Skip to main content
Log in

Numerical approximation of the spectrum of self-adjoint operators in operator preconditioning

  • Original Paper
  • Published:
Numerical Algorithms Aims and scope Submit manuscript

Abstract

We consider operator preconditioning \({\mathscr{B}}^{-1}\mathcal {A}\), which is employed in the numerical solution of boundary value problems. Here, the self-adjoint operators \(\mathcal {A}, {\mathscr{B}}: {H_{0}^{1}}({\Omega }) \to H^{-1}({\Omega })\) are the standard integral/functional representations of the partial differential operators −∇⋅ (k(x)∇u) and −∇⋅ (g(x)∇u), respectively, and the scalar coefficient functions k(x) and g(x) are assumed to be continuous throughout the closure of the solution domain. The function g(x) is also assumed to be uniformly positive. When the discretized problem, with the preconditioned operator \({\mathscr{B}}_{n}^{-1}\mathcal {A}_{n}\), is solved with Krylov subspace methods, the convergence behavior depends on the distribution of the eigenvalues. Therefore, it is crucial to understand how the eigenvalues of \({\mathscr{B}}_{n}^{-1}\mathcal {A}_{n}\) are related to the spectrum of \({\mathscr{B}}^{-1}\mathcal {A}\). Following the path started in the two recent papers published in SIAM J. Numer. Anal. [57 (2019), pp. 1369-1394 and 58 (2020), pp. 2193-2211], the first part of this paper addresses the open question concerning the distribution of the eigenvalues of \({\mathscr{B}}_{n}^{-1}\mathcal {A}_{n}\) formulated at the end of the second paper. The approximation of the spectrum studied in the present paper differs from the eigenvalue problem studied in the classical PDE literature which addresses individual eigenvalues of compact (solution) operators.

In the second part of this paper, we generalize some of our results to general bounded and self-adjoint operators \({\mathcal A, B}: V\to V^{\#}\), where V# denotes the dual of V. More specifically, provided that \({\mathscr{B}}\) is coercive and that the standard Galerkin discretization approximation properties hold, we prove that the whole spectrum of \({\mathscr{B}}^{-1}\mathcal {A}:V\to V\) is approximated to an arbitrary accuracy by the eigenvalues of its finite dimensional discretization \({\mathscr{B}}_{n}^{-1}\mathcal {A}_{n}\).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

Notes

  1. Equivalently, we can also consider the closely related spectral measure (see, e.g., [2]).

  2. Recent beautiful results published in [2, 7] show how to compute smoothed approximations of spectral measures for infinite dimensional self-adjoint operators.

  3. Here, we consider homogeneous Dirichlet boundary conditions. The results will remain valid (with an appropriate choice of the associated spaces) also for homogeneous Neumann boundary conditions (see [9, section 5]) and the numerical experiments section below.

  4. As in [6], we consider conforming finite element (FE) methods using Lagrange elements.

  5. Self-adjoint in the sense that \(\langle \mathcal {A} u, v \rangle = \langle \mathcal {A} v, u \rangle , \langle {\mathscr{B}} u, v \rangle = \langle {\mathscr{B}} v, u \rangle \) for all u,vV, where \(\langle \cdot ,\cdot \rangle : V^\#\times V \to \mathbb {R}\) is the duality pairing. Equivalently, \(\tau \mathcal {A}\), \(\tau {\mathscr{B}}: V \rightarrow V\) are self-adjoint, where τ is the Riesz map.

  6. With g(x) =1.

  7. An interesting application inspired by this result that uses a different approach is presented in [18]

  8. Supports of all discretization functions are contained in \(\overline {\Omega }\).

  9. See [19, Section 1.2] for the definition of the second order derivative.

  10. Note that no limit of vr, as \(r \rightarrow 0\), is needed in this proof. Only the existence of a set of functions satisfying (24) and (25) is required.

  11. Since \({\mathscr{B}}^{-1}\mathcal {A} = (\tau {\mathscr{B}})^{-1} (\tau \mathcal {A})\), one can as an alternative investigate approximations of the spectrum of the symmetrized operator \((\tau {\mathscr{B}})^{-1/2} \tau \mathcal {A} (\tau {\mathscr{B}})^{-1/2}\) that is self-adjoint with respect to the inner product (34).

  12. Recall the assumptions on \(\mathcal A\) and \(\mathcal B\), which guarantee that \(\mathcal B\) is continuously invertible and that \(\mathcal B^{-1} \mathcal A\) is self-adjoint with respect to the inner product (35).

  13. Since the norms \(\| \cdot \|_{{\mathscr{B}}}\) and ∥⋅∥ are equivalent, it does not matter which of these we use in (36).

  14. See [25, Lemma 3.16].

References

  1. von Neumann, J: Mathematical foundations of quantum mechanics. Princeton Landmarks in Mathematics. Princeton University Press, Princeton (1996). Translated from the 1932 German original and with a preface by R. T. Beyer

    Google Scholar 

  2. Colbrook, M, Horning, A, Towsend, A: Computing spectral measures of self-adjoint operators. SIAM Rev. 63, 489–524 (2021)

    Article  MathSciNet  Google Scholar 

  3. Málek, J, Strakoš, Z: Preconditioning and the conjugate gradient method in the context of solving PDEs. SIAM Spotlight Series, vol. 1. Society for Industrial and Applied Mathematics (SIAM), Philadelphia (2015)

  4. Vorobyev, Y V: Methods of moments in applied mathematics. Translated from the Russian by Bernard Seckler. Gordon and Breach Science Publishers, New York (1965)

    Google Scholar 

  5. Liesen, J, Strakoš, Z: Krylov subspace methods: principles and analysis. Numerical Mathematics and Scientific Computation. Oxford University Press, Oxford (2013)

    MATH  Google Scholar 

  6. Gergelits, T, Mardal, K A, Nielsen, B F, Strakoš, Z: Laplacian preconditioning of elliptic PDEs: localization of the eigenvalues of the discretized operator. SIAM J. Numer. Anal. 57(3), 1369–1394 (2019)

    Article  MathSciNet  Google Scholar 

  7. Colbrook, M, Horning, A: Specsolve: spectral methods for spectral measures. arXiv:2201.01314 (2022)

  8. Nielsen, B F, Tveito, A, Hackbusch, W: Preconditioning by inverting the Laplacian; an analysis of the eigenvalues. IMA J. Numer. Anal. 29(1), 24–42 (2009)

    Article  MathSciNet  Google Scholar 

  9. Gergelits, T, Nielsen, B F, Strakoš, Z: Generalized spectrum of second order differential operators. SIAM J. Numer. Anal. 58(4), 2193–2211 (2020)

    Article  MathSciNet  Google Scholar 

  10. Axelsson, O, Karátson, J: Equivalent operator preconditioning for elliptic problems. Numer. Algorithms 50(3), 297–380 (2009)

    Article  MathSciNet  Google Scholar 

  11. Karátson, J: Operator preconditioning with efficient applications for elliptic problems. Cent. Eur. J. Math. 10(3), 231–249 (2012)

    Article  MathSciNet  Google Scholar 

  12. Blaheta, R, Margenov, S, Neytcheva, M: Uniform estimate of the constant in the strengthened CBS inequality for anisotropic non-conforming FEM systems. Numer. Lin. Alg. with Appl. 11, 309–326 (2004)

    Article  MathSciNet  Google Scholar 

  13. Faber, V, Manteuffel, T A, Parter, S V: On the theory of equivalent operators and application to the numerical solution of uniformly elliptic partial differential equations. Adv. in Appl. Math. 11(2), 109–163 (1990)

    Article  MathSciNet  Google Scholar 

  14. Mardal, K A, Winther, R: Preconditioning discretizations of systems of partial differential equations. Numerical Linear Algebra with Applications 18 (1), 1–40 (2011)

    Article  MathSciNet  Google Scholar 

  15. Hrnčíř, J, Pultarová, I, Strakoš, Z: Decomposition of subspaces preconditioning: abstract framework. Numerical Algorithms 83, 57–98 (2020)

    Article  MathSciNet  Google Scholar 

  16. Leute, R J, Ladecký, M, Falsafi, A, Jödicke, I, Pultarová, I, Zeman, J, Junge, T, Pastewka, L: Elimination of ringing artifacts by finite-element projection in fft-based homogenization. J. Comput. Phys. 453, 110931 (2022)

    Article  MathSciNet  Google Scholar 

  17. Hiptmair, R: Operator preconditioning. Computers & Mathematics with Applications. An International Journal 52(5), 699–706 (2006)

    Article  MathSciNet  Google Scholar 

  18. Ladecký, M, Pultarová, I, Zeman, J: Guaranteed two-sided bounds on all eigenvalues of preconditioned diffusion and elasticity problems solved by the finite element method. Appl. Math. (2020)

  19. Ciarlet, P G: The finite element method for elliptic problems. Classics in Applied Mathematics, vol. 40. Society for Industrial and Applied Mathematics (SIAM), Philadelphia (2002). Reprint of the 1978 original [North-Holland, Amsterdam; MR0520174 (58 #25001)]

    Book  Google Scholar 

  20. Colbrook, M, Roman, B, Hansen, A: How to compute spectra with error control. Phys. Rev. Lett. 122, 250201 (2019)

    Article  MathSciNet  Google Scholar 

  21. Gergelits, T: Krylov subspace methods: analysis and applications. Ph.D. Thesis, Charles University (2020)

  22. Bondy, J A, Murty, U S R: Graph theory with applications. American Elsevier Publishing Co., Inc., New York (1976)

    Book  Google Scholar 

  23. Stewart, G W, Sun, J G: Matrix perturbation theory. Computer Science and Scientific Computing. Academic Press, Inc., Boston (1990)

    Google Scholar 

  24. Kato, T: Perturbation theory for linear operators. Springer, Berlin (1980)

    MATH  Google Scholar 

  25. Chatelin, F: Spectral approximation of linear operators. Academic Press, New York (1983)

    MATH  Google Scholar 

  26. Descloux, J, Nassif, N, Rappaz, J: On spectral approximation. Part 1. The problem of convergence. RAIRO - Analyse Numérique 12, 97–112 (1978)

    Article  MathSciNet  Google Scholar 

  27. Rappaz, J, Sanchez Hubert, J, Sanchez Palencia, E, Vassiliev, D: On spectral pollution in the finite element approximation of thin elastic “membrane” shells. Numer. Math. 75, 473–500 (1997)

    Article  MathSciNet  Google Scholar 

  28. Arnold, D N, Falk, R S, Winther, R: Finite element exterior calculus: from Hodge theory to numerical stability. Bull. Amer. Math. Soc. (N.S.) 47, 281–354 (2010)

    Article  MathSciNet  Google Scholar 

  29. Lin, L, Saad, Y, Yang, C: Approximating spectral densities of large matrices. SIAM Rev. 58, 34–65 (2016)

    Article  MathSciNet  Google Scholar 

  30. Ciarlet, P G: Linear and nonlinear functional analysis with applications. Society for Industrial and Applied Mathematics (SIAM), Philadelphia (2015)

    Google Scholar 

Download references

Acknowledgements

The authors are indebted to Miroslav Bačák for strengthening Theorem 4.2. We also wish to thank Roland Herzog, David Krejčiřík, Václav Kučera, Josef Málek, and Ivana Pultarová for stimulating discussions during the work on this paper. Finally, we would like to thank the anonymous referee for many suggestions that has led to improvements of the text.

Funding

Tomáš Gergelits was supported by Lawrence Livermore National Security, LLC Subcontract Award B639388 under Prime Contract No. DE-AC52-07NA27344. Nielsen’s work was supported by The Research Council of Norway, project number 239070. Zdeněk Strakoš was supported by the Grant Agency of the Czech Republic under the contract no. 17-04150J.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zdeněk Strakoš.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Availability of data and materials

Data sharing not applicable to this article as no datasets are generated or analyzed during the current study.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix: : Approximations of the spectrum of self-adjoint operators

Appendix: : Approximations of the spectrum of self-adjoint operators

Using [25, Chapter 3] and [24, Chapter 8], we first recall several results concerning the convergence of linear self-adjoint operators defined on infinite dimensional Hilbert spaces. By the Hellinger-Toeplitz Theorem (see [30, Theorem 5.7.2, p. 260]), any linear self-adjoint operator \(\mathcal {Z}: V \to V\) on a Hilbert space V is closed and, according to the Banach closed-graph theorem, bounded (continuous).

Consider a bounded linear operator \(\mathcal {G}: V \to V\) (not necessarily self-adjoint, therefore we for the moment change the notation) and a sequence of its bounded linear approximations \(\{\mathcal {G}_{n} \}, \mathcal {G}_{n}: V \to V\), that can converge to \(\mathcal {G}\) in different ways:

  • pointwise (strongly), i.e., \(\mathcal {G}_{n} \stackrel {p}{\rightarrow } \mathcal {G}\)

    $$ \text{iff for all} x \in V, \quad \lim_{n \rightarrow \infty} \| \mathcal{G} x - \mathcal{G}_{n} x\| = 0 ;$$
  • uniformly (in norm), iff \(\lim _{n \rightarrow \infty } \| \mathcal {G} - \mathcal {G}_{n} \| = 0 ;\)

  • stably, i.e. \(\mathcal {G}_{n} \stackrel {s}{\rightarrow } \mathcal {G}\) iff

    • \(\mathcal {G}_{n} \stackrel {p}{\rightarrow } \mathcal {G}\), and

    • the inverse operators \(\{ \mathcal {G}_{n}^{-1} \}\) are uniformly bounded, i.e., for some C >0, \(\|\mathcal {G}_{n}^{-1}\| \leq C\) for all n.

Clearly, uniform convergence implies pointwise convergence, but the converse implication does not hold. Since the class of compact operators is closed with respect to uniform convergence, the uniform convergence concept can not be used to investigate the convergence of compact to non-compact operators, such as to bounded continuously invertible operators defined on infinite dimensional Hilbert spaces.

The spectral theory for bounded linear operators is based on the concept of the operator resolvent

$$ \mathcal{R}(\mu) := (\mu \mathcal{I} - \mathcal{G})^{-1}$$

and on the resolvent set

$$ \rho(\mathcal{G}) := \left\{ \mu \in \mathbb{C}; \mu \mathcal{I} - \mathcal{G} \text{has a bounded inverse} \right\}. $$
(42)

It is interesting to notice that, for any \(\mu \in \rho (\mathcal {G})\), a sequence of shifted operators \(\{\mu \mathcal {I} - \mathcal {G}_{n}\}\) converge to \(\mu \mathcal {I} - \mathcal {G}\) stably if, and only if, \(\{\mathcal {G}_{n}\}\) and the resolvents \(\{\mathcal {R}_{n}(\mu )\}, \mathcal {R}_{n}(\mu ):= (\mu \mathcal {I} - \mathcal {G}_{n})^{-1}\), converge to \(\mathcal {G}\) and \(\mathcal {R}(\mu )\) pointwise, respectively, i.e.,Footnote 14

$$ \mu \mathcal{I} - \mathcal{G}_{n} \stackrel{s}{\rightarrow} \mu \mathcal{I} - \mathcal{G} \quad \text{iff} \quad \mathcal{G}_{n} \stackrel{p}{\rightarrow} \mathcal{G} \text{and} \mathcal{R}_{n}(\mu) \stackrel{p}{\rightarrow} \mathcal{R}(\mu). $$
(43)

Indeed, using the resolvent identity

$$ \mathcal{R}_{n}(\mu) - \mathcal{R}(\mu) = \mathcal{R}_{n}(\mu) (\mathcal{G}_{n} - \mathcal{G}) \mathcal{R}(\mu), $$

the right implication follows immediately from the definition of stable convergence. Conversely, from the pointwise convergence of \(\mathcal {R}_{n}(\mu )\) and the uniform boundedness principle (Banach–Steinhaus theorem) we conclude that \(\{\mathcal {R}_{n}(\mu )\}=\{ (\mu \mathcal {I} - \mathcal {G}_{n})^{-1} \}\) is uniformly bounded and the result follows.

We will now present a proof of Theorem 4.1; cf. also [24, chapter VIII, § 1.2, Theorem 1.14, p. 431] and [25, chapter 5, section 4, Theorem 5.12, p. 239-240].

Theorem A.1 (Approximation of the spectrum of self-adjoint operators)

Let \(\mathcal {Z}\) be a linear self-adjoint operator on a Hilbert space V and let \(\{\mathcal {Z}_{n}\}\) be a sequence of linear self-adjoint operators on V converging to \(\mathcal {Z}\) pointwise (strongly). Then, for any point \(\lambda \in \text {sp} (\mathcal {Z})\) in the spectrum of \(\mathcal {Z}\), and for any of its neighbourhoods, the intersection of the spectrum \(\text {sp}(\mathcal {Z}_{j})\) with this neighbourhood is, for j sufficiently large, nonempty.

Proof

Consider any point \(\lambda \in \text {sp}(\mathcal {Z}) \subset \mathbb {R}\) in the spectrum of \(\mathcal {Z}\). Then, for any ε >0, the point μ := λ + ιε, where ι is the complex unit, belongs to the resolvent set \(\rho (\mathcal {Z})\) (because \(\mathcal {Z}\) is self-adjoint). For self-adjoint operators, the norm of the resolvent at any point in the resolvent set is equal to the inverse of the distance of the given point to the spectrum (see, e.g., [25, Proposition 2.32]). Therefore, for all n,

$$ \begin{array}{@{}rcl@{}} \| \mathcal{R}(\mu) \| &:=&\|(\mu \mathcal{I} - \mathcal{Z})^{-1}\| = \frac{1}{\text{dist}(\mu, \text{sp}(\mathcal{Z}))}= \frac{1}{\varepsilon} , \end{array} $$
(44)
$$ \begin{array}{@{}rcl@{}} \| \mathcal{R}_{n}(\mu) \| &:=&\|(\mu \mathcal{I} - \mathcal{Z}_{n})^{-1}\| = \frac{1}{\text{dist}(\mu, \text{sp}(\mathcal{Z}_{n}))}\leq \frac{1}{\varepsilon} . \end{array} $$
(45)

The inequality in (45) follows from the assumption that \(\{\mathcal {Z}_{n}\}\) is a sequence of self-adjoint operators, i.e., \(\text {sp}(\mathcal {Z}_{n}) \subset \mathbb {R}\). Note that inequality (45) provides the uniform boundedness of \(\{ \mathcal {R}_{n}(\mu ) \}\), which, together with the pointwise convergence of \(\{\mathcal {Z}_{n}\}\), yields that

$$ \mu \mathcal{I} - \mathcal{Z}_{n} \stackrel{s}{\rightarrow} \mu \mathcal{I} - \mathcal{Z}. $$

Using (43), we thus have the pointwise convergence of \(\{\mathcal {R}_{n}(\mu ) \}\), i.e., for any xV,∥x∥ =1,

$$ \| \mathcal{R}(\mu) x \| = \lim_{n \rightarrow \infty} \| \mathcal{R}_{n}(\mu) x \|. $$

Consider a fixed xV,||x∥ =1, such that

$$ \frac{1}{2 \varepsilon} \leq \| \mathcal{R}(\mu) x \| \leq \frac{1}{\varepsilon}. $$

(The existence of such a xV follows from (44).) Then, from the pointwise convergence, there must exist n such that for all nn

$$ \| \mathcal{R}_{n}(\mu)\| \geq \| \mathcal{R}_{n}(\mu) x \| \geq \frac{1}{3 \varepsilon}. $$

Recall that \(\| \mathcal {R}_{n}(\mu ) \|=\mathrm {[dist}(\mu , \text {sp}(\mathcal {Z}_{n}))]^{-1}\). Therefore, there exists a point \(\lambda _{n} \in \text {sp}(\mathcal {Z}_{n})\) such that

$$ |\lambda_{n} - \mu| \leq 3 \varepsilon \quad \text{and, consequently,} \quad |\lambda_{n} - \lambda| \leq 4 \varepsilon, $$

which finishes the proof. □

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gergelits, T., Nielsen, B.F. & Strakoš, Z. Numerical approximation of the spectrum of self-adjoint operators in operator preconditioning. Numer Algor 91, 301–325 (2022). https://doi.org/10.1007/s11075-022-01263-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11075-022-01263-5

Keywords

Navigation