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Roland Assaraf
Researcher at University of Paris
Publications - 43
Citations - 1301
Roland Assaraf is an academic researcher from University of Paris. The author has contributed to research in topics: Quantum Monte Carlo & Monte Carlo method. The author has an hindex of 20, co-authored 40 publications receiving 1093 citations. Previous affiliations of Roland Assaraf include Pierre-and-Marie-Curie University & Centre national de la recherche scientifique.
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Zero-Variance Principle for Monte Carlo Algorithms
Roland Assaraf,Michel Caffarel +1 more
TL;DR: In this paper, the authors present a general approach to greatly increase at little cost the efficiency of Monte Carlo algorithms by associating a renormalized observable (improved estimator) having the same average but a different variance.
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Zero-variance zero-bias principle for observables in quantum Monte Carlo: Application to forces
Roland Assaraf,Michel Caffarel +1 more
TL;DR: In this article, a simple and stable method for computing accurate expectation values of observables with variational Monte Carlo or diffusion Monte Carlo (DMC) algorithms is presented, which consists in replacing the usual "bare" estimator associated with the observable by an improved or normalized estimator.
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Quantum Package 2.0: An Open-Source Determinant-Driven Suite of Programs.
Yann Garniron,Thomas Applencourt,Kevin Gasperich,Kevin Gasperich,Anouar Benali,Anthony Ferté,Julien Paquier,Barthélémy Pradines,Roland Assaraf,Peter Reinhardt,Julien Toulouse,Pierrette Barbaresco,Nicolas Renon,Grégoire David,Jean-Paul Malrieu,Mickaël Véril,Michel Caffarel,Pierre-François Loos,Emmanuel Giner,Anthony Scemama +19 more
TL;DR: A renormalized second-order perturbative correction for efficient extrapolation to the full CI limit and a stochastic version of the CIPSI selection performed simultaneously to the PT2 calculation at no extra cost are introduced.
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Diffusion monte carlo methods with a fixed number of walkers
TL;DR: A rigorous proof of the divergence of pure diffusion Monte Carlo methods (DMC without branching in which the weights are carried along trajectories) is given and a bias-free Monte Carlo method combining DMC and PDMC approaches, and based on a minimal stochastic reconfiguration of the population is discussed.
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Computing forces with quantum Monte Carlo
Roland Assaraf,Michel Caffarel +1 more
TL;DR: In this paper, a stable quantum Monte Carlo approach for computing forces between atoms in a molecule is presented, using the standard Hellmann-Feynman expression (local force expressed as the derivative of the total potential energy with respect to the internuclear coordinates).