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Complex Langevin simulations are an attempt to solve the sign (or complex-action) problem encountered in various physical systems of interest. The method is based on a complexification of the underlying degrees of freedom and an evolution…

High Energy Physics - Lattice · Physics 2025-04-08 Michael W. Hansen , Michael Mandl , Erhard Seiler , Dénes Sexty

Monte Carlo simulations away from half-filling suffer from a sign problem that can be reduced by deforming the contour of integration. Such a transformation, which induces a Jacobian determinant in the Boltzmann weight, can be implemented…

Computational Physics · Physics 2022-09-30 Marcel Rodekamp , Evan Berkowitz , Christoph Gäntgen , Stefan Krieg , Thomas Luu , Johann Ostmeyer

The origin of our four-dimensional space-time has been pursued through the dynamical aspects of the IIB matrix model via the improved mean field approximation. Former works have been focused on the specific choice of configurations as…

High Energy Physics - Theory · Physics 2008-11-26 T. Aoyama , H. Kawai , Y. Shibusa

We construct a Lie 3-algebra extended model of the IIB matrix model. It admits any Lie 3-algebra and possesses the same supersymmetry as the original matrix model, and thus as type IIB superstring theory. We examine dynamics of the model by…

High Energy Physics - Theory · Physics 2015-06-15 Matsuo Sato

Various classical solutions to lower dimensional IKKT-like Lorentzian matrix models are examined in their commutative limit. Poisson manifolds emerge in this limit, and their associated induced and effective metrics are computed. Signature…

High Energy Physics - Theory · Physics 2018-10-22 A. Stern , Chuang Xu

We study the properties of the space-time that emerges dynamically from the matrix model for type IIB superstrings in ten dimensions. We calculate the free energy and the extent of space-time using the Gaussian expansion method up to the…

High Energy Physics - Theory · Physics 2015-05-30 Jun Nishimura , Toshiyuki Okubo , Fumihiko Sugino

D-dimensional constrained systems are studied with stochastic Lagrangian and\break Hamiltonian. It is shown that stochastic consistency conditions are second class constraints and Lagrange multiplier fields can be determined in…

High Energy Physics - Theory · Physics 2017-02-01 R. Mochizuki , K. Yoshida

Simulation-based inference (SBI) enables Bayesian analysis when the likelihood is intractable but model simulations are available. Recent advances in statistics and machine learning, including Approximate Bayesian Computation and deep…

Methodology · Statistics 2025-09-15 Haoyu Jiang , Yuexi Wang , Yun Yang

The three-dimensional SU(3) spin model is an effective Polyakov loop model for QCD at nonzero temperature and density. It suffers from a sign problem at nonzero chemical potential. We revisit this model using complex Langevin dynamics and…

High Energy Physics - Lattice · Physics 2015-06-03 Gert Aarts , Frank A. James

We pursue the study of the type IIB matrix model as a constructive definition of superstring. In this paper, we justify the interpretation of space-time as distribution of eigenvalues of the matrices by showing that some low energy…

High Energy Physics - Theory · Physics 2014-11-18 S. Iso , H. Kawai

Lattice simulations of non-zero density QCD introduce the so-called sign problem (complex or negative probabilities), which invalidates importance sampling methods. To circumvent this, we use the Complex Langevin Equation (CLE), to measure…

High Energy Physics - Lattice · Physics 2023-03-01 Michael W. Hansen , Dénes Sexty

We argue that the large-$N$ master field of the Lorentzian IIB matrix model can give the points and metric of a classical spacetime.

High Energy Physics - Theory · Physics 2021-02-02 F. R. Klinkhamer

We show that complex Langevin simulation converges to a wrong result, by relating it to the Lefschetz-thimble path integral, when the path-integral weight has different phases among dominant complex saddle points. Equilibrium solution of…

High Energy Physics - Lattice · Physics 2017-02-07 Tomoya Hayata , Yoshimasa Hidaka , Yuya Tanizaki

Recently, the task of image generation has attracted much attention. In particular, the recent empirical successes of the Markov Chain Monte Carlo (MCMC) technique of Langevin Dynamics have prompted a number of theoretical advances; despite…

Machine Learning · Statistics 2020-06-24 Adam Block , Youssef Mroueh , Alexander Rakhlin , Jerret Ross

The use of a transfer matrix method to solve the 3D Ising model is straightforwardly generalized from the 2D case. We follow B.Kaufman's approach. No approximation is made, however the largest eigenvalue cannot be identified. This problem…

Statistical Mechanics · Physics 2007-05-23 S. L. Lou , S. H. Wu

We provide a new approach for establishing hardness of approximation results, based on the theory recently introduced by the author. It allows one to directly show that approximating a problem beyond a certain threshold requires…

Computational Complexity · Computer Science 2024-02-23 Ali Çivril

We study three possible ways to circumvent the sign problem in the O(3) nonlinear sigma model in 1+1 dimensions. We compare the results of the worm algorithm to complex Langevin and multiparameter reweighting. Using the worm algorithm, the…

High Energy Physics - Lattice · Physics 2017-03-22 Sandor D. Katz , Ferenc Niedermayer , Daniel Nogradi , Csaba Torok

The dual formulation of the compact U(1) lattice gauge theory in three spacetime dimensions allows to finely study the squared width and the profile of the confining flux tube on a wide range of physical interquark distances. The results…

High Energy Physics - Lattice · Physics 2017-03-27 Michele Caselle , Marco Panero , Davide Vadacchino

We investigate finite-dimensional constrained structured optimization problems, featuring composite objective functions and set-membership constraints. Offering an expressive yet simple language, this problem class provides a modeling…

Optimization and Control · Mathematics 2023-02-09 Alberto De Marchi , Xiaoxi Jia , Christian Kanzow , Patrick Mehlitz

In order to solve tasks like uncertainty quantification or hypothesis tests in Bayesian imaging inverse problems, we often have to draw samples from the arising posterior distribution. For the usually log-concave but high-dimensional…

Computation · Statistics 2025-01-23 Matthias J. Ehrhardt , Lorenz Kuger , Carola-Bibiane Schönlieb
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