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Mixed VariationalLike Inequality for Fuzzy Mappings in Reflexive Banach Spaces
Journal of Inequalities and Applications volume 2009, Article number: 209485 (2009)
Abstract
Some existence theorems for the mixed variationallike inequality for fuzzy mappings (FMVLIP) in a reflexive Banach space are established. Further, the auxiliary principle technique is used to suggest a novel and innovative iterative algorithm for computing the approximate solution. Consequently, not only the existence of solutions of the FMVLIP is shown, but also the convergence of iterative sequences generated by the algorithm is also proven. The results proved in this paper represent an improvement of previously known results.
1. Introduction
The concept of fuzzy set theory was introduced by Zadeh [1]. The applications of the fuzzy set theory can be found in many branches of mathematical and engineering sciences including artificial intelligence, control engineering, management sciences, computer science, and operations research [2]. On the other hand, the concept of variational inequality was introduced by Hartman and Stampacchia [3] in early 1960s. These have been extended and generalized to study a wide class of problems arising in mechanics, physics, optimization and control, economics and transportation equilibrium, and so forth. The generalized mixed variationallike inequalities, which are generalized forms of variational inequalities, have potential and significant applications in optimization theory [4, 5], structural analysis [6], and economics [4, 7]. Motivated and inspired by the recent research work going on these two different fields, Chang [8], Chang and Huang [9], Chang and Zhu [10] and Noor [11] introduced and studied the concept of variational inequalities and complementarity problems for fuzzy mappings in different contexts.
It is noted that there are many effective numerical methods for finding approximate solutions of various variational inequalities (e.g., the projection method and its variant forms, linear approximation, descent and Newton's methods), and there are very few methods for general variationallike inequalities. For example, among the most effective numerical technique is the projection method and its variant forms; however, the projection type techniques cannot be extended for constructing iterative algorithms for mixed variationallike inequalities, since it is not possible to find the projection of the solution. Thus, the development of an efficient and implementable technique for solving variationallike inequalities is one of the most interesting and important problems in variational inequality theory. These facts motivated Glowinski et al. [12] to suggest another technique, which does not depend on the projection. The technique is called the auxiliary principle technique.
Recently, the auxiliary principle technique was extended by Huang and Deng [13] to study the existence and iterative approximation of solutions of the setvalued strongly nonlinear mixed variationallike inequality, under the assumptions that the operators are bounded closed values. On the other hand, by using the concept of strongly mixed monotone of a fuzzy mapping on a bounded closed convex set, the auxiliary principle technique was extended by Chang et al. [14] to study the existence and iterative approximation of solutions of the mixed variationallike inequality problem for fuzzy mappings in a Hilbert space.
In this paper, the mixed variationallike inequality problem for fuzzy mapping (FMVLIP) in a reflexive Banach space is studied, and some existence theorems for the problem are proved. We also prove the existence theorem for auxiliary problem of FMVLIP. Further, by exploiting the theorem, we construct and analyze an iterative algorithm for finding the solution of the FMVLIP. Finally, we discuss the convergence analysis of iterative sequence generated by the iterative algorithm.
2. Preliminaries
Throughout this paper, we assume that is a real Banach space with its topological dual , a nonempty convex subset of , is the generalized duality pairing between and , is the family of all nonempty bounded and closed subsets of , and is the Hausdorff metric on defined by
In the sequel we denote the collection of all fuzzy sets on by . A mapping from to is called a fuzzy mapping. If is a fuzzy mapping, then the set , for , is a fuzzy set in (in the sequel we denote by ) and , for each is the degree of membership of in .
A fuzzy mapping is said to be closed, if, for each , the function is upper semicontinuous; that is, for any given net satisfying , we have .
For and , the set
is called a cut set of .
A closed fuzzy mapping is said to satisfy condition , if there exists a function such that for each the set
is a nonempty bounded subset of .
Remark 2.1.
It is worth mentioning that if is a closed fuzzy mapping satisfying condition , then for each , the set . Indeed, let be a net and , then for each . Since the fuzzy mapping is closed, we have
This implies that , and so .
Let be a real reflexive Banach space with the dual space . In this paper, we devote our study to a class of mixed variationallike inequality problem for fuzzy mappings, which is stated as follows.
Let are two closed fuzzy mappings satisfying the condition with functions , respectively. and are two singlevalued mappings. Let be a real bifunction. We shall study the following problem :
The problem (2.5) is called a fuzzy mixed variationallike inequality problem, and we will denote by the solution set of the problem (2.5).
Now, let us consider some special cases of problem (2.5).
() Let be two ordinary setvalued mappings, and let be the mappings as in problem (2.5). Define two fuzzy mappings as follows:
where and are the characteristic functions of the sets and , respectively. It is easy to see that and both are closed fuzzy mappings satisfying condition with constant functions and , for all , respectively. Furthermore,
Thus, problem (2.5) is equivalent to the following problem:
This kind of problem is called the setvalued strongly nonlinear mixed variationallike inequality, which was studied by Huang and Deng [13], when .
() If is a Hilbert space, then problem (2.5) collapses to the following problem: Let are two closed fuzzy mappings satisfying the condition with functions , respectively. are two singlevalued mappings. Let be a real bifunction. We consider the following problem:
The inequality of type (2.9) was studied by Chang et al. [14] under the additional condition that is a nonempty bounded closed subset of .

(3)
If is a Hilbert space and f(u,v)=0, then problem (2.5) is equivalent to the following problem:
This is also a class of special fuzzy variationallike inequalities, which has been studying by many authors.
Evidently, for appropriate and suitable choice of the fuzzy mappings , mappings , the bifunction , and the space , one can obtain a number of the known classes of variational inequalities and variationallike inequalities as special cases from problem (2.5) (see [1, 4, 5, 7–19]).
The following basic concepts will be needed in the sequel.
Definition 2.2.
Let be a nonempty subset of a Banach space . Let be two closed fuzzy mappings satisfying the condition with functions , respectively. Let be mappings. Then
(i) is said to be cocoercive with respect to the first argument of , if there exists a constant , such that
for each , and for all ;
(ii) is Lipschitz continuous in the second argument with respect to the fuzzy mapping , if there exists a constant such that
for any and ;
(iii) is strongly monotone in the first argument with respect to the fuzzy mapping if there exists a constant such that
for any and . Similarly, strongly monotone of in the second argument with respect to the fuzzy mapping can be defined;
(iv) is said to be Lipschitz continuous if there exists a constant such that
for any ;
(v) is Lipschitz continuous, if there exists a constant such that
for any .
Definition 2.3.
The bifunction is said to be skewsymmetric, if
for all .
Remark 2.4.
The skewsymmetric bifunctions have properties which can be considered as an analogs of monotonicity of gradient and nonnegativity of a second derivative for a convex function. As for the investigations of the skewsymmetric bifunction, we refer the reader to [20].
Definition 2.5 (see [15, 21]).
Let be a nonempty convex subset of a Banach space . Let be a Fréchet differentiable function and . Then is said to be
(i)convex, if
for all
(ii)strongly convex, if there exists a constant such that
for all
Note that if for all , then is said to be strongly convex.
Throughout this paper, we shall use the notations "" and "" for weak convergence and strong convergence, respectively.
Remark 2.6.
() Assume that for each fixed the mapping is continuous from the weak topology to the weak topology. Let and be fixed, and let be a functional defined by
Then, it is easy to see that is a weakly continuous functional on .
() Let be a Fréchet differentiable function, and let be a mapping such that . If is an strongly convex functional with constant on a convex subset of then is strongly monotone with constant (see [19], Proposition??2.1).
The following lemma due to Zeng et al. [19] will be needed in proving our results.
Lemma 2.7 (see [19, Lemma??2]).
Let be a nonempty convex subset of a topological vector space and let be such that
(i)for each is lower semicontinuous on each nonempty compact subset of ;
(ii)for each finite set and for each ;
(iii)there exists a nonempty compact convex subset of such that for some , there holds
Then there exists , such that ??for all .
We also need the following lemma.
Lemma 2.8 (see [22]).
Let be a complete metric space and let and be any real number. Then, for every there exists such that .
In the sequel, we assume that and satisfy the following assumption.
Assumption 2.9.
Let be two mappings satisfying the following conditions:
(a) for each ;
(b)for each fixed is a concave function;
(c)for each fixed , the functional is weakly lower semicontinuous function from to , that is,
Remark 2.10.
It follows from Assumption 2.9(a) that and .
3. The Existence Theorems
Theorem 3.1.
Let be a real reflexive Banach space with the dual space , and be a nonempty convex subset of . Let be two closed fuzzy mappings satisfying the condition with functions , respectively. Let , and . Let be skewsymmetric and weakly continuous such that and is a proper convex, for each . Suppose that
(i) is cocoercive with respect to the first argument of with constant ;
(ii) is Lipschitz continuous with constant ;
(iii) is Lipschitz continuous and strongly monotone in the second argument with respect to with constant and , respectively.
If Assumption 2.9 is satisfied, then .
Proof.
For any , we define a function by
where .
Observe that, by is weakly continuous functional and since each fixed the functional is weakly lower semicontinuous, we have the functional is weakly lower semicontinuous for each . This shows that condition (i) in Lemma 2.7 holds. Next, we claim that satisfies condition (ii) in Lemma 2.7. If it is not true, then there exist a finite set and , such that for all , that is,
This gives
Note that for each is a convex functional, that is Hence,
From Assumption 2.9, we obtain
which is a contradiction. Thus condition (ii) in Lemma 2.7 holds. Since for each is a proper convex weakly lower semicontinuous functional and int, the element intcan be found. Moreover, by Proposition I.2.6 of Pascali and Sburlan [23, page 27], is subdifferentiable at . This means
Since is skewsymmetric, it follows that
Letting and be fixed, by using conditions (ii) and (iv) and equality , we have
Define and . Then is a weakly compact convex subset of . Furthermore, it is easy to see that for all . Thus, condition (iii) of Lemma 2.7 is satisfied. By Lemma 2.7, there exists such that for all , this means that
where . Hence, is a solution of the fuzzy variational like inequality (2.5), that is, . This completes the proof.
Remark 3.2.
If all assumptions to Theorem 3.1 hold and is strongly monotone in the first argument with respect to with constant , then the solution of problem (2.5) is unique up to the element . Indeed, supposing that and are elements in , we have
Taking in (3.10) and in (3.11) and adding two inequalities, since is skewsymmetric, we obtain
Using this one, in view of Remark 2.10, we have
Since is strongly monotone in the first argument with respect to with the constant and strongly monotone in the second argument with respect to with constant , we obtain
Since , we must have .
4. Convergence Analysis
4.1. Auxiliary Problem and Algorithm
In this section, we extend the auxiliary principle technique to study the fuzzy mixed variationallike inequality problem (2.5) in a reflexive Banach space . First, we give the existence theorem for the auxiliary problem for the problem (2.5). Consequently, we construct the iterative algorithm for solving the problem of type (2.5).
Let be a mapping, let be a given Fréchet differentiable convex functional, and let be a given positive real number. Given , we consider the following problem : find such that
The problem is called the auxiliary problem for fuzzy mixed variationallike inequality problem (2.5).
Theorem 4.1.
If the conditions of Theorem 3.1 hold and for each fixed is continuous from the weak topology to the weak topology. If the function is strongly convex with constant and the functional is weakly upper semicontinuous on for each , then the auxiliary problem has a unique solution.
Proof.
Let and be fixed. Define a functional by
Note that, for each fixed , the functional is weakly upper semicontinuous on , is continuous from the weak topology to the weak topology, and is weakly continuous. Thus, it is easy to see that for each fixed the function is weakly lower semicontinuous continuous on each weakly compact subset of and so condition (i) in Lemma 2.7 is satisfied. We claim that condition (ii) in Lemma 2.7 holds. If this is false, then there exist a finite set and a with and , such that
By Assumption 2.9, in light of Remark 2.10, together with the convexity of , we have
which is a contradiction. Thus, condition (ii) in Lemma 2.7 is satisfied. Note that the strong convexity of implies that is strongly monotone with constant ; see Remark 2.6(ii). By using the similar argument as in the proof of Theorem 3.1, we can readily prove that condition (iii) of Lemma 2.7 is also satisfied. By Lemma 2.7 there exists a point , such that for all . This implies that is a solution to the problem .
Now we prove that the solution of problem is unique. Let and be two solutions of problem (4.1). Then,
Taking in (4.5) and in (4.6), and adding these two inequalities, since and is skewsymmetric, we obtain
Thus, by is strongly monotone, we have
This implies that and the proof is completed.
By virtue of Theorem 4.1, we now construct an iterative algorithm for solving the fuzzy mixed variationallike inequalities problem (2.5) in a reflexive Banach space .
Let be fixed. For given , from Theorem 4.1, there is such that
Since , by Lemma 2.8, there exist and such that
Again by Theorem 4.1, there is such that
Since , by Lemma 2.8, there exist and such that
Continuing in this way, we can obtain the iterative algorithm for solving problem (2.5) as follows.
Algorithm 4.2.
Let be fixed. For given there exist the sequences and such that
4.2. Convergence Theorems
Now, we shall prove that the sequences and generated by Algorithm 4.2 converge strongly to a solution of problem (2.5).
Theorem 4.3.
Suppose that conditions of Theorem 4.1 hold, and the mapping are Lipschitzian continuous fuzzy mappings with Lipschitzian constant and , respectively. If then the iterative sequences obtained from Algorithm 4.2 converge strongly to a solution of problem (2.5).
Proof.
Let . Define a function by
By the strong convexity of , we have
Note that for all and is skewsymmetric. Since and , from the strong convexity of , and Algorithm 4.2 with it follows that
where .
Consider
Therefore, we have
Since , the inequality (4.18) implies that the sequence is strictly decreasing (unless ) and is nonnegative by (4.15). Hence it converges to some number. Thus, the difference of two consecutive terms of the sequence goes to zero, and so the sequence converges strongly to . Further, from Algorithm 4.2, we have
These imply that and are Cauchy sequence in , since is a convergence sequence. Thus, we can assume that and (as ). Noting and , we have
Hence we must have . Similarly, we can obtain . Finally, we will show that . In regarded of Assumption 2.9(c), for each fixed we have that the functional is an upper semicontinuous functional; this together with the weak continuity of the function , we obtain
This implies that , and the proof is completed.
Remark 4.4.
() Theorems 3.1 and 4.3 are the extension of the results by Chang et al. [14], from Hilbert setting to a general reflexive Banach space, but it is worth noting that the bounded condition of the convex set is not imposed here.
() Since every setvalued mapping is the fuzzy mapping, hence, all results obtained in this paper are still hold for any setvalued mappings .
Thus, our results can be view as a refinement and improvement of the previously known results for variational inequalities.
References
 1.
Zadeh LA: Fuzzy sets. Information and Computation 1965, 8: 338–353.
 2.
Zimmermann HJ: Fuzzy Set Theory and Its Applications. Kluwer Academic Publishers, Dordrecht, The Netherlands; 1988.
 3.
Hartman P, Stampacchia G: On some nonlinear elliptic differentialfunctional equations. Acta Mathematica 1966, 115: 271–310. 10.1007/BF02392210
 4.
Tian GQ: Generalized quasivariationallike inequality problem. Mathematics of Operations Research 1993,18(3):752–764. 10.1287/moor.18.3.752
 5.
Yao JC: The generalized quasivariational inequality problem with applications. Journal of Mathematical Analysis and Applications 1991,158(1):139–160. 10.1016/0022247X(91)902733
 6.
Panagiotopoulos PD, Stavroulakis GE: New types of variational principles based on the notion of quasidifferentiability. Acta Mechanica 1992,94(3–4):171–194. 10.1007/BF01176649
 7.
Cubiotti P: Existence of solutions for lower semicontinuous quasiequilibrium problems. Computers & Mathematics with Applications 1995,30(12):11–22. 10.1016/08981221(95)00171T
 8.
Chang SS: Variational Inequalities and Complementarity Problems Theory and Applications. Shanghai Scientific and Technological Literature, Shanghai, China; 1991.
 9.
Chang S, Huang NJ: Generalized complementarity problems for fuzzy mappings. Fuzzy Sets and Systems 1993,55(2):227–234. 10.1016/01650114(93)901355
 10.
Chang S, Zhu YG: On variational inequalities for fuzzy mappings. Fuzzy Sets and Systems 1989,32(3):359–367. 10.1016/01650114(89)902686
 11.
Noor MA: Variational inequalities for fuzzy mappings—I. Fuzzy Sets and Systems 1993,55(3):309–312. 10.1016/01650114(93)90257I
 12.
Glowinski R, Lions JL, Trémolières R: Numerical Analysis of Variational Inequalities, Studies in Mathematics and Its Applications. Volume 8. NorthHolland, Amsterdam, The Netherlands; 1981:xxix+776.
 13.
Huang N, Deng C: Auxiliary principle and iterative algorithms for generalized setvalued strongly nonlinear mixed variationallike inequalities. Journal of Mathematical Analysis and Applications 2001,256(2):345–359. 10.1006/jmaa.2000.6988
 14.
Chang SS, O'Regan D, Tan KK, Zeng LC: Auxiliary principle and fuzzy variationallike inequalities. Journal of Inequalities and Applications 2005,2005(5):479–494. 10.1155/JIA.2005.479
 15.
Ansari QH, Yao JC: Iterative schemes for solving mixed variationallike inequalities. Journal of Optimization Theory and Applications 2001,108(3):527–541. 10.1023/A:1017531323904
 16.
Fang YP, Huang NJ: Variationallike inequalities with generalized monotone mappings in Banach spaces. Journal of Optimization Theory and Applications 2003,118(2):327–338. 10.1023/A:1025499305742
 17.
Liu Z, Chen Z, Kang SM, Ume JS: Existence and iterative approximations of solutions for mixed quasivariationallike inequalities in Banach spaces. Nonlinear Analysis: Theory, Methods & Applications 2008,69(10):3259–3272. 10.1016/j.na.2007.09.015
 18.
Zeng LC: Iterative approximation of solutions to generalized setvalued strongly nonlinear mixed variationallike inequalities. Acta Mathematica Sinica 2005,48(5):879–888.
 19.
Zeng LC, Ansari QH, Yao JC: General iterative algorithms for solving mixed quasivariationallike inclusions. Computers & Mathematics with Applications 2008,56(10):2455–2467. 10.1016/j.camwa.2008.05.016
 20.
Antipin AS: Iterative gradient predictiontype methods for computing fixed points of extremal mapping. In Parametric Optimization and Related Topics, IV, Approximate Optimization. Volume 9. Edited by: Guddat J, Jonden HTh, Nizicka F, Still G, Twitt F. Peter Lang, Frankfurt am Main, Germany; 1997:11–24.
 21.
Hanson MA: On sufficiency of the KuhnTucker conditions. Journal of Mathematical Analysis and Applications 1981,80(2):545–550. 10.1016/0022247X(81)901232
 22.
Rus IA: Generalized Contractions and Applications. Cluj University Press, ClujNapoca, Romania; 2001:198.
 23.
Pascali D, Sburlan S: Nonlinear Mappings of Monotone Type. Martinus Nijhoff, The Hague, The Netherlands; Sijthoff & Noordhoff International, Alphen aan den Rijn, The Netherlands; 1978:x+341.
Acknowledgments
The authors wish to express their gratitude to the referees for a careful reading of the manuscript and helpful suggestions. This research is supported by the Centre of Excellence in Mathematics, the commission on Higher Education, Thailand.
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Kumam, P., Petrot, N. Mixed VariationalLike Inequality for Fuzzy Mappings in Reflexive Banach Spaces. J Inequal Appl 2009, 209485 (2009). https://doi.org/10.1155/2009/209485
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Keywords
 Variational Inequality
 Lower Semicontinuous
 Weak Topology
 Reflexive Banach Space
 Compact Convex Subset