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.
Gradient-free methods for nonconvex nonsmooth stochastic compositional optimization.Advances in Neural Information Processing Systems, 37:45438– 45461, 2024
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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.