  Constrained optimization of the parameters of a simulator plays a crucial
role in a design process. These problems become challenging when the simulator
is stochastic, computationally expensive, and the parameter space is
high-dimensional. One can efficiently perform optimization only by utilizing
the gradient with respect to the parameters, but these gradients are
unavailable in many legacy, black-box codes. We introduce the algorithm
Scout-Nd (Stochastic Constrained Optimization for N dimensions) to tackle the
issues mentioned earlier by efficiently estimating the gradient, reducing the
noise of the gradient estimator, and applying multi-fidelity schemes to further
reduce computational effort. We validate our approach on standard benchmarks,
demonstrating its effectiveness in optimizing parameters highlighting better
performance compared to existing methods.
