Shadow hybrid Monte Carlo: an efficient propagator in phase space of macromolecules

By Jesus A. Izaguirre, Scott S. Hampton. 2004

104 citations recorded by the source corpus.

Subfields: Molecular Biology, Statistical and Nonlinear Physics, Statistics and Probability, Evaluation & Benchmarks

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

1. Jascha Narain Sohl-Dickstein
2. Daniel Lévy
3. Andrea L. Jochim
4. Mayur Mudigonda
5. A. Berger
6. Mark E. Tuckerman
7. Matthew D. Hoffman
8. Michael Robert DeWeese

In Statistical and Nonlinear Physics

1. Jascha Narain Sohl-Dickstein
2. Daniel Lévy
3. Mayur Mudigonda
4. A. Berger
5. Matthew D. Hoffman
6. Michael Robert DeWeese
7. Greg Yang
8. Yang Song

In Statistics and Probability

1. Jascha Narain Sohl-Dickstein
2. Matthew D. Hoffman
3. Daniel Lévy
4. Mayur Mudigonda
5. Michael Robert DeWeese
6. A. Berger
7. Mark E. Tuckerman
8. David M. Blei

In Evaluation & Benchmarks

1. Jascha Narain Sohl-Dickstein
2. Daniel Lévy
3. Mayur Mudigonda
4. A. Berger
5. Matthew D. Hoffman
6. Michael Robert DeWeese

Citations in the corpus

• Generalizing Hamiltonian Monte Carlo with Neural Networks (2017, methodology): “It would be interesting to explore combining our work with the use of HMC in a minibatch setting (Chen et al., 2014); with shadow Hamiltonians (Izaguirre & Hampton, 2004); with gradient pre-conditioning approaches similar to those used in Riemannian HMC (Girolami et al., 2009; Betancourt, 2013);…”
• Handbook of Markov Chain Monte Carlo (2011, neutral): “…not be able to review here, such as the use of a “shadow Hamiltonian” that is exactly conserved by the inexact simulation of the real Hamiltonian (Izagguirre and Hampton, 2004), and the use of symplectic integration methods more sophisticated than the leapfrog method (eg, Creutz and Gocksch,…”
• A Markov Jump Process for More Efficient Hamiltonian Monte Carlo (2015, methodology): “These include the use of shadow Hamiltonians that are more closely conserved by the approximate Hamiltonian integrator [11], Riemann manifold HMC [12] and other investigation of its geometry [13], quasi-Newton HMC [14], Hilbert space HMC [15], parameter adapta-”
• The Geometric Foundations of Hamiltonian Monte Carlo (2014, unknown): “…optimal step size, however, requires a deeper understanding of the interaction between the geometry of the exact Hamiltonian flow and that of the symplectic integrator developed in backwards error analysis (Leimkuhler and Reich, 2004; Hairer, Lubich and Wanner, 2006; Izaguirre and Hampton, 2004).”
• Optimal tuning of the hybrid Monte Carlo algorithm (2010, unknown): “It has since become popular in a number of application areas, including statistical physics [1,17,18,23,43], computational chemistry [22,24,30,42,45], data assimilation [2], geophysics [30] and neural networks [32,46].”
• A Liouville-operator derived measure-preserving integrator for molecular dynamics simulations in the isothermal–isobaric ensemble (2006, background): “H exists for the harmonic oscillator, convergence of equation (3.18) for nonlinear systems is generally not guaranteed [8], in which case, a truncated series can be employed in practice [30, 12 , 8].”
• Hamiltonian Monte Carlo Without Detailed Balance (2014, methodology): “…HMC (Girolami & Calderhead, 2011), quasi-Newton HMC (Zhang & Sutton, 2011), Hilbert space HMC (Beskos et al., 2011), shadow Hamiltonian methods (Izaguirre & Hampton, 2004), parameter adaptation techniques (Wang et al., 2013), Hamiltonian annealed importance sampling (SohlDickstein & Culpepper,…”