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Related papers: Deterministic Optimisation of Jastrow Factors

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We investigate two kinds of extensions for the variational Monte Carlo (VMC) method with the Pfaffian in the nuclear shell-model calculations. One is the extension to odd-mass nuclei, for which we find a new Pfaffian expression of the VMC…

Nuclear Theory · Physics 2018-11-21 Noritaka Shimizu , Takahiro Mizusaki

Dynamic tensor data are becoming prevalent in numerous applications. Existing tensor clustering methods either fail to account for the dynamic nature of the data, or are inapplicable to a general-order tensor. Also there is often a gap…

Machine Learning · Statistics 2018-09-17 Will Wei Sun , Lexin Li

Ab initio quantum Monte Carlo (QMC) methods are state-of-the-art electronic structure calculations based on highly parallelizable stochastic frameworks for accurate solutions of the many-body Schr{\"o}dinger equation, suitable for modern…

Chemical Physics · Physics 2026-04-07 Kousuke Nakano , Stefano Battaglia , Jürg Hutter

While Diffusion Monte Carlo (DMC) is in principle an exact stochastic method for \textit{ab initio} electronic structure calculations, in practice the fermionic sign problem necessitates the use of the fixed-node approximation and trial…

This paper extends the application of the stochastic variational method to noncentral interactions. Several examples are presented for three- and four-nucleon systems with realistic nuclear forces. The correlated Gaussians easily cope with…

Nuclear Theory · Physics 2009-10-30 K. Varga , Y. Ohbayasi , Y. Suzuki

In the Monte Carlo (MC) method statistical noise is usually present. Statistical noise may become dominant in the calculation of a distribution, usually by iteration, but is less Important in calculating integrals. The subject of the…

Computational Physics · Physics 2013-11-08 Mihály Makai , Zoltán Szatmáry

The tailored coupled cluster (TCC) approach is a promising ansatz that preserves the simplicity of single-reference coupled cluster theory, while incorporating a multi-reference wave function through amplitudes obtained from a preceding…

Chemical Physics · Physics 2021-01-07 Maximilian Mörchen , Leon Freitag , Markus Reiher

In diffusion Monte Carlo (DMC) methods, the nodes (or zeroes) of the trial wave function dictate the magnitude of the fixed-node (FN) error. Within standard DMC implementations, they emanate from short multideterminant expansions,…

Chemical Physics · Physics 2018-05-31 Anthony Scemama , Yann Garniron , Michel Caffarel , Pierre-François Loos

We discuss electronic properties and their evolution for the linear chain of $H_2$ molecules in the presence of a uniform external force $f$ acting along the chain. The system is described by an extended Hubbard model within a fully…

Materials Science · Physics 2018-08-15 Andrzej Biborski , Andrzej P. Kądzielawa , Józef Spałek

We develop a formalism and present an algorithm for optimization of the trial wave-function used in fixed-node diffusion quantum Monte Carlo (DMC) methods. We take advantage of a basic property of the walker configuration distribution…

Strongly Correlated Electrons · Physics 2015-05-13 Fernando A. Reboredo , Randolph Q. Hood , Paul R. C. Kent

Variational and diffusion quantum Monte Carlo (VMC and DMC) methods with Slater-Jastrow-backflow trial wave functions are used to study the spin-polarized three-dimensional uniform electron fluid. We report ground state VMC and DMC energies…

Strongly Correlated Electrons · Physics 2025-11-11 Sam Azadi , N. D. Drummond , Sam. M. Vinko

The accurate but expensive product of geminals ansatz may be approximated by a geminal power, but this approach sacrifices size consistency. Here we show both analytically and numerically that a size consistent form very similar to the…

Strongly Correlated Electrons · Physics 2012-11-19 Eric Neuscamman

Continued demand for accurate and computationally efficient transport methods to solve optically thick, fixed-source transport problems has inspired research on variance-reduction (VR) techniques for Monte Carlo (MC). Methods that use…

Numerical Analysis · Computer Science 2015-02-18 S. C. Wilson , R. N. Slaybaugh

Due to the significant process variations, designers have to optimize the statistical performance distribution of nano-scale IC design in most cases. This problem has been investigated for decades under the formulation of stochastic…

Computational Engineering, Finance, and Science · Computer Science 2023-08-17 Yifan Pan , Zichang He , Nanlin Guo , Zheng Zhang

Markov chain Monte Carlo (MCMC) methods require a large number of samples to approximate a posterior distribution, which can be costly when the likelihood or prior is expensive to evaluate. The number of samples can be reduced if we can…

Computation · Statistics 2019-08-06 V. Roshan Joseph , Dianpeng Wang , Li Gu , Shiji Lv , Rui Tuo

VARCLUST algorithm is proposed for clustering variables under the assumption that variables in a given cluster are linear combinations of a small number of hidden latent variables, corrupted by the random noise. The entire clustering task…

We present the extension of variational Monte Carlo (VMC) to the calculation of electronic excitation energies and oscillator strengths using time-dependent linear-response theory. By exploiting the analogy existing between the linear…

Chemical Physics · Physics 2018-11-08 Bastien Mussard , Emanuele Coccia , Roland Assaraf , Matt Otten , C. J. Umrigar , Julien Toulouse

We propose an efficient ${\cal O}(N^2)$-parameter ansatz that consists of a sequence of exponential operators, each of which is a unitary variant of Neuscamman's cluster Jastrow operator. The ansatz can also be derived as a decomposition of…

Chemical Physics · Physics 2020-07-07 Yuta Matsuzawa , Yuki Kurashige

Reliable robot autonomy hinges on decision-making systems that account for uncertainty without imposing overly conservative restrictions on the robot's action space. We introduce Chance-Constrained Via-Point-Based Stochastic Trajectory…

Practitioners of Bayesian statistics have long depended on Markov chain Monte Carlo (MCMC) to obtain samples from intractable posterior distributions. Unfortunately, MCMC algorithms are typically serial, and do not scale to the large…

Machine Learning · Statistics 2015-06-11 Maxim Rabinovich , Elaine Angelino , Michael I. Jordan
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