Preconditioned matrix norms unify steepest descent, quasi-Newton, and adaptive optimizers, revealing SGD, Adam, Muon, KL-Shampoo, SOAP, and SPlus as special cases and enabling new methods MuAdam and MuAdam-SANIA that are competitive in experiments.
A stable whitening optimizer for efficient neural network training
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A retrospective survey and empirical evaluation of deep learning optimization algorithms that identifies trends, design trade-offs, and future directions.
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Preconditioned Norms: A Unified Framework for Steepest Descent, Quasi-Newton and Adaptive Methods
Preconditioned matrix norms unify steepest descent, quasi-Newton, and adaptive optimizers, revealing SGD, Adam, Muon, KL-Shampoo, SOAP, and SPlus as special cases and enabling new methods MuAdam and MuAdam-SANIA that are competitive in experiments.
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Evolution of Optimization Methods: Algorithms, Scenarios, and Evaluations
A retrospective survey and empirical evaluation of deep learning optimization algorithms that identifies trends, design trade-offs, and future directions.