  Poor (even random) starting points for learning/training/optimization are
common in machine learning. In many settings, the method of Robbins and Monro
(online stochastic gradient descent) is known to be optimal for good starting
points, but may not be optimal for poor starting points -- indeed, for poor
starting points Nesterov acceleration can help during the initial iterations,
even though Nesterov methods not designed for stochastic approximation could
hurt during later iterations. The common practice of training with nontrivial
minibatches enhances the advantage of Nesterov acceleration.
