  The zeroth-order optimization has been widely used in machine learning
applications. However, the theoretical study of the zeroth-order optimization
focus on the algorithms which approximate (first-order) gradients using
(zeroth-order) function value difference at a random direction. The theory of
algorithms which approximate the gradient and Hessian information by
zeroth-order queries is much less studied. In this paper, we focus on the
theory of zeroth-order optimization which utilizes both the first-order and
second-order information approximated by the zeroth-order queries. We first
propose a novel reparameterized objective function with parameters $(\mu,
\Sigma)$. This reparameterized objective function achieves its optimum at the
minimizer and the Hessian inverse of the original objective function
respectively, but with small perturbations. Accordingly, we propose a new
algorithm to minimize our proposed reparameterized objective, which we call
\texttt{MiNES} (mirror descent natural evolution strategy). We show that the
estimated covariance matrix of \texttt{MiNES} converges to the inverse of
Hessian matrix of the objective function with a convergence rate
$\widetilde{\mathcal{O}}(1/k)$, where $k$ is the iteration number and
$\widetilde{\mathcal{O}}(\cdot)$ hides the constant and $\log$ terms. We also
provide the explicit convergence rate of \texttt{MiNES} and how the covariance
matrix promotes the convergence rate.
