Randomized algorithm for global optimization with bounded memory

We describe a class of adaptive algorithms for approximating the global minimum of a function defined on a compact subset of Rd. The algorithms are adaptive versions of Monte Carlo search and use a memory of a fixed number of past observations. By choosing a large enough memory, the convergence rate can be made to exceed any power of the convergence rate obtained with standard Monte Carlo search.