  We study the problem of finding global minimizers of
$V(x):\mathbb{R}^d\rightarrow\mathbb{R}$ approximately via sampling from a
probability distribution $\mu_{\sigma}$ with density
$p_{\sigma}(x)=\dfrac{\exp(-V(x)/\sigma)}{\int_{\mathbb R^d} \exp(-V(y)/\sigma)
dy }$ with respect to the Lebesgue measure for $\sigma \in (0,1]$ small enough.
  We analyze a sampler based on the Euler-Maruyama discretization of the
Schr{\"o}dinger-F{\"o}llmer diffusion processes with stochastic approximation
under appropriate assumptions on the step size $s$ and the potential $V$.
  We prove that the output of the proposed sampler is an approximate global
minimizer of $V(x)$ with high probability at cost of sampling
$\mathcal{O}(d^{3})$ standard normal random variables.
  Numerical studies illustrate the effectiveness of the proposed method and its
superiority to the Langevin method.
