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Solving large-scale eigenvalue problems poses a significant challenge due to the computational complexity and limitations on the parallel scalability of the orthogonalization operation, when many eigenpairs are required. In this paper, we…

Numerical Analysis · Mathematics 2025-11-11 Tianyang Chu , Xiaoying Dai , Shengyue Wang , Aihui Zhou

We propose and analyze a finite element method for the Oseen eigenvalue problem. This problem is an extension of the Stokes eigenvalue problem, where the presence of the convective term leads to a non-symmetric problem and hence, to complex…

Numerical Analysis · Mathematics 2023-11-10 Felipe Lepe , Gonzalo Rivera , Jesus Vellojin

This paper presents a parallel algorithm for finding the smallest eigenvalue of a particular form of ill-conditioned Hankel matrix, which requires the use of extremely high precision arithmetic. Surprisingly, we find that commonly-used…

Numerical Analysis · Mathematics 2009-02-06 Niall Emmart , Charles C. Weems , Yang Chen

This paper studies how to compute all real eigenvalues of a symmetric tensor. As is well known, the largest or smallest eigenvalue can be found by solving a polynomial optimization problem, while the other middle eigenvalues can not. We…

Numerical Analysis · Mathematics 2014-12-16 Chun-Feng Cui , Yu-Hong Dai , Jiawang Nie

Computing more than one eigenvalue for (large sparse) one-parameter polynomial and general nonlinear eigenproblems, as well as for multiparameter linear and nonlinear eigenproblems, is a much harder task than for standard eigenvalue…

Numerical Analysis · Mathematics 2021-10-19 Michiel E. Hochstenbach , Bor Plestenjak

This work studies the problem of maximizing a higher degree real homogeneous multivariate polynomial over the unit sphere. This problem is equivalent to finding the leading eigenvalue of the associated symmetric tensor of higher order,…

Optimization and Control · Mathematics 2019-10-02 Yuning Yang , Guoyin Li

Estimating the number of eigenvalues located in a given interval of a large sparse Hermitian matrix is an important problem in certain applications and it is a prerequisite of eigensolvers based on a divide-and-conquer paradigm. Often an…

Numerical Analysis · Computer Science 2014-08-06 Edoardo Di Napoli , Eric Polizzi , Yousef Saad

Quartic eigenvalue problem $(\lambda^4 A + \lambda^3 B + \lambda^2C + \lambda D + E)x = \mathbf{0}$ naturally arises e.g. when solving the Orr-Sommerfeld equation in the analysis of the stability of the {Poiseuille} flow, in theoretical…

Numerical Analysis · Mathematics 2021-03-10 Zlatko Drmač , Ivana Šain Glibić

The classic method for computing the spectral decomposition of a real symmetric matrix, the Jacobi algorithm, can be accelerated by using mixed precision arithmetic. The Jacobi algorithm is aiming to reduce the off-diagonal entries…

Numerical Analysis · Mathematics 2025-09-03 Zhengbo Zhou

It is significant and challenging to solve eigenvalue problems of partial differential operators when many highly accurate eigenpair approximations are required. The adaptive finite element discretization based parallel orbital-updating…

Numerical Analysis · Mathematics 2025-09-24 Xiaoying Dai , Yan Li , Bin Yang , Aihui Zhou

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

Quadratic forms of Hermitian matrix resolvents involve the solutions of shifted linear systems. Efficient iterative solutions use the shift-invariance property of Krylov subspaces The Hermitian Lanczos method reduces a given vector and…

Numerical Analysis · Mathematics 2020-10-15 Keiichi Morikuni

In this paper we propose an accurate, highly parallel algorithm for the generalized eigendecomposition of a matrix pair $(H, S)$, given in a factored form $(F^{\ast} J F, G^{\ast} G)$. Matrices $H$ and $S$ are generally complex and…

Numerical Analysis · Mathematics 2020-09-22 Sanja Singer , Edoardo Di Napoli , Vedran Novaković , Gayatri Čaklović

We investigate numerically efficient approximations of eigenspaces associated to symmetric and general matrices. The eigenspaces are factored into a fixed number of fundamental components that can be efficiently manipulated (we consider…

Machine Learning · Computer Science 2021-09-29 Cristian Rusu , Lorenzo Rosasco

We present an iterative algorithm, called the symmetric tensor eigen-rank-one iterative decomposition (STEROID), for decomposing a symmetric tensor into a real linear combination of symmetric rank-1 unit-norm outer factors using only…

Numerical Analysis · Mathematics 2016-02-18 Kim Batselier , Ngai Wong

Accurate computation of multiple eigenvalues of quantum Hamiltonians is essential in quantum chemistry, materials science, and molecular spectroscopy. Estimating excited-state energies is challenging for classical algorithms due to…

Quantum Physics · Physics 2026-05-22 Grzegorz Rajchel-Mieldzioć , Szymon Pliś , Emil Zak

We consider heuristic algorithm for solving graph isomorphism problem. The algorithm based on a successive splitting of the eigenvalues of the matrices which are modifications (to positive defined) of graphs' adjacency matrices.…

General Mathematics · Mathematics 2007-05-23 R. T. Faizullin , A. V. Prolubnikov

Dual quaternion matrices have various applications in robotic research and its spectral theory has been extensively studied in recent years. In this paper, we extend Jacobi method to compute all eigenpairs of dual quaternion Hermitian…

Numerical Analysis · Mathematics 2024-06-26 Yongjun Chen , Liping Zhang

Peter Denton, Stephen Parke, Terence Tao and Xining Zhang [arxiv 2019] presented a basic and important identity in linear commutative algebra, so-called {\bf the eigenvector-eigenvalue identity} (formally named in [BAMS, 2021]), which is a…

Rings and Algebras · Mathematics 2022-07-11 Yuchao He , Mengda Wu , Yonghui Xia

First, we derive explicit computable expressions of structured backward errors of approximate eigenelements of structured matrix polynomials including symmetric, skew-symmetric, Hermitian, skew-Hermitian, even and odd polynomials. We also…

Numerical Analysis · Mathematics 2009-07-16 Bibhas Adhikari , Rafikul Alam