Stochastic Gradient Descent on Separable Data: Exact Convergence with a Fixed Learning Rate
This provides theoretical insights into SGD behavior for separable data, potentially explaining similar patterns in deep networks, though it is incremental as it focuses on a specific linear case.
The paper proves that Stochastic Gradient Descent (SGD) converges to zero loss with a fixed learning rate for homogeneous linear classifiers on linearly separable data, achieving O(1/t) loss convergence and O(1/log(t)) directional convergence to the max margin vector for logistic loss.
Stochastic Gradient Descent (SGD) is a central tool in machine learning. We prove that SGD converges to zero loss, even with a fixed (non-vanishing) learning rate - in the special case of homogeneous linear classifiers with smooth monotone loss functions, optimized on linearly separable data. Previous works assumed either a vanishing learning rate, iterate averaging, or loss assumptions that do not hold for monotone loss functions used for classification, such as the logistic loss. We prove our result on a fixed dataset, both for sampling with or without replacement. Furthermore, for logistic loss (and similar exponentially-tailed losses), we prove that with SGD the weight vector converges in direction to the $L_2$ max margin vector as $O(1/\log(t))$ for almost all separable datasets, and the loss converges as $O(1/t)$ - similarly to gradient descent. Lastly, we examine the case of a fixed learning rate proportional to the minibatch size. We prove that in this case, the asymptotic convergence rate of SGD (with replacement) does not depend on the minibatch size in terms of epochs, if the support vectors span the data. These results may suggest an explanation to similar behaviors observed in deep networks, when trained with SGD.