Robust Learning Rate Selection for Stochastic Optimization via Splitting Diagnostic

• URL: http://arxiv.org/abs/1910.08597v5
• Date: Sat, 17 Feb 2024 00:18:21 GMT
• Title: Robust Learning Rate Selection for Stochastic Optimization via Splitting Diagnostic
• Authors: Matteo Sordello, Niccol\`o Dalmasso, Hangfeng He and Weijie Su
• Abstract summary: SplitSGD is a new dynamic learning schedule for optimization. The method decreases the learning rate for better adaptation to the local geometry of the objective function. It essentially does not incur additional computational cost than standard SGD.
• Score: 5.395127324484869
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper proposes SplitSGD, a new dynamic learning rate schedule for stochastic optimization. This method decreases the learning rate for better adaptation to the local geometry of the objective function whenever a stationary phase is detected, that is, the iterates are likely to bounce at around a vicinity of a local minimum. The detection is performed by splitting the single thread into two and using the inner product of the gradients from the two threads as a measure of stationarity. Owing to this simple yet provably valid stationarity detection, SplitSGD is easy-to-implement and essentially does not incur additional computational cost than standard SGD. Through a series of extensive experiments, we show that this method is appropriate for both convex problems and training (non-convex) neural networks, with performance compared favorably to other stochastic optimization methods. Importantly, this method is observed to be very robust with a set of default parameters for a wide range of problems and, moreover, can yield better generalization performance than other adaptive gradient methods such as Adam.

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