Abstract
In this paper we introduce a Fenchel-type conjugate, given as the supremum of convex functions, via Busemann functions. It is known that Busemann functions are smooth convex functions with constant norm gradient. Our study ensures that our proposal on Fenchel conjugate is the most adequate to cover the absence of approximations by non-constant affine functions on Hadamard manifolds. More precisely, as a first contribution of the paper we prove that any affine function is constant in a complete and connected Riemannian manifold of nonpositive Ricci curvature on some open set. Moreover, we show the influence of the sectional curvature in obtaining the main results. In particular, we illustrate that the difference between a proper, lsc, convex function and its biconjugate is a constant that depends on the sectional curvature of the manifold, which show that a Fenchel-Moreau-type theorem is directly influenced by the sectional curvature in general. We also present some applications formulated in terms of the Fenchel conjugate.
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References
Afsari, B.: Riemannian \(l^p\) center of mass: existence, uniqueness, and convexity. Proc. Am. Math. Soc. 139(2), 655–673 (2011)
Afsari, B., Tron, R., Vidal, R.: On the convergence of gradient descent for finding the Riemannian center of mass. SIAM J. Control Optim. 51(3), 2230–2260 (2013)
Alvarez, F., Bolte, J., Brahic, O.: Hessian Riemannian gradient flows in convex programming. SIAM J. Control Optim. 43(2), 477–501 (2004)
Alvarez, F., Bolte, J., Munier, J.: A unifying local convergence result for Newton’s method in Riemannian manifolds. FoCM 8(2), 197–226 (2008)
Ballmann, W., Gromov, M., Schroeder, V.: Manifolds of nonpositive curvature, vol. 61. Springer Science & Business Media, Berlin (2013)
Batista, E., Bento, G.C., Ferreira, O.P.: An extragradient-type algorithm for variational inequality on Hadamard manifolds. ESAIM CONTR OPTIM CA. https://doi.org/10.1051/cocv/2019040 (2020)
Bauschke, H.H., Combettes, P.L., et al.: Convex analysis and monotone operator theory in hilbert spaces, vol. 408. Springer, New York (2011)
Bento, G.C., Cruz Neto, J.X., Melo, Í.D.L.: Combinatorial convexity in Hadamard manifolds: existence for equilibrium problems. J. Optim Theory Appl. 195, 1087–1105 (2022)
Bergmann, R., Herzog, R.: Intrinsic formulation of KKT conditions and constraint qualifications on smooth manifolds. SIAM J. Optim. 29(4), 2423–2444 (2019)
Bergmann, R., Herzog, R., Silva Louzeiro, M., Tenbrinck, D., Vidal-Núñez, J.: Fenchel duality theory and a primal-dual algorithm on Riemannian manifolds. FoCM 21(6), 1465–1504 (2021)
Bergmann, R., Persch, J., Steidl, G.: A parallel Douglas–Rachford algorithm for minimizing rof-like functionals on images with values in symmetric Hadamard manifolds. SIAM J. Imaging Sci. 9(3), 901–937 (2016)
Besse, A.L.: Einstein Manifolds. Springer Science & Business Media (2007)
Bot, R.I.: Conjugate Duality in Convex Optimization, vol. 637. Springer Science & Business Media (2009)
Bridson, M.R., Haefliger, A.: Metric Spaces of Non-Positive Curvature, vol. 319. Springer Science & Business Media (2013)
Busemann, H.: The Geometry of Geodesics. Acad Press, New York (1955)
Busemann, H., Phadke, B.: Novel results in the geometry of geodesics. Adv. Math. 101(2), 180–219 (1993)
Carmo, M.P.d.: Riemannian Geometry. Birkhäuser, Basel (1992)
Bento, G.C., Bitar, S.D.B., Cruz Neto, J.X., Oliveira, P.R., Souza, J.C.O.: Computing Riemannian center of mass on Hadamard manifolds. J. Optim Theory Appl. 183(3), 977–992 (2019)
Combettes, P.L., Hirstoaga, S.A., et al.: Equilibrium programming in Hilbert spaces. J. Nonlinear Convex Anal. 6(1), 117–136 (2005)
Cruz Neto, J.X., Melo, I.D.L., Sousa, P.A., Silva, J.: A note on the paper proximal point methods for quasiconvex and convex functions with Bregman distances on Hadamard manifolds. J. Convex Anal. 24(2), 679–684 (2017)
Eckstein, J., Bertsekas, D.P.: On the Douglas-Rachford splitting method and the proximal point algorithm for maximal monotone operators. Math. Program. 55(1), 293–318 (1992)
Esser, E., Zhang, X., Chan, T.F.: A general framework for a class of first order primal-dual algorithms for convex optimization in imaging science. SIAM J. Imaging Sci. 3(4), 1015–1046 (2010)
Fenchel, W.: On conjugate convex functions. Can. J. Math. 1(1), 73–77 (1949)
Ferreira, O.P., Louzeiro, M.S., Prudente, L.: Gradient method for optimization on Riemannian manifolds with lower bounded curvature. SIAM J. Optim. 29(4), 2517–2541 (2019)
Giselsson, P., Boyd, S.: Linear convergence and metric selection for Douglas–Rachford splitting and admm. IEEE Trans. Automat. Contr. 62(2), 532–544 (2016)
Gutman, D.H., Ho-Nguyen, N.: Coordinate descent without coordinates: Tangent subspace descent on Riemannian manifolds. Math. Oper. Res. (2022)
Hosseini, R., Sra, S.: An alternative to em for gaussian mixture models: batch and stochastic Riemannian optimization. Math Program. 181(1), 187–223 (2020)
Hoseini Monjezi, N., Nobakhtian, S., Pouryayevali, M.R.: A proximal bundle algorithm for nonsmooth optimization on Riemannian manifolds. IMA J. Numer. 3(1), 293–325 (2023)
Innami, N.: Splitting theorems of Riemannian manifolds. Compos. Math. 47(3), 237–247 (1982)
Iusem, A.N., Svaiter, B., Cruz Neto, J.X.: Central paths, generalized proximal point methods, and Cauchy trajectories in Riemannian manifolds. SIAM J. Control Optim. 37(2), 566–588 (1999)
Kakavandi, B.A., Amini, M.: Duality and subdifferential for convex functions on complete CAT(0) metric spaces. Nonlinear Anal Theory Methods Appl. 73(10), 3450–3455 (2010)
Kristály, A., Li, C., López-Acedo, G., Nicolae, A.: What do ‘convexities’ imply on Hadamard manifolds? J. Optim. Theory Appl. 170(3), 1068–1074 (2016)
Lewis, A.S., Malick, J.: Alternating projections on manifolds. Math. Op. Res. 33(1), 216–234 (2008)
Li, C., Mordukhovich, B.S., Wang, J., Yao, J.C.: Weak sharp minima on Riemannian manifolds. SIAM J. Optim. 21(4), 1523–1560 (2011)
Li, P., Tam, L.F.: Positive harmonic functions on complete manifolds with non-negative curvature outside a compact set. Ann Math. 125(1), 171–207 (1987)
Liu, C., Boumal, N.: Simple algorithms for optimization on Riemannian manifolds with constraints. Appl Math Optim. 82(3), 949–981 (2020)
Martínez-Legaz, J.E.: Generalized convex duality and its economic applicatons. In: Nicolas Hadjisavvas, Sándor Komlósi, Siegfried Schaible. (eds.) Handbook of generalized convexity and generalized monotonicity, pp. 237–292. Springer, New York (2005)
Paternain, G.P.: Geodesic Flows, vol. 180. Springer Science & Business Media (2012)
Petersen, P.: Riemannian Geometry, vol. 171. Springer, New York (2006)
Rockafellar, R.T.: Conjugate Duality and Optimization, vol. 16. Society for Industrial and Applied Mathematics, Philadelphia (1974)
Sakai, T.: On Riemannian manifolds admitting a function whose gradient is of constant norm. Kodai Math. J. 19(1), 39–51 (1996)
Shiohama, K.: Busemann functions and total curvature. Invent. Math. 53(3), 281–297 (1979)
Louzeiro, M.S., Bergmann, R., Herzog, R.: Fenchel duality and a separation theorem on Hadamard manifolds. SIAM J. Optim. 32(2), 854–873 (2022)
Singer, I.: A general theory of dual optimization problems. J. Math. Anal. Appl. 116(1), 77–130 (1986)
Sormani, C.: Busemann functions on manifolds with lower bounds on Ricci curvature and minimal volume growth. J. Differ. 48(3), 557–585 (1998)
Tao, P.D., Souad, E.B.: Duality in dc (difference of convex functions) optimization. subgradient methods. Trends Math. Optim. 277–293 (1988)
Tao, P.D., et al.: Algorithms for solving a class of nonconvex optimization problems. methods of subgradients. North-Holland Math. Stud. vol. 129, pp. 249–271. Elsevier (1986)
Udriste, C.: Convex Functions and Optimization Methods on Riemannian Manifolds, vol. 297. Springer Science & Business Media (1994)
Wang, X., Li, C., Yao, J.C.: On some basic results related to affine functions on Riemannian manifolds. J. Optim. Theory Appl. 170(3), 783–803 (2016)
Wang, J., Wang, X., Li, C., Yao, J.C.: Convergence analysis of gradient algorithms on Riemannian manifolds without curvature constraints and application to Riemannian mass. SIAM J. Optim. 31(1), 172–199 (2021)
Zhang, H., Reddi, J., S., Sra, S,: Riemannian SVRG: Fast stochastic optimization on Riemannian manifolds. Adv. Neural Inf. Process Syst. (2016). https://doi.org/10.1016/j.genrep.2022.101717
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This work was funded by the CNPq Grants 314106/2020-0, 302156/2022-4, FAPEPI.
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Bento, G.d.C., Neto, J.C. & Melo, Í.D.L. Fenchel Conjugate via Busemann Function on Hadamard Manifolds. Appl Math Optim 88, 83 (2023). https://doi.org/10.1007/s00245-023-10060-y
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DOI: https://doi.org/10.1007/s00245-023-10060-y