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An inertial proximal point method for difference of maximal monotone vector fields in Hadamard manifolds

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Abstract

We propose an inertial proximal point method for variational inclusion involving difference of two maximal monotone vector fields in Hadamard manifolds. We prove that if the sequence generated by the method is bounded, then every cluster point is a solution of the non-monotone variational inclusion. Some sufficient conditions for boundedness and full convergence of the sequence are presented. The efficiency of the method is verified by numerical experiments comparing its performance with classical versions of the method for monotone and non-monotone problems.

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References

  1. Ansari, Q.H., Babu, F.: Existence and boundedness of solutions to inclusion problems for maximal monotone vector fields in Hadamard manifolds. Optim. Lett. 14(3), 711–727 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  2. Ansari, Q.H., Babu, F., Yao, J.-C.: Inexact proximal point algorithms for inclusion problems on Hadamard manifolds. J. Nonlinear Convex Anal. 21(10), 2417–2432 (2020)

    MathSciNet  MATH  Google Scholar 

  3. Ansari, Q.H., Babu, F.: Proximal point algorithm for inclusion problems in Hadamard manifolds with applications. Optim. Lett. 15(3), 901–921 (2021)

    Article  MathSciNet  MATH  Google Scholar 

  4. Martinet, B.: Regularisation d’inéquations variationelles par approximations succesives. Rev. Française d’Informatique. et de Rech. Oper. 4, 154–159 (1970)

    MATH  Google Scholar 

  5. Li, C., López, G., Martín-Márquez, V.: Monotone vector fields and the proximal point algorithm on Hadamard manifolds. J. Lond. Math. Soc. 79, 663–683 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  6. Ferreira, O.P., Oliveira, P.R.: Proximal point algorithm on Riemannian manifolds. Optimization 51, 257–270 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  7. Moreau, J.J.: Proximité et dualité dans un espace Hilbertien. Bull. Soc. Math. Fr. 93, 273–299 (1965)

    Article  MATH  Google Scholar 

  8. Douglas, J., Rachford, H.H.: On the numerical solution of heat conduction problems in two and three space variables. Trans. Am. Math. Soc. 82, 421–439 (1956)

    Article  MathSciNet  MATH  Google Scholar 

  9. Moudafi, A.: On the difference of two maximal monotone operators: regularization and algorithmic approach. Appl. Math. Comput. 202, 446–452 (2008)

    MathSciNet  MATH  Google Scholar 

  10. Alimohammady, M., Ramazannejad, M., Roohi, M.: Notes on the difference of two monotone operators. Optim. Lett. 8(1), 81–84 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  11. Alimohammady, M., Ramazannejad, M.: Inertial proximal algorithm for difference of two maximal monotone operators. Indian J. Pure Appl. Math. 47(1), 1–8 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  12. Moudafi, A.: On critical points of the difference of two maximal monotone operators. Afr. Mat. 26(3), 457–463 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  13. Noor, M.A., Noor, K.I., Hamdi, A., El-Shemas, E.H.: On difference of two monotone operators. Optim. Lett. 3, 329–335 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  14. Souza, J.C.O., Oliveira, P.R.: A proximal point method for DC functions on Hadamard manifolds. J. Glob. Optim. 63, 797–810 (2015)

    Article  MATH  Google Scholar 

  15. Polyak, B.T.: Some methods of speeding up the convergence of iteration methods. U.S.S.R. Comput. Math. Math. Phys. 4(5), 1–17 (1964)

    Article  Google Scholar 

  16. Alvarez, F., Attouch, H.: An inertial proximal method for maximal monotone operators via discretization of a nonlinear oscillator with damping. Set-Valued Anal. 9(1–2), 3–11 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  17. Maingé, P.E., Moudafi, A.: Convergence of new inertial proximal methods for DC programming. SIAM J. Optim. 19, 397–413 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  18. Oliveira, W., Tcheou, M.: Level an inertial algorithm for DC programming. Set-Valued Var. Anal. 27, 895–919 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  19. Attouch, H., Théra, M.: A general duality principle for the sum of two operators. J. Convex Anal. 3, 1–24 (1996)

    MathSciNet  MATH  Google Scholar 

  20. Sakai, T.: Riemannian geometry. Translations of mathematical monographs. American Mathematical Society, Providence (1996)

  21. Udriste, C.: Convex functions and optimization algorithms on Riemannian manifolds. Mathematics and its applications, Kluwer Academic, Dordrecht (1994)

    Book  MATH  Google Scholar 

  22. do Carmo, M.P.: Riemannian geometry. Birkhauser, Boston (1992)

    Book  MATH  Google Scholar 

  23. Almeida, Y.T., Cruz Neto, J.X., Oliveira, P.R., Souza, J.C.O.: A modified proximal point method for DC functions on Hadamard manifolds. Comput. Optim. Appl. 76, 649–673 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  24. Li, C., López, G., Martín-Márquez, V., Wang, J.H.: Resolvents of set valued monotone vector fields in Hadamard manifolds. Set-Valued Anal. 19, 361–383 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  25. Polyak, B.T.: Introduction to optimization. Optimization Software Inc., New York (1987)

    MATH  Google Scholar 

  26. Aragon Artacho, F.J., Vuong, P.T.: The boosted difference of convex functions algorithm for non-smooth functions. SIAM J. Optim. 30, 980–1006 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  27. Cruz Neto, J.X., Oliveira, P.R., Soubeyran, A., Souza, J.C.O.: A generalized proximal linearized algorithm for DC functions with application to the optimal size of the firm problem. Ann. Oper. Res. 289, 313–339 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  28. Ferreira, O.P., Santos, E.M., Souza, J.C.O.: Boosted scaled subgradient method for DC programming. arXiv:2103.10757 (2021)

  29. Le Thi, H.A., Huynh, V.N., Dinh, T.P.: Convergence analysis of difference-of-convex algorithm with sub-analytic data. J. Optim. Theory Appl. 179, 103–126 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  30. Bento, G.C., Ferreira, O.P., Oliveira, P.R.: Proximal point method for a special class of non-convex functions on Hadamard manifolds. Optimization 64(2), 289–319 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  31. Rothaus, O.S.: Domains of positivity. Abh. Math. Sem. Univ. Hambg. 24, 189–235 (1960)

    Article  MathSciNet  MATH  Google Scholar 

  32. Nesterov, Y.E., Todd, M.J.: On the Riemannian geometry defined by self-concordant barriers and interior-point methods. Found. Comput. Math. 2, 333–361 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  33. Lang, S.: Fundamentals of differential geometry. Volume 191 of graduate texts in mathematics. Springer, New York, (1999)

  34. Lenglet, C., Rousson, M., Deriche, R., Faugeras, O.: Statistics on the manifold of multivariate normal distributions: theory and application to diffusion tensor MRI processing. J. Math. Imaging Vis. 25, 423–444 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  35. Bhatia, R.: Positive definite matrices, vol. 24. Princeton University Press, Princeton (2009)

    Book  MATH  Google Scholar 

  36. Boumal, N., Mishira, B., Absil, P.-A., Sepulchre, R.: Manopt, a Matlab toolbox for optimization on manifolds. J. Mach. Learn. Res. 15, 1455–1459 (2014) http://www.manopt.org

  37. Rockafellar, R.T.: Monotone operators and the proximal point algorithm. SIAM J. Control Optim. 14, 877–898 (1976)

    Article  MathSciNet  MATH  Google Scholar 

  38. Rockafellar, R.: Characterization of the subdifferentials of convex functions. Pac. J. Math. 17(3), 497–510 (1966)

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgements

We would like to thank the referees for their constructive remarks which allow us to improve our work. J.C.O. Souza was supported in part by CNPq Grants 424169/2018-5 and 313901/2020-1. The project leading to this publication has received funding from the French government under the “France 2030” investment plan managed by the French National Research Agency (reference: ANR-17-EURE-0020) and from Excellence Initiative of Aix-Marseille University - A*MIDEX.

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Correspondence to João Carlos de O. Souza.

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Andrade, J.S., Lopes, J.d.O. & Souza, J.C.d.O. An inertial proximal point method for difference of maximal monotone vector fields in Hadamard manifolds. J Glob Optim 85, 941–968 (2023). https://doi.org/10.1007/s10898-022-01240-1

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