  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.
