  The Nesterov's accelerated quasi-Newton (L)NAQ method has shown to accelerate
the conventional (L)BFGS quasi-Newton method using the Nesterov's accelerated
gradient in several neural network (NN) applications. However, the calculation
of two gradients per iteration increases the computational cost. The Momentum
accelerated Quasi-Newton (MoQ) method showed that the Nesterov's accelerated
gradient can be approximated as a linear combination of past gradients. This
abstract extends the MoQ approximation to limited memory NAQ and evaluates the
performance on a function approximation problem.
