Skip to main content
Log in

Accurate Eigenvalues of Some Generalized Sign Regular Matrices via Relatively Robust Representations

  • Published:
Journal of Scientific Computing Aims and scope Submit manuscript

Abstract

In this paper, we consider how to accurately solve the nonsymmetric eigenvalue problem for a class of generalized sign regular matrices including extremely ill-conditioned quasi-Cauchy and quasi-Vandermonde matrices. The problem of performing accurate computations with structured matrices is very much a representation problem. We first develop a relatively robust representation (RRR) for this class of matrices by introducing a free parameter, which exceeds an essential threshold, into an indefinite factorization. We then design a new \(O(n^{3})\) algorithm to compute all the eigenvalues of such matrices with high relative accuracy, as warranted by the RRR. Error analysis and numerical experiments are performed to illustrate the high relative accuracy.

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

Similar content being viewed by others

References

  1. Alfa, A.S., Xue, J., Ye, Q.: Accurate computation of the smallest eigenvalue of a diagonally dominant M-matrix. Math. Comput. 71, 217–236 (2002)

    Article  MathSciNet  Google Scholar 

  2. Ando, T.: Totally positive matrices. Linear Algebra Appl. 90, 165–219 (1987)

    Article  MathSciNet  Google Scholar 

  3. Dailey, M., Dopico, F.M., Ye, Q.: Relative perturbation theory for diagonally dominant matrices. SIAM J. Matrix Anal. Appl. 35, 1303–1328 (2014)

    Article  MathSciNet  Google Scholar 

  4. Dailey, M., Dopico, F.M., Ye, Q.: A new perturbation bound for the LDU factorization of diagonally dominant matrices. SIAM J. Matrix Anal. Appl. 35, 904–930 (2014)

    Article  MathSciNet  Google Scholar 

  5. Delgado, J., Peña, J.M.: Accurate computations with collocation matrices of q-Bernstein polynomials. SIAM J. Matrix Anal. Appl. 36, 880–893 (2015)

    Article  MathSciNet  Google Scholar 

  6. Demmel, J.: Accurate singular value decompositions of structured matrices. SIAM J. Matrix Anal. Appl. 21, 562–580 (1999)

    Article  MathSciNet  Google Scholar 

  7. Demmel, J., Dumitriu, I., Holtz, O., Koev, P.: Accurate and efficient expression evaluation and linear algebra. Acta Numer. 17, 87–145 (2008)

    Article  MathSciNet  Google Scholar 

  8. Demmel, J., Gragg, W.: On computing accurate singular values and eigenvalues of acyclic matrices. Linear Algebra Appl. 185, 203–218 (1993)

    Article  MathSciNet  Google Scholar 

  9. Demmel, J., Gu, M., Eisenstat, S., Slapničar, I., Veselić, K., Drmač, Z.: Computing the singular value decomposition with high relative accuracy. Linear Algebra Appl. 299, 21–80 (1999)

    Article  MathSciNet  Google Scholar 

  10. Demmel, J., Kahan, W.: Accurate singular values of bidiagonal matrices. SIAM J. Sci Stat. Comput. 11, 873–912 (1990)

    Article  MathSciNet  Google Scholar 

  11. Demmel, J., Koev, P.: Accurate SVDs of weakly diagonally dominant \(M\)-matrices. Numer. Math. 98, 99–104 (2004)

    Article  MathSciNet  Google Scholar 

  12. Demmel, J., Koev, P.: The accuracy and efficient solution of a totally positive generailized Vandermonde linear system. SIAM J. Matrix Anal. Appl. 27, 142–152 (2005)

    Article  MathSciNet  Google Scholar 

  13. Dhillon, I.S., Parlett, B.N.: Orthogonal eigenvectors and relative gaps. SIAM J. Matrix Anal. Appl. 25, 858–899 (2004)

    Article  MathSciNet  Google Scholar 

  14. Dopico, F.M., Molera, J.M.: Accurate solution of structured linear systems via rank-revealing decompositions. IMA J. Numer. Anal. 32, 1096–1116 (2012)

    Article  MathSciNet  Google Scholar 

  15. Dopico, F.M., Molera, J.M., Moro, J.: An orthogonal high relative accuracy algorithm for the symmetric eigenproblem. SIAM J. Matrix Anal. Appl. 25, 301–351 (2003)

    Article  MathSciNet  Google Scholar 

  16. Dopico, F.M., Koev, P.: Accurate symmetric rank revealing and eigendecompositions of symmetric structured matrices. SIAM J. Matrix Anal. Appl. 28, 1126–1156 (2006)

    Article  MathSciNet  Google Scholar 

  17. Dopico, F.M., Koev, P.: Perturbation theory for the LDU factorization and accurate computations for diagonally dominant matrices. Numer. Math. 119, 337–371 (2011)

    Article  MathSciNet  Google Scholar 

  18. Dopico, F.M., Koev, P., Molera, J.M.: Implicit standard Jacobi gives high relative accuracy. Numer. Math. 113, 519–553 (2009)

    Article  MathSciNet  Google Scholar 

  19. Dopico, F.M., Pomés, K.: Structured eigenvalue condition numbers for parameterized quasiseparable matrices. Numer. Math. 134, 473–512 (2016)

    Article  MathSciNet  Google Scholar 

  20. Gasca, M., Peña, J.M.: Totally positivity, QR factorization and Neville elimination. SIAM J. Matrix Anal. Appl. 14, 1132–1140 (1993)

    Article  MathSciNet  Google Scholar 

  21. Gasca, M., Peña, J.M.: A matricial description of Neville elimination with applications to total positivity. Linear Algebra Appl. 202, 33–35 (1994)

    Article  MathSciNet  Google Scholar 

  22. Huang, R.: A test and bidiagonal factorization for certain sign regular matrices. Linear Algebra Appl. 438, 1240–1251 (2013)

    Article  MathSciNet  Google Scholar 

  23. Huang, R.: A periodic qd-type reduction for computing eigenvalues of structured matrix products to high relative accuracy. J. Sci. Comput. 75, 1229–1261 (2018)

    Article  MathSciNet  Google Scholar 

  24. Huang, R.: Accurate solutions of product linear systems associated with rank-structured matrices. J. Comput. Appl. Math. 347, 108–127 (2019)

    Article  MathSciNet  Google Scholar 

  25. Huang, R.: Accurate solutions of weighted least squares problems associated with rank-structured matrices. Appl. Numer. Math. 146, 416–435 (2019)

    Article  MathSciNet  Google Scholar 

  26. Huang, R.: A qd-type method for computing generalized singular values of BF matrix pairs with sign regularity to high relative accuracy. Math. Comput. 89, 229–252 (2020)

    Article  MathSciNet  Google Scholar 

  27. Huang, R., Chu, D.L.: Relative perturbation analysis for eigenvalues and singular values of totally nonpositive matrices. SIAM J. Matrix Anal. Appl. 36, 476–495 (2015)

    Article  MathSciNet  Google Scholar 

  28. Huang, R., Chu, D.L.: Computing singular value decompositions of parameterized matrices with total nonpositivity to high relative accuracy. J. Sci. Comput. 71, 682–711 (2017)

    Article  MathSciNet  Google Scholar 

  29. Li, C.-K., Mathias, R.: Interlacing inequalities for totally nonnegative matrices. Linear Algebra Appl. 341, 35–44 (2002)

    Article  MathSciNet  Google Scholar 

  30. Marco, A., Martínez, J.-J.: Accurate computations with Said–Ball–Vandemonde matrices. Linear Algebra Appl. 432, 2894–2908 (2010)

    Article  MathSciNet  Google Scholar 

  31. Parlett, B.N., Dhillon, I.S.: Relatively robust representations of symmetric tridiagonals. Linear Algebra Appl. 309, 121–151 (2000)

    Article  MathSciNet  Google Scholar 

  32. Koev, P.: Accurate eigenvalues and SVDs of totally nonnegative matrices. SIAM J. Matrix Anal. Appl. 27, 1–23 (2005)

    Article  MathSciNet  Google Scholar 

  33. Koev, P.: Accurate computations with totally nonnegative matrices. SIAM J. Matrix Anal. Appl. 29, 731–751 (2007)

    Article  MathSciNet  Google Scholar 

  34. Koev, P.: Accurate eigenvalues and exact zero Jordan blocks of totally nonnegative matrices. Numer. Math. 141, 693–713 (2019)

    Article  MathSciNet  Google Scholar 

  35. Koev, P.: http://math.mit.edu/~plamen/software/TNTool.html

  36. Koev, P., Dopico, F.M.: Accurate eigenvalues of certain sign regular matrices. Linear Algebra Appl. 424, 435–447 (2007)

    Article  MathSciNet  Google Scholar 

  37. Slapničar, I.: Accurate symmetric eigenreduction by a Jacobi method. Ph.D. thesis, Fachbereich mathematik Frenuniversität, Gesamthochschule Hagen, Hagen, Germany (1992)

  38. Veselić, K.: A Jacobi eigenreduction algorithm for definite matrix pairs. Numer. Math. 64, 241–269 (1993)

    Article  MathSciNet  Google Scholar 

  39. Watkins, D.S.: Product eigenvalue problems. SIAM Rev. 47, 3–40 (2005)

    Article  MathSciNet  Google Scholar 

  40. Ye, Q.: Computing singular values of diagonally dominant matrices to high relative accuracy. Math. Comput. 77, 2195–2230 (2008)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

The author would like to thank the Editor and the anonymous referees for their valuable comments and suggestions which have helped to improve the overall presentation of the paper.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Rong Huang.

Additional information

Publisher's Note

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

Research supported by the National Natural Science Foundation of China (Grant No. 11871020), the Natural Science Foundation for Distinguished Young Scholars of Hunan Province (Grant No. 2017JJ1025) and the Research Foundation of Education Bureau of Hunan Province (Grant No. 18A198).

Appendix: The Proof of Theorem 2

Appendix: The Proof of Theorem 2

Denote by \(Q_{k,n}\) the set of strictly increasing sequences of k positive integer numbers less than or equal to n. The following result is derived by using a similar argument as that of [27, Theorem 2.6].

Lemma 7

Let \(T=:\mathbb {PM}(T)\in {{\mathbb {R}}}^{n \times n}\) (\(n> 2\)) be nonsingular tridiagonal with \(\mathbb {PM}(T)\ge 0\), and let \(\tilde{T}\in {{\mathbb {R}}}^{n \times n}\) be obtained from T only by replacing one entry x of \(\mathbb {PM}(T)\) with \(\tilde{x}=x(1+\epsilon _{x})\), where \(|\epsilon _{x}|\le \epsilon \) and \(7\epsilon <1\). Then

$$\begin{aligned} |\mathrm{det}\tilde{T}[\mu |\nu ]-\mathrm{det}T[\mu |\nu ]|\le \frac{7\epsilon }{1-7\epsilon }|\mathrm{det}T[\mu |\nu ]|,~\forall ~\mu ,\nu \in Q_{k,n},~1\le k\le n. \end{aligned}$$

Proof

Set \(T\in {{\mathbb {R}}}^{n \times n}\) be of the form (3). Then \(\mathrm{det}T=-d_{1}d_{2}\ldots d_{n}\), and trivially, \(|\mathrm{det}\tilde{T}-\mathrm{det}T|\le \frac{\epsilon }{1-\epsilon }|\mathrm{det}T|\). Now consider any minor \(\mathrm{det}T[\mu |\nu ]\) for \(\mu =(\mu _{i}),\nu =(\nu _{i})\in Q_{k,n}\) with \(1\le k \le n-1\). Assume that \(1\le z_{1}<\ldots <z_{r}\le n\) are all indices such that \(\mu _{z_{s}}\ne \nu _{z_{s}}\) for \(s=1,2,\ldots ,r\), and let \(\gamma =\mu {\setminus } \{\mu _{z_{1}},\ldots ,\mu _{z_{r}}\}\). Since \(T=(t_{ij})\in {{\mathbb {R}}}^{n \times n}\) is tridiagonal, we have

$$\begin{aligned} \mathrm{det}T[\mu |\nu ]=t_{\mu _{z_{1}},\nu _{z_{1}}}\ldots t_{\mu _{z_{r}},\nu _{z_{r}}}\cdot \mathrm{det}T[\gamma ], \end{aligned}$$
(40)

where for all \(1\le s\le r\),

$$\begin{aligned} \left\{ \begin{array}{lll}t_{\mu _{z_{s}},\nu _{z_{s}}}=0,&{}\quad \mathrm{if}~|\mu _{z_{s}}-\nu _{z_{s}}|>1,\\ t_{\mu _{z_{s}},\nu _{z_{s}}}=\beta _{\mu _{z_{s}},\nu _{z_{s}}}d_{\nu _{z_{s}}},&{}\quad \mathrm{if}~ \mu _{z_{s}}-\nu _{z_{s}}=1~\mathrm{and}~\mu _{z_{s}}\ne n,\\ t_{\mu _{z_{s}},\nu _{z_{s}}}= d_{n-1},&{}\quad \mathrm{if}~ \mu _{z_{s}}-\nu _{z_{s}}=1~\mathrm{and}~\mu _{z_{s}}= n,\\ t_{\mu _{z_{s}},\nu _{z_{s}}}=\alpha _{\mu _{z_{s}},\nu _{z_{s}}}d_{\mu _{z_{s}}},&{}\quad \mathrm{if}~ \nu _{z_{s}}-\mu _{z_{s}}=1~\mathrm{and}~\nu _{z_{s}}\ne n, \end{array}\right. \end{aligned}$$
(41)

and

$$\begin{aligned} t_{n-1,n}=d_{n}+d_{n-1}\beta _{n,n-1}\alpha _{n-1,n},~\mathrm{if}~ \nu _{z_{s}}-\mu _{z_{s}}=1~\mathrm{and}~\nu _{z_{s}}= n. \end{aligned}$$

Remind that only one parameter in \(\mathbb {PM}(T)\) is perturbed. Thus, there is at most one entry \(t_{\mu _{z_{s}},\nu _{z_{s}}}\) of (41) to be perturbed as

$$\begin{aligned} |\tilde{t}_{\mu _{z_{s}},\nu _{z_{s}}}-t_{\mu _{z_{s}},\nu _{z_{s}}}|\le \frac{\epsilon }{1-\epsilon }|t_{\mu _{z_{s}},\nu _{z_{s}}}|. \end{aligned}$$
(42)

For the entry \(t_{n-1,n}\), by considering that \(\alpha _{n-1,n}=\theta +\mathbb {PM}(T)_{n-1,n}\), where \(\theta >0\) is computed in a subtraction-free manner by applying (8) and (9) to \(\mathbb {PM}(T)\), we get that \(|\tilde{\theta }-\theta |\le \frac{2\epsilon }{1-2\epsilon }|\theta |\), thus,

$$\begin{aligned} |\tilde{\alpha }_{n-1,n}-\alpha _{n-1,n}|\le \frac{2\epsilon }{1-2\epsilon }|\alpha _{n-1,n}|, \end{aligned}$$

consequently,

$$\begin{aligned} |\tilde{t}_{n-1,n}-t_{n-1,n}|\le \frac{3\epsilon }{1-3\epsilon }|t_{n-1,n}|. \end{aligned}$$
(43)

In addition, for the minor \(\mathrm{det}T[\gamma ]\) with \(\gamma =(\gamma _{i})\in \mathbb {R}^{k-r}\), the following statements hold.

  • The case \(\gamma _{k-r}\ne n\). By [27, the equalities (2.12) and (2.13)], considering that only one parameter in \(\mathbb {PM}(T)\) is perturbed, we have

    $$\begin{aligned} |\mathrm{det}\tilde{T}[\gamma ]-\mathrm{det}T[\gamma ]|\le \frac{\epsilon }{1-\epsilon }|\mathrm{det}T[\gamma ]|. \end{aligned}$$
    (44)
  • The case \(\gamma _{k-r}=n\). By using (10) and (7) in a subtraction-free manner,

    $$\begin{aligned} {\left\{ \begin{array}{ll}\mathrm{det}T[2:n]=\delta d_{n-1} \prod \nolimits _{t=3}^{n}d_{t-2}\alpha _{t-2,t-1}\beta _{t-1,t-2},\\ \mathrm{det}T[t:n]=\frac{\mathrm{det}T[t-1:n]+\prod \limits _{i=t-1}^{n}d_{i}}{d_{t-2}\beta _{t-1,t-2}\alpha _{t-2,t-1}},~t=3,\ldots ,n,\end{array}\right. } \end{aligned}$$

    we have

    $$\begin{aligned} |\mathrm{det}\tilde{T}[t:n]-\mathrm{det}T[t:n]|\le \frac{2\epsilon }{1-2\epsilon }|\mathrm{det}T[t:n]|,\quad t=2,\ldots ,n. \end{aligned}$$
    (45)

    Thus, since

    $$\begin{aligned} \mathrm{det}T[\gamma ]=\mathrm{det}T[l:n]\cdot \mathrm{det}T[\gamma '], \quad \gamma '=\gamma \backslash \{l,\ldots ,n\} \end{aligned}$$

    for some \(n-k+r+1\le l\le n\), we have by (44) and (45) that

    $$\begin{aligned} |\mathrm{det}\tilde{T}[\gamma ]-\mathrm{det}T[\gamma ]|\le \frac{3\epsilon }{1-3\epsilon }|\mathrm{det}T[\gamma ]|. \end{aligned}$$
    (46)

Therefore, combining (40) with (42), (43), (44) and (46), we get that

$$\begin{aligned} |\mathrm{det}\tilde{T}[\mu |\nu ]-\mathrm{det}T[\mu |\nu ]|\le \frac{7\epsilon }{1-7\epsilon }|\mathrm{det}T[\mu |\nu ]|. \end{aligned}$$

The result is proved. \(\square \)

Now we are ready to prove Theorem 2.

Proof of Theorem 2

By Lemma 1, A or \(-A\) is similar to \(\bar{A}=:|\mathbb {PM}(A)|\). According to the form (3) of A, denote

$$\begin{aligned} K=|B_{1}| \ldots |B_{n-2}|,~ T=|B_{n-1}| P |B_{n}| |D| |C_{n-1}|,~ M=|C_{n-2}|\ldots |C_{1}|. \end{aligned}$$

Then \(\bar{A}=KTM\), where K and M are TN, and T is SR with signature \((1,\ldots ,1,-1)\). So, by [2, Theorem 3.1], \(\bar{A}\) is SR with signature \((1,\ldots ,1,-1)\). Thus, by [2, Corollary 6.6], all the eigenvalues of \(\bar{A}\), and so A, are real. Moreover, by the Cauchy-Binet identity,

$$\begin{aligned} \mathrm{det}\bar{A}[\alpha |\beta ]= & {} \sum \limits _{\omega \in Q_{k,n}}\sum \limits _{\nu \in Q_{k,n}}\mathrm{det}K[\alpha |\omega ]\mathrm{det}T[\omega |\nu ]\mathrm{det}M[\nu |\beta ]\\&\ge 0,~\forall ~\alpha ,\beta \in Q_{k,n},~1\le k\le n-1. \end{aligned}$$

Denote by \(\tilde{\bar{A}}=\tilde{K}\tilde{T}\tilde{M}\) the matrix obtained from \(\bar{A}\) by perturbing one entry of \(|\mathbb {PM}(A)|\). Observe that if the perturbed parameter is from the factor of K or M, then

$$\begin{aligned} |\mathrm{det}\tilde{K}[\alpha |\beta ]-\mathrm{det}K[\alpha |\beta ]|\le & {} \frac{\epsilon }{1-\epsilon }|\mathrm{det}K[\alpha |\beta ]|,~\mathrm{or}~\\ |\mathrm{det}\tilde{M}[\alpha |\beta ]-\mathrm{det}M[\alpha |\beta ]|\le & {} \frac{\epsilon }{1-\epsilon }|\mathrm{det}M[\alpha |\beta ]|; \end{aligned}$$

otherwise, Lemma 7 implies that

$$\begin{aligned} |\mathrm{det}\tilde{T}[\alpha |\beta ]-\mathrm{det}T[\alpha |\beta ]|\le \frac{7\epsilon }{1-7\epsilon }|\mathrm{det}T[\alpha |\beta ]|, \end{aligned}$$

where \(\alpha ,\beta \in Q_{k,n}\) with any \(1\le k\le n\). So,

$$\begin{aligned} |\mathrm{det}\tilde{\bar{A}}[\alpha |\beta ]-\mathrm{det}\bar{A}[\alpha |\beta ]|\le \frac{7\epsilon }{1-7\epsilon }|\mathrm{det}\bar{A}[\alpha |\beta ]|,~\forall ~\alpha ,\beta \in Q_{k,n},~1\le k\le n. \end{aligned}$$

This also means that all the minors of orders less than n of both \(\bar{A}\) and \(\tilde{\bar{A}}\) are nonnegative. Thus, the kth (\(1\le k\le n-1\)) compound matrices \(\mathcal {B}^{(k)}=(b^{(k)}_{ij})\) and \(\tilde{\mathcal {B}}^{(k)}=(\tilde{b}^{(k)}_{ij})\) of \(\bar{A}\) and \(\tilde{\bar{A}}\) are nonnegative satisfying that

$$\begin{aligned} |\tilde{b}^{(k)}_{ij}-b^{(k)}_{ij}|\le \frac{7\epsilon }{1-7\epsilon }b^{(k)}_{ij},\quad \forall ~ i,j. \end{aligned}$$

Therefore, using a similar argument as that of [27, Theorem 3.4], we conclude that the result is true by considering Lemma 1. \(\square \)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Huang, R. Accurate Eigenvalues of Some Generalized Sign Regular Matrices via Relatively Robust Representations. J Sci Comput 82, 78 (2020). https://doi.org/10.1007/s10915-020-01182-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10915-020-01182-4

Keywords

Mathematics Subject Classification

Navigation