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%24 oracle complexity to find an %24varepsilon%24-stationary point. Some works indicate that this complexity can be improved to %24mathcal{O}(varepsilon^{-3})%24 when the loss gradient is Lipschitz continuous. The question of achieving enhanced convergence rates under distinct conditions%2C remains unresolved. In this work%2C we address this question for optimization problems that are nonconvex in the minimization variable and strongly concave or Polyak-Lojasiewicz (PL) in the maximization variable. We introduce novel bias-corrected momentum algorithms utilizing efficient Hessian-vector products. We establish convergence conditions and demonstrate a lower iteration complexity of %24mathcal{O}(varepsilon^{-3})%24 for the proposed algorithms. The effectiveness of the method is validated through applications to robust logistic regression using real-world datasets.?quality=80&w=800)
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