2020
DOI: 10.48550/arxiv.2003.10312
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A termination criterion for stochastic gradient descent for binary classification

Sina Baghal,
Courtney Paquette,
Stephen A. Vavasis

Abstract: We propose a new, simple, and computationally inexpensive termination test for constant step-size stochastic gradient descent (SGD) applied to binary classification on the logistic and hinge loss with homogeneous linear predictors. Our theoretical results support the effectiveness of our stopping criterion when the data is Gaussian distributed. This presence of noise allows for the possibility of non-separable data. We show that our test terminates in a finite number of iterations and when the noise in the dat… Show more

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