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.
Accelerated gradient methods through variable and operator splitting, 2025
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AAMD combines preconditioning, acceleration, and adaptivity in mirror descent using a Lyapunov budget to achieve O(1/k^2) rates under dual relative smoothness and bounded sublevel sets.
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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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Adaptive Accelerated Mirror Descent in Primal and Dual Spaces
AAMD combines preconditioning, acceleration, and adaptivity in mirror descent using a Lyapunov budget to achieve O(1/k^2) rates under dual relative smoothness and bounded sublevel sets.