Establishes O(d² δ^{-3} ε^{-3}) SZO complexity to reach (δ,ε)-Goldstein stationary points in non-smooth non-convex stochastic zeroth-order optimization with decision-dependent distributions, plus improved rates for smooth and Hessian-Lipschitz cases.
Zeroth-order gradient estimators for stochastic problems with decision- dependent distributions
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Stochastic Non-Smooth Non-Convex Optimization with Decision-Dependent Distributions
Establishes O(d² δ^{-3} ε^{-3}) SZO complexity to reach (δ,ε)-Goldstein stationary points in non-smooth non-convex stochastic zeroth-order optimization with decision-dependent distributions, plus improved rates for smooth and Hessian-Lipschitz cases.