arXiv subjects

Zdeněk Strakoš

Publications and source records attributed to Zdeněk Strakoš.

6 recordsLinked to original sources

arXiv2026

A note on the convergence of the eigenvalues in a subdomain to the continuous spectrum

The SIGEST paper Nielsen and Strakoš (2024) characterized the spectrum of the preconditioned operator $Δ^{-1}[\nabla \cdot (K\nabla u)]$ in a bounded open two-dimensional domain $Ω$, where $Δ$ denotes the Laplacian and $K(x,y)$ is a continuous symmetric matrix-valued function. An important part of the analysis states that for a diagonal tensor $K$ constant in an open subdomain $S \subset Ω$, the closed interval defined by its diagonal elements belongs to the spectrum of the preconditioned operator. This result is correct, but the proof in Nielsen and Strakoš (2024) must be refined. This paper presents a refined proof and extends the previous work. As shown in the cited papers, for any point $λ$ in the open interval defined by the elements of the diagonal tensor constant in $S$ and any point $(x_0,y_0)\in S$, a rectangular subdomain $Σ_l\subset S$ can be constructed such that the generalized eigenvalue problem associated with the preconditioned operator restricted to $Σ_l$, of arbitrarily small size, has the eigenvalue $λ$ and infinitely many eigenfunctions. These are given by solutions of a locally defined wave equation. However, such solutions of the locally restricted generalized eigenvalue problem cannot be extended to the whole domain $Ω$. Using instead rectangular subdomains whose size shrinks to zero, the present paper constructs a Weyl singular sequence of \emph{approximate} eigenfunctions associated with $λ$, proving that $λ$ belongs to the spectrum of the preconditioned operator. Since self-adjoint operators in a separable Hilbert space can have at most a countable set of eigenvalues, this shows that the eigenvalues of the locally defined operator converge to points of the continuous spectrum of the preconditioned operator on the entire domain.

math.AP↗

arXiv2024

A posteriori error estimates based on multilevel decompositions with large problems on the coarsest level

Multilevel methods represent a powerful approach in numerical solution of partial differential equations. The multilevel structure can also be used to construct estimates for total and algebraic errors of computed approximations. This paper deals with residual-based error estimates that are based on properties of quasi-interpolation operators, stable-splittings, or frames. We focus on the settings where the system matrix on the coarsest level is still large and the associated terms in the estimates can only be approximated. We show that the way in which the error term associated with the coarsest level is approximated is substantial. It can significantly affect both the efficiency (accuracy) of the overall error estimates and their robustness with respect to the size of the coarsest problem. The newly proposed approximation of the coarsest-level term is based on using the conjugate gradient method with an appropriate stopping criterion. We prove that the resulting estimates are efficient and robust with respect to the size of the coarsest-level problem. Numerical experiments illustrate the theoretical findings.

math.NA↗

arXiv2024

Towards understanding CG and GMRES through examples

When the CG method for solving linear algebraic systems was formulated about 70 years ago by Lanczos, Hestenes, and Stiefel, it was considered an iterative process possessing a mathematical finite termination property. CG was placed into a rich mathematical context, including links with Gauss quadrature and continued fractions. The optimality property of CG was described via a normalized weighted polynomial least squares approximation to zero. This highly nonlinear problem explains the adaptation of CG iterates to the given data. Karush and Hayes immediately considered CG in infinite dimensional Hilbert spaces and investigated its superlinear convergence. Since then, the view of CG and other Krylov subspace methods has changed. Today these methods are primarily used as computational tools, and their behavior is typically characterized using linear upper bounds or heuristics based on clustering of eigenvalues. Such simplifications limit the mathematical understanding and also negatively affect their practical application. This paper offers a different perspective. Focusing on CG and GMRES, it presents mathematically important and practically relevant phenomena that uncover their behavior through a discussion of computed examples. These examples provide an easily accessible approach that enables understanding of the methods, while pointers to more detailed analyses in the literature are given. This approach allows readers to choose the level of depth and thoroughness appropriate for their intentions. Some of the points made in this paper illustrate well known facts. Others challenge mainstream views and explain existing misunderstandings. Several points refer to recent results leading to open problems. We consider CG and GMRES crucially important for the mathematical understanding, further development, and practical applications also of other Krylov subspace methods.

math.NA↗

arXiv2021

Numerical approximation of the spectrum of self-adjoint continuously invertible operators

This paper deals with the generalized spectrum of continuously invertible linear operators defined on infinite dimensional Hilbert spaces. More precisely, we consider two bounded, coercive, and self-adjoint operators $\bc{A, B}: V\mapsto V^{\#}$, where $V^{\#}$ denotes the dual of $V$, and investigate the conditions under which the whole spectrum of $\bc{B}^{-1}\bc{A}:V\mapsto V$ can be approximated to an arbitrary accuracy by the eigenvalues of the finite dimensional discretization $\bc{B}_n^{-1}\bc{A}_n$. Since $\bc{B}^{-1}\bc{A}$ is continuously invertible, such an investigation cannot use the concept of uniform (normwise) convergence, and it relies instead on the pointwise (strong) convergence of $\bc{B}_n^{-1}\bc{A}_n$ to $\bc{B}^{-1}\bc{A}$. The paper is motivated by operator preconditioning which is employed in the numerical solution of boundary value problems. In this context, $\bc{A}, \bc{B}: H_0^1(Ω) \mapsto H^{-1}(Ω)$ are the standard integral/functional representations of the differential operators $ -\nabla \cdot (k(x)\nabla u)$ and $-\nabla \cdot (g(x)\nabla u)$, respectively, and $k(x)$ and $g(x)$ are scalar coefficient functions. The investigated question differs from the eigenvalue problem studied in the numerical PDE literature which is based on the approximation of the eigenvalues within the framework of compact operators. This work follows the path started by the two recent papers published in [SIAM J. Numer. Anal., 57 (2019), pp.~1369-1394 and 58 (2020), pp.~2193-2211] and addresses one of the open questions formulated at the end of the second paper.

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arXiv2020

Generalized spectrum of second order differential operators

We analyze the spectrum of the operator $Δ^{-1} [\nabla \cdot (K\nabla u)]$, where $Δ$ denotes the Laplacian and $K=K(x,y)$ is a symmetric tensor. Our main result shows that this spectrum can be derived from the spectral decomposition $K=Q ΛQ^T$, where $Q=Q(x,y)$ is an orthogonal matrix and $Λ=Λ(x,y)$ is a diagonal matrix. More precisely, provided that $K$ is continuous, the spectrum equals the convex hull of the ranges of the diagonal function entries of $Λ$. The involved domain is assumed to be bounded and Lipschitz, and both homogeneous Dirichlet and homogeneous Neumann boundary conditions are considered. We study operators defined on infinite dimensional Sobolev spaces. Our theoretical investigations are illuminated by numerical experiments, using discretized problems. The results presented in this paper extend previous analyses which have addressed elliptic differential operators with scalar coefficient functions. Our investigation is motivated by both preconditioning issues (efficient numerical computations) and the need to further develop the spectral theory of second order PDEs (core analysis).

math.AP↗

arXiv2018

Laplacian preconditioning of elliptic PDEs: Localization of the eigenvalues of the discretized operator

In the paper \textit{Preconditioning by inverting the {L}aplacian; an analysis of the eigenvalues. IMA Journal of Numerical Analysis 29, 1 (2009), 24--42}, Nielsen, Hackbusch and Tveito study the operator generated by using the inverse of the Laplacian as preconditioner for second order elliptic PDEs $\nabla \cdot (k(x) \nabla u) = f$. They prove that the range of $k(x)$ is contained in the spectrum of the preconditioned operator, provided that $k$ is continuous. Their rigorous analysis only addresses mappings defined on infinite dimensional spaces, but the numerical experiments in the paper suggest that a similar property holds in the discrete case. % Motivated by this investigation, we analyze the eigenvalues of the matrix $\bf{L}^{-1}\bf{A}$, where $\bf{L}$ and ${\bf{A}}$ are the stiffness matrices associated with the Laplace operator and general second order elliptic operators, respectively. Without any assumption about the continuity of $k(x)$, we prove the existence of a one-to-one pairing between the eigenvalues of $\bf{L}^{-1}\bf{A}$ and the intervals determined by the images under $k(x)$ of the supports of the FE nodal basis functions. As a consequence, we can show that the nodal values of $k(x)$ yield accurate approximations of the eigenvalues of $\bf{L}^{-1}\bf{A}$. Our theoretical results are illuminated by several numerical experiments.

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