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Sufficient conditions to compute any solution of a quasivariational inequality via a variational inequality

Author

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  • Didier Aussel

    (University of Perpignan)

  • Simone Sagratella

    (Sapienza University of Rome)

Abstract

We define the concept of reproducible map and show that, whenever the constraint map defining the quasivariational inequality (QVI) is reproducible then one can characterize the whole solution set of the QVI as a union of solution sets of some variational inequalities (VI). By exploiting this property, we give sufficient conditions to compute any solution of a generalized Nash equilibrium problem (GNEP) by solving a suitable VI. Finally, we define the class of pseudo-Nash equilibrium problems, which are (not necessarily convex) GNEPs whose solutions can be computed by solving suitable Nash equilibrium problems.

Suggested Citation

  • Didier Aussel & Simone Sagratella, 2017. "Sufficient conditions to compute any solution of a quasivariational inequality via a variational inequality," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 85(1), pages 3-18, February.
    • Handle: RePEc:spr:mathme:v:85:y:2017:i:1:d:10.1007_s00186-016-0565-x
    DOI: 10.1007/s00186-016-0565-x
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    References listed on IDEAS

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    1. Vittorio Latorre & Simone Sagratella, 2016. "A canonical duality approach for the solution of affine quasi-variational inequalities," Journal of Global Optimization, Springer, vol. 64(3), pages 433-449, March.
    2. Axel Dreves & Christian Kanzow & Oliver Stein, 2012. "Nonsmooth optimization reformulations of player convex generalized Nash equilibrium problems," Journal of Global Optimization, Springer, vol. 53(4), pages 587-614, August.
    3. Francisco Facchinei & Christian Kanzow & Sebastian Karl & Simone Sagratella, 2015. "The semismooth Newton method for the solution of quasi-variational inequalities," Computational Optimization and Applications, Springer, vol. 62(1), pages 85-109, September.
    4. Jong-Shi Pang & Masao Fukushima, 2005. "Quasi-variational inequalities, generalized Nash equilibria, and multi-leader-follower games," Computational Management Science, Springer, vol. 2(1), pages 21-56, January.
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    Cited by:

    1. Simone Sagratella, 2017. "Computing equilibria of Cournot oligopoly models with mixed-integer quantities," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 86(3), pages 549-565, December.
    2. Cesarone, Francesco & Lampariello, Lorenzo & Sagratella, Simone, 2019. "A risk-gain dominance maximization approach to enhanced index tracking," Finance Research Letters, Elsevier, vol. 29(C), pages 231-238.
    3. Francesco Cesarone & Lorenzo Lampariello & Davide Merolla & Jacopo Maria Ricci & Simone Sagratella & Valerio Giuseppe Sasso, 2023. "A bilevel approach to ESG multi-portfolio selection," Computational Management Science, Springer, vol. 20(1), pages 1-23, December.
    4. Axel Dreves & Simone Sagratella, 2020. "Nonsingularity and Stationarity Results for Quasi-Variational Inequalities," Journal of Optimization Theory and Applications, Springer, vol. 185(3), pages 711-743, June.
    5. Pin-Bo Chen & Gui-Hua Lin & Xide Zhu & Fusheng Bai, 2021. "Smoothing Newton method for nonsmooth second-order cone complementarity problems with application to electric power markets," Journal of Global Optimization, Springer, vol. 80(3), pages 635-659, July.
    6. Rahman Khorramfar & Osman Y. Özaltın & Karl G. Kempf & Reha Uzsoy, 2022. "Managing Product Transitions: A Bilevel Programming Approach," INFORMS Journal on Computing, INFORMS, vol. 34(5), pages 2828-2844, September.
    7. Rahman Khorramfar & Osman Ozaltin & Reha Uzsoy & Karl Kempf, 2024. "Coordinating Resource Allocation during Product Transitions Using a Multifollower Bilevel Programming Model," Papers 2401.17402, arXiv.org.
    8. Jiawang Nie & Xindong Tang & Lingling Xu, 2021. "The Gauss–Seidel method for generalized Nash equilibrium problems of polynomials," Computational Optimization and Applications, Springer, vol. 78(2), pages 529-557, March.
    9. Tommaso Colombo & Simone Sagratella, 2020. "Distributed algorithms for convex problems with linear coupling constraints," Journal of Global Optimization, Springer, vol. 77(1), pages 53-73, May.
    10. Yekini Shehu & Aviv Gibali & Simone Sagratella, 2020. "Inertial Projection-Type Methods for Solving Quasi-Variational Inequalities in Real Hilbert Spaces," Journal of Optimization Theory and Applications, Springer, vol. 184(3), pages 877-894, March.
    11. Lorenzo Lampariello & Simone Sagratella, 2017. "A Bridge Between Bilevel Programs and Nash Games," Journal of Optimization Theory and Applications, Springer, vol. 174(2), pages 613-635, August.
    12. Sagratella, Simone & Schmidt, Marcel & Sudermann-Merx, Nathan, 2020. "The noncooperative fixed charge transportation problem," European Journal of Operational Research, Elsevier, vol. 284(1), pages 373-382.
    13. Christian Kanzow & Daniel Steck, 2018. "Augmented Lagrangian and exact penalty methods for quasi-variational inequalities," Computational Optimization and Applications, Springer, vol. 69(3), pages 801-824, April.
    14. Lorenzo Lampariello & Christoph Neumann & Jacopo M. Ricci & Simone Sagratella & Oliver Stein, 2020. "An explicit Tikhonov algorithm for nested variational inequalities," Computational Optimization and Applications, Springer, vol. 77(2), pages 335-350, November.
    15. Lampariello, Lorenzo & Neumann, Christoph & Ricci, Jacopo M. & Sagratella, Simone & Stein, Oliver, 2021. "Equilibrium selection for multi-portfolio optimization," European Journal of Operational Research, Elsevier, vol. 295(1), pages 363-373.
    16. Lorenzo Lampariello & Simone Sagratella, 2020. "Numerically tractable optimistic bilevel problems," Computational Optimization and Applications, Springer, vol. 76(2), pages 277-303, June.
    17. Simone Sagratella, 2017. "Algorithms for generalized potential games with mixed-integer variables," Computational Optimization and Applications, Springer, vol. 68(3), pages 689-717, December.

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