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Relativistic dynamics of a charged particle in time-dependent electromagnetic fields has theoretical significance and a wide range of applications. It is often multi-scale and requires accurate long-term numerical simulations using…

Plasma Physics · Physics 2018-10-24 Ruili Zhang , Yulei Wang , Yang He , Jianyuan Xiao , Jian Liu , Hong Qin , Yifa Tang

Some explicit algorithms for higher order symplectic integration of a large class of Hamilton's equations have recently been discussed by Mushtaq \emph{et. al}. Here we present a Python program for automatic numerical implementation of…

Numerical Analysis · Mathematics 2015-06-17 Asif Mushtaq , Kåre Olaussen

An improved algorithm to evaluate the nonrelativistic three-electron Hylleraas-Configuration Interaction (Hy-CI) kinetic energy integrals over Slater orbitals and the Hamiltonian in Hylleraas coordinates is shown. The resulting analytical…

Atomic Physics · Physics 2016-01-29 Maria Belen Ruiz

Numerical lattice gauge theory computations to generate gauge field configurations including the effects of dynamical fermions are usually carried out using algorithms that require the molecular dynamics evolution of gauge fields using…

High Energy Physics - Lattice · Physics 2015-06-11 A. D. Kennedy , P. J. Silva , M. A. Clark

Most numerical integration algorithms are not designed specifically for Hamiltonian systems and do not respect their characteristic properties, which include the preservation of phase space volume with time. This can lead to spurious…

Astrophysics · Physics 2015-06-24 David JD Earn

Numerical methods that preserve geometric invariants of the system, such as energy, momentum or the symplectic form, are called geometric integrators. In this paper we present a method to construct symplectic-momentum integrators for…

Numerical Analysis · Mathematics 2014-11-07 Leonardo Colombo , Sebastián Ferraro , David Martín de Diego

We introduce a novel numerical method to integrate partial differential equations representing the Hamiltonian dynamics of field theories. It is a multi-symplectic integrator that locally conserves the stress-energy tensor with an excellent…

Numerical Analysis · Mathematics 2017-02-23 Hugo Ricateau , Leticia F. Cugliandolo

We introduce a recent symplectic integration scheme derived for solving physically motivated systems with non-separable Hamiltonians. We show its relevance to Riemannian manifold Hamiltonian Monte Carlo (RMHMC) and provide an alternative to…

Machine Learning · Statistics 2019-10-15 Adam D. Cobb , Atılım Güneş Baydin , Andrew Markham , Stephen J. Roberts

We show that symplectic Runge-Kutta methods provide effective symplectic integrators for Hamiltonian systems with index one constraints. These include the Hamiltonian description of variational problems subject to position and velocity…

Numerical Analysis · Mathematics 2014-02-28 Robert I McLachlan , Klas Modin , Olivier Verdier , Matt Wilkins

By exploiting the error functions of explicit symplectic integrators for solving separable Hamiltonians, I show that it is possible to develop explicit, time-reversible symplectic integrators for solving non-separable Hamiltonians of the…

Computational Physics · Physics 2009-09-25 Siu A. Chin

We compare the performances of symplectic and non-symplectic integrators for the computation of normal geodesics and conjugate points in sub-Riemannian geometry at the example of the Martinet case. For this case study we consider first the…

Numerical Analysis · Mathematics 2007-05-23 Monique Chyba , Ernst Hairer , Gilles Vilmart

In order to perform numerical studies of long-term stability in nonlinear Hamiltonian systems, one needs a numerical integration algorithm which is symplectic. Further, this algorithm should be fast and accurate. In this paper, we propose…

Exactly Solvable and Integrable Systems · Physics 2009-11-07 Govindan Rangarajan

In the recent years a generalization $H=p^2 +x^2(ix)^\epsilon$ of the harmonic oscillator using a complex deformation was investigated, where \epsilon\ is a real parameter. Here, we will consider the most simple case: \epsilon even and x…

Quantum Physics · Physics 2015-05-30 Tomas Azizov , Carsten Trunk

A variational framework for accelerated optimization was recently introduced on normed vector spaces and Riemannian manifolds in Wibisono et al. (2016) and Duruisseaux and Leok (2021). It was observed that a careful combination of…

Optimization and Control · Mathematics 2023-05-16 Valentin Duruisseaux , Melvin Leok

This paper studies diagonal implicit symplectic extended Runge--Kutta--Nystr\"{o}m (ERKN) methods for solving the oscillatory Hamiltonian system $H(q,p)=\dfrac{1}{2}p^{T}p+\dfrac{1}{2}q^{T}Mq+U(q)$. Based on symplectic conditions and order…

Numerical Analysis · Mathematics 2017-12-04 Mingxue Shi , Hao Zhang , Bin Wang

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

We present a practical algorithm based on symplectic splitting methods to integrate numerically in time the Schr\"odinger equation. When discretized in space, the Schr\"odinger equation can be recast as a classical Hamiltonian system…

Numerical Analysis · Mathematics 2015-02-24 S. Blanes , F. Casas , A. Murua

This article considers non-relativistic charged particle dynamics in both static and non-static electromagnetic fields, which are governed by nonseparable, possibly time-dependent Hamiltonians. For the first time, explicit symplectic…

Numerical Analysis · Mathematics 2016-11-23 Molei Tao

We suggest a numerical integration procedure for solving the equations of motion of certain classical spin systems which preserves the underlying symplectic structure of the phase space. Such symplectic integrators have been successfully…

Statistical Mechanics · Physics 2007-05-23 Robin Steinigeweg , Heinz-Jürgen Schmidt

Recently, continuous-time dynamical systems have proved useful in providing conceptual and quantitative insights into gradient-based optimization, widely used in modern machine learning and statistics. An important question that arises in…

Optimization and Control · Mathematics 2021-04-29 Guilherme França , Michael I. Jordan , René Vidal