GT-DSGD achieves order-optimal high-probability rates O(log(1/δ)/sqrt(nT)) for non-convex and O(log(1/δ)/(nT)) for PL costs, matching the conditions used for mean-squared error bounds.
Linear Convergence of Gradient and Proximal- Gradient Methods Under the Polyak- Lojasiewicz Condition,
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High-Probability Convergence in Decentralized Stochastic Optimization with Gradient Tracking
GT-DSGD achieves order-optimal high-probability rates O(log(1/δ)/sqrt(nT)) for non-convex and O(log(1/δ)/(nT)) for PL costs, matching the conditions used for mean-squared error bounds.