Averaged SA-Adam iterates have exactly the SGD Polyak-Ruppert covariance H^{-1} S H^{-1} under sub-linear momentum gain and local stabilization.
Sgd with adaptive preconditioning: Unified analysis and momentum acceleration
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Clipped AdamW with exponentially weighted accumulation achieves superior global convergence rates for convex stochastic generalized Lipschitz optimization compared to SGD and AdaGrad.
Proving stability of Leon's preconditioner enables the first tuning-free Nesterov-accelerated projection-free adaptive SGD variant with improved non-smooth non-convex rates.
Refines subspace preconditioning for randomized linear solvers via QR-like factorization, enabling implicit use and proving expected linear convergence while reducing to a smaller system with good singular values.
citing papers explorer
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A Polyak-Ruppert Central Limit Theorem for SA-Adam with Momentum and Non-Convergent Adaptive Preconditioning
Averaged SA-Adam iterates have exactly the SGD Polyak-Ruppert covariance H^{-1} S H^{-1} under sub-linear momentum gain and local stabilization.
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Stochastic Non-Smooth Convex Optimization with Unbounded Gradients
Clipped AdamW with exponentially weighted accumulation achieves superior global convergence rates for convex stochastic generalized Lipschitz optimization compared to SGD and AdaGrad.
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Optimal Projection-Free Adaptive SGD for Matrix Optimization
Proving stability of Leon's preconditioner enables the first tuning-free Nesterov-accelerated projection-free adaptive SGD variant with improved non-smooth non-convex rates.
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On subspace-constrained preconditioning for randomized iterative methods
Refines subspace preconditioning for randomized linear solvers via QR-like factorization, enabling implicit use and proving expected linear convergence while reducing to a smaller system with good singular values.