Affordable Access

Implicit Mass-Matrix Penalization of Hamiltonian dynamics with application to exact sampling of stiff systems

  • Plechac, Petr
  • Rousset, Mathias
Publication Date
May 28, 2009
Submission Date
May 28, 2009
External links


An implicit mass-matrix penalization (IMMP) of Hamiltonian dynamics is proposed, and associated dynamical integrators, as well as sampling Monte-Carlo schemes, are analyzed for systems with multiple time scales. The penalization is based on an extended Hamiltonian with artificial constraints associated with some selected DOFs. The penalty parameters enable arbitrary tuning of timescales for the selected DOFs. The IMMP dynamics is shown to be an interpolation between the exact Hamiltonian dynamics and the dynamics with rigid constraints. This property translates in the associated numerical integrator into a tunable trade-off between stability and dynamical modification. Moreover, a penalty that vanishes with the time-step yields order two convergent schemes for the exact dynamics. Moreover, by construction, the resulting dynamics preserves the canonical equilibrium distribution in position variables, up to a computable geometric correcting potential, leading to Metropolis-like unbiased sampling algorithms. The algorithms can be implemented with a simple modification of standard geometric integrators with algebraic constraints imposed on the selected DOFs, and has no additional complexity in terms of enforcing the constraints and force evaluations. The properties of the IMMP method are demonstrated numerically on the $N$-alkane model, showing that the time-step stability region of integrators and the sampling efficiency can be increased with a gain that grows with the size of the system. This feature is mathematically analyzed for a harmonic atomic chain model. When a large stiffness parameter is introduced, the IMMP method is shown to be asymptotically stable and to converge towards the heuristically expected Markovian effective dynamics on the slow manifold.

Report this publication


Seen <100 times