Generalizing Parallel Replica Dynamics: Trajectory Fragments, Asynchronous Computing, and PDMPs
Copyright policy )Generalizing Parallel Replica Dynamics: Trajectory Fragments, Asynchronous Computing, and PDMPs
We study the Parallel Replica Dynamics in a general setting. We introduce a trajectory fragment framework that can be used to design and prove consistency of Parallel Replica algorithms for generic Markov processes. We use our framework to formulate a novel condition that guarantees an asynchronous algorithm is consistent. Exploiting this condition and our trajectory fragment framework, we present new synchronous and asynchronous Parallel Replica algorithms for piecewise deterministic Markov processes.
32 pages, 9 figures
- Colorado State University United States
- BOARD OF GOVERNORS OF THE COLORADO STATE UNIVERSITY SYSTEM United States
Monte Carlo methods, Parallel numerical computation, Numerical Analysis (math.NA), asynchronous computing, stationary distributions, Stochastic methods (Fokker-Planck, Langevin, etc.) applied to problems in time-dependent statistical mechanics, long-time dynamics, FOS: Mathematics, Numerical analysis or methods applied to Markov chains, parallel replica dynamics, Mathematics - Numerical Analysis, piecewise deterministic Markov processes, 65C05, 65C20, 65C40, 65Y05, 82C80
Monte Carlo methods, Parallel numerical computation, Numerical Analysis (math.NA), asynchronous computing, stationary distributions, Stochastic methods (Fokker-Planck, Langevin, etc.) applied to problems in time-dependent statistical mechanics, long-time dynamics, FOS: Mathematics, Numerical analysis or methods applied to Markov chains, parallel replica dynamics, Mathematics - Numerical Analysis, piecewise deterministic Markov processes, 65C05, 65C20, 65C40, 65Y05, 82C80
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