Skip to main content

Advertisement

Log in

Pareto solutions as limits of collective traps: an inexact multiobjective proximal point algorithm

  • S.I. : CLAIO 2018
  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

In this paper we introduce a definition of approximate Pareto efficient solution as well as a necessary condition for such solutions in the multiobjective setting on Riemannian manifolds. We also propose an inexact proximal point method for nonsmooth multiobjective optimization in the Riemannian context by using the notion of approximate solution. The main convergence result ensures that each cluster point (if any) of any sequence generated by the method is a Pareto critical point. Furthermore, when the problem is convex on a Hadamard manifold, full convergence of the method for a weak Pareto efficient solution is obtained. As an application, we show how a Pareto critical point can be reached as a limit of traps in the context of the variational rationality approach of stay and change human dynamics.

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

  • Apolinário, H. C. F., Papa Quiroz, E. A., & Oliveira, P. R. (2016). A scalarization proximal point method for quasiconvex multiobjective minimization. Journal of Global Optimization, 64, 79–96.

    Article  Google Scholar 

  • Auslender, A., & Teboulle, M. (2006). Interior gradient and proximal methods for convex and conic optimization. SIAM Journal on Optimization, 16(3), 697–725.

    Article  Google Scholar 

  • Bento, G. C., Ferreira, O. P., & Oliveira, P. R. (2012). Unconstrained steepest descent method for multicriteria optimization on Riemannian manifolds. Journal of Optimization Theory and Applications, 154(1), 88–107.

    Article  Google Scholar 

  • Bento, G. C., & Cruz Neto, J. X. (2013). A subgradient method for multiobjective optimization on Riemannian manifolds. Journal of Optimization Theory and Applications, 159(1), 125–137.

    Article  Google Scholar 

  • Bento, G. C., Cruz Neto, J. X., & Soubeyran, A. (2014). A proximal point-type method for multicriteria optimization. Set-Valued and Variational Analysis, 22, 557–573.

    Article  Google Scholar 

  • Bento, G. C., Cruz Neto, J. X., López, G., Soubeyran, A., & Souza, J. C. O. (2018). The proximal point method for locally Lipschitz functions in multiobjective optimization with application to the compromise problem. SIAM Journal on Optimization, 28(2), 1104–1120.

    Article  Google Scholar 

  • Bento, G., Cruz Neto, J. X., & Meireles, L. V. (2018). Proximal point method for locally Lipschitz functions in multiobjective optimization of Hadamard manifolds. Journal of Optimization Theory and Applications, 179(1), 37–52.

    Article  Google Scholar 

  • Bento, G. C., Neto, J. C., Soares, P. A., & Soubeyran, A. (2021). A new regularization of equilibrium problems on Hadamard manifolds: Applications to theories of desires. Annals of Operations Research. https://doi.org/10.1007/s10479-021-04052-w.

    Article  Google Scholar 

  • Bento, G. C., Cruz Neto, J. X., Soubeyran, A., & Sousa Júnior, V. L. (2018). Inexact multi-objective local search proximal algorithms: Application to group dynamic and distributive justice problems. Journal of Optimization Theory and Applications, 177(1), 181–200.

    Article  Google Scholar 

  • Bento, G. C., Ferreira, O. P., & Pereira, Y. R. L. (2018). Proximal point method for vector optimization on Hadamard manifolds. Operations Research Letters, 46(1), 13–18.

    Article  Google Scholar 

  • Bonnel, H., Iusem, A. N., & Svaiter, B. F. (2005). Proximal methods in vector optimization. SIAM Journal on Optimization, 15(4), 953–970.

    Article  Google Scholar 

  • Ceng, L. C., & Yao, J. C. (2007). Approximate proximal methods in vector optimization. European Journal of Operational Research, 183(1), 1–19.

    Article  Google Scholar 

  • Ceng, L. C., Mordukhovich, B. S., & Yao, J. C. (2010). Hybrid approximate proximal method with auxiliary variational inequality for vector optimization. Journal of Optimization Theory and Applications, 146, 267–303.

    Article  Google Scholar 

  • Chen, G. Y., Huang, X., & Yang, X. (2005). Vector optimization: Set-valued and variational analysis Lecture notes in economics and mathematical systems (1st ed., Vol. 541). Springer-Verlag.

  • Choung, T. D., Mordukhovich, B. S., & Yao, J. C. (2011). Hybrid approximate proximal algorithms for efficient solutions in vector optimization. Journal of Nonlinear and Convex Analysis, 12, 257–286.

    Google Scholar 

  • Chuong, T. D., & Kim, D. S. (2016). Approximate solutions of multiobjective optimization problems. Positivity, 20(1), 187–207.

    Article  Google Scholar 

  • Carmo, Do., & M.P.a. (1992). Riemannian geometry. Mathematics: Theory & applications. Birkhäuser Boston Inc.

  • Gregório, R., & Oliveira, P. R. (2011). A logarithmic-quadratic proximal point scalarization method for multiobjective programming. Journal of Global Optimization, 49, 281–291.

    Article  Google Scholar 

  • Huang, X. X., & Yang, X. Q. (2004). Duality for multiobjective optimization via nonlinear Lagrangian functions. Journal of Optimization Theory and Applications, 120(1), 111–127.

    Article  Google Scholar 

  • Jahn, J. (2004). Vector optimization: Theory, applications, and extensions. Springer.

  • Ledyaev, Y. S., & Zhu, Q. J. (2007). Nonsmooth analysis on smooth manifolds. Transactions of the American Mathematical Society, 359(8), 3687–3732.

    Article  Google Scholar 

  • Lewin, K. (1952). Frontiers in group dynamics. In D. Cartwright (Ed.), (1947) and Field Theory in social Science. Social Science Paperbacks.

  • Lewin, K. (1959). Group decisions and social change. In T. M. Newcomb & E. L. Hartley (Eds.), Reading in social psychology. Henry Holt.

  • Li, C., Mordukhovich, B. S., Wang, J., & Yao, J. C. (2011). Weak sharp minima on Riemannian manifolds. SIAM Journal on Optimization, 21(4), 1523–1560.

    Article  Google Scholar 

  • Loridan, P. (1984). \(\epsilon \)-solutions in vector minimization problems. Journal of Optimization Theory and Applications, 43(2), 265–276.

  • Luc, D. T. (1989). Theory of vector optimization lecture notes in economics and mathematical systems (Vol. 319). Springer.

  • Meireles, L. V. (2019). Proximal point methods for multiobjective optimization in riemann- ian manifolds (PhD Thesis),

  • Minami, M. (1983). Weak Pareto-optimal necessary conditions in a nondifferentiable multiobjective program on a Banach space. Journal of Optimization Theory and Applications, 41, 451–461.

    Article  Google Scholar 

  • Quiroz, E. A. P., Cusihuallpa, N. B., & Maculan, N. (2020). Inexact proximal point methods for multiobjective quasiconvex minimization on hadamard manifolds. Journal of Optimization Theory and Applications, 186(3), 879–898.

    Article  Google Scholar 

  • Rockafellar, R. T. (1976). Monotone operators and the proximal point algorithm. SIAM Journal on Control and Optimization, 14(5), 877–898.

  • Sakai, T. (1996). Riemannian geometry, translations of mathematical monographs (Vol. 149). American Mathematical Society.

  • Solodov, M. V., & Svaiter, B. F. (1999). An inexact hybrid extragradient-proximal point algorithm using the enlargement of a maximal monotone operator. Set-Valued Analysis, 7, 323–345.

    Article  Google Scholar 

  • Soubeyran, A. (2009). Variational rationality, a theory of individual stability and change: worthwhile and ambidextry behaviors. Preprint. GREQAM: Aix Marseille University.

  • Soubeyran, A. (2010). Variational rationality and the “unsatisfied man”: routines and the course pursuit between aspirations, capabilities and beliefs. Preprint GREQAM: Aix Marseille University.

  • Soubeyran, A. (2021). Variational rationality: towards a grand theory of motivation driven by worthwhile moves. Preprint: GREQAM-AMSE, Aix Marseille University.

  • Soubeyran, A. (2021). Variational rationality: The concepts of motivation and motivational force. Aix-Marseille University.

  • Soubeyran, A., (2021). Variational rationality. The resolution of goal conflicts via stop and go approach-avoidance dynamics. Preprint. AMSE, Aix-Marseille University

  • Soubeyran, A., (2021). Variational rationality. A general theory of moving goals and intentions as satisficing worthwhile moves. Preprint. AMSE, Aix-Marseille University

  • Souza, J. C. O. (2018). Proximal point methods for Lipschitz functions on Hadamard manifolds: scalar and vectorial cases. Journal of Optimization Theory and Applications, 179(3), 745–760.

    Article  Google Scholar 

  • Tang, F. M., & Huang, P. L. (2017). On the convergence rate of a proximal point algorithm for vector function on Hadamard manifolds. Journal of the Operations Research Society of China, 5, 405–417.

  • Udriste, C. (1994). Convex functions and optimization methods on riemannian manifolds, mathematics and its applications (Vol. 297). Kluwer Academic Publishers Group.

  • Villacorta, K. D. V., & Oliveira, P. R. (2011). An interior proximal method in vector optimization. European Journal of Operational Research, 214, 485–492.

  • Vinter, R. B. (2000). Optimal control. Birkhauser.

Download references

Acknowledgements

The authors was supported in part by CAPES, FAPEG/PRONEM- (grants 201710267000532) and CNPq (grants 308330/2018-8, 314106/2020-0)

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to G. C. Bento.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bento, G.C., Cruz Neto, J.X., Meireles, L.V. et al. Pareto solutions as limits of collective traps: an inexact multiobjective proximal point algorithm. Ann Oper Res 316, 1425–1443 (2022). https://doi.org/10.1007/s10479-022-04719-y

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-022-04719-y

Keywords

Mathematics Subject Classification

Navigation