  We study the error in approximating the minimum of a Brownian motion on the
unit interval based on finitely many point evaluations. We construct an
algorithm that adaptively chooses the points at which to evaluate the Brownian
path. In contrast to the $1/2$ convergence rate of optimal nonadaptive
algorithms, the proposed adaptive algorithm converges at an arbitrarily high
polynomial rate.
