Adam-SHANG is a convergent Adam variant for stochastic smooth convex optimization that uses a stable lagged-preconditioner update and a computable trace-ratio stepsize rule.
Convergence rates for the adam optimizer
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
Establishes a central limit theorem for averaged Adam with n^{-1/2} convergence rate to an attracting zero and covariance determined by the algorithm at the attractor.
citing papers explorer
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Adam-SHANG: A Convergent Adam-Type Method for Stochastic Smooth Convex Optimization
Adam-SHANG is a convergent Adam variant for stochastic smooth convex optimization that uses a stable lagged-preconditioner update and a computable trace-ratio stepsize rule.
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Central limit theorem for the averaged Adam optimizer
Establishes a central limit theorem for averaged Adam with n^{-1/2} convergence rate to an attracting zero and covariance determined by the algorithm at the attractor.