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Traditional numerical methods for calculating matrix eigenvalues are prohibitively expensive for high-dimensional problems. Iterative random sparsification methods allow for the estimation of a single dominant eigenvalue at reduced cost by…

Numerical Analysis · Mathematics 2023-10-03 Samuel M. Greene , Robert J. Webber , Timothy C. Berkelbach , Jonathan Weare

This paper introduces a novel a posteriori error estimation framework for the enriched Galerkin (EG) finite element method applied to linear parabolic equations. While the EG method has been recognized for its local conservation property…

Numerical Analysis · Mathematics 2026-04-29 Hyun-Geun Shin , Yi-Yung Yang , Sanghyun Lee

We consider a 1D periodic atomistic model, for which we formulate and analyze an adaptive variant of a quasicontinuum method. We establish a posteriori error estimates for the energy norm and for the energy, based on a posteriori residual…

Numerical Analysis · Mathematics 2017-02-15 Christoph Ortner , Hao Wang

In this paper, we propose a new trace finite element method for the {Laplace-Beltrami} eigenvalue problem. The method is proposed directly on a smooth manifold which is implicitly given by a level-set function and require high order…

Numerical Analysis · Mathematics 2022-01-17 Song Lu , Xianmin Xu

In this work, we present the a posteriori error analysis of Stabilization-Free Virtual Element Methods for the 2D Poisson equation. The abscence of a stabilizing bilinear form in the scheme allows to prove the equivalence between a suitably…

Numerical Analysis · Mathematics 2026-01-30 Stefano Berrone , Andrea Borio , Davide Fassino , Francesca Marcon

A local and parallel algorithm based on the multilevel discretization is proposed in this paper to solve the eigenvalue problem by the finite element method. With this new scheme, solving the eigenvalue problem in the finest grid is…

Numerical Analysis · Mathematics 2014-01-21 Yu Li , Xiaole Han , Hehu Xie , Chunguang You

We present an algorithmic solution to the problem of incremental belief updating in the context of Monte Carlo inference in Bayesian statistical models represented by probabilistic programs. Given a model and a sample-approximated…

Machine Learning · Statistics 2024-02-13 David Tolpin

We derive a posteriori error estimates for the hybridizable discontinuous Galerkin (HDG) methods, including both the primal and mixed formulations, for the approximation of a linear second-order elliptic problem on conforming simplicial…

Numerical Analysis · Mathematics 2017-06-20 Mark Ainsworth , Guosheng Fu

A type of parallel augmented subspace scheme for eigenvalue problems is proposed by using coarse space in the multigrid method. With the help of coarse space in multigrid method, solving the eigenvalue problem in the finest space is…

Numerical Analysis · Mathematics 2020-08-19 Fei Xu , Hehu Xie , Ning Zhang

We present an a posteriori error estimate based on equilibrated stress reconstructions for the finite element approximation of a unilateral contact problem with weak enforcement of the contact conditions. We start by proving a guaranteed…

Numerical Analysis · Mathematics 2021-09-27 Daniele Antonio Di Pietro , Ilaria Fontana , Kyrylo Kazymyrenko

For the solution of linear discrete ill-posed problems, in this paper we consider the Arnoldi-Tikhonov method coupled with the Generalized Cross Validation for the computation of the regularization parameter at each iteration. We study the…

Numerical Analysis · Mathematics 2013-04-02 Paolo Novati , Maria Rosaria Russo

Variational inference has become an increasingly attractive fast alternative to Markov chain Monte Carlo methods for approximate Bayesian inference. However, a major obstacle to the widespread use of variational methods is the lack of…

Machine Learning · Statistics 2020-03-03 Jonathan H. Huggins , Mikołaj Kasprzak , Trevor Campbell , Tamara Broderick

Fully coupled McKean-Vlasov forward-backward stochastic differential equations (MV-FBSDEs) arise naturally from large population optimization problems. Judging the quality of given numerical solutions for MV-FBSDEs, which usually require…

Numerical Analysis · Mathematics 2023-06-08 Christoph Reisinger , Wolfgang Stockinger , Yufei Zhang

Classical a posteriori error analysis for differential equations quantifies the error in a Quantity of Interest (QoI) which is represented as a bounded linear functional of the solution. In this work we consider a posteriori error estimates…

Numerical Analysis · Mathematics 2020-07-07 Jehanzeb H. Chaudhry , Donald Estep , Zachary Stevens , Simon J. Tavener

A fundamental task in numerical computation is the solution of large linear systems. The conjugate gradient method is an iterative method which offers rapid convergence to the solution, particularly when an effective preconditioner is…

Methodology · Statistics 2018-12-18 Jon Cockayne , Chris Oates , Ilse Ipsen , Mark Girolami

We devise and analyze a reliable and efficient a posteriori error estimator for a semilinear control-constrained optimal control problem in two and three dimensional Lipschitz, but not necessarily convex, polytopal domains. We consider a…

Numerical Analysis · Mathematics 2019-11-22 Alejandro Allendes , Francisco Fuica , Enrique Otarola , Daniel Quero

We introduce two a posteriori error estimators for N\'ed\'elec finite element discretizations of the curl-curl problem. These estimators pertain to a new Prager-Synge identity and an associated equilibration procedure. They are reliable and…

Numerical Analysis · Mathematics 2021-08-24 T. Chaumont-Frelet

A posteriori error estimators for the symmetric mixed finite element methods for linear elasticity problems of Dirichlet and mixed boundary conditions are proposed. Stability and efficiency of the estimators are proved. Finally, we provide…

Numerical Analysis · Mathematics 2017-05-12 Long Chen , Jun Hu , Xuehai Huang , Hongying Man

A methodology to analyze the properties of the first (largest) eigenvalue and its eigenvector is developed for large symmetric random sparse matrices utilizing the cavity method of statistical mechanics. Under a tree approximation, which is…

Optimization and Control · Mathematics 2015-05-18 Yoshiyuki Kabashima , Hisanao Takahashi , Osamu Watanabe

Generalized polynomial chaos expansions are a powerful tool to study differential equations with random coefficients, allowing in particular to efficiently approximate random invariant sets associated to such equations. In this work, we use…

Numerical Analysis · Mathematics 2022-03-07 Maxime Breden