Efficient Safe Zeroth-Order Optimization Using Quadratic Local Approximations

This paper proposes a novel safe zeroth-order optimization method that iteratively constructs quadratic approximations of the constraint functions, builds local feasible sets, and optimizes over them. The method guarantees that all samples are feasible and returns an η-KKT pair within a polynomial number of iterations and samples.