I am using SGD to solve for MSE function. My training set is around 50K, and I am monitoring the gradient at every epoch (once a pass is completed over all the training data). I played around a lot with the step sizes however I don't think they make that big of a difference. After running for 30 iterations, I don't think I have seen any convergence based on the gradient values. The norm of the gradient values change around 0.1 to 0.01. Not a constant decrease but it is erratic.
I am aware that it is not converging to optimal, I am okay with an approximate converge. I am just not really sure how to understand whether 0.1 norm(gradient) is close to the optimal and is relatively a good approximation or not.