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The aim of this paper is to develop an algebraic multigrid method to solve eigenvalue problems based on the combination of the multilevel correction scheme and the algebraic multigrid method for linear equations. Our approach uses the…

Numerical Analysis · Mathematics 2020-03-02 Ning Zhang , Xiaole Han , Yunhui He , Hehu Xie , Chun'guang You

We consider smooth bounded surfaces with a smooth boundary and a prescribed background metric g_0. We now consider all metrics g conformal to g_0 which have a prescribed volume M. We now minimize the first eigenvalue of the Laplace operator…

Analysis of PDEs · Mathematics 2012-09-11 Sagun Chanillo

Thanks to its versatility, its simplicity, and its fast convergence, ADMM is among the most widely used approaches for solving a convex problem in distributed form. However, making it running efficiently is an art that requires a fine…

Optimization and Control · Mathematics 2019-07-10 Tomaso Erseghe

We consider the problem of deriving upper bounds on the parameters of sum-rank-metric codes, with focus on their dimension and block length. The sum-rank metric is a combination of the Hamming and the rank metric, and most of the available…

Combinatorics · Mathematics 2023-10-30 Aida Abiad , Antonina P. Khramova , Alberto Ravagnani

This paper is a tutorial for eigenvalue and generalized eigenvalue problems. We first introduce eigenvalue problem, eigen-decomposition (spectral decomposition), and generalized eigenvalue problem. Then, we mention the optimization problems…

Machine Learning · Statistics 2023-05-23 Benyamin Ghojogh , Fakhri Karray , Mark Crowley

We show that correlation matrices with particular average and variance of the correlation coefficients have a notably restricted spectral structure. Applying geometric methods, we derive lower bounds for the largest eigenvalue and the…

Mathematical Physics · Physics 2021-08-25 Yuriy Stepanov , Hendrik Herrmann , Thomas Guhr

The standard approach for finding eigenvalues and eigenvectors of matrix polynomials starts by embedding the coefficients of the polynomial into a matrix pencil, known as linearization. Building on the pioneering work of Nakatsukasa and…

Numerical Analysis · Mathematics 2018-08-15 Javier Perez

It is well-known that the finite difference discretization of the Laplacian eigenvalue problem $-\Delta u = \lambda u$ leads to a matrix eigenvalue problem (EVP) $A x= \lambda x$ where the matrix $A$ is Toeplitz-plus-Hankel. Analytical…

Numerical Analysis · Mathematics 2021-04-13 Quanling Deng

For a given graph $G$, we aim to determine the possible realizable spectra for a generalized (or sometimes referred to as a weighted) Laplacian matrix associated with $G$. This new specialized inverse eigenvalue problem is considered for…

Combinatorics · Mathematics 2024-12-03 Shaun Fallat , Himanshu Gupta , Jephian C. -H. Lin

We study the generalized finite element methods (GFEMs) for the second-order elliptic eigenvalue problem with an interface in 1D. The linear stable generalized finite element methods (SGFEM) were recently developed for the elliptic source…

Numerical Analysis · Mathematics 2018-10-25 Quanling Deng , Victor Calo

Generalized eigenvalue problems involving a singular pencil may be very challenging to solve, both with respect to accuracy and efficiency. While Part I presented a rank-completing addition to a singular pencil, we now develop two…

Numerical Analysis · Mathematics 2023-10-26 Michiel E. Hochstenbach , Christian Mehl , Bor Plestenjak

The spectral transformation Lanczos method for the sparse symmetric definite generalized eigenvalue problem for matrices $A$ and $B$ is an iterative method that addresses the case of semidefinite or ill conditioned $B$ using a shifted and…

Numerical Analysis · Mathematics 2024-11-07 Michael Stewart

Eigenvalue interlacing is a versatile technique for deriving results in algebraic combinatorics. In particular, it has been successfully used for proving a number of results about the relation between the (adjacency matrix or Laplacian)…

Combinatorics · Mathematics 2012-06-05 M. A. Fiol

We derive bounds on the eigenvalues of saddle-point matrices with singular leading blocks. The technique of proof is based on augmentation. Our bounds depend on the principal angles between the ranges or kernels of the matrix blocks.…

Numerical Analysis · Mathematics 2022-06-01 Susanne Bradley , Chen Greif

The maximum (or minimum) generalized eigenvalue of symmetric positive semidefinite matrices that depend on optimization variables often appears as objective or constraint functions in structural topology optimization when we consider…

Optimization and Control · Mathematics 2024-05-09 Akatsuki Nishioka , Yoshihiro Kanno

It is shown how mixed finite element methods for symmetric positive definite eigenvalue problems related to partial differential operators can provide guaranteed lower eigenvalue bounds. The method is based on a classical compatibility…

Numerical Analysis · Mathematics 2024-01-10 Dietmar Gallistl

Let $(P,\preceq)$ be a lattice, $S$ a finite subset of $P$ and $f_1,f_2,...,f_n$ complex-valued functions on $P$. We define row-adjusted meet and join matrices on $S$ by $(S)_{f_1,...,f_n}=(f_i(x_i\wedge x_j))$ and…

Number Theory · Mathematics 2011-10-25 Mika Mattila , Pentti Haukkanen

We analyze the systematic errors made when using the generalized eigenvalue problem to extract energies and matrix elements in lattice gauge theory. Effective theories such as HQET are also discussed. Numerical results are shown for the…

High Energy Physics - Lattice · Physics 2010-01-21 B. Blossier , G. von Hippel , T. Mendes , R. Sommer , M. Della Morte

We prove existence, uniqueness and regularity results for mixed boundary value problems associated with fully nonlinear, possibly singular or degenerate elliptic equations. Our main result is a global H\"older estimate for solutions,…

Analysis of PDEs · Mathematics 2021-04-07 Isabeau Birindelli , Francoise Demengel , Fabiana Leoni

Let $(P,\preceq)$ be a lattice and $f$ a complex-valued function on $P$. We define meet and join matrices on two arbitrary subsets $X$ and $Y$ of $P$ by $(X,Y)_f=(f(x_i\wedge y_j))$ and $[X,Y]_f=(f(x_i\vee x_j))$ respectively. Here we…

Number Theory · Mathematics 2011-10-25 Mika Mattila , Pentti Haukkanen