Presents a model-based proximal framework for adaptive momentum in first-order optimizers by using a two-plane approximation of the objective to dynamically set the memory coefficient online.
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Parameter-free first-order methods attain optimal oracle complexity O(ε^{-2/(1+3ρ)}) for convex function-constrained optimization under Hölder smoothness by combining modified Polyak steps, Nesterov momentum, and APL level-set methods.
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Adaptive Memory Momentum via a Model-Based Framework for Deep Learning Optimization
Presents a model-based proximal framework for adaptive momentum in first-order optimizers by using a two-plane approximation of the objective to dynamically set the memory coefficient online.
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Uniformly Optimal and Parameter-free First-order Methods for Convex and Function-constrained Optimization
Parameter-free first-order methods attain optimal oracle complexity O(ε^{-2/(1+3ρ)}) for convex function-constrained optimization under Hölder smoothness by combining modified Polyak steps, Nesterov momentum, and APL level-set methods.