Regularization method for Noor’s variational inequality problem induced by a hemicontinuous monotone operator
© Cho and Petrot; licensee Springer 2012
Received: 8 May 2012
Accepted: 13 September 2012
Published: 3 October 2012
In this paper, we provide a regularization method for finding a solution of Noor’s variational inequality problem induced by a hemicontinuous monotone operator. Moreover, such a solution is related to the set of zero of inverse strongly monotone mappings. Consequently, since we do not assume the strong monotonicity of the considered operator, our results are general and extend some well-known results in the literature.
1 Introduction and preliminaries
It is well known that the variational inequalities theory, which was introduced by Hartman and Stampacchia  in early 1960s, provides the most natural, direct, simple, unified and efficient framework for a general treatment of a wide class of linear and nonlinear problems. Of course, it has been extended and generalized in several directions.
for all with , where K is a nonempty closed convex subset of H and are mappings.
It is known that a class of nonsymmetric and odd-order obstacle, unilateral and moving boundary value problems arising in pure and applied sciences can be studied in the unified framework of general variational inequality (1.1), see [3–12] and the references therein. However, it is worth knowing that to guarantee the existence and uniqueness of a solution of the problem (1.1), one has to impose some conditions on the operator A and g (see  for an example in a more general case). Indeed, it has been shown that if A fails to be Lipschitz continuous or strongly monotone, then the solution set of the problem (1.1), which will be denoted by , may be empty.
for all . For a mapping , an element is called a fixed point of T if . Here, we will denote the set of all fixed points of the mapping T by . It is well known that the problem of finding the fixed point sets for the class of nonexpansive mappings is the subject of current interest in nonlinear functional analysis. A large number of mathematicians are interested in this field, one may see some important results in [14–17]. Moreover, it is well known that there are some relations between the fixed point problems and many types of variational inequality problems. Hence, of course, it is natural to consider a unified approach to these two different problems (see [13, 18–21] for examples).
for all .
For a mapping , we put . Further, if , then x is called a zero of A.
Remark 1.1 It is easy to see that any 1-inverse strongly monotone mapping is a nonexpansive mapping. Moreover, if T is a nonexpansive mapping, then it is well known that is -inverse strongly monotone mapping (see [22, 23]). Also, let us notice that the problem of finding an element of is equivalent to the problem of finding an element of . These mean that the problem of finding zeros of the inverse strongly monotone mappings contains the problem of finding fixed points of nonexpansive mappings as a special case.
for all with , where . Further, denotes a set of solutions of the problem (1.2).
If (: the zero operator) for all , then the problem (1.2) reduces to (1.1).
If (: the identity operator), then the problem (1.2) is reduced to the problem of finding the common element of the solution of classical variational inequality problem and the set S, which has been studied by many authors (see [23–25] for examples).
We need the following concepts in order to prove our results.
Throughout this paper, we let and stand for the set of real numbers and the set of natural numbers, respectively. Let be a bifunction with for all .
- (1)monotone if, for all ,
- (2)strongly monotone with constant τ if, for all ,
- (3)hemicontinuous in the first variable u if, for any fixed ,
for all .
for all .
Considering the problem (1.3), the following are very useful.
Lemma 1.4 
If is hemicontinuous in the first variable and has the monotonic property, then , where is the solution set of (1.3), is the solution set of for all . Moreover, in this case, they are closed and convex;
If is hemicontinuous in the first variable for each and F is strongly monotone, then is a nonempty singleton. In addition, if F is a strongly monotone mapping, then is a singleton set.
In order to prove our results, we also need the following concepts and facts.
for all .
for all , where I is the identity operator on K.
Remark 1.7 It is well known that if is a k-strictly pseudocontrative mapping, then the mapping is a -inverse strongly monotone. Conversely, if is a λ-inverse strongly monotone with , then is -strictly pseudocontractive mapping (see ).
Lemma 1.8 
Let K be a nonempty closed convex subset of a Hilbert space H and be a k-strictly pseudocontractive mapping. Then is demiclosed at zero, that is, whenever is a sequence in K such that converges weakly to and converges strongly to 0, we have .
From now on, the symbols ‘⇀’ and ‘→’ stand for ‘weak convergence’ and ‘strong convergence’, respectively.
Lemma 1.9 Let H be a real Hilbert space and be a sequence of H. If and , then .
Lemma 1.10 
, where ;
Then as .
for all with , where is a fixed real number.
For each , the problem (2.1) has the unique solution ;
for some ;
- (3)There exists a positive constant M such that(2.2)
for all .
for all . Note that since g is an affine continuous mapping, we have is a closed convex subset of H. Moreover, it is easy to see that and are monotone bifunctions on for each .
- (1)Observe that the problem (2.1) is equivalent to the problem of finding such that(2.4)
- (2)Observe that for each , we have and for each . This implies that(2.5)
for all . Thus, is a bounded subset of K. Consequently, the set of weak limit points as of the net , denoted by , is nonempty. This allows us to pick and a null sequence in the interval such that weakly converges to z as . Notice that since K is closed and convex, we know that K is weakly closed, and so . Consequently, since , we let be such that .
Now, we claim that . To prove this claim, we divide the proof into two steps:
On the other hand, we know that the mapping is strictly pseudocontractive for each , and hence by Lemma 1.8, we have is demiclosed at zero. It follows that . This means . Consequently, since is arbitrary chosen, we conclude that . This proves Step 1.
for all . Since the operator is a Lipschitzian mapping for each , it follows that is a bounded mapping for each . Thus, letting , since and as , it follows from (2.11) that for any , . Consequently, in view of Lemma 1.4(1), Step 2 is proved.
Hence, from Steps 1 and 2, we conclude that as required.
- (3)Let and , be the solutions of the problem (2.1) associated with α and β, respectively. Without loss of generality, we assume that . For each , we know that and are monotone mappings. Then by applying (2.4), we have
for some .
where . This completes the proof. □
Remark 2.2 As a special case of Theorem 2.1, if g is the identity operator on H, then we recover a recent result presented by Kim and Buong .
3 Main results
for all , , where and are sequences of positive real numbers.
The well-definedness of algorithm (3.1) is guaranteed by the following proposition:
for all . Then there exists a unique element such that for all .
This means is -strongly monotone. Consequently, by Lemma 1.4(2), the proof is completed. □
Now, we provide some sufficient conditions for the convergence of the regularization inertial proximal point algorithm (3.1).
then the sequence defined by (3.1) converges strongly to an element for some .
Consequently, by the condition (c), we have . Meanwhile, the conditions (b) and (c) imply . Thus, all the conditions of Lemma 1.10 are satisfied, and so as . Moreover, by the condition (a) and Theorem 2.1(2), we know that there exists such that converges strongly to . This implies that converges strongly to as . This completes the proof. □
Example 3.3 Let be a fixed real number. Let and for each . Then we can check that the sequences and satisfy all the conditions in Theorem 3.2.
Remark 3.4 Note that because of the condition (b) of Theorem 3.2, the choice is not included in the class of the parameters . This may lead to an important further research work, that is, finding other (regularization inertial proximal point) algorithms for the problem (1.2) including the natural parameter choice .
In this paper, we provide a regularization method for general monotone variational inequality, where the regularizer is a Lipschitz continuous and strongly monotone operator. Also, an iterative method as discretization of regularization method is introduced. We would like to point out, as we have mentioned in Remark 1.1 and Remark 1.2, that our results are general and extend some well-known results which have been investigated in literature; for more examples, see [29–34]. Further, let us notice that although many authors have proved results for finding the solution of the variational inequality problem and the solution set of a finite inverse strongly monotone mapping, it is clear that it cannot be directly applied to our main considered problem (1.2) due to the presence of g.
The authors wish to express their gratitude to the referees for a careful reading of the manuscript and helpful suggestions. This work is supported by the Centre of Excellence in Mathematics, the commission on Higher Education, Thailand and by the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education, Science and Technology (Grant Number: 2010-0021821)
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