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Related papers: A new approach to spectral approximation

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In this paper we explore a certain class of non-selfadjoint operators acting in a complex separable Hilbert space. We consider a perturbation of a non-selfadjoint operator by an operator that is also non-selfadjoint. Our consideration is…

Functional Analysis · Mathematics 2019-03-26 M. V. Kukushkin

We propose an efficient numerical method for a non-selfadjoint Steklov eigenvalue problem. The Lagrange finite element is used for discretization. The convergence is proved using the spectral perturbation theory for compact operators. The…

Numerical Analysis · Mathematics 2018-04-10 Juan Liu , Jiguang Sun , Tiara Turner

We study evolution equations with non-self-adjoint generators, for example the convection-diffusion equation. Spectral expansions are not a reliable method of solving such equations, because they are so ill-conditioned. We introduce a new…

Spectral Theory · Mathematics 2007-05-23 E. B. Davies

A mechanism is described to symmetrize the ultraspherical spectral method for self-adjoint problems. The resulting discretizations are symmetric and banded. An algorithm is presented for an adaptive spectral decomposition of self-adjoint…

Numerical Analysis · Mathematics 2020-04-22 Jared Lee Aurentz , Richard Mikael Slevinsky

We demonstrate criteria, purely based on finite subwords of the potential, to guarantee spectral inclusion as well as Hausdorff approximation of pseudospectra or even spectra of generalized Schr\"odinger operators on the discrete line or…

Spectral Theory · Mathematics 2023-01-20 Fabian Gabel , Dennis Gallaun , Julian Großmann , Marko Lindner , Riko Ukena

The inverse problem of spectral analysis for the non-self-adjoint matrix Sturm-Liouville operator on a finite interval is investigated. We study properties of the spectral characteristics for the considered operator, and provide necessary…

Spectral Theory · Mathematics 2014-07-15 Natalia Bondarenko

In this paper spectral theorems for not necessarily continuous normal and self-adjoint random operators on a complex separable Hilbert space are proved.

Spectral Theory · Mathematics 2017-01-24 Pastorel Gaspar

Let A(x) be a holomorphic family of bounded self-adjoint operators on a separable Hilbert space H and let A(x)_n be the orthogonal compressions of A(x) to the span of first n elements of an orthonormal basis of H. The problem considered…

Functional Analysis · Mathematics 2022-07-08 V. B. Kiran Kumar , M. N. N. Namboodiri , S. Serra-Capizzano

We study the spectral convergence of compact, self-adjoint operators on a separable Hilbert space under operator norm perturbations, and derive asymptotic expansions for their eigenvalues and eigenprojections. Our analysis focuses on…

Statistics Theory · Mathematics 2026-02-10 Eunseong Bae , Wolfgang Polonik

In this article we are interested for the numerical computation of spectra of non-self adjoint quadratic operators, in two and three spatial dimensions. Indeed, in the multidimensional case very few results are known on the location of the…

Numerical Analysis · Mathematics 2024-12-04 Fatima Aboud , François Jauberteau , Didier Robert

The problem of approximating the discrete spectra of families of self-adjoint operators that are merely strongly continuous is addressed. It is well-known that the spectrum need not vary continuously (as a set) under strong perturbations.…

Spectral Theory · Mathematics 2016-03-08 Jonathan Ben-Artzi , Thomas Holding

We present a streamlined approach for generalized strong and norm convergence of self-adjoint operators in different Hilbert spaces. In particular, we establish convergence of associated (semi-)groups, (essential) spectra and spectral…

Spectral Theory · Mathematics 2026-01-16 Gerald Teschl , Yifei Wang , Bing Xie , Zhe Zhou

We present the convergence rates and the explicit error bounds of Hill's method, which is a numerical method for computing the spectra of ordinary differential operators with periodic coefficients. This method approximates the operator by a…

Numerical Analysis · Mathematics 2015-07-28 Ken'ichiro Tanaka , Sunao Murashige

In \cite{Os} a general spectral approximation theory was developed for compact operators on a Banach space which does not require that the operators be self-adjoint and also provides a first order correction term. Here we extend some of the…

Mathematical Physics · Physics 2016-01-20 Shari Moskow

The so-called equation of motion method is useful to obtain the explicit form of the eigenvectors and eigenvalues of certain non self-adjoint bosonic Hamiltonians with real eigenvalues. These operators can be diagonalized when they are…

Quantum Physics · Physics 2015-09-03 Natalia Bebiano , Joao da Providencia , Joao P. da Providencia

A method for approximate solution of spectral problems for Sturm-Liouville equations based on the construction of the Delsarte transmutation operators is presented. In fact the problem of numerical approximation of solutions and eigenvalues…

Classical Analysis and ODEs · Mathematics 2014-08-21 Vladislav V. Kravchenko , Sergii M. Torba

Adjoint methods can speed up stellarator optimisation by providing gradient information more efficiently compared to finite-difference evaluations. Adjoint methods are herein applied to vacuum magnetic fields, with objective functions…

Plasma Physics · Physics 2022-02-16 Richard Nies , Elizabeth J. Paul , Stuart R. Hudson , Amitava Bhattacharjee

In this paper we study spectral properties of self-adjoint operators on a large class of geometries given via sofic groups. We prove that the associated integrated densities of states can be approximated via finite volume analogues. This is…

Spectral Theory · Mathematics 2012-12-21 Christoph Schumacher , Fabian Schwarzenberger

This paper addresses the problem of computing the eigenvalues lying in the gaps of the essential spectrum of a periodic Schrodinger operator perturbed by a fast decreasing potential. We use a recently developed technique, the so called…

Spectral Theory · Mathematics 2009-11-13 Lyonell Boulton , Michael Levitin

In many areas of machine learning, it becomes necessary to find the eigenvector decompositions of large matrices. We discuss two methods for reducing the computational burden of spectral decompositions: the more venerable Nystom extension…

Machine Learning · Statistics 2011-07-22 Darren Homrighausen , Daniel J. McDonald