Skip to main content
Log in

On manifolds of tensors of fixed TT-rank

  • Published:
Numerische Mathematik Aims and scope Submit manuscript

Abstract

Recently, the format of TT tensors (Hackbusch and Kühn in J Fourier Anal Appl 15:706–722, 2009; Oseledets in SIAM J Sci Comput 2009, submitted; Oseledets and Tyrtyshnikov in SIAM J Sci Comput 31:5, 2009; Oseledets and Tyrtyshnikov in Linear Algebra Appl 2009, submitted) has turned out to be a promising new format for the approximation of solutions of high dimensional problems. In this paper, we prove some new results for the TT representation of a tensor \({U \in \mathbb{R}^{n_1\times \cdots\times n_d}}\) and for the manifold of tensors of TT-rank \({\underline{r}}\) . As a first result, we prove that the TT (or compression) ranks r i of a tensor U are unique and equal to the respective separation ranks of U if the components of the TT decomposition are required to fulfil a certain maximal rank condition. We then show that the set \({\mathbb{T}}\) of TT tensors of fixed rank \({\underline{r}}\) locally forms an embedded manifold in \({\mathbb{R}^{n_1\times\cdots\times n_d}}\) , therefore preserving the essential theoretical properties of the Tucker format, but often showing an improved scaling behaviour. Extending a similar approach for matrices (Conte and Lubich in M2AN 44:759, 2010), we introduce certain gauge conditions to obtain a unique representation of the tangent space \({\mathcal{T}_U\mathbb{T}}\) of \({\mathbb{T}}\) and deduce a local parametrization of the TT manifold. The parametrisation of \({\mathcal{T}_{U}\mathbb{T}}\) is often crucial for an algorithmic treatment of high-dimensional time-dependent PDEs and minimisation problems (Lubich in From quantum to classical molecular dynamics: reduced methods and numerical analysis, 2008). We conclude with remarks on those applications and present some numerical examples.

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

  1. Beck M.H., Jäckle A., Worth G.A., Meyer H.D.: The multiconfiguration time dependent Hartree (MCTDH) method: a highly efficient algorithm for propagating wavepackets. Phys. Rep. 324, 1 (2000)

    Article  Google Scholar 

  2. Bellman R.E.: Adaptive Control Processes. Princeton University Press, Princeton (1961)

    MATH  Google Scholar 

  3. Beylkin G., Garcke J., Mohlenkamp M.J.: Multivariate regression and machine learning with sums of separable functions. SIAM J. Sci. Comput. 31(3), 1840 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  4. Conte D., Lubich C.: An error analysis of the multi-configuration time-dependent Hartree method of quantum dynamics. M2AN 44, 759 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  5. Crawford T.D., Schaeffer H.F. III: An introduction to coupled cluster theory for computational chemists. Rev. Comput. Chem. 14, 33 (2000)

    Article  Google Scholar 

  6. De Lathauwer L., De Moor B., Vandewalle J.: A multilinear singular value decomposition. SIAM J. Matrix Anal. Appl. 21(4), 1253–1278 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  7. De Lathauwer L., De Moor B., Vandewalle J.: On the best rank-1 and rank-(R 1, R 2, . . . , R N) approximation and applications of higher-order tensors. SIAM J. Matrix Anal. Appl. 21(4), 1324 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  8. de Silva V., Lim L.-H.: Tensor rank and the ill-posedness of the best low-rank approximation problem. SIAM J. Matrix Anal. Appl.: Special Issue on Tensor Decompositions and Applications 30(3), 1084–1127 (2008)

    Google Scholar 

  9. Eldén L., Savas B.: A Newton–Grassmann method for computing the best multi-linear rank-(r 1,r 2,r 3) approximation of a tensor. SIAM J. Matrix Anal. Appl. 31, 248 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  10. Espig, M.: Effziente Bestapproximation mittels Summen von Elementartensoren in hohen Dimensionen. Ph.D. thesis (2007)

  11. Espig, M., Hackbusch, W., Rohwedder, T., Schneider, R.: Variational calculus with sums of elementary tensors of fixed rank. Numer. Math. (submitted)

  12. Falcó, A., Hackbusch, W.: On minimal subspaces in tensor representations. Preprint, Max-Planck-Institut für Mathematik in den Naturwissenschaften, Leipzig. http://www.mis.mpg.de/publications/preprints/2010/prepr2010-70.html (2010)

  13. Fannes M., Nachtergaele B., Werner R.F.: Finitely correlated states on quantum spin chains. Commun. Math. Phys. 144, 443 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  14. Friedman J.H.: Multivariate adaptive regression splines (with discussion). Ann. Stat. 191, 1 (1991)

    Article  Google Scholar 

  15. Grasedyck L.: Hierarchical singular value decomposition of tensors. SIAM. J. Matrix Anal. Appl. 31, 2029 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  16. Hackbusch W., Kühn S.: A new scheme for the tensor representation. J. Fourier Anal. Appl. 15, 706–722 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  17. Hairer E., Lubich C., Wanner G.: Geometrical Numerical Integration—Structure-Preserving Algorithms for Ordinary Differential Equations, 2nd edn. Springer, Berlin (2006)

    Google Scholar 

  18. Helgaker T., J#x00F8;rgensen P., Olsen J.: Molecular Electronic-Structure Theory. Wiley, New York (2000)

    Google Scholar 

  19. Holtz, S., Rohwedder, T., Schneider, R.: The alternating linear scheme for tensor optimisation in the TT format. SISC (2010, submitted)

  20. Huckle, T., Waldherr, K., Schulte-Herbrüggen, T.: Computations in quantum tensor networks. Linear Algebra Appl.: Special Issue on Tensors. http://www5.in.tum.de/pub/CompQuantTensorNetwork.pdf (2010, submitted)

  21. Kapteyn A., Neudecker H., Wansbeek T.: An approach to n-mode components analysis. Psychometrika 51, 269 (1986)

    Article  MathSciNet  MATH  Google Scholar 

  22. Khoromskij B.N., Khoromskaia V.: Multigrid accelerated tensor approximation of function related multidimensional arrays. SIAM J. Sci. Comput. 31(4), 3002 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  23. Klümper A., Schadschneider A., Zittartz J.: Groundstate properties of a generalized VBS-model. Z. Phys. B: Condensed Matter 87, 281–287 (1992)

    Article  Google Scholar 

  24. Koch O., Lubich C.: Dynamical low rank approximation. SIAM J. Matrix Anal. Appl. 29(2), 434 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  25. Koch O., Lubich C.: Dynamical low-rank approximation of tensors. SIAM J. Matrix Anal. Appl. 31, 2360 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  26. Kolda T.G., Bader B.W.: Tensor decompositions and applications. SIAM Rev 51(3), 455–500 (2008)

    Article  MathSciNet  Google Scholar 

  27. Kroonenberg P.M., De Leeuw J.: Principal component analysis of three-mode data by means of alternating least squares algorithms. Psychometrika 45, 69 (1980)

    Article  MathSciNet  MATH  Google Scholar 

  28. Kunkel P., Mehrmann V.: Differential-Algebraic Equations Analysis and Numerical Solution. EMS Publishing House, Zurich (2006)

    Book  MATH  Google Scholar 

  29. Lang, S.: Fundamentals of Differential Geometry. Springer (2001)

  30. Lee, J.M.: Manifolds and differential geometry. In: Graduate Studies in Mathematics, vol. 107. AMS (2009)

  31. Lubich, C.: From quantum to classical molecular dynamics: reduced methods and numerical analysis. In: Zürich Lectures in Advanced Mathematics. EMS (2008)

  32. Marti K.H., Bauer B., Reiher M., Troyer M., Verstraete F.: Complete-graph tensor network states: a new fermionic wave function ansatz for molecules. New J. Phys. 12, 103008 (2010)

    Article  Google Scholar 

  33. Oseledets, I.: Compact matrix form of the d-dimensional tensor decomposition. SIAM J. Sci. Comput. (2009, submitted)

  34. Oseledets I.: On a new tensor decomposition. Doklady Math 80(1), 495–496 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  35. Oseledets, I.: Tensors inside matrices give logarithmic complexity. Preprint 2009-04, IMA RAS April 2009. SIAM J. Matrix Anal. Appl. (accepted)

  36. Oseledets, I.: TT Toolbox 1.0: Fast multidimensional array operations in MATLAB. Preprint 2009-06, INM RAS, August 2009

  37. Oseledets I., Tyrtyshnikov E.E.: Breaking the curse of dimensionality, or how to use SVD in many dimensions. SIAM J. Sci. Comput. 31, 5 (2009)

    Article  MathSciNet  Google Scholar 

  38. Oseledets, I.V., Tyrtyshnikov, E.E.: Tensor tree decomposition does not need a tree. Linear Algebra Appl. (2009, submitted)

  39. Savas, B., Lim, L.-H.: Quasi-Newton methods on Grassmannians and multilinear approximations of tensors. ARXIV, eprint arXiv:0907.2214, http://arxiv.org/abs/0907.2214, 2009. Also accepted in SIAM J. Sci. Comput. (2010)

  40. Schneider R., Rohwedder T., Blauert J., Neelov A.: Direct minimization for calculating invariant subspaces in density functional computations of the electronic structure. J. Comput. Math. 27, 360 (2009)

    MathSciNet  MATH  Google Scholar 

  41. Schollwöck U.: The density-matrix renormalization group. Rev. Mod. Phys. 77(1), 259 (2005)

    Article  Google Scholar 

  42. Szabo A., Ostlund N.S.: Modern Quantum Chemistry. Dover, New York (1992)

    Google Scholar 

  43. Tucker L.R.: Some mathematical notes on three-mode factor analysis. Psychometrica 31(3), 279–311 (1966)

    Article  Google Scholar 

  44. Van Loan, C.F.: Tensor network computations in quantum chemistry. http://www.cs.cornell.edu/cv/OtherPdf/ZeuthenCVL.pdf (2008)

  45. Vidal G.: Efficient classical simulation of slightly entangled quantum computation. Phys. Rev. Lett. 91(14), 147902 (2003)

    Article  Google Scholar 

  46. White S.: Density matrix formulation for quantum renormalization groups. Phys. Rev. Lett. 69, 2863 (1992)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Reinhold Schneider.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Holtz, S., Rohwedder, T. & Schneider, R. On manifolds of tensors of fixed TT-rank. Numer. Math. 120, 701–731 (2012). https://doi.org/10.1007/s00211-011-0419-7

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00211-011-0419-7

Mathematics Subject Classification (2010)

Navigation