Implementation of a modified Nesterov's Accelerated quasi-Newton Method on Tensorflow

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 T ensorflow. 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. Index T erms --Neural networks, training algorithm, quasi-Newton method, Nesterov's accelerated gradient, Global convergence, T ensorflow, highly-nonlinear function modeling.