Skip to main content
Log in

The Geometry of the Newton Method on Non-Compact Lie Groups

  • Published:
Journal of Global Optimization Aims and scope Submit manuscript

Abstract

An important class of optimization problems involve minimizing a cost function on a Lie group. In the case where the Lie group is non-compact there is no natural choice of a Riemannian metric and it is not possible to apply recent results on the optimization of functions on Riemannian manifolds. In this paper the invariant structure of a Lie group is exploited to provide a strong interpretation of a Newton iteration on a general Lie group. The paper unifies several previous algorithms proposed in the literature in a single theoretical framework. Local asymptotic quadratic convergence is proved for the algorithms considered.

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.

Similar content being viewed by others

References

  • Absil, P.-A., Mahony, R., Sebulchre, R. and VanDooren, P., (2002). A Grassman-Rayleigh quotient iteration for computing invariant subspaces. SIAM Review 44(1), 57–73.

    Google Scholar 

  • Boothby, W.M. (1986), An Introduction to Differentiable Manifolds and Riemannian Geometry. Academic Press, London.

    Google Scholar 

  • Botsaris, C.A. (1978), Differential gradient methods. Journal of Mathematical Analysis and Applications 63, 177–198.

    Google Scholar 

  • Botsaris, C.A. (1981a), A class of differential descent methods for constrained optimization. Journal Mathematical Analysis and Applications 79, 96–112.

    Google Scholar 

  • Botsaris, C.A. (1981b), Constrained optimization along geodesics. Journal Mathematical Analysis and Applications 79, 295–306.

    Google Scholar 

  • Brockett, R.W. (1989), Least squares matching problems. Linear Algebra and its Applications, 122-124, 761–777.

    Google Scholar 

  • Brockett, R.W. (1993), Differential geometry and the design of gradient algorithms. Proceedings of Symposia in Pure Mathematics 54, 69–92.

    Google Scholar 

  • Bruyne, F. De, Anderson, B.D.O., Gevers, M. and Linard, N. (1999), Gradient expressions for a closed-loop identification scheme with a tailor-made parametrization. Automatica, 35(11), 1867–1871.

    Google Scholar 

  • Chu, M.T., (1988). On the continuous realization of iterative processes. SIAM Review 30(3), 375–387.

    Google Scholar 

  • Chu, M.T. and Driessel, K.R. (1990), The projected gradient method for least squares matrix approximations with spectral constraints. SIAM Journal of Numerical Analysis 27(4), 1050–1060.

    Google Scholar 

  • Dehaene, J. (1995), Continuous-time matrix algorithms systolic algorithms and adaptive neural networks. Ph.D. thesis, Faculteit Toegepaste Wetenschappen, Department Elektrotechniek-ESAT, Katholieke Universiteit Leuven.

  • Deift, P., Nanda, T. and Tomei, C., (1983). Ordinary differential equations for the symmetric eigenvalue problem. SIAM Journal of Numerical Analysis 20(1), 1–22.

    Google Scholar 

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

    Google Scholar 

  • do Carmo, M.P. (1992), Riemannian Geometry. Mathematics: Theory and Applications. Birkhäuser, Boston, MA.

    Google Scholar 

  • Edelman, A., Arias, T.A. and Smith, S.T. (1998), The geometry of algorithms with orthogonality constraints. SIAM Journal on Matrix Analysis and Applications 20(2), 303–353.

    Google Scholar 

  • Flashka, H. (1974), The Toda lattice, II. Existence of integrals. Physical Review b 9(4), 1924–1925.

    Google Scholar 

  • Fleming, W. (1977), Functions of Several Variables. Undergraduate Texts in Mathematics, Springer, NY.

    Google Scholar 

  • Fletcher, R. (1996), Practical Methods of Optimization, 2nd edn. Wiley, Oxford.

    Google Scholar 

  • Gabay, D. (1982), Minimizing a differentiable function over a differentiable manifold, Journal of Optimization Theory and Applications 37(2), 177–219.

    Google Scholar 

  • Gevers, M.R. and Li, G. (1993), Parametrizations in control, estimation and filtering problems: accuracy aspects. In: Communications in Control Engineering. Springer, NY.

    Google Scholar 

  • Helgason, S. (1978), Differential Geometry, Lie Groups and Symmetric Spaces. Academic Press, NY.

    Google Scholar 

  • Helmke, U. and Moore, J.B. (1994), Optimization and dynamical systems. In: Communications and Control Engineering. Springer, London.

    Google Scholar 

  • Liu, W.Q., Yan, W.Y., Sreeram, V. and Teo, K.L. (1996), Gradient flow approach to LQ cost improvement for simultaneous stabilization problem, Optimal Control Applications and Methods 17, 367–375.

    Google Scholar 

  • Madievski, A.G., Anderson, B.D.O. and Gevers, M.R. (1994), Optimum realizations of sampled data controllers for FWL sensitivity minimization, Automatica 31(3), 367–379.

    Google Scholar 

  • Mahony, R.E. (1994), Optimization algorithms on homogeneous spaces: with applications in linear systems theory. Ph.D. thesis, Department of Systems Engineering, Canberra, Australia.

    Google Scholar 

  • Mahony, R.E. (1996), The constrained Newton method on a Lie-group and the symmetric eivenvalue problem. Linear Algebra and its Applications 248, 67–89.

    Google Scholar 

  • Manton, J. (2001), Optimisation algorithms exploiting unitary constraints, IEEE Transactions on Signal Processing. Accepted for publication.

  • Moore, J.B., Mahony, R.E. and Helmke, U. (1994), Numerical gradient algorithms for eigenvalue and singular value calculations. SIAM Journal of Matrix Analysis 15(3), 881–902.

    Google Scholar 

  • Nomizu (1954), Invariant affine connections on homogeneous spaces. American Journal of Mathematics 76, 33–65.

    Google Scholar 

  • Owren, B. and Welfert, B. (2000), The Newton method on Lie groups. BIT 40(1), 121–145.

    Google Scholar 

  • Perkins, J.E., Helmke, U. and Moore, J.B. (1990), Balanced realizations via gradient flow techniques Systems and Control Letters 14, 369–380.

    Google Scholar 

  • Smith, S. (1994), Optimization techniques on Riemannian manifolds. Fields Institute Communications 3, 113–136. Proceedings Fields Inst. Workshop on Hamiltonian and Gradient Flows, Algorithms and Control.

    Google Scholar 

  • Smith, S.T. (1993), Geometric optimization methods for adaptive filtering. Ph.D. thesis, Division of Applied Science.

  • Tseng, C.H., Teo, K.L., Cantoni, A. and Zang, Z. (1998), Iterative methods for optimal envelope-constrained filter design using gradient flow approach. Proceedings of Optimization Techniques and Applications 1, 534–541.

    Google Scholar 

  • Udriste, C. (1994), Convex Functions and Optimization Methods on Riemannian Manifolds. Kluwer Academic Publishers, Dordrecht.

    Google Scholar 

  • Varadarajan, V.S. (1984), Lie groups, Lie algebras and their representations. In: Graduate Texts in Mathematics. Springer, NY.

    Google Scholar 

  • Warner, F.W. (1983), Foundations of differentiable manifolds and Lie groups. Graduate Texts in Mathematics. Springer, NY.

    Google Scholar 

  • Watkins, D.S. and Elsner, L. (1988), Self similar flows. Linear Algebra and its Applications 110, 213–242.

    Google Scholar 

  • Yan, W.-Y., Helmke, U. and Moore, J.B. (1994), Global analysis of Oja's flow for a neural networks. IEEE Transactions on Neural Networks, 5(5).

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Mahony, R., Manton, J.H. The Geometry of the Newton Method on Non-Compact Lie Groups. Journal of Global Optimization 23, 309–327 (2002). https://doi.org/10.1023/A:1016586831090

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1016586831090

Keywords

Navigation