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On Search of a Nash Equilibrium in Quasiconcave Quadratic Games

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Abstract

The Nash equilibrium problem with nonconcave quadratic payoff functions is considered. We analyze conditions that provide quasiconcavity of payoff functions in their own variables on the respective strategy sets and hence guarantee the existence of an equilibrium point. One such condition is that the matrix of every payoff function has exactly one positive eigenvalue; this condition is viewed as a basic assumption in the paper. We propose an algorithm that either converges to an equilibrium point or declares that the game has no equilibria. It is shown that some stages of the algorithm are noticeably simplified for quasiconcave games. The algorithm is tested on small-scale instances.

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REFERENCES

  1. N. S. Vasil’ev, “On computing Nash equilibrium in quadratic games,” Vopr. Kibern. 154, 64–69 (1989) [in Russian].

    MATH  Google Scholar 

  2. A. S. Antipin, Gradient and Extragradient Approaches in Bilinear Equilibrium Programming (Vychisl. Tsentr im. A. A. Dorodnitsyna RAN, Moscow, 2002) [in Russian].

    Google Scholar 

  3. D. A. Schiro, J.-S. Pang, and U. V. Shanbhag, “On the solution of affine generalized Nash equilibrium problems with shared constraints by Lemke’s method,” Math. Program. Ser. A 142, 1–46 (2013).

    Article  MathSciNet  MATH  Google Scholar 

  4. A. Dreves, “Finding all solutions of affine generalized Nash equilibrium problems with one-dimensional strategy sets,” Math. Meth. Oper. Res. 80 (2), 139–159 (2014).

    Article  MathSciNet  MATH  Google Scholar 

  5. A. Haurie and J. B. Krawczyk, “Optimal charges on river effluent from lumped and distributed sources,” Environ. Model. & Assess. 2 (3), 177–189 (1997).

    Article  Google Scholar 

  6. H. Yin, U. V. Shanbhag, and P. G. Mehta, “Nash equilibrium problems with scaled congestion costs and shared constraints,” IEEE Trans. Autom. Control. 56 (7), 1702–1708 (2011).

    Article  MathSciNet  MATH  Google Scholar 

  7. B. F. Hobbs and J.-S. Pang, “Nash–Cournot equilibria in electric power markets with piecewise linear demand functions and joint constraints,” Oper. Res. 55 (1), 113–127 (2007).

    Article  MathSciNet  MATH  Google Scholar 

  8. A. von Heusinger and C. Kanzow, “Relaxation methods for generalized Nash equilibrium problems with inexact line search,” J. Optim. Theory Appl. 143 (1), 159–183 (2009).

    Article  MathSciNet  MATH  Google Scholar 

  9. A. Dreves, A. von Heusinger, C. Kanzow, and M. Fukushima, “A globalized Newton method for the computation of normalized Nash equilibria,” J. Global Optim. 56 (2), 327–340 (2013).

    Article  MathSciNet  MATH  Google Scholar 

  10. J. B. Rosen, “Existence and uniqueness of equilibrium points for concave \( n \)-person games,” Econometrica 33 (3), 520–534 (1965).

    Article  MathSciNet  MATH  Google Scholar 

  11. A. S. Antipin, “Equilibrium programming: Gradient type methods,” Avtom. Telemekh. (8), 125–137 (1997) [Autom. Remote Control 58 (8), 1337–1347 (1997)].

    MathSciNet  MATH  Google Scholar 

  12. S. I. Zukhovitskii, R. A. Polyak, and M. E. Primak, “Concave many-person games,” Ekon. Mat. Metody 7 (6), 888–900 (1971) [in Russian].

    Google Scholar 

  13. I. Minarchenko, “ ‘Search of Nash equilibrium in quadratic \( n \)-person game,” in Discrete Optimization and Operations Research 2016, vol. 9869 of Lect. Notes Comput. Sci. (Vladivostok, Russia, September 19–23, 2016) (Springer, Cham, 2016), 509–521.

  14. S. Kakutani, “A generalization of Brouwer’s fixed point theorem,” Duke Math. J. 8 (3), 457–459 (1941).

    Article  MathSciNet  MATH  Google Scholar 

  15. W. K. Kim and K. H. Lee, “The existence of Nash equilibrium in \( n \)-person games with \( C \)-concavity,” Comput. Math. Appl. 44 (8), 1219–1228 (2002).

    Article  MathSciNet  MATH  Google Scholar 

  16. L. H. Yen and L. D. Muu, “A parallel subgradient projection algorithm for quasiconvex equilibrium problems under the intersection of convex sets,” Optimization (2021). Published online at https://doi.org/10.1080/02331934.2021.1946057

  17. J. X. Cruz Neto, J. O. Lopes, and P. A. Soares, “A minimization algorithm for equilibrium problems with polyhedral constraints,” Optimization 65 (5), 1061–1068 (2016).

    Article  MathSciNet  MATH  Google Scholar 

  18. S. Boyd and L. Vandenberghe, Convex Optimization (Cambridge Univ. Press, Cambridge, 2004).

    Book  MATH  Google Scholar 

  19. O. L. Mangasarian, “Pseudo-convex functions,” J. SIAM Control Ser. A 3 (2), 281–290 (1965).

    MathSciNet  MATH  Google Scholar 

  20. M. Avriel, W. E. Diewert, S. Schaible, and I. Zang, Generalized Concavity (Soc. Ind. Appl. Math, Philadelphia, 2010).

    Book  MATH  Google Scholar 

  21. C. H. Papadimitriou, “On the complexity of the parity argument and other inefficient proofs of existence,” J. Comput. Syst. Sci. 48 (3), 498–532 (1994).

    Article  MathSciNet  MATH  Google Scholar 

  22. K. C. Kiwiel, “Convergence and efficiency of subgradient methods for quasiconvex minimization,” Math. Program. 90 (1), 1–25 (2001).

    Article  MathSciNet  MATH  Google Scholar 

  23. R. Murray, B. Swenson, and S. Kar, “Revisiting normalized gradient descent: Fast evasion of saddle points,” IEEE Trans. Autom. Control 64 (11), 4818–4824 (2019).

    Article  MathSciNet  MATH  Google Scholar 

  24. H. Nikaidô and K. Isoda, “Note on non-cooperative convex game,” Pac. J. Math. 5 (5), 807–815 (1955).

    Article  MathSciNet  MATH  Google Scholar 

  25. O. V. Khamisov, “A global optimization approach to solving equilibrium programming problems,” in Series on Computers and Operations Research 1: Optimization and Optimal Control (2003), 155–164.

  26. N. Nisan, T. Roughgarden, É. Tardos, and V. V. Vazirani, Eds., Algorithmic Game Theory (Cambridge Univ. Press, Cambridge, 2007).

  27. V. P. Bulatov and T. I. Belykh, “Numerical solution methods for multiextremal problems connected with inverse problems in mathematical programming,” Izv. VUZov. Mat. 6, 14–20 (2007) [Russ. Math. 51 (6), 11–17 (2007)].

    Article  MATH  Google Scholar 

  28. I. Minarchenko and O. Khamisov, “On minimization of a quadratic function with one negative eigenvalue,” Optim. Lett. 15 (4), 1447–1455 (2021).

    Article  MathSciNet  Google Scholar 

  29. The Advanced Interactive Multidimensional Modeling System (AIMMS B. V., Haarlem, 2022). Available at https://www.aimms.com . Accessed May 21, 2022.

  30. IBM ILOG CPLEX Optimization Studio (IBM Corp., Armonk, 2022). Available at https://www.ibm.com/products/ilog-cplex-optimization-studio . Accessed May 21, 2022.

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Funding

This research was carried out within the state assignment under the Program of Fundamental Research in Russia for 2021–2030, project no. FWEU–2021–0006 [AAAA–A21–121012090034–3].

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Correspondence to I. M. Minarchenko.

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Translated by V. Potapchouck

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Minarchenko, I.M. On Search of a Nash Equilibrium in Quasiconcave Quadratic Games. J. Appl. Ind. Math. 17, 120–130 (2023). https://doi.org/10.1134/S1990478923010131

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  • DOI: https://doi.org/10.1134/S1990478923010131

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