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.
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References
Abaidoo R, Agyapong EK (2022) Financial development and institutional quality among emerging economies. J Econ Dev 24:198–216
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
Antipin AS (1976) On a method for convex programs using a symmetrical modification of the Lagrange function. Ekonomika i Matematicheskie Metody 12:1164–1173
Aubin JP, Ekeland I (1984) Applied Nonlinear Analysis. Wiley, New York
Bauschke HH, Combettes PL (2011) Convex analysis and monotone operator theory in hilbert spaces. Springer, New York
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
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
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
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
Cottle RW, Yao JC (1992) Pseudo-monotone complementarity problems in Hilbert space. J Optim Theory Appl 75:281–295
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
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
Dong QL, Lu YY, Yang JF (2016) The extragradient algorithm with inertial effects for solving the variational inequality. Optimization 65:2217–2226
Facchinei F, Pang JS (2003) Finite-Dimensional Variational Inequalities and Complementarity Problems, vol I. Springer Series in Operations Research, Springer, New York
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
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
Gibali A, Reich S, Zalas R (2017) Outer approximation methods for solving variational inequalities in Hilbert space. Optimization 66:417–437
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
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
Karamardian S, Schaible S (1990) Seven kinds of monotone maps. J Optim Theory Appl 66:37–46
Khanh PD, Vuong PT (2014) Modified projection method for strongly pseudomonotone variational inequalities. J Glob Optim 58:341–350
Kinderlehrer D, Stampacchia G (1980) An Introduction to Variational Inequalities and Their Applications. Academic Press, New York
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
Konnov IV (2001) Combined Relaxation Methods for Variational Inequalities. Springer-Verlag, Berlin
Korpelevich GM (1976) The extragradient method for finding saddle points and other problems. Ekonomikai Matematicheskie Metody 12:747–756
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
Maingé PE (2008) A hybrid extragradient-viscosity method for monotone operators and fixed point problems. SIAM J Control Optim 47:1499–1515
Malitsky YV, Semenov VV (2015) A hybrid method without extrapolation step for solving variational inequality problems. J Glob Optim 61:193–202
Malitsky YV (2015) Projected reflected gradient methods for monotone variational inequalities. SIAM J Optim 25:502–520
Opial Z (1967) Weak convergence of the sequence of successive approximations for nonexpansive mappings. Bull Amer Math Soc 73:591–597
Ortega JM, Rheinboldt WC (1970) Iterative Solution of Nonlinear Equations in Several Variables. Academic Press, New York
Shehu Y, Dong QL, Jiang D (2019) Single projection method for pseudo-monotone variational inequalbity in Hilbert spaces. Optimization 68:385–409
Shehu Y, Iyiola OS, Reich S (2022) A modified inertial subgradient extragradient method for solving variational inequalities. Optim Eng 23:42-C449
Solodov MV, Svaiter BF (1999) A new projection method for variational inequality problems. SIAM J Control Optim 37:765–776
Thong DV, Hieu DV (2018) Modified subgradient extragradient method for inequality variational problems. Numer Algorithms 79:597–610
Vuong PT (2018) On the weak convergence of the extragradient method for solving pseudo-monotone variational inequalities. J Optim Theory Appl 176:399–409
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
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
Xiao YB, Huang NJ, Cho YJ (2012) A class of generalized evolution variational inequalities in Banach space. Appl Math Lett 25:914–920
Yang J, Liu H (2018) A modified projected gradient method for monotone variational inequalities. J Optim Theory Appl 179:197–211
Yao Y (2012) Postolache, M: Iterative methods for pseudomonotone variational inequalities and fixed point problems. J Optim Theory Appl 155:273–287
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
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
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.
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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
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DOI: https://doi.org/10.1007/s11067-023-09606-y
Keywords
- Subgradient extragradient method
- Double inertial steps
- Variational inequality
- Pseudomonotone mapping
- Lipschitz continuity
- R-linear convergence rate