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We present an exact Monte Carlo algorithm designed to sample theories where the energy is a sum of many couplings of decreasing strength. The algorithm avoids the computation of almost all non-leading terms. Its use is illustrated by…

High Energy Physics - Lattice · Physics 2009-10-31 T. Bakeyev , Ph. de Forcrand

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…

Strongly Correlated Electrons · Physics 2013-10-24 F. R. Petruzielo , A. A. Holmes , Hitesh J. Changlani , M. P. Nightingale , C. J. Umrigar

The sign problem is a notorious problem, which occurs in Monte Carlo simulations of a system with a partition function whose integrand is not positive. One way to simulate such a system is to use the factorization method where one enforces…

High Energy Physics - Lattice · Physics 2012-11-08 Konstantinos N. Anagnostopoulos , Takehiro Azuma , Jun Nishimura

The development of Monte Carlo algorithms for generating gauge field configurations with dynamical fermions and methods for extracting the most information from ensembles are summarised.

High Energy Physics - Lattice · Physics 2009-11-07 Mike Peardon

Monte Carlo (MC) simulations of many systems, in particular those with conflicting constraints, can be considerably speeded up by using multicanonical or related methods. Some of these approaches sample with a-priori unknown weight factors.…

High Energy Physics - Lattice · Physics 2009-10-30 Bernd A. Berg

Previous investigations have shown that the canonical approach to simulating QCD at finite density is promising. The algorithm we used in our earlier work employs an exact calculation of the fermionic determinant which limits the size of…

High Energy Physics - Lattice · Physics 2008-11-26 Andrei Alexandru , Anyi Li , Keh-Fei Liu

A quantum Monte Carlo method combining update of the loop algorithm with the global flip of the world line is proposed as an efficient method to study the magnetization process in an external field, which has been difficult because of…

Statistical Mechanics · Physics 2009-10-31 Hiroaki Onishi , Masamichi Nishino , Naoki Kawashima , Seiji Miyashita

A recent reformulation [1] of the problem of Coulomb gases in the presence of a dynamical dielectric medium showed that finite temperature simulations of such systems can be accomplished on the basis of completely local Hamiltonians on a…

Soft Condensed Matter · Physics 2009-11-11 A. Duncan , R. D. Sedgewick

Direct sampling of multi-dimensional systems with quantum Monte Carlo methods allows exact account of many-body effects or particle correlations. The most straightforward approach to solve the Schr\"odinger equation, Diffusion Monte Carlo,…

Quantum Physics · Physics 2017-09-07 Ilkka Ruokosenmäki , Tapio T. Rantala

The wavelength of a periodic spatial structure of Turing type is an intrinsic property of the considered reaction-diffusion dynamics and we address the question of its control at the microscopic scale for given dynamical parameters. The…

Pattern Formation and Solitons · Physics 2018-09-26 Gabriel Morgado , Bogdan Nowakowski , Annie Lemarchand

In this work, within the framework of path integral Monte Carlo, we construct a pseudo-fermion propagator by replacing the original fermionic determinant with its absolute value. This modified propagator defines an auxiliary system free…

Computational Physics · Physics 2026-03-31 Yunuo Xiong , Hongwei Xiong

The Wilson fermion determinant can be written in the form of a series expansion in fugacity $\xi=\exp(\mu/T)$, provided that the eigenmodes of the temporally reduced operator are obtained. Since the calculation of all eigenmodes rapidly…

High Energy Physics - Lattice · Physics 2014-11-18 Yasunori Futamura , Shoji Hashimoto , Akira Imakura , Keitaro Nagata , Tetsuya Sakurai

A class of Monte Carlo algorithms which incorporate absorbing Markov chains is presented. In a particular limit, the lowest-order of these algorithms reduces to the $n$-fold way algorithm. These algorithms are applied to study the escape…

Condensed Matter · Physics 2009-10-22 M. A. Novotny

Fermion sampling is to generate probability distribution of a many-body Slater-determinant wavefunction, which is termed "determinantal point process" in statistical analysis. For its inherently-embedded Pauli exclusion principle, its…

Quantum Physics · Physics 2023-01-31 Haoran Sun , Jie Zou , Xiaopeng Li

Compact and accurate wave functions can be constructed by quantum Monte Carlo methods. Typically, these wave functions consist of a sum of a small number of Slater determinants multiplied by a Jastrow factor. In this paper we study the…

Condensed Matter · Physics 2009-10-30 Chien-Jung Huang , C. J. Umrigar , M. P. Nightingale

We propose a general framework for finding the ground state of many-body fermionic systems by using feed-forward neural networks. The anticommutation relation for fermions is usually implemented to a variational wave function by the Slater…

Strongly Correlated Electrons · Physics 2021-12-21 Koji Inui , Yasuyuki Kato , Yukitoshi Motome

A Monte Carlo program is presented that computes all four fermion processes in $e^+ e^-$ annihilation. QED initial state corrections and QCD contributions are included. Fermions are taken to be massless, allowing a very fast evaluation of…

High Energy Physics - Phenomenology · Physics 2009-10-28 F. Berends , R , Kleiss , R. Pittau

The past few years have seen considerable progress in algorithmic development for the generation of gauge fields including the effects of dynamical fermions. The Rational Hybrid Monte Carlo (RHMC) algorithm, where Hybrid Monte Carlo is…

High Energy Physics - Lattice · Physics 2008-11-26 M. A. Clark

An algorithm for separating the high- and low-frequency molecular dynamics modes in Hybrid Monte Carlo simulations of gauge theories with dynamical fermions is presented. The separation is based on splitting the pseudo-fermion action into…

High Energy Physics - Lattice · Physics 2008-11-26 A. Ali Khan , T. Bakeyev , M. Göckeler , R. Horsley , D. Pleiter , P. Rakow , A. Schäfer , G. Schierholz , H. Stüben

Many machine learning problems optimize an objective that must be measured with noise. The primary method is a first order stochastic gradient descent using one or more Monte Carlo (MC) samples at each step. There are settings where…

Machine Learning · Computer Science 2021-04-22 Sifan Liu , Art B. Owen
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