  In this paper, we propose a new zero order optimization method called
minibatch stochastic three points (MiSTP) method to solve an unconstrained
minimization problem in a setting where only an approximation of the objective
function evaluation is possible. It is based on the recently proposed
stochastic three points (STP) method (Bergou et al., 2020). At each iteration,
MiSTP generates a random search direction in a similar manner to STP, but
chooses the next iterate based solely on the approximation of the objective
function rather than its exact evaluations. We also analyze our method's
complexity in the nonconvex and convex cases and evaluate its performance on
multiple machine learning tasks.
