Nesterov Accelerated Gradient (NAG) is a powerful optimization algorithm that enhances gradient descent by leveraging a momentum term and a lookahead step. It accelerates convergence by approximating the future trajectory of the function and adjusting the step direction accordingly. Compared to gradient descent, NAG achieves faster convergence rates and improved stability, particularly for non-convex and ill-conditioned problems. The algorithm involves calculating a momentum term that incorporates past gradients and a lookahead step that estimates the future gradient. These enhancements contribute to NAG’s ability to navigate complex optimization landscapes and effectively minimize objective functions.
