Presents classical Õ(n²/ε^{1.5}) and quantum Õ(n/ε^{1.5}) query algorithms for ε-stationary points of twice-differentiable non-convex functions with Lipschitz gradient and Hessian via comparison oracles.
arXiv preprint arXiv:1710.11606 , year=
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Proposes federated adaptive optimizers (FedAdagrad, FedAdam, FedYogi) with convergence analysis for non-convex objectives under data heterogeneity and reports empirical gains over FedAvg.
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Finding Stationary Points by Comparisons
Presents classical Õ(n²/ε^{1.5}) and quantum Õ(n/ε^{1.5}) query algorithms for ε-stationary points of twice-differentiable non-convex functions with Lipschitz gradient and Hessian via comparison oracles.
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Adaptive Federated Optimization
Proposes federated adaptive optimizers (FedAdagrad, FedAdam, FedYogi) with convergence analysis for non-convex objectives under data heterogeneity and reports empirical gains over FedAvg.