Adam-HNAG is a splitting-based reformulation of Adam that yields the first convergence proof for Adam-type methods, including accelerated rates, in convex smooth optimization.
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SHANG++ delivers faster convergence and stronger robustness to multiplicative noise in stochastic optimization for both convex and strongly convex problems, with explicit parameters and competitive deep-learning results.
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Adam-HNAG: A Convergent Reformulation of Adam with Accelerated Rate
Adam-HNAG is a splitting-based reformulation of Adam that yields the first convergence proof for Adam-type methods, including accelerated rates, in convex smooth optimization.
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SHANG++: Robust Stochastic Acceleration under Multiplicative Noise
SHANG++ delivers faster convergence and stronger robustness to multiplicative noise in stochastic optimization for both convex and strongly convex problems, with explicit parameters and competitive deep-learning results.