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The Lagrange mesh method is a very simple procedure to accurately solve eigenvalue problems starting from a given nonrelativistic or semirelativistic two-body Hamiltonian with local or nonlocal potential. We show in this work that it can be…

Computational Physics · Physics 2007-05-23 F. Buisseret , C. Semay

We present a highly accurate method for solving single-active-electron (SAE) atomic eigenset in momentum space. The trouble of Coulomb kernel singularity is bypassed with numerical quadrature, which is simple but effective. The complicated…

Quantum Physics · Physics 2017-06-20 Shih-Da Jheng , Tsin-Fu Jiang

The method reducing the solution of the Schroedinger equation for several types of power potentials to the solution of the eigenvalue problem for the infinite system of algebraic equations is developed. The finite truncation of this system…

High Energy Physics - Phenomenology · Physics 2014-11-17 R. N. Faustov , V. O. Galkin , A. V. Tatarintsev , A. S. Vshivtsev

A global approximation method of Nystr\"om type is explored for the numerical solution of a class of nonlinear integral equations of the second kind. The cases of smooth and weakly singular kernels are both considered. In the first…

Numerical Analysis · Mathematics 2024-07-16 Luisa Fermo , Anna Lucia Laguardia , Concetta Laurita , Maria Grazia Russo

We demonstrate a general three-dimensional semiclassical model as a powerful technique for the study of correlated multi-electron escape in atoms driven by infrared laser pulses at intensities where electron-electron correlation prevails.…

Atomic Physics · Physics 2023-04-26 Agapi Emmanouilidou , Matthew Benjamin Peters , Georgios Petros Katsoulis

A fast method for solving boundary integral equations with the generalized Neumann kernel and the adjoint generalized Neumann kernel is presented. The method is based on discretizing the integral equations by the Nystr\"om method with the…

Numerical Analysis · Mathematics 2015-04-10 Mohamed M. S. Nasser

The Nystr\"{o}m method is routinely used for out-of-sample extension of kernel matrices. We describe how this method can be applied to find the singular value decomposition (SVD) of general matrices and the eigenvalue decomposition (EVD) of…

Numerical Analysis · Computer Science 2013-05-02 Arik Nemtsov , Amir Averbuch , Alon Schclar

Cornell potential is known to represent the quark-antiquark confinement interaction. In addition to the Cornell potential, there have been other interactions in the literature that demonstrate confining structure in the quark-antiquark…

General Relativity and Quantum Cosmology · Physics 2023-05-26 S. Habib Mazharimousavi

A practical method to solve cut-off Coulomb problems of two-cluster systems in the momentum space is given. When a sharply cut-off Coulomb force with a cut-off radius $\rho$ is introduced at the level of constituent particles, two-cluster…

Nuclear Theory · Physics 2012-09-03 Yoshikazu Fujiwara , Kenji Fukukawa

To solve boundary integral equations for potential problems using collocation Boundary Element Method (BEM) on smooth curved 3D geometries, an analytical singularity extraction technique is employed. By adopting the isoparametric approach,…

Numerical Analysis · Mathematics 2022-07-20 Tadej Kanduc

The numerical solution of problems in nonlinear magnetostatics is typically based on a variational formulation in terms of magnetic potentials, the discretization by finite elements, and iterative solvers like the Newton method. The vector…

Numerical Analysis · Mathematics 2024-05-03 Herbert Egger , Felix Engertsberger , Bogdan Radu

We formulate a damped oscillating particle method to solve the stationary nonlinear Schr\"{o}dinger equation (NLSE). The ground state solutions are found by a converging damped oscillating evolution equation that can be discretized with…

Computational Physics · Physics 2016-03-03 P. Sandin , M. Ogren , M. Gulliksson

The Lagrange-mesh method is a powerful method to solve eigenequations written in configuration space. It is very easy to implement and very accurate. Using a Gauss quadrature rule, the method requires only the evaluation of the potential at…

Mathematical Physics · Physics 2012-08-10 G. Lacroix , C. Semay , F. Buisseret

This paper presents a general high-order kernel regularization technique applicable to all four integral operators of Calder\'on calculus associated with linear elliptic PDEs in two and three spatial dimensions. Like previous density…

Numerical Analysis · Mathematics 2021-03-02 Luiz M. Faria , Carlos Pérez-Arancibia , Marc Bonnet

We introduce a method for reducing anisotropic heavy-quark potentials to isotropic potentials by using an effective screening mass that depends on the quantum numbers $l$ and $m$ of a given state. We demonstrate that, using the resulting 1D…

High Energy Physics - Phenomenology · Physics 2023-01-11 Ajaharul Islam , Lihua Dong , Yun Guo , Alexander Rothkopf , Michael Strickland

In this study, we solved Schr\"odinger equation with Cornell potential (Coulomb-plus-linear potential) by using neural network approach. Four different types of Cornell potential were used without a physical relevance. Besides that…

Computational Physics · Physics 2019-04-15 Halil Mutuk

Direct numerical solution of the coordinate-space integral-equation version of the two-particle Lippmann Schwinger (LS) equation is considered as a means of avoiding the shortcomings of partial-wave expansion at high energies and in the…

Computational Physics · Physics 2016-11-15 Zeki C. Kuruoglu

An algorithm for the direct inversion of the linear systems arising from Nystrom discretization of integral equations on one-dimensional domains is described. The method typically has O(N) complexity when applied to boundary integral…

Numerical Analysis · Mathematics 2011-05-27 Adrianna Gillman , Patrick Young , Per-Gunnar Martinsson

We develop an improved bound for the approximation error of the Nystr\"{o}m method under the assumption that there is a large eigengap in the spectrum of kernel matrix. This is based on the empirical observation that the eigengap has a…

Machine Learning · Computer Science 2012-09-04 Mehrdad Mahdavi , Tianbao Yang , Rong Jin

Kernel mean embeddings are a powerful tool to represent probability distributions over arbitrary spaces as single points in a Hilbert space. Yet, the cost of computing and storing such embeddings prohibits their direct use in large-scale…

Machine Learning · Statistics 2022-06-16 Antoine Chatalic , Nicolas Schreuder , Alessandro Rudi , Lorenzo Rosasco