Outer approximation algorithms for equilibriums

Abstract: Equilibrium problems appear frequently in many practical problems arising from, for instance, physics, engineering, game theory, transportation, economics and network. They have become an attractive field for many researchers in both theory and applications. The paper concerns with some methods for finding an approximate solution of equilibrium problems. Using theoretical development methods, we present two new outer approximation algorithms for solving equilibrium problems on a convex subset C, where the underlying function is Lipschitz-type continuous and pseudomonotone. The algorithm is to define proximal point subproblems or projector point subproblems on the convex domains C C k ⊇ , C C C k k k k + = 1 0 ⊂ ∩ = , ∞ C, k = 0,1,. The strongly convergent theorems are established under standard assumptions imposed on cost mappings.

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35 TẠP CHÍ KHOA HỌC – ĐẠI HỌC TÂY BẮC Khoa học Tự nhiên và Công nghệ 1. Introduction Let C be a nonempty closed convex subset of nR� . We present outer approximation algorithms for finding a solution of equilibrium problems (shortly EP): * *Find such that ( , ) 0, .x C f x y y C∈ ≥ ∀ ∈ (EP) The area of equilibrium problems has attracted attention of researchers from applications in physics, engineering, game theory, transportation, economics and network ([1- 9]). problems are models whose formulation includes optimization, variational inequalities, multi-objective optimization problems, fixed point problems, saddle point problems, Nash equilibria and complementarity problems as particular cases ([5, 7, 11]). Recently, researchers have presented some methods to solve EP(f, C) with a monotone bifunction f. In [5], the authors used projection methods. The interior quadratic regularization methods is presented in [1,2]. In [4,12], the interior quadratic regularization technique hasbeen used to develop proximal iterative algorithm for variational inequalities. In [3], P.N. Anh, T. Kuno used a cutting hyperplane method for pseudomonotone equilibrium problems. In this paper we present two new outer approximation algorithms for solving equilibrium problems on a convex subset C. The first algorithm is to define proximal point subproblems on the convex domains kC C⊇ , 0 , k kC C ∞ =∩ = 1 ,k kC C+ ⊂ 0,1,...k = , which forms a generalized iteration scheme for finding a global equilibrium point. The second algorithm is to define projector point subproblems on the convex domain, which forms a generalized iteration scheme for finding two projector points on the kC . The paper is organized as follows. In sections 2 we review some definitions and basic results that will be used in our subsequent analysis. Section 3, will present two outer approximation algorithms for solving equilibrium problems on a convex subset C and prove convergence of the algorithms. 2. Preliminaries This section contains some definitions and basic results that will be used in our subsequent analysis. We first state the formal definition of some classes of functions that play an essential role in this paper. OUTER APPROXIMATION ALGORITHMS FOR EQUILIBRIUMS Tran Van Thang *, Nguyen Minh Khoa Electric Power University Abstract: Equilibrium problems appear frequently in many practical problems arising from, for instance, physics, engineering, game theory, transportation, economics and network. They have become an attractive field for many researchers in both theory and applications. The paper concerns with some methods for finding an approximate solution of equilibrium problems. Using theoretical development methods, we present two new outer approximation algorithms for solving equilibrium problems on a convex subset C, where the underlying function is Lipschitz-type continuous and pseudomonotone. The algorithm is to define proximal point subproblems or projector point subproblems on the convex domains kC C⊇ , 1 0, 0,1,, ...k k k kC C C kC ∞ + = =⊂ ∩ = The strongly convergent theorems are established under standard assumptions imposed on cost mappings. Keywords: Equilibrium problem; Proximal point problem; Pseudonomotone; Lipschitz-type continuous; Outer approximation algorithm. Trần Văn Thắng, Nguyễn Minh Khoa (2020) (18): 35 - 40 36 Let C be a nonempty closed convex subset of a real space nR� . A bifunction : nRf C C× → � is called to be (i) β -strongly monotone, if 2( , ) ( , ) , ;f x y f y x x y x y Cβ+ ≤ − − ∀ ∈\ \ (ii) monotone, if ( , ) ( , ) 0 , ;f x y f y x x y C+ ≤ ∀ ∈ (iii) pseudonomotone, if (x, y) 0f ≥ thì (y,x) 0, ,f x y C≥ ∀ ∈ (iv) Lipschitz-type continuous with constants 1 0c > and 2 0c > ([8]), if 2 1 2 2 ( , ) ( , ) ( , ) , , . f x y f y z f x z c x y c y z x y z C + ≥ − − − − ∀ ∈ 。 ャ。 ャ 。 ャ。 ャ Lemma 2.1. ([13]) Let { }ka , { }kb and { }kc be the three positive real numerical satisfying the following conditions: 1 0 (1 ) , 0, ,n n n n n n a b a c n b ∞ + = ≤ + + ∀ ≥ < +∞∑ 0 .n n c ∞ = < +∞∑ Then, lim na exists. 3. Outer approximation algorithms In this section, we present new outer approximation algorithms for solving equilibrium problems on a convex subset C. Its convergence analysis is postponed. We first state the assumptions that we will assume to hold through the rest of this paper. Assumption: (A1) f is pseumonotone on nR� (A2) f satisfies Lipschitz-type condition on nR� with two constants 1c and 2c . (A3) ( ,.)f x is convex and subdifferentiable on nR� for every fixed .nx R∈� (A4) f is lower semicontinuous on n nR R× �� . 3.1 Algorithm 1 Initialization: Choose the sequence kC such that kC C⊇ , 0 , k kC C ∞ =∩ = 1 ,k kC C+ ⊂ 0,1,...k = , 0 intx C∈ , positive sequences kλ Iteration k: 1 1 1 1 1 1 Find [ ( , ) ( , )] ( , ) , 0, . (1) k k k k k k k k k k k x C f x x f x x f x x x x x x x C λ + + + + + +  ∈  − + − − − ≥ ∀ ∈ Lemma 3.1.1. We fix sequences { }kC of closed convex subsets of nR� such that 1 0,k k k kC C C C ∞ + =⊂ ∩ = . Let { }kx be a sequences generated by Algorithm 1. If *x is a solution of EP(f , C) satisfying *x is a solution of problems ( , )kEP f C for all 0,1,...k = then 1 * 2 * 2 2 1 21 2 1 || || || || 1 2 1 2 || || , 1 2 k k k k kk k x x x x c c x x c λ λ λ + + − ≤ − − − − − − where 1 2 1 1 { , } 2 2k min c c λ < for all 0,1,...k = . Proof. Since ( , )f x y Lipschitz-type on nR� with two positive constants 1 2,c c , i.e., 2 1 2 2 ( , ) ( , ) ( , ) || || || || , , , , f x y f y z f x z c x y c y z x y z C + ≥ − − − − ∀ ∈ (2) It is equivalent to * 1 1 * 1 2 1 * 2 1 2 ( , ) ( , ) [ ( , ) + || || || || ]. k k k k k k k k k f x x f x x f x x c x x c x x λ λ+ + + +  − ≤  − + − (3) Since * 1( , )kx Sol f C +∈ for all 0,1,...k = and ( , )f x y is pseudomonotone on nR� we have * 1 *( , ) 0, ( , ) 0.k kf x x f x x+≤ ≤ (4) From (2), (3) and (4), it follows that 1 1 * 1 2 1 1 * 2 2 [ , || || || || . ] k k k k k k k x x x x c x x c x x λ+ + + + − − ≤ − + − (5) On the other hand, 1 * 2 * 2 1 2 1 1 * || || || || | | || 2 , . k k k k k k k x x x x x x x x x x + + + + − = − − − + − − (6) This together with (5) implies that 1 * 2 * 2 2 1 2 1 (1 2 ) || || || || (1 2 ) || || . k k k k k k c x x x x c x x λ λ + + − − ≤ − − − − So, we obtain 1 * 2 * 2 2 1 21 2 1 || || || || 1 2 1 2 | | || . 1 2 k k k k kk k x x x x c c x x c λ λ λ + + − ≤ − − − − − − � 37 Theorem 3.1.2. Let { }kx be a sequences generated by Algorithm 1, 1 2 1 1 0 min{ , } 2 2k c c λ< < and 1 k k λ +∞ = < +∞∑ . If there exists *x C∈ such that *x is a solution of problems ( , )kEP f C for all 0,1,...k = . Then { }kx is weakly convergent to *x . .Proof By Lemma 3.1.1, we have 1 * 2 * 2 2 1 || || || || , 1 2 k k k x x x x cλ + − ≤ − − or, ( )1 * 2 * 2 ,|| || 1 || ||k kkx x x xα+ − ≤ + − where 2 2 2 1 2 k k k c c λ α λ = − . Since 1 k k λ +∞ = < +∞∑ , we have 2 1 1 2 2 . 1 2 k k k k k c c λ α λ +∞ +∞ = = = < +∞ −∑ ∑ By Lemma 2.1, the sequence *|| ||kx x− is convergence, and so { }kx is bounded. Thus, there exists a subsequence { }jkx converging to x . From (1) and (3), it follows that 1 1 1* * 1 12 * 2 * 1 2 , ( , ) || || || || ( , ). [ ] j j j j j j j k k kk k k k kk j x x x x f x x c x x c x x f x x λ+ + + + + − − ≤ + − + − + (7) From (6) and (7), it follows that 1 1* 2 2 2 1 1* 2 * * (1 2 ) || || (1 2 ) || || || || 2 ( , ) ( , ). j j j j j j j j k k k k k j k k k k c x x c x x x x f x x f x x λ λ λ + + + − − + − − − − ≤ + Using (., )f y is continuous, let j →∞ , we obtain *( , ) 0.f x x ≥ By the monotonicity of f and * Sol( , )kx f C∈ , we conclude that *0 ( , ) 0.f x x≤ ≤ It follows that *x x= . 3.2 Algorithm 2 Initialization: Choose the sequence kC such that kC C⊇ , 0 , k kC C ∞ =∩ = 1 ,k kC C+ ⊂ 0 intx C∈ , positive sequences { }kλ . Iteration k: 2 1 2 1 argmin ( , ) 2 1 argmin ( , ) 2 k k k k k k y C k k k k y C y f x y y x x f y y y x λ λ ∈ + ∈   = + −        = + −    。 ャ。 ャ 。 ャ。 ャ Lemma 3.2.1. We fix sequences { }kC of closed convex subsets of nR� such that ,kC C⊂ and 1 00,1,2...,k k k kC C k C C ∞ + =⊂ ∀ = ∩ = . Let { }kx be a sequences generated by Algorithm (2). If *x is an solution of (EP) satisfying *x is a solution of problems ( , )kEP f C for all 0,1,...k = then 1 * 2 * 2 1 2 2 1 || || || || (1 2 ) || || (1 2 ) || || , k k k k k k k k x x x x c x y c x y λ λ + +− ≤ − − − − − − − for all 0,1,...k = . .Proof Since 1 21argmin{ ( , ) || || } 2k k k k k x C x f y y y xλ+ ∈ = + − , we have 2 1 2 1 1 0 ( , ) || || ( ) 2 ( ). k k k k k k C f y y y x x N x λ + +  ∈∂ + −    + Thus, there exist 12 ( , ) k kw f y x +∈∂ and 1( )kCw N x +∈ such that 1 0.k kk w x x wλ ++ − + = Hence, it follows from the definition of kC N that 1 1 1 1 1 , , , 0, . k k k k k k k k k k w x x x x w x x x x x x x C λ λ+ + + + + + − − = − − − − ≥ ∀ ∈So, 1 1 1, , k k k kkx x x x w x xλ + + +− − ≤ − .kx C∀ ∈ (8) By 12 ( , ) k kw f y x +∈∂ , we have 1 1, ( , ) ( , ),k k k kw x x f y x f y x+ +− ≤ − .nx R∀ ∈� (9) From relations (8) and (9), we obtain ( )1 1 1 ( , ) ( , ) , , . k k k k k k k k f y x f y x x x x x x C λ + + + − ≥ − − ∀ ∈ (10) Since * kx C∈ , we have 1 1 * * 1 , ( , ) ( , ) k k k k k k k x x x x f y x f y xλ + + + − − ≤  −  . Since * ( , )kx Sol f C∈ and ( , )f x y is pseudomonotone we have *( , ) 0.kf y x ≤ This together with (10) implies that 38 1 * 1 1, ( , )k k k k kkx x x x f y xλ + + +− − ≥ (11) Since ( , )f x y Lipschitz-type on nR� with two positive constants 1 2,c c , i.e., 2 2 1 2 ( , ) ( , ) ( , ) || || || || , , , . f x y f y z f x z c x y c y z x y z + ≥ − − − − ∀ Appying above inequality with 1,k k kx x y y z x += = = we have 1 1 2 1 2 1 2 ( , ) ( , ) ( , ) || || || || . k k k k k k k k k k f y x f x x f x y c y x c x y + + + ≥ − − − − − This together with (11) implies that 1 * 1 1 2 1 2 1 2 , [ ( , ) ( , ) || || || || ]. k k k k k k k k k k k k x x x x f x x f x y c y x c x y λ+ + + + − − ≥ − − − − − (12) Since 21argmin ( , ) 2k k k k k y C y f x y y xλ ∈  = + −    。 ャ。 ャ, by an argument similar to the above, we obtain , ( , ) ( , ) ,k k k k k kky x y y f x y f x yλ  − − ≥ −  .ky C∀ ∈ Appying above inequality with 1ky x += , we get 1 1, ( , ) ( , )k k k k k k k kky x y x f x x f x yλ + + − − ≥ −  This together with (12) and 1 * 2 * 2 1 2 1 1 * || || || | | || || 2 , , k k k k k k k x x x x x x x x x x + + + + − = − − − + − − implies that * 2 1 2 1 * 2 2 1 1 2 2 || || || || || || 2 , 2 || || 2 || || . k k k k k k k k k k k k k x x x x x x y x y y c y x c x y λ λ + + + − − − − − ≥ − − − − − − So that, 1 * 2 * 2 1 2 1 2 1 1 2 2 || || || || || || 2 , 2 || || 2 || || k k k k k k k k k k k k k k x x x x x x y x y x c y x c x y λ λ + + + + − ≤ − − − − − − + − + − * 2 1 2 1 2 1 1 2 2 || || || ( ) ( ) || 2 , 2 || || 2 || || k k k k k k k k k k k k k k k x x x y x y y x y x c y x c x y λ λ + + + = − − − − − − − − + − + − * 2 2 1 1 2 2 || || (1 2 ) || || (1 2 ) || || . k k k k k k k x x c y x c x y λ λ + = − − − − − − − Theorem 3.2.2. Assume that (A 1 )-(A 4 ) hold, sequence { }kλ satisfies kklim λ λ→+∞ = 1 2 1 1 0 min{ , }, 2 2( )k c c ρ λ γ < < ≤ − and there exists *x C∈ such that *x is a solution of problems ( , )kEP f C for all 0,1,...k = . Then { }kx is weakly convergent to *x . .Proof Using the assumption of { }kλ , we have 11 2 0kcλ− > and 21 2 0kcλ− > . By using Lemma 3.2.1, we obtain 1 * *|| || || ||, 0.k kx x x x k+ − ≤ − ∀ ≥ It follows that *{|| ||}kx x− is nondecreasing and lower bounded by 0 , and so *|| ||k k lim x x c →+∞ − = < +∞ . Hence, the *{|| ||}kx x− is bounded and there exists a subsequence { }jkx converging to x . Since 1 0,1,2...k kC C k+ ⊂ ∀ = and 0k kC C ∞ =∩ = , it is easily to check that x C∈ . By using Lemma 3.2.1, we get 2 2 1 1 * 2 1 * 2 (1 2 ) || || (1 2 ) || || || || || || . k k k k k k k c y x c y x x x x x ρ λ + − − ≤ − − ≤ − − − Due to 11 2 0cρ− > and *|| ||k k lim x x c →+∞ − = , it follows that || || 0.k k k lim x y →+∞ − = This implies that { }jky convergent to x because { }jkx convergent to x . On the other hand 21argmin ( , ) . 2 j j j j k j k k k k y C y f x y y xλ ∈  = + −    。 ャ。 ャ Since f is lower semicontinuous on C C× , letting j →+∞ yields 21argmin ( , ) : . 2 jk x f x y y x y Cλ = + − ∈    \ \ It follows that x is a solution of problems ( , ) jk EP f C for all 0,1,...j = and a solution of the problem (EP) (because jk C C⊂ ). From *|| ||jk k lim x x c →+∞ − = and replacing *x by x we have || || 0.jk k c lim x x →+∞ = − = Hence, the { }kx convergent to .x REFERENCE 1. P. N. Anh (2009), A logarithmic quadratic regularization method for 39 solving pseudomonotone equilibrium problems, Acta Mathematica Vietnamica, 34, 183–200. 2. P. N. Anh (2008), An LQP regularization method for equilibrium problems on polyhedral, Vietnam Journal of Mathematics, 36, 209-228. 3. P. N. Anh, T. Kuno (1999), A cutting hyperplane method for generalized monotone nonlipschitzian multivalued variational inequalities, in: Proceedings of 4th International Conference on High Performance Scientific Computing, Hanoi, 2010 (in press). 4. P. N. Anh (2008), An interior-quadratic proximal method for solving monotone generalized variational inequalities, East- West Journal of Mathematics, 10, 81-100. 5. P. Daniele, F. Giannessi, A. Maugeri (2003), Equilibrium Problems and Variational Models, Kluwer. 6. I.V. Konnov (2000), Combined Relaxation Methods for Variational Inequalities, Springer-Verlag, Berlin. 7. E. Blum, W. Oettli (1994), From optimization and variational inequality to equilibrium problems, The Mathematics Student, 63, 127-149. 8. G. Mastroeni (2004), Gap function for equilibrium problems, Journal of Global Optimization, 27, 411-426. 9. A. Moudafi (1999), Proximal point algorithm extended to equilibrium problem, Journal of Natural Geometry, 15, 91-100. 10. M.A. Noor (2004), Auxiliary principle technique for equilibrium problems, Journal of Optimization Theory and Applications, 122, 371-386. 11. G. Bigi, M. Castellani, M. Pappalardo (2009), A new solution method for equilibrium problems, Optimization Methods & Software, 24, 895-911. 12. A. Auslender, M. Teboulle, S. Bentiba (1999), A logarithmic-quadratic proximal method for ariational inequalities, Journal of Computational Optimization and Applications, 12, 31-40. 13. L. C. Ceng, J. C. Yao (2008), Approximate proximal algorithms for generalized variational inequalities with pseudomonone multifunction, Journal of Computational and Applied Mathematics, 213, 423-438. CONCLUSIONS we present two new outer approximation algorithms for solving equilibrium problems on a convex subset C with the cost function (x, y)f is pseumonotone and satisfies Lipschitz-type condition on nR� with two constants 1c and 2c . The algorithm is to define proximal point subproblems or projector point subproblems on the convex domains kC C⊇ , 1 0, 0,1,, ...k k k kC C C kC ∞ + = =⊂ ∩ = Under standard assumptions imposed on cost mappings we obtained strongly convergence theorem of the algorithms. 40 THUẬT TOÁN XẤP XỈ NGOÀI GIẢI BÀI TOÁN CÂN BẰNG Trần Văn Thắng*, Nguyễn Minh Khoa Trường Đại học Điện lực Tóm tắt: Gần đây, các bài toán cân bằng được ứng dụng một cách rộng rãi và hiệu quả trong nhiều lĩnh vực thực tế, như vật lý, kỹ thuật, lý thuyết trò chơi, giao thông, kinh tế và mạng internet. Bài toán đã thu hút nhiều nhà khoa học tham gia nghiên cứu cả về lý thuyết lẫn ứng dụng. Bài báo này quan tâm nghiên cứu phương pháp tìm nghiệm xấp xỉ của bài toán cân bằng. Sử dụng phương pháp phát triển lý thuyết, chúng tôi đưa ra hai thuật toán xấp xỉ ngoài tìm nghiệm gần đúng của bài toán cân bằng trên tập con lồi C với gải thiết hàm giá là liên tục kiểu Lipschitz và giả đơn điệu. Thuật toán được xác định bởi các dãy các bài toán điểm gần kề hay bài toán tìm điểm chiếu trên các miền lồi ,kC C⊇ 1 0, 0,1,, ...k k k kC C C kC ∞ + = =⊂ ∩ = Với các giả thiết cơ bản của hàm giá, chúng tôi đã thu các định lý hội tụ mạnh của các thuật toán trong bài báo. Từ khóa: Bài toán cân bằng; Bài toán điểm gần kề; Giả đơn điệu; Liên tục kiểu Lipschitz; Thuật toán xấp xỉ ngoài. ______________________________________________ Ngày nhận bài: 27/9/2019. Ngày nhận đăng: 10/11/2019 Liên lạc: Trần Văn Thắng; Email: thangtv@epu.edu.vn .
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