A Variant of Gradient Descent Algorithm Based on Gradient Averaging

• URL: http://arxiv.org/abs/2012.02387v2
• Date: Thu, 10 Dec 2020 06:48:52 GMT
• Title: A Variant of Gradient Descent Algorithm Based on Gradient Averaging
• Authors: Saugata Purkayastha and Sukannya Purkayastha
• Abstract summary: In regression tasks, it is observed that the behaviour of Grad-Avg is almost identical with Gradient Descent (SGD) We show that Grad-Avg converges faster than the other state-of-the-arts for the classification task on two benchmark datasets.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we study an optimizer, Grad-Avg to optimize error functions. We establish the convergence of the sequence of iterates of Grad-Avg mathematically to a minimizer (under boundedness assumption). We apply Grad-Avg along with some of the popular optimizers on regression as well as classification tasks. In regression tasks, it is observed that the behaviour of Grad-Avg is almost identical with Stochastic Gradient Descent (SGD). We present a mathematical justification of this fact. In case of classification tasks, it is observed that the performance of Grad-Avg can be enhanced by suitably scaling the parameters. Experimental results demonstrate that Grad-Avg converges faster than the other state-of-the-art optimizers for the classification task on two benchmark datasets.

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