IDEAS home Printed from https://ideas.repec.org/a/wly/jnlaaa/v2012y2012i1n206345.html

A New Modified Hybrid Steepest‐Descent by Using a Viscosity Approximation Method with a Weakly Contractive Mapping for a System of Equilibrium Problems and Fixed Point Problems with Minimization Problems

Author

Listed:
  • Uamporn Witthayarat
  • Thanyarat Jitpeera
  • Poom Kumam

Abstract

The purpose of this paper is to consider a modified hybrid steepest‐descent method by using a viscosity approximation method with a weakly contractive mapping for finding the common element of the set of a common fixed point for an infinite family of nonexpansive mappings and the set of solutions of a system of an equilibrium problem. The sequence is generated from an arbitrary initial point which converges in norm to the unique solution of the variational inequality under some suitable conditions in a real Hilbert space. The results presented in this paper generalize and improve the results of Moudafi (2000), Marino and Xu (2006), Tian (2010), Saeidi (2010), and some others. Finally, we give an application to minimization problems and a numerical example which support our main theorem in the last part.

Suggested Citation

  • Uamporn Witthayarat & Thanyarat Jitpeera & Poom Kumam, 2012. "A New Modified Hybrid Steepest‐Descent by Using a Viscosity Approximation Method with a Weakly Contractive Mapping for a System of Equilibrium Problems and Fixed Point Problems with Minimization Problems," Abstract and Applied Analysis, John Wiley & Sons, vol. 2012(1).
    • Handle: RePEc:wly:jnlaaa:v:2012:y:2012:i:1:n:206345
    DOI: 10.1155/2012/206345
    as

    Download full text from publisher

    File URL: https://doi.org/10.1155/2012/206345
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2012/206345?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Yair Censor & Wei Chen & Patrick Combettes & Ran Davidi & Gabor Herman, 2012. "On the effectiveness of projection methods for convex feasibility problems with linear inequality constraints," Computational Optimization and Applications, Springer, vol. 51(3), pages 1065-1088, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Peichao Duan & Xubang Zheng & Jing Zhao, 2018. "Strong Convergence Theorems of Viscosity Iterative Algorithms for Split Common Fixed Point Problems," Mathematics, MDPI, vol. 7(1), pages 1-14, December.
    2. Xianfu Wang & Xinmin Yang, 2015. "On the Existence of Minimizers of Proximity Functions for Split Feasibility Problems," Journal of Optimization Theory and Applications, Springer, vol. 166(3), pages 861-888, September.
    3. Yair Censor & Ran Davidi & Gabor T. Herman & Reinhard W. Schulte & Luba Tetruashvili, 2014. "Projected Subgradient Minimization Versus Superiorization," Journal of Optimization Theory and Applications, Springer, vol. 160(3), pages 730-747, March.
    4. Howard Heaton & Yair Censor, 2019. "Asynchronous sequential inertial iterations for common fixed points problems with an application to linear systems," Journal of Global Optimization, Springer, vol. 74(1), pages 95-119, May.
    5. Paulo Oliveira, 2014. "A strongly polynomial-time algorithm for the strict homogeneous linear-inequality feasibility problem," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 80(3), pages 267-284, December.
    6. Stéphane Chrétien & Pascal Bondon, 2020. "Projection Methods for Uniformly Convex Expandable Sets," Mathematics, MDPI, vol. 8(7), pages 1-19, July.
    7. Chin How Jeffrey Pang, 2019. "Dykstra’s Splitting and an Approximate Proximal Point Algorithm for Minimizing the Sum of Convex Functions," Journal of Optimization Theory and Applications, Springer, vol. 182(3), pages 1019-1049, September.
    8. Norihisa Obata & Omar M. Abou Al-Ola & Ryosei Nakada & Takeshi Kojima & Tetsuya Yoshinaga, 2024. "Variable Dose-Constraints Method for Enhancing Intensity-Modulated Radiation Therapy Treatment Planning," Mathematics, MDPI, vol. 12(23), pages 1-26, December.
    9. Brendan O’Donoghue & Eric Chu & Neal Parikh & Stephen Boyd, 2016. "Conic Optimization via Operator Splitting and Homogeneous Self-Dual Embedding," Journal of Optimization Theory and Applications, Springer, vol. 169(3), pages 1042-1068, June.
    10. Yanni Guo & Xiaozhi Zhao, 2019. "Bounded Perturbation Resilience and Superiorization of Proximal Scaled Gradient Algorithm with Multi-Parameters," Mathematics, MDPI, vol. 7(6), pages 1-14, June.
    11. Aviv Gibali & Karl-Heinz Küfer & Daniel Reem & Philipp Süss, 2018. "A generalized projection-based scheme for solving convex constrained optimization problems," Computational Optimization and Applications, Springer, vol. 70(3), pages 737-762, July.
    12. Ron Aharoni & Yair Censor & Zilin Jiang, 2018. "Finding a best approximation pair of points for two polyhedra," Computational Optimization and Applications, Springer, vol. 71(2), pages 509-523, November.
    13. Yair Censor & Alexander Zaslavski, 2013. "Convergence and perturbation resilience of dynamic string-averaging projection methods," Computational Optimization and Applications, Springer, vol. 54(1), pages 65-76, January.
    14. Hongjin He & Chen Ling & Hong-Kun Xu, 2015. "A Relaxed Projection Method for Split Variational Inequalities," Journal of Optimization Theory and Applications, Springer, vol. 166(1), pages 213-233, July.
    15. Yair Censor & Alexander J. Zaslavski, 2015. "Strict Fejér Monotonicity by Superiorization of Feasibility-Seeking Projection Methods," Journal of Optimization Theory and Applications, Springer, vol. 165(1), pages 172-187, April.
    16. Jiawei Chen & Qamrul Hasan Ansari & Yeong-Cheng Liou & Jen-Chih Yao, 2016. "A proximal point algorithm based on decomposition method for cone constrained multiobjective optimization problems," Computational Optimization and Applications, Springer, vol. 65(1), pages 289-308, September.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:jnlaaa:v:2012:y:2012:i:1:n:206345. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://onlinelibrary.wiley.com/journal/4058 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.