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Adapt or Forget: Provable Tradeoffs Between Adam and SGD in Nonstationary Optimization
We provide a theoretical analysis of Adam under non-stationary stochastic objectives, separating two regimes: Euclidean tracking under adaptive strong monotonicity of the Adam-preconditioned mean-gradient operator, and high-probability projected stationarity guarantees under general $L$-smooth objec
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