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Riemannian inexact Newton method for structured inverse eigenvalue and singular value problems

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Abstract

Inverse eigenvalue and singular value problems have been widely discussed for decades. The well-known result is the Weyl-Horn condition, which presents the relations between the eigenvalues and singular values of an arbitrary matrix. This result by Weyl-Horn then leads to an interesting inverse problem, i.e., how to construct a matrix with desired eigenvalues and singular values. In this work, we do that and more. We propose an eclectic mix of techniques from differential geometry and the inexact Newton method for solving inverse eigenvalue and singular value problems as well as additional desired characteristics such as nonnegative entries, prescribed diagonal entries, and even predetermined entries. We show theoretically that our method converges globally and quadratically, and we provide numerical examples to demonstrate the robustness and accuracy of our proposed method.

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References

  1. Absil, P.-A., Mahony, R., Sepulchre, R.: Optimization Algorithms on Matrix Manifolds. Princeton University Press, Princeton (2008)

    Book  MATH  Google Scholar 

  2. Baker, C.G., Absil, P.-A., Gallivan, K.A.: An implicit Riemannian trust-region method for the symmetric generalized eigenproblem. In: Computational Science—ICCS 2006, LNCS. Springer, Berlin (2006)

  3. Boley, D.L., Golub, G.H.: A survey of matrix inverse eigenvalue problems. Inverse Problems 3(4), 595–622 (1987)

    Article  MathSciNet  MATH  Google Scholar 

  4. Boyd, S., Xiao, L.: Least-squares covariance matrix adjustment. SIAM J. Matrix Anal. Appl. 27(2), 532–546 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  5. Chan, N.N., Li, K.H.: Diagonal elements and eigenvalues of a real symmetric matrix. J. Math. Anal. Appl. 91(2), 562–566 (1983)

    Article  MathSciNet  MATH  Google Scholar 

  6. Chu, E.K., Datta, B.N.: Numerically robust pole assignment for second-order systems. Int. J. Control 64(6), 1113–1127 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  7. Chu, M.T.: Numerical methods for inverse singular value problems. SIAM J. Numer. Anal. 29(3), 885–903 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  8. Chu, M.T.: On constructing matrices with prescribed singular values and diagonal elements. Linear Algebra Appl. 288(1–3), 11–22 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  9. Chu, M.T.: A fast recursive algorithm for constructing matrices with prescribed eigenvalues and singular values. SIAM J. Numer. Anal. 37(3), 1004–1020 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  10. Chu, M.T.: Linear algebra algorithms as dynamical systems. Acta Numer. 17, 1–86 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  11. Chu, M.T.: On the first degree Fejér–Riesz factorization and its applications to \(X+A^\ast X^{-1}A=Q\). Linear Algebra Appl. 489, 123–143 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  12. Chu, M.T., Driessel, K.R.: The projected gradient method for least squares matrix approximations with spectral constraints. SIAM J. Numer. Anal. 27(4), 1050–1060 (1990)

    Article  MathSciNet  MATH  Google Scholar 

  13. Chu, M.T., Driessel, K.R.: Constructing symmetric nonnegative matrices with prescribed eigenvalues by differential equations. SIAM J. Math. Anal. 22(5), 1372–1387 (1991)

    Article  MathSciNet  MATH  Google Scholar 

  14. Chu, M.T., Golub, G.H.: Inverse Eigenvalue Problems: Theory, Algorithms, and Applications. Numerical Mathematics and Scientific Computation. Oxford University Press, New York (2005)

    Book  Google Scholar 

  15. Chu, M.T., Lin, W.-W., Xu, S.-F.: Updating quadratic models with no spillover effect on unmeasured spectral data. Inverse Problems 23(1), 243–256 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  16. Chu, M.T., Wright, J.W.: The educational testing problem revisited. IMA J. Numer. Anal. 15(1), 141–160 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  17. Cotae, P., Aguirre, M.: On the construction of the unit tight frames in code division multiple access systems under total squared correlation criterion. AEU Int. J. Electron. Commun. 60(10), 724–734 (2006)

    Article  Google Scholar 

  18. Datta, B.N.: Finite-element model updating, eigenstructure assignment and eigenvalue embedding techniques for vibrating systems. Mech. Sys. Signal Process. 16(1), 83–96 (2002)

    Article  Google Scholar 

  19. Datta, B.N., Elhay, S., Ram, Y.M., Sarkissian, D.R.: Partial eigenstructure assignment for the quadratic pencil. J. Sound Vib. 230(1), 101–110 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  20. Datta, B.N., Lin, W.-W., Wang, J.-N.: Robust partial pole assignment for vibrating systems with aerodynamic effects. IEEE Trans. Autom. Control 51(12), 1979–1984 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  21. Deakin, A.S., Luke, T.M.: On the inverse eigenvalue problem for matrices (atomic corrections). J. Phys. A Math. Gen. 25(3), 635 (1992)

    Article  MATH  Google Scholar 

  22. Dhillon, I.S., Heath Jr., R.W., Sustik, M.A., Tropp, J.A.: Generalized finite algorithms for constructing Hermitian matrices with prescribed diagonal and spectrum. SIAM J. Matrix Anal. Appl. 27(1), 61–71 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  23. Eisenstat, S.C., Walker, H.F.: Globally convergent inexact Newton methods. SIAM J. Optim. 4(2), 393–422 (1994)

    Article  MathSciNet  MATH  Google Scholar 

  24. Gladwell, G.M.L.: Inverse Problems in Vibration, Volume 119 of Solid Mechanics and Its Applications, 2nd edn. Kluwer Academic Publishers, Dordrecht (2004)

    Google Scholar 

  25. Gohberg, I., Lancaster, P., Rodman, L.: Matrix Polynomials. Society for Industrial and Applied Mathematics, Philodelphia (2009)

    Book  MATH  Google Scholar 

  26. Golub, G.H.: Some modified matrix eigenvalue problems. SIAM Rev. 15(2), 318–334 (1973)

    Article  MathSciNet  MATH  Google Scholar 

  27. Grubišić, I., Pietersz, R.: Efficient rank reduction of correlation matrices. Linear Algebra Appl. 422(2–3), 629–653 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  28. Higham, N.J.: Accuracy and Stability of Numerical Algorithms. SIAM, Philadelphia (1996)

    MATH  Google Scholar 

  29. Horn, A.: On the eigenvalues of a matrix with prescribed singular values. Proc. Am. Math. Soc. 5, 4–7 (1954)

    Article  MathSciNet  MATH  Google Scholar 

  30. Jacek, K.: Inverse problems in quantum chemistry. Int. J. Quantum Chem. 109(11), 2456–2463 (2009)

    Article  Google Scholar 

  31. Kelley, C.T.: Iterative Methods for Linear and Nonlinear Equations, Volume 16 of Frontiers in Applied Mathematics. SIAM, Philadelphia (1995)

    Book  Google Scholar 

  32. Kosowski, P., Smoktunowicz, A.: On constructing unit triangular matrices with prescribed singular values. Computing 64(3), 279–285 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  33. Li, C.-K., Mathias, R.: Construction of matrices with prescribed singular values and eigenvalues. BIT Numer. Math. 41(1), 115–126 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  34. Li, N.: A matrix inverse eigenvalue problem and its application. Linear Algebra Appl. 266, 143–152 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  35. Luenberger, D.G.: Optimization by Vector Space Methods. Wiley, New York (1969)

    MATH  Google Scholar 

  36. Mirsky, L.: Matrices with prescribed characteristic roots and diagonal elements. J. Lond. Math. Soc. 33, 14–21 (1958)

    Article  MathSciNet  MATH  Google Scholar 

  37. Möller, M., Pivovarchik, V.: Inverse Sturm–Liouville Problems. Springer, Cham (2015)

    Book  Google Scholar 

  38. Nichols, N.K., Kautsky, J.: Robust eigenstructure assignment in quadratic matrix polynomials: nonsingular case. SIAM J. Matrix Anal. Appl. 23(1), 77–102 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  39. Rao, R., Dianat, S.: Basics of Code Division Multiple Access (CDMA). SPIE Tutorial Texts. SPIE Press, Washington (2005)

    Book  Google Scholar 

  40. Simonis, J.P.: Inexact Newton methods applied to under-determined systems. Ph.D. dissertation, Worcester Polytechnic Institute, Worcester, MA (2006)

  41. Sing, F.Y.: Some results on matrices with prescribed diagonal elements and singular values. Can. Math. Bull. 19(1), 89–92 (1976)

    Article  MathSciNet  MATH  Google Scholar 

  42. Thompson, R.C.: Singular values, diagonal elements, and convexity. SIAM J. Appl. Math. 32(1), 39–63 (1977)

    Article  MathSciNet  MATH  Google Scholar 

  43. Tropp, J.A., Dhillon, I.S., Heath Jr., R.W.: Finite-step algorithms for constructing optimal CDMA signature sequences. IEEE Trans. Inf. Theory 50(11), 2916–2921 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  44. Wang, L., Chu, M.T., Bo, Y.: A computational framework of gradient flows for general linear matrix equations. Numer. Algorithms 68(1), 121–141 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  45. Weyl, H.: Inequalities between the two kinds of eigenvalues of a linear transformation. Proc. Natl. Acad. Sci. USA 35, 408–411 (1949)

    Article  MathSciNet  MATH  Google Scholar 

  46. Wu, S.-J., Chu, M.T.: Solving an inverse eigenvalue problem with triple constraints on eigenvalues, singular values, and diagonal elements. Inverse Problems 33(8), 085003, 21 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  47. Yao, T.-T., Bai, Z.-J., Zhao, Z., Ching, W.-K.: A Riemannian Fletcher-Reeves conjugate gradient method for doubly stochastic inverse eigenvalue problems. SIAM J. Matrix Anal. Appl. 37(1), 215–234 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  48. Zha, H., Zhang, Z.: A note on constructing a symmetric matrix with specified diagonal entries and eigenvalues. BIT 35(3), 448–451 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  49. Zhao, Z., Bai, Z.-J., Jin, X.-Q.: A Riemannian Newton algorithm for nonlinear eigenvalue problems. SIAM J. Matrix Anal. Appl. 36(2), 752–774 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  50. Zhao, Z., Bai, Z.-J., Jin, X.-Q.: A Riemannian inexact Newton-CG method for constructing a nonnegative matrix with prescribed realizable spectrum. Numer. Math. 140(4), 827–855 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  51. Zhao, Z., Jin, X.-Q., Bai, Z.-J.: A geometric nonlinear conjugate gradient method for stochastic inverse eigenvalue problems. SIAM J. Numer. Anal. 54(4), 2015–2035 (2016)

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgements

This research work is also supported by the National Center for Theoretical Sciences in Taiwan. The authors wish to thank Prof. Michiel E. Hochstenbach for his highly valuable comments. They also thank Prof. Zheng-Jian Bai and Dr. Zhi Zhao for helpful discussions.

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Correspondence to Matthew M. Lin.

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Communicated by Michiel E. Hochstenbach.

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Chun-Yueh Chiang: This research was supported in part by the Ministry of Science and Technology of Taiwan under Grant 107-2115-M-150-002. Matthew M. Lin: This research was supported in part by the Ministry of Science and Technology of Taiwan under Grants 107-2115-M-006-007-MY2 and 108-2636-M-006-006. Xiao-Qing Jin: This research was supported in part by the research Grant MYRG2016-00077-FST from University of Macau.

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Chiang, CY., Lin, M.M. & Jin, XQ. Riemannian inexact Newton method for structured inverse eigenvalue and singular value problems. Bit Numer Math 59, 675–694 (2019). https://doi.org/10.1007/s10543-019-00754-7

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