In a Hilbert setting, we develop fast methods for convex unconstrained optimization. We rely
on the asymptotic behavior of an inertial system combining geometric damping with
temporal scaling. The convex function to minimize enters the dynamic via its gradient. The
dynamic includes three coefficients varying with time, one is a viscous damping coefficient,
the second is attached to the Hessian-driven damping, the third is a time scaling coefficient.
We study the convergence rate of the values under general conditions involving the …