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Self-adjoint operators on infinite-dimensional spaces with continuous spectra are abundant but do not possess a basis of eigenfunctions. Rather, diagonalization is achieved through spectral measures. The SpecSolve package [SIAM Rev., 63(3)…

Numerical Analysis · Mathematics 2022-01-06 Matthew J. Colbrook , Andrew Horning

We introduce a simple, general, and convergent scheme to compute generalized eigenfunctions of self-adjoint operators with continuous spectra on rigged Hilbert spaces. Our approach does not require prior knowledge about the eigenfunctions,…

Numerical Analysis · Mathematics 2024-10-14 Matthew J. Colbrook , Andrew Horning , Tianyiwa Xie

It is known that convergence of l.s.b. closed symmetric sesquilinear forms implies norm resolvent convergence of the associated self-adjoint operators and this in turn convergence of discrete spectra. In this paper in both cases sharp…

Mathematical Physics · Physics 2017-12-12 Johannes F. Brasche , Robert Fulsche

A new technique for approximating eigenvalues and eigenvectors of a self-adjoint operator is presented. The method does not incur spectral pollution, uses trial spaces from the form domain, has a self-adjoint algorithm, and exhibits…

Spectral Theory · Mathematics 2014-03-28 Michael Strauss

We establish spectral convergence results of approximations of unbounded non-selfadjoint linear operators with compact resolvents by operators that converge in generalized strong resolvent sense. The aim is to establish general assumptions…

Spectral Theory · Mathematics 2016-04-27 Sabine Bögli

An inverse problem in spectroscopy is considered. The objective is to restore the discrete spectrum from observed spectrum data, taking into account the spectrometer's line spread function. The problem is reduced to solution of a system of…

Numerical Analysis · Mathematics 2017-01-23 Valery Sizikov , Denis Sidorov

We present a general and automated approach for computing model gradients for PDE solvers built on sparse spectral methods, and implement this capability in the widely used open-source Dedalus framework. We apply reverse-mode automatic…

Numerical Analysis · Mathematics 2026-04-15 Calum S. Skene , Keaton J. Burns

Let $L$ be a non-negative self adjoint operator acting on $L^2(X)$ where $X$ is a space of homogeneous type. Assume that $L$ generates a holomorphic semigroup $e^{-tL}$ whose kernels $p_t(x,y)$ satisfy generalized $m$-th order Gaussian…

Analysis of PDEs · Mathematics 2012-11-07 Adam Sikora , Lixin Yan , Xiaohua Yao

We prove a semiclassical resolvent estimate for a broad class of non-self-adjoint, non-elliptic pseudodifferential operators in the low-lying spectral regime. The proof relies on improved ellipticity properties for the symbol of the…

Spectral Theory · Mathematics 2026-01-27 Stepan Malkov

A certified strategy for determining sharp intervals of enclosure for the eigenvalues of matrix differential operators with singular coefficients is examined. The strategy relies on computing the second order spectrum relative to subspaces…

Numerical Analysis · Mathematics 2016-02-17 Lyonell Boulton , Monika Winklmeier

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 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

We develop the method of similar operators to study the spectral properties of unbounded perturbed linear operators that can be represented by matrices of various kinds. The class of operators under consideration includes various…

Functional Analysis · Mathematics 2019-08-13 Anatoly G. Baskakov , Ilya A. Krishtal , Natalia B. Uskova

We introduce computational strategies for measuring the ``size'' of the spectrum of bounded self-adjoint operators using various metrics such as the Lebesgue measure, fractal dimensions, the number of connected components (or gaps), and…

Spectral Theory · Mathematics 2024-07-31 Matthew J. Colbrook , Mark Embree , Jake Fillman

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

Spectral measures arise in numerous applications such as quantum mechanics, signal processing, resonances, and fluid stability. Similarly, spectral decompositions (pure point, absolutely continuous and singular continuous) often…

Spectral Theory · Mathematics 2021-03-02 Matthew John Colbrook

In this article, we introduce a general theoretical framework to analyze non-consistent approximations of the discrete eigenmodes of a self-adjoint operator. We focus in particular on the discrete eigenvalues laying in spectral gaps. We…

Functional Analysis · Mathematics 2012-05-03 Eric Cancès , Virginie Ehrlacher , Yvon Maday

We study the approximation of the spectrum of a second-order elliptic differential operator by the Hybrid High-Order (HHO) method. The HHO method is formulated using cell and face unknowns which are polynomials of some degree $k\geq0$. The…

Numerical Analysis · Mathematics 2018-07-23 Victor Calo , Matteo Cicuttin , Quanling Deng , Alexandre Ern

We consider the problem of how to compute eigenvalues of a self-adjoint operator when a direct application of the Galerkin (finite-section) method is unreliable. The last two decades have seen the development of the so-called quadratic…

Spectral Theory · Mathematics 2015-06-16 James Hinchcliffe , Michael Strauss

Many computational algorithms applied to geometry operate on discrete representations of shape. It is sometimes necessary to first simplify, or coarsen, representations found in modern datasets for practicable or expedited processing. The…

Computational Geometry · Computer Science 2023-02-10 Alexandros Dimitrios Keros , Kartic Subr
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