Summary
This detailed tutorial provides an incremental approach to understanding the Muon optimizer for machine learning. It explains the underlying math and concepts including gradient descent, momentum, preconditioning, and how the Muon optimizer efficiently approximates whitening with minimal overhead. The post emphasizes the benefits of treating gradient matrices as structure-aware entities, positioning Muon as a significant advancement over previous optimizers like Adam and AdaGrad, particularly for neural network training.