Opuscula Mathematica

On the rate of asymptotic regularity of iterative methods for nonexpansive mappings in CAT(0) spaces and hyperbolic optimization

Vittorio Colao
Katherine Rossella Foglia

Abstract. The Krasnosel'skiĭ-Mann and Halpern iterations are classical schemes for approximating fixed points of nonexpansive mappings in Banach spaces, and have been widely studied in more general frameworks such as \(CAT(\kappa)\) and, more generally, geodesic spaces. Convergence results and convergence rate estimates in these nonlinear settings are already well established. The contribution of this paper is twofold: first, we extend to complete \(CAT(0)\) spaces proof techniques originally developed in the linear setting of Banach and Hilbert spaces, thereby recovering the same asymptotic regularity bounds; second, we introduce a Halpern-type optimizer for hyperbolic optimization as a nonlinear counterpart of the Euclidean HalpernSGD scheme.

Keywords: geodesic spaces, Hadamard spaces, metric fixed point theory, optimization, hyperbolic deep learning, Halpern iterates, Krasnosel'skiĭ-Mann iterates.

Mathematics Subject Classification: 47H10, 46N10, 53C25, 58C30.

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  1. M. Bačák, Convex Analysis and Optimization in Hadamard Spaces, Walter de Gruyter, Berlin–Boston, 2014.
  2. J. Baillon, G. Haddad, Quelques propriétés des opérateurs angle-bornés et \(n\)-cycliquement monotones, Israel J. Math. 26 (1977), 137-150. https://doi.org/10.1007/bf03007664
  3. J. Baillon, R.E. Bruck, The rate of asymptotic regularity is \(\mathcal{O}(1/\sqrt{n})\), [in:] A.G. Kartsatos (ed.), Theory and Applications of Nonlinear Operators of Accretive and Monotone Type, Lecture Notes in Pure and Appl. Math. 178, Dekker, New York, 1996, 51-81.
  4. H.H. Bauschke, P.L. Combettes, The Baillon-Haddad Theorem Revisited, J. Convex Anal. 17 (2010), 781-787.
  5. S. Bonnabel, Stochastic gradient descent on Riemannian manifolds, IEEE Trans. Automat. Control 58 (2013), 2217-2229. https://doi.org/10.1109/tac.2013.2254619
  6. R.I. Boţ, D. Nguyen, Fast Krasnosel'skiĭ-Mann algorithm with a convergence rate of the fixed point iteration of \(o(1/k)\), arXiv:2206.09462, 2022.
  7. M. Bravo, R. Cominetti, Sharp convergence rates for averaged nonexpansive maps, Israel J. Math. 227 (2018), 163-188. https://doi.org/10.1007/s11856-018-1723-z
  8. M.R. Bridson, A. Haefliger, Metric Spaces of Non-Positive Curvature, Springer, Berlin, 2013.
  9. M.M. Bronstein, J. Bruna, Y. LeCun, A. Szlam, P. Vandergheynst, Geometric deep learning: going beyond Euclidean data, IEEE Signal Process. Mag. 34 (2017), 18-42. https://doi.org/10.1109/msp.2017.2693418
  10. F.E. Browder, Nonexpansive nonlinear operators in a Banach space, Proc. Nat. Acad. Sci. U.S.A. 54 (1965), 1041-1044. https://doi.org/10.1073/pnas.54.4.1041
  11. V. Colao, K.R. Foglia, On the Convergence of HalpernSGD, arXiv:2601.18906, 2026.
  12. V. Colao, K.R. Foglia, A. Giordano, E. Ritacco, W. Spataro, An optimizer derived from Halpern's method for enhanced neural network convergence and reduced carbon emissions, J. Intell. Inf. Syst. 64 (2026), 77-96. https://doi.org/10.1007/s10844-025-00969-x
  13. R. Cominetti, J.A. Soto, J. Vaisman, On the rate of convergence of Krasnosel'skiĭ-Mann iterations and their connection with sums of Bernoullis, Israel J. Math. 199 (2014), 757-772. https://doi.org/10.1007/s11856-013-0045-4
  14. J.P. Contreras, R. Cominetti, Optimal error bounds for non-expansive fixed-point iterations in normed spaces, Math. Program. 199 (2023), 343-374. https://doi.org/10.1007/s10107-022-01830-7
  15. S. Dhompongsa, W.A. Kirk, B. Sims, Fixed points of uniformly Lipschitzian mappings, Nonlinear Anal. 65 (2006), 762-772. https://doi.org/10.1016/j.na.2005.09.044
  16. S. Dhompongsa, B. Panyanak, On \(\Delta\)-convergence theorems in CAT(0) spaces, Comput. Math. Appl. 56 (2008), 2572-2579. https://doi.org/10.1016/j.camwa.2008.05.036
  17. R. Espínola, A. Fernández-León, CAT(k)-spaces, weak convergence and fixed points, J. Math. Anal. Appl. 353 (2009), 410-427. https://doi.org/10.1016/j.jmaa.2008.12.015
  18. K.R. Foglia, V. Colao, E. Ritacco, HalpernSGD: A Halpern-Inspired Optimizer for Accelerated Neural Network Convergence and Reduced Carbon Footprint, [in:] A. Appice, H. Azzag, M.S. Hacid, A. Hadjali, Z.W. Ras (eds.), Foundations of Intelligent Systems, Lecture Notes in Comput. Sci. 14670, Springer, Cham, 2024, 296-305. https://doi.org/10.1007/978-3-031-62700-2_26
  19. O.-E. Ganea, G. Bécigneul, T. Hofmann, Hyperbolic neural networks, Adv. Neural Inf. Process. Syst. 31 (2018), 5345-5355.
  20. B. Halpern, Fixed points of nonexpanding maps, Bull. Amer. Math. Soc. 73 (1967), 957-961. https://doi.org/10.1090/s0002-9904-1967-11864-0
  21. J. He, D. Fang, G. L{ó}pez, C. Li, Mann's algorithm for nonexpansive mappings in CAT(\(\kappa\)) spaces, Nonlinear Anal. 75 (2012), 445-452. https://doi.org/10.1016/j.na.2011.07.070
  22. S. Ishikawa, Fixed points and iteration of a nonexpansive mapping in a Banach space, Proc. Amer. Math. Soc. 59 (1976), 65-71. https://doi.org/10.1090/s0002-9939-1976-0412909-x
  23. W.A. Kirk, Geodesic geometry and fixed point theory, [in:] Seminar of Mathematical Analysis (Málaga/Sevilla, 2002/2003), Colecc. Abierta 64, Univ. Sevilla Secr. Publ., Sevilla, 2003, 195-225.
  24. W.A. Kirk, Geodesic geometry and fixed point theory II, [in:] International Conference on Fixed Point Theory and Applications, Yokohama Publ., Yokohama, 2004, 113-142.
  25. W.A. Kirk, B. Panyanak, A concept of convergence in geodesic spaces, Nonlinear Anal. 68 (2008), 3689-3696. https://doi.org/10.1016/j.na.2007.04.011
  26. L. Leuştean, P. Pinto, Quantitative results on a Halpern-type proximal point algorithm, Comput. Optim. Appl. 79 (2021), no. 1, 101-125. https://doi.org/10.1007/s10589-021-00263-w
  27. L. Leuştean, P. Pinto, Rates of asymptotic regularity for the alternating Halpern-Mann iteration, J. Math. Anal. Appl. 515 (2022), 126477.
  28. F. Lieder, On the convergence rate of the Halpern-iteration, Optim. Lett. 15 (2021), 405-418. https://doi.org/10.1007/s11590-020-01617-9
  29. T.C. Lim, Remarks on some fixed point theorems, Proc. Amer. Math. Soc. 60 (1976), 179-182. https://doi.org/10.1090/s0002-9939-1976-0423139-x
  30. W.R. Mann, Mean value methods in iteration, Proc. Amer. Math. Soc. 4 (1953), 506-510. https://doi.org/10.1090/s0002-9939-1953-0054846-3
  31. W. Peng, T. Varanka, A. Mostafa, H. Shi, G. Zhao, Hyperbolic deep neural networks: a survey, IEEE Trans. Pattern Anal. Mach. Intell. 44 (2022), no. 12, 10023-10044. https://doi.org/10.1109/tpami.2021.3136921
  32. P. Pinto, N. Pischke, On the Halpern method with adaptive anchoring parameters, Math. Comp., published electronically in 2026, DOI: 10.1090/mcom/4182. https://doi.org/10.1090/mcom/4182
  33. R.T. Rockafellar, Monotone operators associated with saddle-functions and minimax problems, [in:] F.E. Browder (ed.), Nonlinear Functional Analysis, Part 1, Proc. Sympos. Pure Math. 18, Amer. Math. Soc., Providence, RI, 1970, 241-250. https://doi.org/10.1090/pspum/018.1/0285942
  34. R.T. Rockafellar, Monotone operators and the proximal point algorithm, SIAM J. Control Optim. 14 (1976), 877-898. https://doi.org/10.1137/0314056
  35. S. Sabach, S. Shtern, A first order method for solving convex bilevel optimization problems, SIAM J. Optim. 27 (2017), 640-660. https://doi.org/10.1137/16m105592x
  36. S. Saejung, Halpern's iteration in CAT(0) spaces, Fixed Point Theory Appl. 2010 (2009), 1-13. https://doi.org/10.1155/2010/471781
  37. F. Sala, C. De Sa, A. Gu, C. Ré, Representation tradeoffs for hyperbolic embeddings, [in:] J. Dy, A. Krause (eds.), Proceedings of the 35th International Conference on Machine Learning, Proc. Mach. Learn. Res. 80, PMLR, 2018, 4460-4469.
  38. A. Sipoş, Abstract strongly convergent variants of the proximal point algorithm, Comput. Optim. Appl. 83 (2022), no. 1, 349-380. https://doi.org/10.1007/s10589-022-00397-5
  39. C. Udrişte, Convex Functions and Optimization Methods on Riemannian Manifolds, Math. Appl., Kluwer Acad. Publ., Dordrecht, 1994. https://doi.org/10.1007/978-94-015-8390-9_3
  40. R. Wittmann, Approximation of fixed points of nonexpansive mappings, Arch. Math. (Basel) 58 (1992), 486-491. https://doi.org/10.1007/bf01190119
  41. H.-K. Xu, Viscosity approximation methods for nonexpansive mappings, J. Math. Anal. Appl. 298 (2004), 279-291. https://doi.org/10.1016/j.jmaa.2004.04.059
  • Vittorio Colao
  • ORCID iD https://orcid.org/0000-0003-0743-0137
  • University of Calabria, Department of Mathematics and Computer Science, Ponte P. Bucci, 30B, Arcavacata di Rende (CS), 87036, Italy
  • Katherine Rossella Foglia (corresponding author)
  • ORCID iD https://orcid.org/0009-0009-0192-2684
  • University of Calabria, Department of Mathematics and Computer Science, Ponte P. Bucci, 30B, Arcavacata di Rende (CS), 87036, Italy
  • Communicated by Marek Galewski.
  • Received: 2025-10-31.
  • Revised: 2026-03-25.
  • Accepted: 2026-05-08.
  • Published online: 2026-06-23.
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