  Andersen dynamics is a standard method for molecular simulations, and a
precursor of the Hamiltonian Monte Carlo algorithm used in MCMC inference. The
stochastic process corresponding to Andersen dynamics is a PDMP (piecewise
deterministic Markov process) that iterates between Hamiltonian flows and
velocity randomizations of randomly selected particles. Both from the viewpoint
of molecular dynamics and MCMC inference, a basic question is to understand the
convergence to equilibrium of this PDMP particularly in high dimension. Here we
present couplings to obtain sharp convergence bounds in the Wasserstein sense
that do not require global convexity of the underlying potential energy.
