Nesterov's Accelerated Gradient and Momentum as approximations to   Regularised Update Descent
  We present a unifying framework for adapting the update direction in gradient-based iterative optimization methods. As natural special cases we re-derive classical momentum and Nesterov's accelerated gradient method, lending a new intuitive interpretation to the latter algorithm. We show that a new algorithm, which we term Regularised Gradient Descent, can converge more quickly than either Nesterov's algorithm or the classical momentum algorithm. 
Aleksandar Botev, Guy Lever, David Barber
stat.ML cs.LG