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Riemannian Geometry of Grassmann Manifolds with a View on Algorithmic Computation

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Abstract

We give simple formulas for the canonical metric, gradient, Lie derivative, Riemannian connection, parallel translation, geodesics and distance on the Grassmann manifold of p-planes in R n. In these formulas, p-planes are represented as the column space of n×p matrices. The Newton method on abstract Riemannian manifolds proposed by Smith is made explicit on the Grassmann manifold. Two applications – computing an invariant subspace of a matrix and the mean of subspaces – are worked out.

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References

  1. Absil, P.-A.: Invariant subspace computation: A geometric approach, Ph.D. thesis, Faculté des Sciences Appliquées, Université de Liège, Secrétariat de la FSA, Chemin des Chevreuils 1 (Bât. B52), 4000 Liège, Belgium, 2003.

    Google Scholar 

  2. Absil, P.-A., Mahony, R., Sepulchre, R. and Van Dooren, P.: A Grassmann–Rayleigh quotient iteration for computing invariant subspaces, SIAM Rev. 44(1) (2002), 57–73.

    Google Scholar 

  3. Absil, P.-A., Sepulchre, R., Van Dooren, P. and Mahony, R.: Cubically convergent iterations for invariant subspace computation, SIAM J. Matrix Anal. Appl., to appear.

  4. Absil, P.-A. and Van Dooren, P.: Two-sided Grassmann–Rayleigh quotient iteration, SIAM J. Matrix Anal. Appl. (2002), submitted.

  5. Björk, Å. and Golub, G. H.: Numerical methods for computing angles between linear subspaces, Math. Comp. 27 (1973), 579–594.

    Google Scholar 

  6. Boothby, W. M.: An Introduction to Differentiable Manifolds and Riemannian Geometry, Academic Press, 1975.

  7. Chatelin, F.: Simultaneous Newton's iteration for the eigenproblem, Computing 5(Suppl.) (1984), 67–74.

    Google Scholar 

  8. Chavel, I.: Riemannian Geometry – A Modern Introduction, Cambridge Univ. Press, 1993.

  9. Common, P. and Golub, G. H.: Tracking a few extreme singular values and vectors in signal processing, Proc. IEEE 78(8) (1990), 1327–1343.

    Google Scholar 

  10. Demmel, J. W.: Three methods for refining estimates of invariant subspaces, Computing 38 (1987), 43–57.

    Google Scholar 

  11. Dennis, J. E. and Schnabel, R. B.: Numerical Methods for Unconstrained Optimization and Nonlinear Equations, Prentice Hall Series in Computational Mathematics, Prentice-Hall, Englewood Cliffs, NJ, 1983.

    Google Scholar 

  12. do Carmo, M. P.: Riemannian Geometry, Birkhäuser, 1992.

  13. Doolin, B. F. and Martin, C. F.: Introduction to Differential Geometry for Engineers, Monographs and Textbooks in Pure and Applied Mathematics 136, Marcel Deckker, Inc., New York, 1990.

    Google Scholar 

  14. Edelman, A., Arias, T. A. and Smith, S. T.: The geometry of algorithms with orthogonality constraints, SIAM J. Matrix Anal. Appl. 20(2) (1998), 303–353.

    Google Scholar 

  15. Ferrer, J., García, M. I. and Puerta, F.: Differentiable families of subspaces, Linear Algebra Appl. 199 (1994), 229–252.

    Google Scholar 

  16. Gabay, D.: Minimizing a differentiable function over a differential manifold, J. Optim. Theory Appl. 37(2) (1982), 177–219.

    Google Scholar 

  17. Golub, G. H. and Van Loan, C. F.: Matrix Computations, 3rd edn, The Johns Hopkins Univ. Press, 1996.

  18. Helgason, S.: Differential Geometry, Lie Groups, and Symmetric Spaces, Pure Appl. Math. 80, Academic Press, Oxford, 1978.

    Google Scholar 

  19. Helmke, U. and Moore, J. B.: Optimization and Dynamical Systems, Springer, 1994.

  20. Karcher, H.: Riemannian center of mass and mollifier smoothing, Comm. Pure Appl. Math. 30(5) (1977), 509–541.

    Google Scholar 

  21. Kendall, W. S.: Probability, convexity, and harmonic maps with small image. I. Uniqueness and fine existence, Proc. London Math. Soc. 61(2) (1990), 371–406.

    Google Scholar 

  22. Kobayashi, S. and Nomizu, K.: Foundations of Differential Geometry, Vol. 1, 2, Wiley, 1963.

  23. Leichtweiss, K.: Zur riemannschen Geometrie in grassmannschen Mannigfaltigkeiten, Math. Z. 76 (1961), 334–366.

    Google Scholar 

  24. Luenberger, D. G.: Optimization by Vector Space Methods, Wiley, Inc., 1969.

  25. Lundström, E. and Eldén, L.: Adaptive eigenvalue computations using Newton's method on the Grassmann manifold, SIAM J. Matrix Anal. Appl. 23(3) (2002), 819–839.

    Google Scholar 

  26. Machado, A. and Salavessa, I.: Grassmannian manifolds as subsets of Euclidean spaces, Res. Notes in Math. 131 (1985), 85–102.

    Google Scholar 

  27. Mahony, R. E.: The constrained Newton method on a Lie group and the symmetric eigenvalue problem, Linear Algebra Appl. 248 (1996), 67–89.

    Google Scholar 

  28. Mahony, R. and Manton, J. H.: The geometry of the Newton method on non-compact Lie groups, J. Global Optim. 23(3) (2002), 309–327.

    Google Scholar 

  29. Nomizu, K.: Invariant affine connections on homogeneous spaces, Amer. J. Math. 76 (1954), 33–65.

    Google Scholar 

  30. Ran, A. C. M. and Rodman, L.: A class of robustness problems in matrix analysis, In: D. Alpay, I. Gohberg, and V. Vinnikov (eds), Interpolation Theory, Systems Theory and Related Topics, The Harry Dym Anniversary Volume, Operator Theory: Advances and Applications 134, Birkhäuser, 2002, pp. 337–383.

  31. Simoncini, V. and Elden, L.: Inexact Rayleigh quotient-type methods for eigen-value computations, BIT 42(1) (2002), 159–182.

    Google Scholar 

  32. Smith, S. T.: Geometric optimization methods for adaptive filtering, Ph.D. thesis, Division of Applied Sciences, Harvard University, Cambridge, MA, 1993.

    Google Scholar 

  33. Smith, S. T.: Optimization techniques on Riemannian manifolds, In: A. Bloch (ed.), Hamiltonian and Gradient Flows, Algorithms and Control, Fields Institute Communications, Vol. 3, Amer. Math. Soc., 1994, pp. 113–136.

  34. Stewart, G. W.: Error and perturbation bounds for subspaces associated with certain eigen-value problems, SIAM Rev. 15(4) (1973), 727–764.

    Google Scholar 

  35. Udri¸ste, C.: Convex Functions and Optimization Methods on Riemannian Manifolds, Kluwer Acad. Publ., Dordrecht, 1994.

    Google Scholar 

  36. Wong, Y.-C.: Differential geometry of Grassmann manifolds, Proc. Nat. Acad. Sci. U.S.A. 57 (1967), 589–594.

    Google Scholar 

  37. Woods, R. P.: Characterizing volume and surface deformations in an atlas framework: Theory, applications, and implementation, NeuroImage 18(3) (2003), 769–788.

    Google Scholar 

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Absil, PA., Mahony, R. & Sepulchre, R. Riemannian Geometry of Grassmann Manifolds with a View on Algorithmic Computation. Acta Applicandae Mathematicae 80, 199–220 (2004). https://doi.org/10.1023/B:ACAP.0000013855.14971.91

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  • DOI: https://doi.org/10.1023/B:ACAP.0000013855.14971.91

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