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Modified Subgradient Extragradient Algorithms with A New Line-Search Rule for Variational Inequalities

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Abstract

In this paper, we introduce a modified subgradient extragradient algorithm with a new line-search rule for solving pseudomonotone variational inequalities with non-Lipschitz mappings. The new line-search rule is designed by the golden radio \((\sqrt{5}+1)/2\). We prove the strong convergence theorem under some appropriate conditions in real Hilbert spaces. Finally, we give some numerical experiments to illustrate the performances and advantages of the proposed algorithm.

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References

  1. Facchinei, F., Pang, J.S.: Finite-Dimensional Variational Inequalities and Complementarity Problems. Springer Series in Operations Research. Springer, New York (2003)

    MATH  Google Scholar 

  2. Kinderlehrer, D., Stampacchia, G.: An Introduction to Variational Inequalities and Their Applications. Academic Press, New York (1980)

    MATH  Google Scholar 

  3. Bauschke, H.H., Combettes, P.L.: Convex Analysis and Monotone Operator Theory in Hilbert Spaces. Springer, New York (2020)

    MATH  Google Scholar 

  4. Baiocchi, C., Capelo, A.: Variational and Quasivariational Inequalities Applications to Free Boundary problems. Wiley, New York (1984)

    MATH  Google Scholar 

  5. Cottle, R.W., Yao, J.C.: Pseudomonotone complementarity problems in Hilbert space. J. Optim. Theory Appl. 75, 281–295 (1992)

    MathSciNet  MATH  Google Scholar 

  6. Combettes, P.L.: The convex feasibility problem in image recovery. Adv. Imaging Electron Phys. 95, 155–270 (1996)

    Google Scholar 

  7. Dafermos, S., Nagurney, A.: A network formulation of market equilibrim problems and variational inequalities. Oper. Res. Lett. 3, 247–250 (1984)

    MathSciNet  MATH  Google Scholar 

  8. Dautray, R., Lions, J.L.: Mathematical Analysis and Numerical Methods for Science and Technology. Springer, New York (1988)

    MATH  Google Scholar 

  9. Konnov, I.V.: Combined Relaxation Methods for Variational Inequalities. Springer, Berlin (2001)

    MATH  Google Scholar 

  10. Bot, R.I., Csetnek, E.R., Vuong, P.T.: The forward–backward–forward method from continuous and discrete perspective for pseudo-monotone variational inequalities in Hilbert spaces. Eur. J. Oper. Res. 287, 49–60 (2020)

    MathSciNet  MATH  Google Scholar 

  11. Solodov, M.V., Tseng, P.: Modified projection-type methods for monotone variational inequalities. SIAM J. Control Optim. 34, 1814–1830 (1996)

    MathSciNet  MATH  Google Scholar 

  12. Reich, S., Thong, D.V., Cholamjiak, P., Long, L.V.: Inertial projection-type methods for solving pseudomonotone variational inequality problems in Hilbert space. Numer. Algorithms 88, 813–835 (2021)

    MathSciNet  MATH  Google Scholar 

  13. Shehu, Y., Iyiola, O.S.: Projection methods with alternating inertial steps for variatinal inequalities: weak and linear convergence. Appl. Numer Math. 157, 315–337 (2020)

    MathSciNet  MATH  Google Scholar 

  14. Dong, Q.L., Cho, Y.J., Zhong, L.L., Rassias, T.M.: Inertial projection and contraction algorithms for variational inequalities. J. Glob. Optim. 70, 687–704 (2018)

    MathSciNet  MATH  Google Scholar 

  15. Wang, Y.J., Xiu, N.H., Wang, C.Y.: Unified framework of projection methods for pseudomonotone variational inequalities. J. Optim. Theory Appl. 111, 641–656 (2001)

    MathSciNet  MATH  Google Scholar 

  16. Thong, D.V., Vuong, P.T.: Modified Tseng’s extragradient methods for solving pseudo-monotone variational inequalities. Optimization 68, 2207–2226 (2019)

    MathSciNet  MATH  Google Scholar 

  17. Iusem, A.N., Svaiter, B.F.: A variant of Korpelevich’s method for variational inequalities with a new search strategy. Optimization 42, 309–321 (1997)

    MathSciNet  MATH  Google Scholar 

  18. Fan, J.J., Qin, X.L.: Weak and strong convergence of inertial Tseng’s extragradient algorithms for solving variational inequality problems. Optimization 70, 1195–1216 (2021)

    MathSciNet  MATH  Google Scholar 

  19. Long, X.J., He, Y.H.: A fast stochastic approximation-based subgradient extragradient algorithm with variance reduction for solving stochastic variational inequality problems. J. Comput. Appl. Math. 420, 114786 (2023)

    MathSciNet  MATH  Google Scholar 

  20. He, Y.H., Long, X.J.: A variance-based proximal backward-forward algorithm with line search for stochastic mixed variational inequalities. Pac. J. Optim. 18, 713–735 (2022)

    MathSciNet  MATH  Google Scholar 

  21. Korpelevich, G.M.: An extragradient method for finding saddle points and other problems. Ekon. i Mat. Metody 17, 747–756 (1976)

    MathSciNet  MATH  Google Scholar 

  22. Censor, Y., Gibali, A., Reich, S.: The subgradient extragradient method for solving variational inequalities in Hilbert space. J. Optim. Theory Appl. 148, 318–335 (2011)

    MathSciNet  MATH  Google Scholar 

  23. Censor, Y., Gibali, A., Reich, S.: Strong convergence of subgradient extragradient methods for the variational inequality problem in Hilbert space. Optim. Methods Softw. 26, 827–845 (2011)

    MathSciNet  MATH  Google Scholar 

  24. Yang, J., Liu, H.W., Liu, Z.X.: Modified subgradient extragradient algorithms for solving monotone variational inequalities. Optimzation 67, 2247–2258 (2018)

    MathSciNet  MATH  Google Scholar 

  25. Yang, J., Liu, H.W., Li, G.: Convergence of a subgradient extragradient algorithm for solving monotone variational inequalities. Numer. Algorithms 84, 389–405 (2020)

    MathSciNet  MATH  Google Scholar 

  26. Thong, D.V., Hieu, D.V.: Modified subgradient extragradient method for variational equalities problems. Numer. Algorithms 79, 597–610 (2018)

    MathSciNet  MATH  Google Scholar 

  27. Thong, D.V., Vinh, N.T., Cho, Y.J.: Accelerated subgradient extragradient methods for variational inequality problems. J. Sci. Comput. 80, 1438–1462 (2019)

    MathSciNet  MATH  Google Scholar 

  28. Thong, D.V., Shehu, Y., Iyiola, O.S.: Weak and strong convergence theorems for solving pseudo-monotone variational inequalities with non-Lipschitz mappings. Numer. Algorithms 84, 795–823 (2020)

    MathSciNet  MATH  Google Scholar 

  29. Thong, D.V., Yang, J., Cho, Y.J., Rassias, T.M.: Explicit extragradient-like method with adaptive stepsizes for pseudomonotone variational inequalities. Optim. Lett. 15, 2181–2199 (2021)

    MathSciNet  MATH  Google Scholar 

  30. Dong, Q.L., Jiang, D., Gibali, A.: A modified subgradient extragradient method for solving the variational inequality problem. Numer. Algorithms 79, 927–940 (2018)

    MathSciNet  MATH  Google Scholar 

  31. Iyiola, O.S., Shehu, Y.: Inertial version of generalized projected reflected gradient method. J. Sci. Comput. 93, 24 (2022)

    MathSciNet  MATH  Google Scholar 

  32. Izuchukwu, C., Shehu, Y., Yao, J.C.: New inertial forward-backward type for variational inequalities with Quasi-monotonicity. J. Glob. Optim. 84, 441–464 (2022)

    MathSciNet  MATH  Google Scholar 

  33. Izuchukwu, C., Shehu, Y., Yao, J.C.: A simple projection method for solving quasimonotone variational inequality problems. Optim. Eng. (2022). https://doi.org/10.1007/s11081-022-09713-8

    Article  Google Scholar 

  34. Thong, D.V., Gibali, A., Vuong, P.T.: An explicit algorithm for solving monotone variational inequalities. Appl. Numer. Math. 171, 408–425 (2022)

    MathSciNet  MATH  Google Scholar 

  35. Shehu, Y., Iyiola, O.S., Reich, S.: A modified inertial subgradient extragradient method for solving variational inequalities. Optim. Eng. 23, 421–449 (2022)

    MathSciNet  MATH  Google Scholar 

  36. Thong, D.V., Vinh, N.T., Cho, Y.J.: Accelerates subgradient extragradient methods for variational inequality problems. J. Sci. Comput. 80, 1438–1462 (2019)

    MathSciNet  MATH  Google Scholar 

  37. Malitsky, Y.: Golden ratio algorithms for variational inequlities. Math. Program. 184, 384–410 (2020)

    MathSciNet  MATH  Google Scholar 

  38. Yao, Y.H., Iyiola, O.S., Shehu, Y.: Subgradient extragradient method with double inertial steps for variational inequalities. J. Sci. Comput. 90, 71 (2022)

    MathSciNet  MATH  Google Scholar 

  39. Khanh, P.Q., Thong, D.V., Vinh, N.T.: Versions of the subgradient extragradient method for pseudomonotone variational inequalities. Acta Appl. Math. 170, 319–345 (2020)

    MathSciNet  MATH  Google Scholar 

  40. Cai, G., Dong, Q.L., Peng, Y.: Strong convergence theorems for solving variational inequality problems with pseudo-monotone and non-Lipschitz operators. J. Optim. Theory Appl. 188, 447–472 (2021)

    MathSciNet  MATH  Google Scholar 

  41. Xie, Z.B., Cai, G., Li, X.X., Dong, Q.L.: Strong convergence of the modified inertial extragradirnt method with line-search process for solving variational inequality problems in Hilbert spaces. J. Sci. Comput. 88, 50 (2021)

    MATH  Google Scholar 

  42. Tan, B., Li, S.X., Qin, X.L.: On modified subgradient extragradient methods for pseudomonotone variational inequality problems with applications. Comput. Appl. Math. 40, 253 (2021)

    MathSciNet  MATH  Google Scholar 

  43. Dong, Q.L., He, S.N., Liu, L.L.: A general inertial projected gradient method for variational inequality problems. Comput. Appl. Math. 40, 168 (2021)

    MathSciNet  MATH  Google Scholar 

  44. Hieu, D.V., Cho, Y.J., Xiao, Y.B.: Golden ratio algorithms with new stepsize rules for variational inequalities. Math. Methods Appl. Sci. 42, 6067–6082 (2019)

    MathSciNet  MATH  Google Scholar 

  45. Goebel, K., Reich, S.: Uniform Convexity, Hyperbolic Geometry and Nonexpansive Mapping. Marcet Dekker, New York (1984)

    MATH  Google Scholar 

  46. Denisov, S.V., Semenov, V.V., Chabak, L.M.: Convergence of the modified extragradient method for variational inequalities with non-Lipschitz operators. Cybern. Syst. Anal. 51, 757–765 (2015)

    MathSciNet  MATH  Google Scholar 

  47. Maingé, P.E.: A hybrid extragradient-viscosity method for monotone operators and fixed point problems. SIAM J. Control. Optim. 47, 1499–1515 (2008)

    MathSciNet  MATH  Google Scholar 

  48. Xu, H.K.: Iterative algorithms for nonlinear operators. J. Lond. Math. Soc. 66, 240–256 (2002)

    MathSciNet  MATH  Google Scholar 

  49. Suantai, S., Pholasa, N., Cholamjiak, P.: The modified inertial relaxed CQ algorithm for solving the split feasibility problems. J. Ind. Manag. Optim. 13, 1–21 (2018)

    MathSciNet  MATH  Google Scholar 

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Acknowledgements

The authors thank the editors and reviewers sincerely for their insightful suggestions which improved this work significantly.

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Correspondence to Xian-Jun Long.

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Communicated by Anton Abdulbasah Kamil.

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This work was supported by the National Natural Science Foundation of China (11471059), the Natural Science Foundation of Chongqing(cstc2021jcyj-msxmX0721, cstc2018jcyjAX0119), the Education Committee Project Research Foundation of Chongqing (KJZDK201900801), the Team Building Project for Graduate Tutors in Chongqing (yds223010) and the Project of Chongqing Technology and Business University (KFJJ2022055).

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Long, XJ., Yang, J. & Cho, Y.J. Modified Subgradient Extragradient Algorithms with A New Line-Search Rule for Variational Inequalities. Bull. Malays. Math. Sci. Soc. 46, 140 (2023). https://doi.org/10.1007/s40840-023-01522-1

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