithm, which uses feedback from par-
ticles to the design of the algorithm. The feature of this algorithm most critical
to exact optimization is targeted tempering, a new technique developed in this
paper.