English
Related papers

Related papers: Comments on the Discrete Variable Representation

200 papers

We consider time discretization methods for abstract parabolic problems with inhomogeneous linear constraints. Prototype examples that fit into the general framework are the heat equation with inhomogeneous (time dependent) Dirichlet…

Numerical Analysis · Mathematics 2018-06-14 Igor Voulis , Arnold Reusken

We propose a differential difference equation in ${\mathcal R}^1\times {\mathcal Z}^2$ and study it by Hirota's bilinear method. This equation has a singular continuum limit into a system which admits the reduction to the Davey-Stewartson…

Exactly Solvable and Integrable Systems · Physics 2016-09-08 Gegenhasi , Xing-Biao Hu , Decio Levi

In this paper, we propose and analyze a mixed virtual element method for the approximation of the eigenvalues and eigenfunctions of the two-dimensional elasticity eigenvalue problem. Under standard assumptions on polygonal meshes, we prove…

Numerical Analysis · Mathematics 2026-03-24 Felipe Lepe , Gonzalo Rivera

Recently there has been a growing interest in computational methods for quantum scattering equations that avoid the traditional decomposition of wave functions and scattering amplitudes into partial waves.The aim of the present work is to…

Computational Physics · Physics 2015-06-16 Zeki C. Kuruoglu

We construct energy-dependent potentials for which the Schroedinger equations admit solu- tions in terms of exceptional orthogonal polynomials. Our method of construction is based on certain point transformations, applied to the equations…

Mathematical Physics · Physics 2017-04-05 Axel Schulze-Halberg , Pinaki Roy

In this paper we obtain an It\^o differential representation for a class of singular stochastic Volterra integral equations. As an application, we investigate the rate of convergence in the small time central limit theorem for the solution.

Probability · Mathematics 2020-05-12 Nguyen Tien Dung

We introduce a simple calculus, extending a variant of the Steenbrink spectrum, for describing Hodge-theoretic invariants of (smoothings of) isolated singularities with (relative) automorphisms. After computing these "eigenspectra" in the…

Algebraic Geometry · Mathematics 2024-02-21 Ben Castor , Haohua Deng , Matt Kerr , Gregory Pearlstein

This paper studies the numerical approximation of the ground state of the Gross-Pitaevskii (GP) eigenvalue problem with a fully discretized Sobolev gradient flow induced by the $H^1$ norm. For the spatial discretization, we consider the…

Numerical Analysis · Mathematics 2024-09-04 Ziang Chen , Jianfeng Lu , Yulong Lu , Xiangxiong Zhang

This article explains and illustrates the use of a set of coupled dynamical equations, second order in a fictitious time, which converges to solutions of stationary Schr\"{o}dinger equations with additional constraints. We include three…

Computational Physics · Physics 2020-10-21 M. Ogren , M. Gulliksson

We present a new high-order accurate Lagrangian discontinuous Galerkin (DG) hydrodynamic method to simulate material dynamics (for e.g., gasses, fluids, and solids) with up to fourth-order accuracy on cubic meshes. The variables, such as…

Computational Physics · Physics 2021-03-04 Xiaodong Liu , Nathaniel R. Morgan , Evan J. Lieberman , Donald E. Burton

We show that discrete singular Radon transforms along a certain class of polynomial mappings $P:\mathbb{Z}^d\to \mathbb{Z}^n$ satisfy sparse bounds. For $n=d=1$ we can handle all polynomials. In higher dimensions, we pose restrictions on…

Classical Analysis and ODEs · Mathematics 2021-08-02 Theresa C. Anderson , Bingyang Hu , Joris Roos

We develop spectral methods for ODEs and operator eigenvalue problems that are based on a least-squares formulation of the problem. The key tool is a method for rectangular generalized eigenvalue problems, which we extend to quasimatrices…

Numerical Analysis · Mathematics 2021-09-14 Behnam Hashemi , Yuji Nakatsukasa

We present a Ritz-Galerkin discretization on sparse grids using pre-wavelets, which allows to solve elliptic differential equations with variable coefficients for dimension $d=2,3$ and higher dimensions $d>3$. The method applies multilinear…

Numerical Analysis · Mathematics 2016-03-10 Rainer Hartmann , Christoph Pflaum

In this work we propose a new, arbitrary order space-time finite element discretisation for Hamiltonian PDEs in multisymplectic formulation. We show that the new method which is obtained by using both continuous and discontinuous…

Numerical Analysis · Mathematics 2021-08-18 Elena Celledoni , James Jackaman

For the linearized setting of the dynamics of complex bodies we construct variational integrators and prove their convergence by making use of BV estimates on the rate fields. We allow for peculiar substructural inertia and internal…

Mathematical Physics · Physics 2008-03-12 Matteo Focardi , Paolo Maria Mariano

We study the stochastic Riemannian gradient algorithm for matrix eigen-decomposition. The state-of-the-art stochastic Riemannian algorithm requires the learning rate to decay to zero and thus suffers from slow convergence and sub-optimal…

Machine Learning · Computer Science 2016-05-30 Zhiqiang Xu , Yiping Ke

Our main objective in this work is to show how Sobolev orthogonal polynomials emerge as a useful tool within the framework of spectral methods for boundary-value problems. The solution of a boundary-value problem for a stationary…

Numerical Analysis · Mathematics 2026-01-23 Miguel A. Piñar

This paper is on further development of discrete complex analysis introduced by R. Isaacs, J. Ferrand, R. Duffin, and C. Mercat. We consider a graph lying in the complex plane and having quadrilateral faces. A function on the vertices is…

Combinatorics · Mathematics 2013-04-01 Mikhail Skopenkov

In this paper we establish a best approximation property of fully discrete Galerkin finite element solutions of second order parabolic problems on convex polygonal and polyhedral domains in the $L^\infty$ norm. The discretization method…

Numerical Analysis · Mathematics 2018-08-20 Dmitriy Leykekhman , Boris Vexler

We study the problem of privately estimating the parameters of $d$-dimensional Gaussian Mixture Models (GMMs) with $k$ components. For this, we develop a technique to reduce the problem to its non-private counterpart. This allows us to…

Machine Learning · Statistics 2023-06-09 Jamil Arbas , Hassan Ashtiani , Christopher Liaw