  Recent studies incorporate Nesterov's accelerated gradient method for the
acceleration of gradient based training. The Nesterov's Accelerated
Quasi-Newton (NAQ) method has shown to drastically improve the convergence
speed compared to the conventional quasi-Newton method. This paper implements
NAQ for non-convex optimization on Tensorflow. Two modifications have been
proposed to the original NAQ algorithm to ensure global convergence and
eliminate linesearch. The performance of the proposed algorithm - mNAQ is
evaluated on standard non-convex function approximation benchmark problems and
microwave circuit modelling problems. The results show that the improved
algorithm converges better and faster compared to first order optimizers such
as AdaGrad, RMSProp, Adam, and the second order methods such as the
quasi-Newton method.
