  When looking for a solution, deterministic methods have the enormous
advantage that they do find global optima. Unfortunately, they are very
CPU-intensive, and are useless on untractable NP-hard problems that would
require thousands of years for cutting-edge computers to explore. In order to
get a result, one needs to revert to stochastic algorithms, that sample the
search space without exploring it thoroughly. Such algorithms can find very
good results, without any guarantee that the global optimum has been reached;
but there is often no other choice than using them. This chapter is a short
introduction to the main methods used in stochastic optimization.
