  In a Hilbert setting, for convex differentiable optimization, we consider
accelerated gradient dynamics combining Tikhonov regularization with
Hessian-driven damping. The Tikhonov regularization parameter is assumed to
tend to zero as time tends to infinity, which preserves equilibria. The
presence of the Tikhonov regularization term induces a strong convexity
property which vanishes asymptotically. To take advantage of the exponential
convergence rates attached to the heavy ball method in the strongly convex
case, we consider the inertial dynamic where the viscous damping coefficient is
taken proportional to the square root of the Tikhonov regularization parameter,
and therefore also converges towards zero. Moreover, the dynamic involves a
geometric damping which is driven by the Hessian of the function to be
minimized, which induces a significant attenuation of the oscillations. Under
an appropriate tuning of the parameters, based on Lyapunov's analysis, we show
that the trajectories have at the same time several remarkable properties: they
provide fast convergence of values, fast convergence of gradients towards zero,
and strong convergence to the minimum norm minimizer. This study extends a
previous paper by the authors where similar issues were examined but without
the presence of Hessian driven damping.
