Fast convex optimization via inertial systems with asymptotic vanishing viscous and Hessian-driven damping

We study the convergence rate of a family of inertial algorithms, which can be obtained by discretization of an inertial system combining asymptotic vanishing viscous and Hessian-driven damping. We establish a fast sublinear convergence rate in case the objective function is convex and satisfies Polyak-Łojasiewicz inequality. We also establish a linear convergence rate for smooth strongly convex functions. The results can provide more insights into the convergence property of Nesterov's accelerated gradient method.