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Minimization of equilibrium problems, variational inequality problems and fixed point problems

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Abstract

In this paper, we devote to find the solution of the following quadratic minimization problem

$$\min_{x\in \Omega}\|x\|^2,$$

where Ω is the intersection set of the solution set of some equilibrium problem, the fixed points set of a nonexpansive mapping and the solution set of some variational inequality. In order to solve the above minimization problem, we first construct an implicit algorithm by using the projection method. Further, we suggest an explicit algorithm by discretizing this implicit algorithm. Finally, we prove that the proposed implicit and explicit algorithms converge strongly to a solution of the above minimization problem.

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References

  1. Blum E., Oettli W.: From optimization and variational inequalities to equilibrium problems. Math. Stud. 63, 123–145 (1994)

    Google Scholar 

  2. Takahashi S., Takahashi W.: Strong convergence theorem for a generalized equilibrium problem and a nonexpansive mapping in a Hilbert space. Nonlinear Anal. 69, 1025–1033 (2008)

    Article  Google Scholar 

  3. Takahashi W., Toyoda M.: Weak convergence theorems for nonexpansive mappings and monotone mappings. J. Optim. Theory Appl. 118, 417–428 (2003)

    Article  Google Scholar 

  4. Nadezhkina N., Takahashi W.: Weak convergence theorem by an extragradient method for nonexpansive mappings and monotone mappings. J. Optim. Theory Appl. 128, 191–201 (2006)

    Article  Google Scholar 

  5. Moudafi, A., Théra, M.: Proximal and dynamical approaches to equilibrium problems. In: Ill-posed variational problems and regularization techniques (Trier, 1998), pp. 187–201, Lecture Notes in Economics and Mathematical Systems, vol. 477, Springer, Berlin (1999)

  6. Bauschke H.H., Borwein J.M.: On projection algorithms for solving convex feasibility problems. SIAM Rev. 38, 367–426 (1996)

    Article  Google Scholar 

  7. Bauschke H.H., Combettes P.L., Luke D.R.: Finding best approximation pairs relative to two closed convex sets in Hilbert spaces. J. Approx. Theory 127, 178–192 (2004)

    Article  Google Scholar 

  8. Combettes P.L., Hirstoaga A.: Equilibrium programming in Hilbert spaces. J. Nonlinear Convex Anal. 6, 117–136 (2005)

    Google Scholar 

  9. Combettes P.L.: Strong convergence of block-iterative outer approximation methods for convex optimization. SIAM J. Control Optim. 38, 538–565 (2000)

    Article  Google Scholar 

  10. Combettes P.L., Pesquet J.C.: Proximal thresholding algorithm for minimization over orthonormal bases. SIAM J. Optim. 18, 1351–1376 (2007)

    Article  Google Scholar 

  11. Combettes P.L.: Inconsistent signal feasibility problems: least-squares solutions in a product space. IEEE Trans. Signal Process 42, 2955–2966 (1994)

    Article  Google Scholar 

  12. Yamada, I.: The hybrid steepest descent method for the variational inequality problem over the intersection of fixed point sets of nonexpansive mappings. In: Butnariu, D., Censor, Y., Reich, S. (eds.) Inherently Parallel Algorithm for Feasibility and Optimization. Stud. Comput. Math., vol. 8, pp. 473–504. North-Holland, Amsterdam (2001)

  13. Yamada I., Ogura N.: Hybrid steepest descent method for the variational inequality problem over the fixed point set of certain quasi-nonexpansive mappings. Numer. Funct. Anal. Optim. 25, 619–655 (2004)

    Article  Google Scholar 

  14. Yamada, I., Ogura, N. Shirakawa, N.: A numerically robust hybrid steepest descent method for the convexly constrained generalized inverse problems. In: Inverse Problems, Image Analysis, and Medical Imaging, Contemp. Math., vol. 313, pp. 269–305. American Mathematical Society, Providence, RI, (2002)

  15. Yao Y., Liou Y.C., Yao J.C.: An iterative algorithm for approximating convex minimization problem. Appl. Math. Comput. 188, 648–656 (2007)

    Article  Google Scholar 

  16. Yao Y., Yao J.C.: On modified iterative method for nonexpansive mappings and monotone mappings. Appl. Math. Comput. 186, 1551–1558 (2007)

    Article  Google Scholar 

  17. Iusem A.N., Sosa W.: Iterative algorithms for equilibrium problems. Optimization 52, 301–316 (2003)

    Article  Google Scholar 

  18. Suzuki T.: Strong convergence theorems for infinite families of nonexpansive mappings in general Banach spaces. Fixed Point Theory Appl. 2005, 103–123 (2005)

    Article  Google Scholar 

  19. Xu, H.K.: Viscosity method for hierarchical fixed point approach to variational inequalities. To appear in Taiwanese J. Math.

  20. Xu H.K.: An iterative approach to quadratic optimization. J. Optim. Theory Appl. 116, 659–678 (2003)

    Article  Google Scholar 

  21. Lu X., Xu H.K., Yin X.: Hybrid methods for a class of monotone variational inequalities. Nonlinear Anal. 71, 1032–1041 (2009)

    Article  Google Scholar 

  22. Ceng L.C., Guu S.M., Yao J.C.: On generalized implicit vector equilibrium problems in Banach spaces. Comput. Math. Appl. 57, 1682–1691 (2009)

    Article  Google Scholar 

  23. Peng J.W., Yao J.C.: Strong convergence theorems of iterative scheme based on the extragradient method for mixed equilibrium problems and fixed point problems. Math. Comput. Modelling 49, 1816–1828 (2009)

    Article  Google Scholar 

  24. Fang, Y.P., Huang, N.J., Yao, J.C.: Well-posedness by perturbations of mixed variational inequalities in Banach spaces. European J. Oper. Res. 201, 682–692 (2010)

    Google Scholar 

  25. Ceng L.C., Yao J.C.: A hybrid iterative scheme for mixed equilibrium problems and fixed point problems. J. Comput. Appl. Math. 214, 186–201 (2008)

    Article  Google Scholar 

  26. Ceng L.C., Schaible S., Yao J.C.: Implicit iteration scheme with perturbed mapping for equilibrium problems and fixed point problems of finitely many nonexpansive mappings. J. Optim. Theory Appl. 139, 403–418 (2008)

    Article  Google Scholar 

  27. Attouch H., Cominetti R.: A dynamical approach to convex minimization coupling approximation with the steepest descent method. J. Differ. Equ. 128, 519–540 (1996)

    Article  Google Scholar 

  28. Censor Y., Lent A.: Cyclic subgradient projections. Math. Program. 24, 233–235 (1982)

    Article  Google Scholar 

  29. Butnariu, D., Censor, Y., Reich, S. (eds): Inherently Parallel Algorithms in Feasibility and Optimization and Their Applications. Elsevier, New York (2001)

    Google Scholar 

  30. Tseng P.: Applications of a splitting algorithm to decomposition in convex programming and variational inequalities. SIAM J. Control Optim. 29, 119–138 (1991)

    Article  Google Scholar 

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Correspondence to Shin Min Kang.

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The first author was partially supported by National Natural Science Foundation of China Grant 10771050. The second author was partially supported by the grant NSC 98-2622-E-230-006-CC3 and NSC 98-2923- E-110-003-MY3.

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Yao, Y., Liou, YC. & Kang, S.M. Minimization of equilibrium problems, variational inequality problems and fixed point problems. J Glob Optim 48, 643–656 (2010). https://doi.org/10.1007/s10898-009-9512-1

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  • DOI: https://doi.org/10.1007/s10898-009-9512-1

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