Skip to main content
Log in

Using Double Inertial Steps Into the Single Projection Method with Non-monotonic Step Sizes for Solving Pseudomontone Variational Inequalities

  • Research
  • Published:
Networks and Spatial Economics Aims and scope Submit manuscript

Abstract

In this paper, we propose a new modified algorithm for finding an element of the set of solutions of a pseudomonotone, Lipschitz continuous variational inequality problem in real Hilbert spaces. Using the technique of double inertial steps into a single projection method we give weak and strong convergence theorems of the proposed algorithm. The weak convergence does not require prior knowledge of the Lipschitz constant of the variational inequality mapping and only computes one projection onto a feasible set per iteration as well as without using the sequentially weak continuity of the associated mapping. Under additional strong pseudomonotonicity and Lipschitz continuity assumptions, the R-linear convergence rate of the proposed algorithm is presented. Finally, some numerical examples are given to illustrate the effectiveness of the algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Data Availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  • Abaidoo R, Agyapong EK (2022) Financial development and institutional quality among emerging economies. J Econ Dev 24:198–216

    Article  Google Scholar 

  • Alvarez F, Attouch H (2001) An inertial proximal method for maximal monotone operators via discretization of a nonlinear oscillator with damping. Set-Valued Anal 9:3–11

    Article  MathSciNet  Google Scholar 

  • Antipin AS (1976) On a method for convex programs using a symmetrical modification of the Lagrange function. Ekonomika i Matematicheskie Metody 12:1164–1173

    Google Scholar 

  • Aubin JP, Ekeland I (1984) Applied Nonlinear Analysis. Wiley, New York

    Google Scholar 

  • Bauschke HH, Combettes PL (2011) Convex analysis and monotone operator theory in hilbert spaces. Springer, New York

    Book  Google Scholar 

  • Bot RI, Csetnek ER, Vuong PT (2020) The forward-backward-forward method from discrete and continuous perspective for pseudo-monotone variational inequalities in Hilbert spaces. Eur J Oper Res. https://doi.org/10.1016/j.ejor.2020.04.035

    Article  Google Scholar 

  • Ceng LC, Teboulle M, Yao JC (2010) Weak convergence of an iterative method for pseudomonotone variational inequalities and fixed-point problems. J Optim Theory Appl 146:19–31

    Article  MathSciNet  Google Scholar 

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

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

  • Censor Y, Gibali A, Reich S (2012) Extensions of Korpelevich’s extragradient method for the variational inequality problem in Euclidean space. Optimization 61:1119–1132

    Article  MathSciNet  Google Scholar 

  • Chang X, Liu S, Deng Z, Li S (2022) An inertial subgradient extragradient algorithm with adaptive stepsizes for variational inequality problems. Optim Methods Softw. https://doi.org/10.1080/10556788.2021.1910946

    Article  MathSciNet  Google Scholar 

  • Cottle RW, Yao JC (1992) Pseudo-monotone complementarity problems in Hilbert space. J Optim Theory Appl 75:281–295

    Article  MathSciNet  Google Scholar 

  • Denisov SV, Semenov VV, Chabak LM (2015) Convergence of the modified extragradient method for variational inequalities with non-Lipschitz operators. Cybern Syst Anal 51:757–765

    Article  MathSciNet  Google Scholar 

  • Dong QL, Yuan HB, Cho YJ, Rassias ThM (2018) Modified inertial Mann algorithm and inertial CQ-algorithm for nonexpansive mappings. Optim Lett 12:87–102

    Article  MathSciNet  Google Scholar 

  • Dong QL, Lu YY, Yang JF (2016) The extragradient algorithm with inertial effects for solving the variational inequality. Optimization 65:2217–2226

    Article  MathSciNet  Google Scholar 

  • Facchinei F, Pang JS (2003) Finite-Dimensional Variational Inequalities and Complementarity Problems, vol I. Springer Series in Operations Research, Springer, New York

    Google Scholar 

  • Fichera G (1963) Sul problema elastostatico di Signorini con ambigue condizioni al contorno. Atti Accad Naz Lincei VIII Ser Rend Cl Sci Fis Mat Nat 34:138–142

    MathSciNet  Google Scholar 

  • Fichera G (1964) Problemi elastostatici con vincoli unilaterali: il problema di Signorini con ambigue condizioni al contorno. Atti Accad Naz Lincei Mem Cl Sci Fis Mat Nat Sez I VIII Ser 7:91–140

    MathSciNet  Google Scholar 

  • Gibali A, Reich S, Zalas R (2017) Outer approximation methods for solving variational inequalities in Hilbert space. Optimization 66:417–437

    Article  MathSciNet  Google Scholar 

  • Goebel K, Reich S (1984) Uniform Convexity, Hyperbolic Geometry, and Nonexpansive Mappings: Marcel Dekker, New York

  • Hieu DV, Anh PK, Muu LD (2017) Modified hybrid projection methods for finding common solutions to variational inequality problems. Comput Optim Appl 66:75–96

    Article  MathSciNet  Google Scholar 

  • Hu X, Wang J (2006) Solving pseudo-monotone variational inequalities and pseudo-convex optimization problems using the projection neural network. IEEE Trans Neural Netw 17:1487–1499

    Article  PubMed  Google Scholar 

  • Karamardian S, Schaible S (1990) Seven kinds of monotone maps. J Optim Theory Appl 66:37–46

    Article  MathSciNet  Google Scholar 

  • Khanh PD, Vuong PT (2014) Modified projection method for strongly pseudomonotone variational inequalities. J Glob Optim 58:341–350

    Article  MathSciNet  Google Scholar 

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

    Google Scholar 

  • Kraikaew R, Saejung S (2014) Strong convergence of the Halpern subgradient extragradient method for solving variational inequalities in Hilbert spaces. J Optim Theory Appl 163:399–412

    Article  MathSciNet  Google Scholar 

  • Konnov IV (2001) Combined Relaxation Methods for Variational Inequalities. Springer-Verlag, Berlin

    Book  Google Scholar 

  • Korpelevich GM (1976) The extragradient method for finding saddle points and other problems. Ekonomikai Matematicheskie Metody 12:747–756

    MathSciNet  Google Scholar 

  • Liu H, Yang J (2020) Weak convergence of iterative methods for solving quasimonotone variational inequalities. Comput Optim Appl. https://doi.org/10.1007/s10589-020-00217-8

    Article  MathSciNet  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  • Malitsky YV, Semenov VV (2015) A hybrid method without extrapolation step for solving variational inequality problems. J Glob Optim 61:193–202

    Article  MathSciNet  Google Scholar 

  • Malitsky YV (2015) Projected reflected gradient methods for monotone variational inequalities. SIAM J Optim 25:502–520

    Article  MathSciNet  Google Scholar 

  • Opial Z (1967) Weak convergence of the sequence of successive approximations for nonexpansive mappings. Bull Amer Math Soc 73:591–597

    Article  MathSciNet  Google Scholar 

  • Ortega JM, Rheinboldt WC (1970) Iterative Solution of Nonlinear Equations in Several Variables. Academic Press, New York

    Google Scholar 

  • Shehu Y, Dong QL, Jiang D (2019) Single projection method for pseudo-monotone variational inequalbity in Hilbert spaces. Optimization 68:385–409

    Article  MathSciNet  Google Scholar 

  • Shehu Y, Iyiola OS, Reich S (2022) A modified inertial subgradient extragradient method for solving variational inequalities. Optim Eng 23:42-C449

    Article  MathSciNet  Google Scholar 

  • Solodov MV, Svaiter BF (1999) A new projection method for variational inequality problems. SIAM J Control Optim 37:765–776

    Article  MathSciNet  Google Scholar 

  • Thong DV, Hieu DV (2018) Modified subgradient extragradient method for inequality variational problems. Numer Algorithms 79:597–610

    Article  MathSciNet  Google Scholar 

  • Vuong PT (2018) On the weak convergence of the extragradient method for solving pseudo-monotone variational inequalities. J Optim Theory Appl 176:399–409

    Article  MathSciNet  PubMed  PubMed Central  Google Scholar 

  • Vuong PT, Shehu Y (2019) Convergence of an extragradient-type method for variational inequality with applications to optimal control problems. Numer Algorithms 81:269–291

    Article  MathSciNet  Google Scholar 

  • Wang YM, Xiao YB, Wang X, Cho YJ (2016) Equivalence of well-posedness between systems of hemivariational inequalities and inclusion problems. J Nonlinear Sci Appl 9:1178–1192

    Article  MathSciNet  Google Scholar 

  • Xiao YB, Huang NJ, Cho YJ (2012) A class of generalized evolution variational inequalities in Banach space. Appl Math Lett 25:914–920

    Article  MathSciNet  Google Scholar 

  • Yang J, Liu H (2018) A modified projected gradient method for monotone variational inequalities. J Optim Theory Appl 179:197–211

    Article  ADS  MathSciNet  Google Scholar 

  • Yao Y (2012) Postolache, M: Iterative methods for pseudomonotone variational inequalities and fixed point problems. J Optim Theory Appl 155:273–287

    Article  MathSciNet  Google Scholar 

  • Yao Y, Marino G, Muglia L (2014) A modified Korpelevich’s method convergent to the minimum-norm solution of a variational inequality. Optimization 63:559–569

    Article  MathSciNet  Google Scholar 

  • Yao Y, Iyiola OS, Shehu Y (2022) Subgradient extragradient method with double inertial steps for variational inequalities. J Sci Comput 90:71. https://doi.org/10.1007/s10915-021-01751-1

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

The authors are thankful to the handling editor and two anonymous reviewers for comments and remarks which substantially improved the quality of the paper. We also would like to express our gratitude to Professor Terry Friesz, Editor-in-Chief, for giving us the opportunity to revise and resubmit this manuscript. The authors would like to thank Professor Pham Ky Anh for drawing our attention to the subject and for many useful discussions. This research has been done under the research project QG.23.04 of Vietnam National University, Hanoi.

Funding

No funding.

Author information

Authors and Affiliations

Authors

Contributions

D. V. Thong, P. T. H. Huyen and H. T. T. Tam wrote the main manuscript text and Xiao-Huan Li and V. T. Dung prepared Figs. 18. All authors reviewed the manuscript carefully.

Corresponding author

Correspondence to Vu Tien Dung.

Ethics declarations

Ethical Approval

Not Applicable.

Conflict of Interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Thong, D.V., Li, XH., Dung, V.T. et al. Using Double Inertial Steps Into the Single Projection Method with Non-monotonic Step Sizes for Solving Pseudomontone Variational Inequalities. Netw Spat Econ 24, 1–26 (2024). https://doi.org/10.1007/s11067-023-09606-y

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11067-023-09606-y

Keywords

Mathematics Subject Classification (2000)

Navigation