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Variational integrators are derived for structure-preserving simulation of stochastic Hamiltonian systems with a certain type of multiplicative noise arising in geometric mechanics. The derivation is based on a stochastic discrete…

Numerical Analysis · Mathematics 2019-07-31 Darryl D. Holm , Tomasz M. Tyranowski

In this paper, we systematically construct two classes of structure-preserving schemes with arbitrary order of accuracy for canonical Hamiltonian systems. The one class is the symplectic scheme, which contains two new families of…

Numerical Analysis · Mathematics 2023-07-27 Yonghui Bo , Wenjun Cai , Yushun Wang

An overview of Hamiltonian systems with noncanonical Poisson structures is given. Examples of bi-Hamiltonian ode's, pde's and lattice equations are presented. Numerical integrators using generating functions, Hamiltonian splitting,…

Exactly Solvable and Integrable Systems · Physics 2007-05-23 B. Karasözen

Hamiltonian systems are differential equations which describe systems in classical mechanics, plasma physics, and sampling problems. They exhibit many structural properties, such as a lack of attractors and the presence of conservation…

Numerical Analysis · Mathematics 2022-01-14 Christian Offen , Sina Ober-Blöbaum

We have developed a symbolic algebra approach to automatically produce, verify, and optimize computer code for the Fast Multipole Method (FMM) operators. This approach allows for flexibility in choosing a basis set and kernel, and can…

Computational Physics · Physics 2020-05-29 Jonathan P. Coles , Rebekka Bieri

We present the first known efficient decoding algorithm for correcting multiple insertion-deletion errors in Helberg codes and their non-binary generalizations, extending a known algorithm for correcting multiple deletion errors.

Information Theory · Computer Science 2025-08-27 Anthony Segrest , Hieu D. Nguyen

We show how the integrators used for the molecular dynamics step of the Hybrid Monte Carlo algorithm can be further improved. These integrators not only approximately conserve some Hamiltonian $H$ but conserve exactly a nearby shadow…

High Energy Physics - Lattice · Physics 2015-05-30 M. A. Clark , Bálint Joó , A. D. Kennedy , P. J. Silva

Machine learning methods are widely used in the natural sciences to model and predict physical systems from observation data. Yet, they are often used as poorly understood "black boxes," disregarding existing mathematical structure and…

Machine Learning · Computer Science 2023-10-24 Marco David , Florian Méhats

We present a class of non-standard numerical schemes which are modifications of the discrete gradient method. They preserve the energy integral exactly (up to the round-off error). The considered class contains locally exact discrete…

Numerical Analysis · Computer Science 2013-08-08 Jan L. Cieśliński , Bogusław Ratkiewicz

We present the symplectic algorithm in the Lagrangian formalism for the Hamiltonian systems by virtue of the noncommutative differential calculus with respect to the discrete time and the Euler--Lagrange cohomological concepts. We also show…

Computational Physics · Physics 2007-05-23 H. Y. Guo , Y. Q. Li , K. Wu

We develop a generating-function formulation for the symbolic reduction of multi-loop Feynman integrals. In this framework, integration-by-parts identities are rewritten as differential equations for sector-wise generating functions, so the…

High Energy Physics - Phenomenology · Physics 2026-05-12 Bo Feng , Xiang Li , Yuanche Liu , Yanqing Ma , Yang Zhang

We present a brief tutorial on the nuts and bolts computation of a multisymplectic particle-in-cell algorithm using the discretized Lagrangian approach. This approach, originated by Marsden, Shadwick, and others, brings the benefits of…

Plasma Physics · Physics 2014-09-18 Stephen D. Webb

We propose a combinatorial method for computing explicit solutions to multi-parametric quadratic programs, which can be used to compute explicit control laws for linear model predictive control. In contrast to classical methods, which are…

Optimization and Control · Mathematics 2024-04-09 Daniel Arnström , Daniel Axehill

There exist many applications where it is necessary to approximate numerically derivatives of a function which is given by a computer procedure. In particular, all the fields of optimization have a special interest in such a kind of…

Numerical Analysis · Mathematics 2012-03-15 Yaroslav D. Sergeyev

Hamiltonian Monte Carlo can provide powerful inference in complex statistical problems, but ultimately its performance is sensitive to various tuning parameters. In this paper we use the underlying geometry of Hamiltonian Monte Carlo to…

Methodology · Statistics 2015-02-03 M. J. Betancourt , Simon Byrne , Mark Girolami

Structure-preserving algorithms and algorithms with uniform error bound have constituted two interesting classes of numerical methods. In this paper, we blend these two kinds of methods for solving nonlinear Hamiltonian systems with highly…

Numerical Analysis · Mathematics 2021-02-08 Bin Wang , Yaolin Jiang

This paper presents some algorithmic techniques to compute explicitly the noetherian operators associated to a class of ideals and modules over a polynomial ring. The procedures we include in this work can be easily encoded in computer…

Commutative Algebra · Mathematics 2010-03-30 A. Damiano , I. Sabadini , D. C. Struppa

Stochastic computing (SC) is a high density, low-power computation technique which encodes values as unary bitstreams instead of binary-encoded (BE) values. Practical SC implementations require deterministic or pseudo-random number…

Emerging Technologies · Computer Science 2019-02-28 Vincent T. Lee , Samuel Archibald Elliot , Armin Alaghi , Luis Ceze

Higher transcendental function occur frequently in the calculation of Feynman integrals in quantum field theory. Their expansion in a small parameter is a non-trivial task. We report on a computer program which allows the systematic…

Mathematical Physics · Physics 2008-11-26 Stefan Weinzierl

$\renewcommand{\Re}{\mathbb{R}}$ We develop a general randomized technique for solving "implic it" linear programming problems, where the collection of constraints are defined implicitly by an underlying ground set of elements. In many…

Computational Geometry · Computer Science 2021-12-24 Timothy M. Chan , Sariel Har-Peled , Mitchell Jones