Riemannian cubics are critical points of the total squared norm of the accelerations of the curves on Riemannian manifolds with given end-points and end-velocities. Riemannian cubic splines are $ C^2 $ track sums of Riemannian cubics. Determining Riemannian cubics with given boundary data is equivalent to solving fourth-order ordinary differential equations with boundary conditions, while finding Riemannian cubic splines is tantamount to solving boundary and interior value problems, which are both hard in practice. The present paper proposes Riemannian gradient-based methods to tackle the former problem by taking advantage of the variational principle and the shooting method. The core idea is to add a number of junctions and associated velocities between given boundary points and to adjust such junctions and associated velocities according to a variational principle. Based on the obtained Riemannian cubics, Riemannian cubic splines are determined by constructing piecewise Riemannian cubics of class $ C^2 $ at interior points. The effectiveness of the proposed method is assessed by numerical experiments on a unit sphere.
Citation: Erchuan Zhang, Simone Fiori. Determining Riemannian cubics and cubic splines with given boundary conditions[J]. Electronic Research Archive, 2025, 33(10): 6493-6513. doi: 10.3934/era.2025286
Riemannian cubics are critical points of the total squared norm of the accelerations of the curves on Riemannian manifolds with given end-points and end-velocities. Riemannian cubic splines are $ C^2 $ track sums of Riemannian cubics. Determining Riemannian cubics with given boundary data is equivalent to solving fourth-order ordinary differential equations with boundary conditions, while finding Riemannian cubic splines is tantamount to solving boundary and interior value problems, which are both hard in practice. The present paper proposes Riemannian gradient-based methods to tackle the former problem by taking advantage of the variational principle and the shooting method. The core idea is to add a number of junctions and associated velocities between given boundary points and to adjust such junctions and associated velocities according to a variational principle. Based on the obtained Riemannian cubics, Riemannian cubic splines are determined by constructing piecewise Riemannian cubics of class $ C^2 $ at interior points. The effectiveness of the proposed method is assessed by numerical experiments on a unit sphere.
| [1] | S. A. Gabriel, J. T. Kajiya, Spline interpolation in curved space, State of the Art in Image Synthesis, SIGGRAPH'85 Course Notes, 1–14, 1985. Available from: https://archive.computerhistory.org/resources/access/text/2023/05/102724881-05-11-acc.pdf?utm_source |
| [2] |
L. Noakes, G. Heinzinger, B. Paden, Cubic splines on curved spaces, IMA J. Math. Control Inf., 6 (1989), 465–473. https://doi.org/10.1093/imamci/6.4.465 doi: 10.1093/imamci/6.4.465
|
| [3] | D. C. Brody, D. D. Holm, D. M. Meier, Quantum splines, Phys. Rev. Lett., 109 (2012), 100501. https://doi.org/10.1103/PhysRevLett.109.100501 |
| [4] |
K. Kim, I. L. Dryden, H. Le, K. E. Severn, Smoothing splines on Riemannian manifolds, with applications to 3D shape space, J. R. Stat. Soc. Ser. B Stat. Methodol., 83 (2021), 108–132. https://doi.org/10.1111/rssb.12402 doi: 10.1111/rssb.12402
|
| [5] | N. Singh, F. Vialard, M. Niethammer, Splines for diffeomorphisms, Med. Image Anal., 25 (2015), 56–71. https://doi.org/10.1016/j.media.2015.04.012 |
| [6] |
P. T. Fletcher, Geodesic regression and the theory of least squares on Riemannian manifolds, Int. J. Comput. Vision, 105 (2013), 171–185. https://doi.org/10.1007/s11263-012-0591-y doi: 10.1007/s11263-012-0591-y
|
| [7] |
J. Aubray, F. Nicol, Polynomial regression on Lie groups and application to SE(3), Entropy, 26 (2004), 825. https://doi.org/10.3390/e26100825 doi: 10.3390/e26100825
|
| [8] |
M. Hanik, E. Nava-Yazdani, C. von Tycowicz, De Casteljau's algorithm in geometric data analysis: Theory and application, Comput. Aided Geom. Des., 110 (2024), 102288. https://doi.org/10.1016/j.cagd.2024.102288 doi: 10.1016/j.cagd.2024.102288
|
| [9] |
E. Zhang, L. Noakes, The cubic de Casteljau construction and Riemannian cubics, Comput. Aided Geom. Des., 75 (2019), 101789. https://doi.org/10.1016/j.cagd.2019.101789 doi: 10.1016/j.cagd.2019.101789
|
| [10] | J. Hinkle, P. Muralidharan, P. T. Fletcher, S. Joshi, Polynomial regression on Riemannian manifolds, in Computer Vision-ECCV 2012, 7574 (2012), 1–14. https://doi.org/10.1007/978-3-642-33712-3_1 |
| [11] |
C. Belta, V. Kumar, An SVD-based projection method for interpolation on SE(3), IEEE Trans. Rob. Autom., 18 (2002), 334–345. https://doi.org/10.1109/TRA.2002.1019463 doi: 10.1109/TRA.2002.1019463
|
| [12] |
P. S. V. S. S. Kumar, R. Padhi, Minimum jerk based guidance for autonomous soft-landing of quadrotor, IFAC-PapersOnLine, 57 (2024), 107–112. https://doi.org/10.1016/j.ifacol.2024.05.019 doi: 10.1016/j.ifacol.2024.05.019
|
| [13] |
S. Fiori, A coordinate-free variational approach to fourth-order dynamical systems on manifolds: A system and control theoretic viewpoint, Mathematics, 12 (2024), 428. https://doi.org/10.3390/math12030428 doi: 10.3390/math12030428
|
| [14] | M. Camarinha, F. Silva Leite, P. Crouch, Existence and uniqueness for Riemannian cubics with boundary conditions, in CONTROLO 2020, 695 (2020), 322–331. https://doi.org/10.1007/978-3-030-58653-9_31 |
| [15] |
M. Camarinha, F. Silva Leite, P. Crouch, Riemannian cubics close to geodesics at the boundaries, J. Geom. Mech., 14 (2022), 545–558. https://doi.org/10.3934/jgm.2022003 doi: 10.3934/jgm.2022003
|
| [16] |
R. Giambò, F. Giannoni, P. Piccione, Optimal control on Riemannian manifolds by interpolation, Math. Control Signals Syst., 16 (2004), 278–296. https://doi.org/10.1007/s00498-003-0139-3 doi: 10.1007/s00498-003-0139-3
|
| [17] | H. B. Keller, Numerical Methods for Two-Point Boundary-Value Problems, Dover Publications, 2018. |
| [18] |
L. Noakes, Approximating near-geodesic natural cubic splines, Commun. Math. Sci., 12 (2014), 1409–1425. https://doi.org/10.4310/CMS.2014.v12.n8.a2 doi: 10.4310/CMS.2014.v12.n8.a2
|
| [19] |
P. Balseiro, T. J. Stuchi, A. Cabrera, J. Koiller, About simple variational splines from the Hamiltonian viewpoint, J. Geom. Mech., 9 (2017), 257–290. https://doi.org/10.3934/jgm.2017011 doi: 10.3934/jgm.2017011
|
| [20] |
B. Heeren, M. Rumpf, B. Wirth, Variational time discretization of Riemannian splines, IMA J. Numer. Anal., 39 (2019), 61–104. https://doi.org/10.1093/imanum/drx077 doi: 10.1093/imanum/drx077
|
| [21] |
L. Machado, F. Silva Leite, K. Krakowski, Higher-order smoothing splines versus least squares problems on Riemannian manifolds, J. Dyn. Control Syst., 16 (2010), 121–148. https://doi.org/10.1007/s10883-010-9080-1 doi: 10.1007/s10883-010-9080-1
|
| [22] |
T. T. Tran, I. Adouani, C. Samir, Computing regularized splines in the Riemannian manifold of probability measures, Math. Modell. Numer. Anal., 59 (2025), 73–99. https://doi.org/10.1051/m2an/2024056 doi: 10.1051/m2an/2024056
|
| [23] | N. Boumal, B. Mishra, P. Absil, R. Sepulchre, Manopt, a Matlab toolbox for optimization on manifolds, J. Mach. Learn. Res., 15 (2014), 1455–1459, |
| [24] |
B. Afsari, R. Tron, R. Vidal, On the convergence of gradient descent for finding the Riemannian center of mass, SIAM J. Control Optim., 51 (2013), 2230–2260. https://doi.org/10.1137/12086282X doi: 10.1137/12086282X
|
| [25] |
C. Samir, P. Absil, A. Srivastava, E. Klassen, A gradient-descent method for curve fitting on Riemannian manifolds, Found. Comput. Math., 12 (2012), 49–73. https://doi.org/10.1007/s10208-011-9091-7 doi: 10.1007/s10208-011-9091-7
|
| [26] |
X. Zhu, A Riemannian conjugate gradient method for optimization on the Stiefel manifold, Comput. Optim. Appl., 67 (2017), 73–110. https://doi.org/10.1007/s10589-016-9883-4 doi: 10.1007/s10589-016-9883-4
|
| [27] |
H. Sato, Riemannian conjugate gradient methods: General framework and specific algorithms with convergence analyses, SIAM J. Optim., 32 (2022), 2690–2717. https://doi.org/10.1137/21M1464178 doi: 10.1137/21M1464178
|
| [28] | W. Huang, P. Absil, K. A. Gallivan, A Riemannian BFGS method for nonconvex optimization problems, in Numerical Mathematics and Advanced Applications ENUMATH 2015, 112 (2016), 627–634. https://doi.org/10.1007/978-3-319-39929-4_60 |
| [29] |
X. Yuan, W. Huang, P. Absil, K. A. Gallivan, A Riemannian limited-memory BFGS algorithm for computing the matrix geometric mean, Proc. Comput. Sci., 80 (2016), 2147–2157. https://doi.org/10.1016/j.procs.2016.05.534 doi: 10.1016/j.procs.2016.05.534
|
| [30] | M. P. Do Carmo, Riemannian Geometry, $1^{st}$ edition, Birkhäuser, 1992. |
| [31] |
P. Crouch, F. Silva Leite, The dynamic interpolation problem: on Riemannian manifolds, Lie groups, and symmetric spaces, J. Dyn. Control Syst., 1 (1995), 177–202. https://doi.org/10.1007/BF02254638 doi: 10.1007/BF02254638
|
| [32] |
S. Fiori, Manifold calculus in system theory and control-Fundamentals and first-order systems, Symmetry, 13 (2021), 2092. https://doi.org/10.3390/sym13112092 doi: 10.3390/sym13112092
|
| [33] |
S. Fiori, Manifold calculus in system theory and control-Scond order structures and systems, Symmetry, 14 (2022), 1144. https://doi.org/10.3390/sym14061144 doi: 10.3390/sym14061144
|