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UTV Tools: Matlab templates for rank-revealing UTV decompositions

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Abstract

We describe a Matlab 5.2 package for computing and modifying certain rank-revealing decompositions that have found widespread use in signal processing and other applications. The package focuses on algorithms for URV and ULV decompositions, collectively known as UTV decompositions. We include algorithms for the ULLV decomposition, which generalizes the ULV decomposition to a pair of matrices. For completeness a few algorithms for computation of the RRQR decomposition are also included. The software in this package can be used as is, or can be considered as templates for specialized implementations on signal processors and similar dedicated hardware platforms.

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References

  1. G. Adams, M.F. Griffin and G.W. Stewart, Direction-of-arrival estimation using the rank-revealing URV decomposition, in: Proc. of IEEE Internat. Conf. on Acoustics, Speech, and Signal Processing, Washington (1991).

  2. J.L. Barlow and P.A. Yoon, Solving recursive TLS problems using the rank-revealing ULV decomposition, in: Recent Advances in Total Least Squares Techniques and Errors-In-Variables Modeling, ed. S. Van Huffel (SIAM, Philadelphia, PA, 1997) pp. 117–126.

    Google Scholar 

  3. J.L. Barlow, P.A. Yoon and H. Zha, An algorithm and a stability theory for downdating the ULV decomposition, BIT 36 (1996) 15–40.

    Article  MathSciNet  Google Scholar 

  4. M.W. Berry, S.T. Dumais and G.W. O'Brien, Using linear algebra for intelligent information retrieval, SIAM Rev. 37 (1995) 573–595.

    Article  MATH  MathSciNet  Google Scholar 

  5. E. Biglieri and K. Yao, Some properties of singular value decomposition and their applications to digital signal processing, Signal Processing 18 (1989) 277–289.

    Article  MathSciNet  Google Scholar 

  6. C.H. Bischof and G.M. Shroff, On updating signal subspaces, IEEE Trans. Signal Processing 40 (1992) 96–105.

    Article  Google Scholar 

  7. Å. Björck, H. Park and L. Eldén, Accurate downdating of least squares solutions, SIAM J. Matrix Anal. Appl. 15 (1994) 549–568.

    Article  MATH  MathSciNet  Google Scholar 

  8. A.W. Bojanczyk and J.M. Lebak, Downdating a ULLV decomposition of two matrices; in: Applied Linear Algebra, ed. J.G. Lewis (SIAM, Philadelphia, PA, 1994).

    Google Scholar 

  9. J.R. Bunch and N.P. Nielsen, Updating the singular value decomposition, Numer. Math. 31 (1978) 111–129.

    Article  MATH  MathSciNet  Google Scholar 

  10. T.F. Chan, Rank revealing QR factorizations, Linear Algebra Appl. 88/89 (1987) 67–82.

    Article  MATH  MathSciNet  Google Scholar 

  11. T.F. Chan and P.C. Hansen, Some applications of the rank revealing QR factorization, SIAM J. Sci. Statist. Comput. 13 (1992) 727–741.

    Article  MATH  MathSciNet  Google Scholar 

  12. T.F. Chan and P.C. Hansen, Low-rank revealing QR factorizations, Numer. Linear Algebra Appl. 1 (1994) 33–44.

    Article  MATH  MathSciNet  Google Scholar 

  13. A.K. Cline, A.R. Conn and C.F. Van Loan, Generalizing the LINPACK condition estimator, in: Numerical Analysis, ed. J.P. Hennart, Lecture Notes in Mathematics, Vol. 909 (Springer, Berlin, 1882).

    Google Scholar 

  14. P. Comon and G.H. Golub, Tracking a few extreme singular values and vectors in signal processing, Proc. IEEE 78 (1990) 1337–1343.

    Article  Google Scholar 

  15. J.W. Daniel, W.B. Gragg, L. Kaufman and G.W. Stewart, Reorthogonalization and stable algorithms for updating the Gram–Schmidt QR factorization, Math. Comp. 30 (1976) 772–795.

    Article  MATH  MathSciNet  Google Scholar 

  16. B. De Moor, Generalizations of the OSVD: Structure, properties and applications, in: [59] pp. 83–98.

  17. F. Deprettere, SVD and Signal Processing, Algorithms, Applications, and Architectures (North-Holland, Amsterdam, 1988).

    Google Scholar 

  18. L. Eldén and E. Sjöström, Fast computation of the principal singular vectors of Toeplitz matrices arising in exponential data modelling, Signal Processing 50 (1996) 151–164.

    Article  MATH  Google Scholar 

  19. R.D. Fierro, Perturbation analysis for two-sided (or complete) orthogonal decompositions, SIAM J. Matrix Anal. Appl. 17 (1996) 383–400.

    Article  MATH  MathSciNet  Google Scholar 

  20. R.D. Fierro and J.R. Bunch, Bounding the subspaces from rank revealing two-sided orthogonal decompositions, SIAM J. Matrix Anal. Appl. 16 (1995) 743–759.

    Article  MATH  MathSciNet  Google Scholar 

  21. R.D. Fierro and P.C. Hansen, Accuracy of TSVD solutions computed from rank-revealing decompositions, Numer. Math. 70 (1995) 453–471.

    Article  MATH  MathSciNet  Google Scholar 

  22. R.D. Fierro and P.C. Hansen, Low-rank revealing UTV decompositions, Numerical Algorithms 15 (1997) 37–55.

    Article  MATH  MathSciNet  Google Scholar 

  23. R.D. Fierro, L. Vanhamme and S. Van Huffel, Total least squares algorithms based on rank-revealing complete orthogonal decompositions, in: Recent Advances in Total Least Squares Techniques and Errors-in-Variables Modeling, ed. S. Van Huffel (SIAM, Philadelphia, PA, 1997) pp. 99–116.

    Google Scholar 

  24. L. Foster, Rank and null space calculations using matrix decomposition without column interchanges, Linear Algebra Appl. 74 (1986) 47–71.

    Article  MATH  MathSciNet  Google Scholar 

  25. G.H. Golub, V. Klema and G.W. Stewart, Rank degeneracy and least squares problems, Technical Report TR-456, Dept. of Computer Science, University of Maryland, MD (1976).

    Google Scholar 

  26. G.H. Golub and C.F. Van Loan, Matrix Computations, 3rd ed. (Johns Hopkins University Press, Baltimore, MD, 1996).

    MATH  Google Scholar 

  27. M. Gu and S.C. Eisenstat, Downdating the singular value decomposition, SIAM J. Matrix Anal. Appl. 16 (1995) 793–810.

    Article  MATH  MathSciNet  Google Scholar 

  28. P.C. Hansen, The 2-norm of random matrices, J. Comput. Appl. Math. 23 (1988) 117–120.

    Article  MATH  MathSciNet  Google Scholar 

  29. P.C. Hansen, Rank-Deficient and Discrete Ill-Posed Problems: Numerical Aspects of Linear Inversion (SIAM, Philadelphia, PA, 1998).

    Google Scholar 

  30. P.C. Hansen, Rank-deficient prewhitening by quotient SVD and UTV, BIT 38 (1998) 34–43.

    MATH  MathSciNet  Google Scholar 

  31. P.S.K. Hansen, Signal subspace methods for speech enhancement, Ph.D. thesis, Dept. of Mathematical Modelling, Technical University of Denmark (1997).

  32. N.J. Higham, A survey of condition number estimation for triangular matrices, SIAM Rev. 29 (1987) 575–596.

    Article  MATH  MathSciNet  Google Scholar 

  33. S.H. Jensen, P.C. Hansen, S.D. Hansen and J.A. Sørensen, Reduction of broad-band noise in speech by truncated QSVD, IEEE Trans. Audio Speech Proc. 3 (1995) 439–448.

    Article  Google Scholar 

  34. J. Kuczynski and H. Wozniakowski, Estimating the largest eigenvalue by the Power and Lanczos algorithms with a random start, SIAM J. Matrix Anal. 4 (1992) 1094–1122.

    Article  MATH  MathSciNet  Google Scholar 

  35. C.L. Lawson and R.J. Hanson, Solving Least Squares Problems (Prentice-Hall, Englewood Cliffs, NJ, 1974). Reprinted by SIAM, Philadelphia.

    MATH  Google Scholar 

  36. J.M. Lebak and A.W. Bojanczyk, Modifying a rank-revealing ULLV decomposition, Manuscript, School of Electrical Engineering, Cornell University (1994).

  37. K.J.R. Liu, D.P. O'Leary, G.W. Stewart and Y.-J. Wu, URV ESPRIT for tracking time-varying signals, IEEE Trans. Signal Processing 42 (1994) 3441–3448.

    Article  Google Scholar 

  38. F.T. Luk and S. Qiao, A new matrix decomposition for signal processing, Automatica 30 (1994) 39–43.

    Article  MATH  MathSciNet  Google Scholar 

  39. F.T. Luk and S. Qiao, An adaptive algorithm for interference cancelling in array processing, in: Advanced Signal Processing Algorithms, Architectures, and Implementations VI, ed. F.T. Luk, SPIE Proceedings, Vol. 2846 (1996) pp. 151–161.

  40. W. Ma and J.P. Kruth, Mathematical modelling of free-form curves and surfaces from discrete points with NURBS, in: Curves and Surfaces in Geometric Design, eds. P.J. Laurent, A. Le Méhauté and L.L Schumaker (A.K. Peters, Wellesley, MA, 1994).

    Google Scholar 

  41. R. Mathias and G.W. Stewart, A block QR algorithm and the singular value decomposition, Linear Algebra Appl. 182 (1993) 91–100.

    Article  MATH  MathSciNet  Google Scholar 

  42. M. Moonen and B. De Moor, SVD and Signal Processing, III, Algorithms, Architectures and Applications (Elsevier, Amsterdam, 1995).

    MATH  Google Scholar 

  43. M. Moonen, P. Van Dooren and J. Vandewalle, A note on efficient numerically stabilized rank-one eigenstructure updating, IEEE Trans. Signal Processing 39 (1991) 1911–1913.

    Article  Google Scholar 

  44. M. Moonen, P. Van Dooren and J. Vandewalle, A singular value decomposition updating algorithm for subspace tracking, SIAM J. Matrix Anal. Appl. 13 (1992) 1015–1038.

    Article  MATH  MathSciNet  Google Scholar 

  45. H. Park and L. Eldén, Downdating the rank revealing URV decomposition, SIAM J. Matrix Anal. Appl. 16 (1995) 138–155.

    Article  MATH  MathSciNet  Google Scholar 

  46. H. Park, S. Van Huffel and L. Eldén, Fast algorithms for exponential data modeling, in: Proc. of 1994 IEEE Internat. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), April 19–22, Adelaïde, Australia, Vol. 4 (1994) pp. 25–28.

    Google Scholar 

  47. D.J. Pierce and J.G. Lewis, Sparse multifrontal rank-revealing QR factorization, SIAM J. Matrix Anal. Appl. 18 (1997) 159–180.

    Article  MATH  MathSciNet  Google Scholar 

  48. M.A. Rahman and K. Yu, Total least squares approach for frequency estimation using linear prediction, IEEE Trans. ASSP 35 (1987) 1442–1454.

    Article  Google Scholar 

  49. L.L. Scharf, The SVD and reduced rank signal processing, Signal Processing 25 (1991) 113–133.

    Article  MATH  Google Scholar 

  50. G.W. Stewart, Rank degeneracy, SIAM J. Sci. Statist. Comput. 5 (1984) 403–413.

    Article  MATH  MathSciNet  Google Scholar 

  51. G.W. Stewart, An updating algorithm for subspace tracking, IEEE Trans. Signal Processing 40 (1992) 1535–1541.

    Article  Google Scholar 

  52. G.W. Stewart, Updating a rank-revealing ULV decomposition, SIAM J. Matrix Anal. Appl. 14 (1993) 494–499.

    Article  MATH  MathSciNet  Google Scholar 

  53. G.W. Stewart, Determining rank in the presence of error in: Linear Algebra for Large Scale and Real-Time Applications, eds. M.S. Moonen, G.H. Golub and B.L.R. DeMoor (Kluwer Academic, Dordrecht, 1993) pp. 275–292.

    Google Scholar 

  54. G.W. Stewart, UTV decompositions, in: Numerical Analysis, eds. D.F. Griffith and G.A. Watson 1993, Pitman Research Notes in Mathematical Sciences (New York, 1994).

  55. G.W. Stewart, A gap-revealing matrix decomposition, Report TR-3771, Dept. of Computer Science, University of Maryland (1997), to appear in SIAM J. Sci. Comput.

  56. G.W. Stewart, Matrix Algorithms. Vol. I: Basic Decompositions (SIAM, Philadelphia, PA, 1998).

    Google Scholar 

  57. M. Stewart and P. Van Dooren, Updating a generalized URV decomposition, SIAM J. Matrix Anal. Appl., to appear.

  58. D.W. Tufts and R. Kumaresan, Estimation of frequencies of multiple sinusoids: Making linear prediction perform like maximum likelihood, Proc. IEEE 70 (1982) 975–989.

    Article  Google Scholar 

  59. R. Vaccaro, SVD and Signal Processing, II, Algorithms, Analysis and Applications (Elsevier, Amsterdam, 1991).

    Google Scholar 

  60. R.J. Vaccaro, D.W. Tufts and G.F. Boudreaux-Bartels, Advances in principal component signal processing, in: [17] pp. 115–146.

  61. A. van der Veen and E.F. Deprettere, SVD-based low-rank approximations of rational models, in: [59] pp. 431–454.

  62. S. Van Huffel and H. Zha, An efficient total least squares algorithm based on a rank revealing two-sided orthogonal decomposition, Numerical Algorithms 4 (1993) 101–133.

    Article  MATH  MathSciNet  Google Scholar 

  63. G. Xu and T. Kailath, Fast estimation of principle eigenspace using Lanczos algorithm, SIAM J. Matrix Anal. Appl. 15 (1994) 974–994.

    Article  MATH  MathSciNet  Google Scholar 

  64. G. Xu, H. Zha, G.H. Golub and T. Kailath, Fast and robust algorithms for updating signal subspaces, IEEE Trans. Circuits Systems 41 (1994) 537–549.

    Article  MATH  Google Scholar 

  65. P.A. Yoon and J.L. Barlow, An efficient rank detection procedure for modifying the ULV decomposition, BIT 38 (1998) 781–801.

    MATH  MathSciNet  Google Scholar 

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Fierro, R.D., Hansen, P.C. & Hansen, P.S.K. UTV Tools: Matlab templates for rank-revealing UTV decompositions. Numerical Algorithms 20, 165–194 (1999). https://doi.org/10.1023/A:1019112103049

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