PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Powiadomienia systemowe
  • Sesja wygasła!
Tytuł artykułu

Strong convergence inertial projection algorithm with self-adaptive step size rule for pseudomonotone variational inequalities in Hilbert spaces

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper, we introduce a new algorithm for solving pseudomonotone variational inequalities with a Lipschitz-type condition in a real Hilbert space. The algorithm is constructed around two algorithms: the subgradient extragradient algorithm and the inertial algorithm. The proposed algorithm uses a new step size rule based on local operator information rather than its Lipschitz constant or any other line search scheme and functions without any knowledge of the Lipschitz constant of an operator. The strong convergence of the algorithm is provided. To determine the computational performance of our algorithm, some numerical results are presented.
Wydawca
Rocznik
Strony
110--128
Opis fizyczny
Bibliogr. 37 poz., rys., tab.
Twórcy
  • Applied Mathematics Program, Faculty of Science and Technology, Valaya Alongkorn Rajabhat University under the Royal Patronage (VRU), 1 Moo 20 Phaholyothin Road, Klong Neung, Klong Luang, Pathumthani 13180, Thailand
  • Department of Mathematics, Faculty of Science and Technology, Phetchabun Rajabhat University, Phetchabun 67000, Thailand
  • School of Science, University of Phayao, Phayao 56000, Thailand
Bibliografia
  • [1] G. Stampacchia, Formes bilinéaires coercitives sur les ensembles convexes, Académie des Sciences de Paris 258(1964), 4413-4416.
  • [2] I. V. Konnov, On systems of variational inequalities, Izv. Vyssh. Uchebn. Zaved. Mat. 41(1997), 77-86.
  • [3] G. Kassay, J. Kolumbán, and Z. Páles, On Nash stationary points, Publ. Math. Debrecen 54(1999), no. 3-4, 267-279.
  • [4] G. Kassay, J. Kolumbán, and Z. Páles, Factorization of minty and stampacchia variational inequality systems, European J. Oper. Res. 143(2002), no. 2, 377-389.
  • [5] D. Kinderlehrer and G. Stampacchia, An Introduction to Variational Inequalities and Their Applications, Classics in Applied Mathematics, vol. 31, Society for Industrial and Applied Mathematics (SIAM), Philadelphia, PA, 2000.
  • [6] I. Konnov, Equilibrium Models and Variational Inequalities, Mathematics in Science and Engineering, vol. 210, Elsevier B.V., Amsterdam, 2007.
  • [7] C. M. Elliott, Variational and quasivariational inequalities: Applications to free-boundary problems (Claudio Baiocchi and António Capelo), SIAM Rev. 29(1987), no. 2, 314-315.
  • [8] A. Nagurney, Network Economics: A Variational Inequality Approach, Kluwer Academic Publishers Group, Dordrecht, 1999.
  • [9] W. Takahashi, Introduction to Nonlinear and Convex Analysis, Yokohama Publishers, Yokohama, 2009.
  • [10] H. U. Rehman, D. Gopal, and P. Kumam, Generalizations of Darboas fixed point theorem for new condensing operators with application to a functional integral, Demonstr. Math. 52(2019), no. 1, 166-182.
  • [11] H. U. Rehman, P. Kumam, Y. J. Cho, and P. Yordsorn, Weak convergence of explicit extragradient algorithms for solving equilibirum problems, J. Inequal. Appl. 2019(2019), 282, DOI: https://doi.org/10.1186/s13660-019-2233-1.
  • [12] H. U. Rehman, P. Kumam, A. B. Abubakar, and Y. J. Cho, The extragradient algorithm with inertial effects extended toequilibrium problems, Comput. Appl. Math. 39(2020), no. 2, 100, DOI: https://doi.org/10.1007/s40314-020-1093-0.
  • [13] G. Korpelevich, The extragradient method for finding saddle points and other problems, Èkonom. i Mat. Metody 12(1976), no. 4, 747-756.
  • [14] M. A. Noor, Some iterative methods for nonconvex variational inequalities, Math. Comput. Modelling 54(2010), no. 11-12, 97-108.
  • [15] Y. Censor, A. Gibali, and S. Reich, The subgradient extragradient method for solving variational inequalities in Hilbert space, J. Optim. Theory Appl. 148(2010), no. 2, 318-335.
  • [16] Y. Censor, A. Gibali and S. Reich, and S. Reich, Extensions of Korpelevich extragradient method for the variational inequality problem in euclidean space, Optimization 61(2012), no. 9, 1119-1132.
  • [17] Y. V. Malitsky and V. V. Semenov, An extragradient algorithm for monotone variational inequalities, Cybernet. Systems Anal. 50(2014), no. 2, 271-277.
  • [18] P. Tseng, A modified forward-backward splitting method for maximal monotone mappings, SIAM J. Control Optim. 38(2000), no. 2, 431-446.
  • [19] A. Moudafi, Viscosity approximation methods for fixed-points problems, J. Math. Anal. Appl. 241(2000), no. 1, 46-55.
  • [20] L. Zhang, C. Fang, and S. Chen, An inertial subgradient-type method for solving single-valued variational inequalities and fixed point problems, Numer. Algorithms 79(2018), no. 3, 941-956.
  • [21] A. N. Iusem and B. F. Svaiter, A variant of Korpelevichas method for variational inequalities with a new search strategy, Optimization 42(1997), no. 4, 309-321.
  • [22] D. V. Thong and D. V. Hieu, Modified subgradient extragradient method for variational inequality problems, Numer. Algorithms 79(2018), no. 2, 597-610.
  • [23] D. V. Thong and D. V. Hieu, Weak and strong convergence theorems for variational inequality problems, Numer. Algorithms 78(2018), no. 4, 1045-1060.
  • [24] H. U. Rehman, N. Pakkaranang, A. Hussain, and N. Wairojjana, A modified extra-gradient method for a family of strongly pseudomonotone equilibrium problems in real Hilbert spaces, J. Math. Comput. Sci. 22(2021), no. 1, 38-48.
  • [25] N. Wairojjana, H. U. Rehman, I. K. Argyros, and N. Pakkaranang, An accelerated extragradient method for solving pseudomonotone equilibrium problems with applications, Axioms 9(2020), no. 3, 99, DOI: https://doi.org/10.3390/axioms9030099.
  • [26] N. Wairojjana, H. U. Rehman, M. D. la Sen, N. Pakkaranang, A general inertial projection-type algorithm for solving equilibrium problem in Hilbert spaces with applications in fixed-point problems, Axioms 9(2020), no. 3, 101,DOI: https://doi.org/10.3390/axioms9030101.
  • [27] H. U. Rehman, N. Pakkaranang, P. Kumam, and Y. J. Cho, Modified subgradient extragradient method for a family of pseudomonotone equilibrium problems in real a Hilbert space, J. Nonlinear Convex Anal. 21(2020), no. 9, 2011-2025.
  • [28] H. U. Rehman, P. Kumam, Q. L. Dong, and Y. J. Cho, A modified self-adaptive extragradient method for pseudomonotone equilibrium problem in a real Hilbert space with applications, Math. Methods Appl. Sci. 44(2021), no. 5, 3527-3547.
  • [29] A. S. Antipin, On a method for convex programs using a symmetrical modification of the Lagrange function, Economika i Matem. Metody 12(1976), no. 3, 1164-1173.
  • [30] J. Yang, H. Liu, and Z. Liu, Modified subgradient extragradient algorithms for solving monotone variational inequalities, Optimization 67(2018), no. 12, 2247-2258.
  • [31] B. Polyak, Some methods of speeding up the convergence of iteration methods, U.S.S.R. Comput. Math. Math. Phys. 4(1964), no. 5, 1-17.
  • [32] H. H. Bauschke and P. L. Combettes, Convex Analysis and Monotone Operator Theory in Hilbert Spaces, Springer, New York, 2011.
  • [33] H. K. Xu, Another control condition in an iterative method for nonexpansive mappings, Bull. Austral. Math. Soc. 65(2002), no. 1, 109-113.
  • [34] P. E. Maingé, Strong convergence of projected subgradient methods for nonsmooth and nonstrictly convex minimization, Set-Valued Anal. 16(2008), no. 7-8, 899-912.
  • [35] W. Takahashi, Nonlinear Functional Analysis, Yokohama Publishers, Yokohama, 2000.
  • [36] P. T. Harker and J. S. Pang, A damped-Newton method for the linear complementarity problem, in: E. L. Allgower, K. Georg (eds.), Computational Solution of Nonlinear Systems of Equations: AMS Lectures on Applied Mathematics, vol. 26, American Mathematical Society, Providence, RI, 1990, pp. 265-284.
  • [37] X. Hu and J. Wang, Solving pseudomonotone variational inequalities and pseudoconvex optimization problems using the projection neural network, IEEE Trans. Neural Networks 17(2006), no. 6, 1487-1499.
Uwagi
PL
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-f77ddb59-1815-40fd-b966-94c94deac1b7
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.