Results 11 to 20 of about 840,639 (141)

Coupled Electron Ion Monte Carlo Calculations of Atomic Hydrogen [PDF]

, 2004
We present a new Monte Carlo method which couples Path Integral for finite temperature protons with Quantum Monte Carlo for ground state electrons, and we apply it to metallic hydrogen for pressures beyond molecular dissociation.
Ceperley, David M., Holzmann, Markus, Pierleoni, Carlo   +2 more
core   +1 more source

Auxiliary Field Diffusion Monte Carlo calculation of nuclei with A<40 with tensor interactions [PDF]

, 2007
We calculate the ground-state energy of 4He, 8He, 16O, and 40Ca using the auxiliary field diffusion Monte Carlo method in the fixed phase approximation and the Argonne v6' interaction which includes a tensor force.
Fantoni, S., Gandolfi, S., Pederiva, F., Schmidt, K. E.   +3 more
core   +1 more source

Optimization of ground and excited state wavefunctions and van der Waals clusters [PDF]

, 2000
A quantum Monte Carlo method is introduced to optimize excited state trial wavefunctions. The method is applied in a correlation function Monte Carlo calculation to compute ground and excited state energies of bosonic van der Waals clusters of upto seven
Andrei Mushinski, B. Bernu, C. J. Umrigar, C. J. Umrigar, C. J. Umrigar, D. Blume, D. Blume, D. H. Zhang, D. M. Ceperley, D. M. Leitner, G. H. Golub, J. de Boer, J. H. Hetherington, J. K. L. MacDonald, M. Caffarel, M. Caffarel, M. Foulkes, M. Meierovich, M. P. Nightingale, M. P. Nightingale, M. P. Nightingale, V. Melik-Alaverdian, Vilen Melik-Alaverdian, W. H. Press, W. R. Brown   +24 more
core   +3 more sources

Semistochastic Projector Monte Carlo Method

Physical Review Letters, 2012
We introduce a semistochastic implementation of the power method to compute, for very large matrices, the dominant eigenvalue and expectation values involving the corresponding eigenvector. The method is semistochastic in that the matrix multiplication is partially implemented numerically exactly and partially with respect to expectation values only ...
Petruzielo, F. R., Holmes, A. A., Changlani, Hitesh J., Nightingale, M. P., Umrigar, C. J.   +4 more
openaire   +5 more sources

Divergence of the multilevel Monte Carlo Euler method for nonlinear stochastic differential equations

, 2013
The Euler-Maruyama scheme is known to diverge strongly and numerically weakly when applied to nonlinear stochastic differential equations (SDEs) with superlinearly growing and globally one-sided Lipschitz continuous drift coefficients.
Hutzenthaler, Martin, Jentzen, Arnulf, Kloeden, Peter E.   +2 more
core   +1 more source

Efficient Monte Carlo Calculations of the One-Body Density [PDF]

, 2006
An alternative Monte Carlo estimator for the one-body density rho(r) is presented. This estimator has a simple form and can be readily used in any type of Monte Carlo simulation.
Assaraf, Roland, Caffarel, Michel, Scemama, Anthony   +2 more
core   +7 more sources

Self-Learning Monte Carlo Method [PDF]

, 2016
Monte Carlo simulation is an unbiased numerical tool for studying classical and quantum many-body systems. One of its bottlenecks is the lack of general and efficient update algorithm for large size systems close to phase transition or with strong ...
Fu, Liang, Liu, Junwei, Meng, Zi Yang, Qi, Yang   +3 more
core   +2 more sources

Transition Matrix Monte Carlo Method

, 1998
We analyze a new Monte Carlo method which uses transition matrix in the space of energy. This method gives an efficient reweighting technique. The associated artificial dynamics is a constrained random walk in energy, producing the result that ...
Wang, Jian-Sheng
core   +1 more source

Self-Learning Monte Carlo Method: Continuous-Time Algorithm

, 2017
The recently-introduced self-learning Monte Carlo method is a general-purpose numerical method that speeds up Monte Carlo simulations by training an effective model to propose uncorrelated configurations in the Markov chain.
Fu, Liang, Liu, Junwei, Nagai, Yuki, Qi, Yang, Shen, Huitao   +4 more
core   +1 more source

Optimized monte carlo methods [PDF]

, 2008
I discuss optimized data analysis and Monte Carlo methods. Reweighting methods are discussed through examples, like Lee-Yang zeroes in the Ising model and the absence of deconfinement in QCD. I discuss reweighted data analysis and multi-hystogramming. I introduce Simulated Tempering, and as an example its application to the Random Field Ising Model.
openaire   +2 more sources