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A Contour Integral-Based Algorithm for Computing Generalized Singular Values (2401.00121v1)

Published 30 Dec 2023 in math.NA and cs.NA

Abstract: We propose a contour integral-based algorithm for computing a few singular values of a matrix or a few generalized singular values of a matrix pencil. Mathematically, the generalized singular values of a matrix pencil are the eigenvalues of an equivalent Hermitian-definite matrix pencil, known as the Jordan-Wielandt matrix pencil. However, direct application of the FEAST solver does not fully exploit the structure of this problem. We analyze several projection strategies on the Jordan-Wielandt matrix pencil, and propose an effective and robust scheme tailored to GSVD. Both theoretical analysis and numerical experiments demonstrate that our algorithm achieves rapid convergence and satisfactory accuracy.

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References (48)
  1. Generalized singular value decomposition for comparative analysis of genome-scale expression data sets of two different organisms. Proc. Natl. Acad. Sci., 100(6):3351–3356, 2003. doi:10.1073/pnas.0530258100.
  2. A numerical method for polynomial eigenvalue problems using contour integral. Japan J. Indust. Appl. Math., 27(1):73–90, 2010. doi:10.1007/s13160-010-0005-x.
  3. Randomized algorithms for estimating the trace of an implicit symmetric positive semi-definite matrix. J. ACM, 58(2):1–34, 2011. doi:10.1145/1944345.1944349.
  4. Computing the generalized singular decomposition. SIAM J. Sci. Comput., 14(6):1464–1486, 1993. doi:10.1137/0914085.
  5. A new preprocessing algorithm for the computation of the generalized singular value decomposition. SIAM J. Sci. Comput., 14(4):1007–1012, 1993. doi:10.1137/0914060.
  6. Application of generalized singular value decomposition to ionospheric tomography. Ann. Geophys., 22(10):3437–3444, 2004. doi:10.5194/angeo-22-3437-2004.
  7. The rotation of eigenvectors by a perturbation. III. SIAM J. Numer. Anal., 7(1):1–46, 1970. doi:10.1137/0707001.
  8. The university of Florida sparse matrix collection. ACM Trans. Math. Software, 38(1):1:1–1:25, 2011. doi:10.1145/2049662.2049663.
  9. Efficient estimation of eigenvalue counts in an interval. Numer. Linear Algebra Appl., 23(4):674–692, 2016. doi:10.1002/nla.2048.
  10. Canonical correlations and generalized SVD: applications and new algorithms. J. Comput. Appl. Math., 27(1):37–52, 1989. doi:10.1016/0377-0427(89)90360-9.
  11. Efficient algorithm for linear systems arising in solutions of eigenproblems and its application to electronic-structure calculations. In Michel Daydé, Osni Marques, and Kengo Nakajima, editors, High Performance Computing for Computational Science — VECPAR 2012, volume 7851 of Lect. Notes in Comput. Sci., pages 226–235, 2013. doi:10.1007/978-3-642-38718-0_23.
  12. Parallel stochastic estimation method of eigenvalue distribution. JSIAM Lett., 2:127–130, 2010. doi:10.14495/jsiaml.2.127.
  13. An improved contour-integral algorithm for calculating critical eigenvalues of power systems based on accurate number counting. IEEE Trans. Power Syst., 38(1):549–558, 2022. doi:10.1109/TPWRS.2022.3159494.
  14. A. Girard. A fast ‘Monte-Carlo cross-validation’ procedure for large least squares problems with noisy data. Numer. Math., 56(1):1–23, 1989. doi:10.1007/BF01395775.
  15. Zolotarev quadrature rules and load balancing for the FEAST eigensolver. SIAM J. Sci. Comput., 37(4):A2100–A2122, 2015. doi:10.1137/140980090.
  16. Per Christian Hansen. Regularization, GSVD and truncated GSVD. BIT, 29:491–504, 1989. doi:10.1007/BF02219234.
  17. Nicholas J. Higham. The matrix sign decomposition and its relation to the polar decomposition. Linear Algebra Appl., 212/213:3–20, 1994. doi:10.1016/0024-3795(94)90393-X.
  18. M. E. Hochstenbach. A Jacobi–Davidson type method for the generalized singular value problem. Linear Algebra Appl., 431(3–4):471–487, 2009. doi:10.1016/j.laa.2009.03.003.
  19. Structure preserving dimension reduction for clustered text data based on the generalized singular value decomposition. SIAM J. Matrix Anal. Appl., 25(1):165–179, 2003. doi:10.1137/S0895479801393666.
  20. On choices of formulations of computing the generalized singular value decomposition of a large matrix pair. Numer. Algorithms, 87:689–718, 2021. doi:10.1007/s11075-020-00984-9.
  21. A cross-product free Jacobi–Davidson type method for computing a partial generalized singular value decomposition of a large matrix pair. J. Sci. Comput., 94:3, 2023. doi:10.1007/s10915-022-02053-w.
  22. A FEAST SVDsolver based on Chebyshev–Jackson series for computing partial singular triplets of large matrices. J. Sci. Comput., 97(1):21:1–21:36, 2023. doi:10.1007/s10915-023-02342-y.
  23. Bo Kågström. The generalized singular value decomposition and the general (A−λ⁢B)𝐴𝜆𝐵(A-\lambda B)( italic_A - italic_λ italic_B )-problem. BIT, 24:568–583, 1984. doi:10.1007/BF01934915.
  24. Block locally optimal preconditioned eigenvalue xolvers (BLOPEX) in hypre and PETSc. SIAM J. Sci. Comput., 29(5):2224–2239, 2007. doi:10.1137/060661624.
  25. Andrew V. Knyazev. Toward the optimal preconditioned eigensolver: Locally optimal block preconditioned conjugate gradient method. SIAM J. Sci. Comput., 23(2):517–541, 2001. doi:10.1137/S1064827500366124.
  26. Applications of the generalized singular-value decomposition method on the eigenproblem using the incomplete boundary element formulation. J. Sound Vib., 235(5):813–845, 2000. doi:10.1006/jsvi.2000.2946.
  27. Real-time super-resolution sound source localization for robots. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 694–699, 2012. doi:10.1109/IROS.2012.6385494.
  28. A quadrature-based eigensolver with a Krylov subspace method for shifted linear systems for Hermitian eigenproblems in lattice QCD. JSIAM Lett., 2:115–118, 2010. doi:10.14495/jsiaml.2.115.
  29. C. C. Paige. The general linear model and the generalized singular value decomposition. Linear Algebra Appl., 70:269–284, 1985. doi:10.1016/0024-3795(85)90059-X.
  30. C. C. Paige. Computing the generalized singular value decomposition. SIAM J. Sci. Comput., 7(4):1126–1146, 1986. doi:10.1137/0907077.
  31. Towards a generalized singular value decomposition. SIAM J. Numer. Anal., 18(3):398–405, 1981. doi:10.1137/0718026.
  32. A relationship between linear discriminant analysis and the generalized minimum squared error solution. SIAM J. Matrix Anal. Appl., 27(2):474–492, 2005. doi:10.1137/040607599.
  33. An algorithm for the generalized singular value decomposition on massively parallel computers. J. Parallel Distrib. Comput., 17(4):267–276, 1993. doi:10.1006/jpdc.1993.1026.
  34. Beresford N. Parlett. The Symmetric Eigenvalue Problem. SIAM, Philadelphia, PA, USA, 1998. doi:10.1137/1.9781611971163.
  35. Eric Polizzi. Density-matrix-based algorithms for solving eigenvalue problems. Phys. Rev. B, 79:115112, 2009. doi:10.1103/physrevb.79.115112.
  36. Efficient parameter estimation and implementation of a contour integral-based eigensolver, 2013. doi:10.1260/1748-3018.7.3.249.
  37. A projection method for generalized eigenvalue problems using numerical integration. J. Comput. Appl. Math., 159(1):119–128, 2003. doi:10.1016/S0377-0427(03)00565-X.
  38. CIRR: a Rayleigh–Ritz type method with contour integral for generalized eigenvalue problems. Hokkaido Math. J., 36(4):745–757, 2007. doi:10.14492/hokmj/1272848031.
  39. Signal processing computations using the generalized singular value decomposition. In Proceedings of the SPIE, volume 495, pages 47–55, 1984.
  40. Brian D. Sutton. Stable computation of the CS decomposition: Simultaneous bidiagonalization. SIAM J. Matrix Anal. Appl., 33(1):1–21, 2012. doi:10.1137/100813002.
  41. Ping Tak Peter Tang and Eric Polizzi. FEAST as a subspace iteration eigensolver accelerated by approximate spectral projection. SIAM J. Matrix Anal. Appl., 35(2):354–390, 2014. doi:10.1137/13090866X.
  42. The exponentially convergent trapezoidal rule. SIAM Rev., 56(3):385–458, 2014. doi:10.1137/130932132.
  43. Charles F. Van Loan. Generalizing the singular value decomposition. SIAM J. Numer. Anal., 13(1):76–83, 1976. doi:10.1137/0713009.
  44. Charles F. Van Loan. Computing the CS and the generalized singular value decompositions. Numer. Math., 46(4):479–491, 1985. doi:10.1007/BF01389653.
  45. ChASE: Chebyshev accelerated subspace iteration eigensolver for sequences of Hermitian eigenvalue problems. ACM Trans. Math. Software, 45(2):21:1–21:34, 2019. doi:10.1145/3313828.
  46. A fast contour-integral eigensolver for non-Hermitian matrices. SIAM J. Matrix Anal. Appl., 38(4):1268–1297, 2017. doi:10.1137/16m1086601.
  47. A projection method for nonlinear eigenvalue problems using contour integrals. JSIAM Lett., 5:41–44, 2013. doi:10.14495/jsiaml.5.41.
  48. Hongyuan Zha. Computing the generalized singular values/vectors of large sparse or structured matrix pairs. Numer. Math., 72:391–417, 1996. doi:10.1007/s002110050175.
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