Skip to main content
Log in

Geometric means of quasi-Toeplitz matrices

  • Published:
BIT Numerical Mathematics Aims and scope Submit manuscript

Abstract

We study means of geometric type of quasi-Toeplitz matrices, that are semi-infinite matrices \(A=(a_{i,j})_{i,j=1,2,\ldots }\) of the form \(A=T(a)+E\), where E represents a compact operator, and T(a) is a semi-infinite Toeplitz matrix associated with the function a, with Fourier series \(\sum _{k=-\infty }^{\infty } a_k e^{{{\mathfrak {i}}}k t}\), in the sense that \((T(a))_{i,j}=a_{j-i}\). If a is real valued and essentially bounded, then these matrices represent bounded self-adjoint operators on \(\ell ^2\). We prove that if \(a_1,\ldots ,a_p\) are continuous and positive functions, or are in the Wiener algebra with some further conditions, then matrix geometric means, such as the ALM, the NBMP and the weighted mean of quasi-Toeplitz positive definite matrices associated with \(a_1,\ldots ,a_p\), are quasi-Toeplitz matrices associated with the function \((a_1\cdots a_p)^{\frac{1}{p}}\), which differ only by the compact correction. We introduce numerical algorithms for their computation and show by numerical tests that these operator means can be effectively approximated numerically.

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.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Notes

  1. Here and in the proof of the theorem, we have \(s_1=1\) and thus the notation could be simplified, but we prefer to keep \(s_1\) because this makes the proof valid for the NBMP and weighted means.

References

  1. Ando, T., Li, C.-K., Mathias, R.: Geometric means. Linear Algebra Appl. 385, 305–334 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  2. Appell, J., Bana\(\acute{s}\), J., Merentes, N.: Bounded Variation and Around. De Gruyter, Berlin, (2013)

  3. Bernstein, M.S.: Sur la convergence absolue des séries trigonométriques. Comptes rendu, t. 158, 1161–1163 (1914)

    MATH  Google Scholar 

  4. Bhatia, R.: Positive definite matrices. Princeton Series in Applied Mathematics. Princeton University Press, Princeton, NJ (2007)

    MATH  Google Scholar 

  5. Bini, D.A., Iannazzo, B.: A note on computing matrix geometric means. Adv. Comput. Math. 35(2–4), 175–192 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  6. Bini, D.A., Iannazzo, B.: Computing the Karcher mean of symmetric positive definite matrices. Linear Algebra Appl. 438(4), 1700–1710 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  7. Bini, D.A., Iannazzo, B., Jeuris, B., Vandebril, R.: Geometric means of structured matrices. BIT 54(1), 55–83 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  8. Bini, D. A., Iannazzo, B., Meng, J.: Algorithms for approximating means of semi-infinite quasi-Toeplitz matrices. International Conference on Geometric Science of Information. GSI 2021:Geometric Science of Information, pages 405–414 (2021)

  9. Bini, D. A., Latouche, G., Meini, B.: Numerical Methods for Structured Markov Chains. Numerical Mathematics and Scientific Computation. Oxford University Press, New York, Oxford Science Publications (2005)

  10. Bini, D.A., Massei, S., Robol, L.: Quasi-Toeplitz matrix arithmetic: a MATLAB toolbox. Numer. Algorithms 81, 741–769 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  11. Bini, D.A., Meini, B., Poloni, F.: An effective matrix geometric mean satisfying the Ando-Li-Mathias properties. Math. Comp. 79(269), 437–452 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  12. Böttcher, A., Grudsky, S.M.: Toeplitz matrices, asymptotic linear algebra, and functional analysis. Birkhäuser Verlag, Basel (2000)

    Book  MATH  Google Scholar 

  13. Böttcher, A., Grudsky, S.M.: Spectral properties of banded Toeplitz matrices. Society for Industrial and Applied Mathematics (SIAM), Philadelphia, PA (2005)

    Book  MATH  Google Scholar 

  14. Böttcher, A., Silbermann, B.: Introduction to large truncated Toeplitz matrices. Universitext. Springer-Verlag, New York (1999)

    Book  MATH  Google Scholar 

  15. Fasi, M., Iannazzo, B.: Computing the weighted geometric mean of two large-scale matrices and its inverse times a vector. SIAM J. Matrix Anal. Appl. 39(1), 178–203 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  16. Higham, N.J.: Functions of matrices: theory and computation. Society for Industrial and Applied Mathematics, Philadelphia, PA, USA (2008)

    Book  MATH  Google Scholar 

  17. Hildebrandt, S.: The closure of the numerical range of an operator as spectral set. Comm. Pure Appl. Math. 17, 415–421 (1964)

    Article  MathSciNet  MATH  Google Scholar 

  18. Iannazzo, B.: A note on computing the matrix square root. Calcolo 40(4), 273–283 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  19. Iannazzo, B.: The geometric mean of two matrices from a computational viewpoint. Numer. Linear Algebra Appl. 23(2), 208–229 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  20. Iannazzo, B., Jeuris, B., Pompili, F.: The Derivative of the Matrix Geometric Mean with an Application to the Nonnegative Decomposition of Tensor Grids. In Structured Matrices in Numerical Linear Algebra, pages 107–128. Springer (2019)

  21. Iannazzo, B., Porcelli, M.: The Riemannian Barzilai-Borwein method with nonmonotone line search and the matrix geometric mean computation. IMA J. Numer. Anal., 38(1):495–517, 04 (2017)

  22. Jeuris, B., Vandebril, R.: The Kähler mean of block-Toeplitz matrices with Toeplitz structured blocks. SIAM J. Matrix Anal. Appl. 37(3), 1151–1175 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  23. Jeuris, B., Vandebril, R., Vandereycken, B.: A survey and comparison of contemporary algorithms for computing the matrix geometric mean. Electron. Trans. Numer. Anal. 39, 379–402 (2012)

    MathSciNet  MATH  Google Scholar 

  24. Kadison, R. V., Ringrose, J. R.: Fundamentals of the Theory of Operator Algebras. Vol. I, volume 100 of Pure and Applied Mathematics. Academic Press, Inc. [Harcourt Brace Jovanovich, Publishers], New York. Elementary theory (1983)

  25. Katznelson, Y.: An introduction to harmonic analysis, 3rd edn. Cambridge University Press, New York (2004)

    Book  MATH  Google Scholar 

  26. Lang, S.: Complex analysis, 4th edn. Springer-Verlag, New York (1999)

    Book  MATH  Google Scholar 

  27. Lapuyade-Lahorgue, J., Barbaresco, F.: Radar detection using Siegel distance between autoregressive processes, application to HF and X-band radar. In 2008 IEEE Radar Conference, pages 1–6 (2008)

  28. Lawson, J.: Existence and uniqueness of the Karcher mean on unital \(C^\ast \)-algebras. J. Math. Anal. Appl., 483(2):123625, 16 (2020)

  29. Lawson, J., Lee, H., Lim, Y.: Weighted geometric means. Forum Math. 24(5), 1067–1090 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  30. Lawson, J., Lim, Y.: Karcher means and Karcher equations of positive definite operators. Trans. Amer. Math. Soc. Ser. B 1, 1–22 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  31. Lee, H., Lim, Y., Yamazaki, T.: Multi-variable weighted geometric means of positive definite matrices. Linear Algebra Appl. 435(2), 307–322 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  32. Lévy, P.: Sur la convergence absolue des séries de Fourier. Compositio Math. 1, 1–14 (1935)

    MathSciNet  MATH  Google Scholar 

  33. Moakher, M.: A differential geometric approach to the geometric mean of symmetric positive-definite matrices. SIAM J. Matrix Anal. Appl. 26(3), 735–747 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  34. Moakher, M.: On the averaging of symmetric positive-definite tensors. J. Elasticity 82(3), 273–296 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  35. Nakamura, N.: Geometric means of positive operators. Kyungpook Math. J. 49(1), 167–181 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  36. Nobari, E.: A monotone geometric mean for a class of Toeplitz matrices. Linear Algebra Appl. 511, 1–18 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  37. Nobari, E., Ahmadi Kakavandi, B.: A geometric mean for Toeplitz and Toeplitz-block block-Toeplitz matrices. Linear Algebra Appl., 548:189–202 (2018)

  38. Rathi, Y., Tannenbaum, A., Michailovich, O.: Segmenting images on the tensor manifold. In 2007 IEEE Conference on Computer Vision and Pattern Recognition, pages 1–8 (2007)

  39. Robol, L.: Rational Krylov and ADI iteration for infinite size quasi-Toeplitz matrix equations. Linear Algebra Appl. 604, 210–235 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  40. Siddiqi, A. H.: Functional Analysis and Applications. Springer (2018)

  41. Thompson, A.C.: On certain contraction mappings in a partially ordered vector space. Proc. Amer. Math. soc. 14, 438–443 (1963)

    MathSciNet  MATH  Google Scholar 

  42. Wang, Y., Qiu, S., Ma, X., He, H.: A prototype-based SPD matrix network for domain adaptation EEG emotion recognition. Pattern Recognit. 110, 107626 (2021)

  43. Widom, H.: Asymptotic behavior of block Toeplitz matrices and determinants. II. Advances in Math. 21, 1–29 (1976)

    Article  MathSciNet  MATH  Google Scholar 

  44. Yang, L., Arnaudon, M., Barbaresco, F.: Geometry of covariance matrices and computation of median. In Bayesian inference and maximum entropy methods in science and engineering, volume 1305 of AIP Conf. Proc., pages 479–486. Amer. Inst. Phys., Melville, NY (2010)

  45. Yger, F., Berar, M., Lotte, F.: Riemannian Approaches in Brain-Computer Interfaces: A Review. IEEE Trans. Neural Syst. Rehabilitation Eng. 25(10), 1753–1762 (2017)

    Article  Google Scholar 

  46. Yuan, X., Huang, W., Absil, P.-A., Gallivan, K.A.: Computing the matrix geometric mean: Riemannian versus Euclidean conditioning, implementation techniques, and a Riemannian BFGS method. Numer. Linear Algebra Appl. 27(5), e2321 (2020)

  47. Zanini, P., Congedo, M., Jutten, C., Said, S., Berthoumieu, Y.: Transfer Learning: A Riemannian Geometry Framework With Applications to Brain-Computer Interfaces. IEEE Trans. Biomed. Eng. 65(5), 1107–1116 (2018)

    Article  Google Scholar 

  48. Zygmund, A.: Trigonometric series, 2nd edn. Cambridge University Press, Cambridge (1959)

    MATH  Google Scholar 

Download references

Acknowledgements

The authors wish to thank the anonymous referee and the editor for providing useful comments that helped to improve this presentation.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jie Meng.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Communicated by Michiel E. Hochstenbach.

Publisher's Note

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

The work of the first two authors was partly supported by INdAM (Istituto Nazionale di Alta Matematica) through a GNCS Project. A part of this research has been done during a visit of the third author to the University of Perugia. The work of the second author was partly supported by the project “Non-Euclidean geometries with applications” funded by the Università degli Studi di Perugia, Fondo Ricerca di Base 2019. The work of the third author was partly supported by the National Natural Science Foundation of China under grant Nos.12201591,12001262.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bini, D.A., Iannazzo, B. & Meng, J. Geometric means of quasi-Toeplitz matrices. Bit Numer Math 63, 20 (2023). https://doi.org/10.1007/s10543-023-00962-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10543-023-00962-2

Keywords

Mathematics Subject Classification

Navigation