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A general iterative algorithm for monotone operators with λ-hybrid mappings in Hilbert spaces
Journal of Inequalities and Applications volume 2014, Article number: 264 (2014)
Abstract
Let C be a nonempty closed convex subset of a Hilbert space ℋ, let B, G be two set-valued maximal monotone operators on C into ℋ, and let be a k-contraction with . is an α-inverse strongly monotone mapping, is a -strongly monotone and L-Lipschitzian mapping with and , is a λ-hybrid mapping. In this paper, a general iterative scheme for approximating a point of is introduced, where is the set of fixed points of T, and a strong convergence theorem of the sequence generated by the iterative scheme is proved under suitable conditions. As applications of our strong convergence theorem, the related equilibrium and variational problems are also studied.
MSC:47H05, 47H10, 58E35.
1 Introduction
Throughout this paper, ℋ denotes a real Hilbert space, C a nonempty closed convex subset of ℋ, ℕ the set of all natural numbers and ℝ the set of all real numbers. For a self-mapping T on ℋ, denotes the set of all fixed points of T.
A set-valued map with domain is called monotone if
for all and for any , ; B is said to be maximal monotone if its graph is not properly contained in the graph of any other monotone operator. For a positive real number r, the resolvent of a monotone operator B for r is a single-valued mapping defined by if and only if , that is, for any , where I is the identity mapping on ℋ. The Yosida approximation of B for is defined as . It is known [1] that for all .
The fixed point theory for nonexpansive mappings can be applied to the problem of finding a zero point v of a maximal monotone operator B on ℋ, that is, finding a point satisfying . In the sequel, we shall denote the set of all zero points of B by .
A self-mapping V on ℋ is called -strongly monotone if there is a positive real number such that
V is called L-Lipschitzian if there is a positive real number L such that
A mapping is said to be α-inverse strongly monotone if there is a positive real number α such that
As easily seen, an α-inverse strongly monotone mapping is -Lipschitzian on C.
For , a mapping is said to be λ-hybrid if
When , T is called nonspreading. It is known that is closed and convex provided T is a λ-hybrid self-mapping on C, cf. [2].
Recently, Lin and Takahashi [3] introduced an algorithm for finding a point , where A is an α-inverse strongly monotone mapping of C into ℋ, and B, G are two set-valued maximal monotone operators with and . More precisely, let g be a k-contraction and V be a -strongly monotone and L-Lipschitzian mapping. Choose so that and . Then the algorithm starts with any and generates a sequence iteratively by
where , , and satisfy
They proved that has a unique fixed point in Ω, and this is also a unique solution to the hierarchical variational inequality
As Lin and Takahashi said in [3], their idea for this algorithm comes from the works of Tian [4].
On the other hand, Manaka and Takahashi [5] used the algorithm
to find a point for a nonspreading mapping T, an α-inverse strongly monotone mapping A and a maximal monotone operator B under the conditions
where are fixed. They proved that the sequence constructed above converges weakly to a point .
Very recently, Liu et al. [6] modified the iterative scheme (2) to approximate a point for a nonspreading mapping T, an α-inverse strongly monotone mapping and a maximal monotone operator B. For any , they put to be any point of ℋ and define recursively for all ,
where is a suitable sequence in .
Motivated by the above works, in this paper we introduce a general iterative scheme for approximating a point of , where T is a λ-hybrid self-mapping on C, is an α-inverse strongly monotone mapping and B and G are two maximal monotone operators. A strong convergence theorem of the sequence generated by our iterative scheme is proved under suitable conditions. Our result improves and generalizes the main theorem of Lin and Takahashi [3]. As applications of our strong convergence theorem, the related equilibrium and variational problems are also studied.
2 Preliminaries
In order to facilitate our investigation, in what follows we recall some basic facts. A mapping is said to be
-
(i)
nonexpansive if
-
(ii)
firmly nonexpansive if
The metric projection from ℋ onto C is the mapping that assigns each the unique point in C with the property
It is known that is nonexpansive and characterized by the inequality: for any ,
cf. [7].
For any , one has
Lemma 2.1 (Demiclosedness principle) [8]
Let T be a nonexpansive self-mapping on a nonempty closed convex subset C of ℋ, and suppose that is a sequence in C such that converges weakly to some and . Then .
For , the resolvent of the maximal monotone operator B on ℋ has the following properties, cf. [9].
Lemma 2.2 Let B be a maximal monotone operator on ℋ. Then, for any ,
-
(a)
is single-valued and firmly nonexpansive;
-
(b)
and .
The following lemma can be derived easily from the resolvent identity of a monotone operator B:
Lemma 2.3 Let B be a monotone operator on ℋ. Then, for any with and for any ,
When B is maximal monotone, a different proof may be found in Takahashi et al. [9].
Lemma 2.4 [5]
Let be an α-inverse strongly monotone mapping, and let B be a maximal monotone operator on ℋ with . Then, for any , one has .
Lemma 2.5 [3]
Let be an α-inverse strongly monotone mapping. Then, for any , is nonexpansive.
Lemma 2.6 [3]
Let be a k-contraction with , let be a -strongly monotone and L-Lipschitzian mapping with and , and let γ be a real number satisfying . Then is a -strongly monotone and -Lipschitzian mapping. Furthermore, for any nonempty closed convex subset Ω of ℋ, has a unique fixed point , which is also a unique solution of the variational inequality
Lemma 2.7 [10]
Let be a sequence of nonnegative real numbers satisfying
where , and verify the following conditions:
-
(i)
, ;
-
(ii)
;
-
(iii)
and .
Then .
3 Strong convergence theorems
We begin the proof of the main result of this paper. As the proof is rather lengthy, we divide the proof into many assertions.
Theorem 3.1 Suppose that
(3.1.1) and are two maximal monotone operators with and ;
(3.1.2) is a k-contraction, is an α-inverse strongly monotone mapping, and is a -strongly monotone and L-Lipschitzian mapping with and ;
(3.1.3) is a λ-hybrid mapping;
(3.1.4) ;
(3.1.5) μ and γ are two real numbers satisfying and .
Start with any and define a sequence iteratively by
where the sequences , and verify the following conditions:
(3.1.6) is a sequence in with and ;
(3.1.7) and are sequences in so that and there are with for all .
Then the sequence constructed by algorithm (6) converges strongly to a point , where is the unique fixed point of , and this point is also a unique solution of the hierarchical inequality
Proof In what follows, p is a point in Ω, , and for all .
-
Assertion (A): There issuch that
Since , there is such that and for all . Then, as V is -strongly monotone and L-Lipschitzian, we have that for all ,
and so the assertion holds.
-
Assertion (B): The sequences, , , , , , andare bounded.
We firstly show that
On account of by Lemma 2.4, by assumption and the facts that a resolvent is nonexpansive and A is α-inverse strongly monotone, we have
where the last inequality follows from the hypothesis that . Therefore,
Since T is λ-hybrid, we have, for any ,
and so, by induction, it comes easily that
Consequently,
Next, we show that is bounded. Indeed, as , we have
which together with Assertion (A) implies that for all ,
from which we inductively deduce that
Thus, is bounded, and so are , by (9). And then the boundedness of follows from (12). The fact that and are bounded is due to the fact that V and g are L-Lipschitzian and k-contraction, respectively.
Finally, from , we deduce that is bounded, and is bounded comes from A is -Lipschitzian.
-
Assertion (C): .
We at first show that . By Assertion (B), we can choose a positive real number M so that
Then, for all ,
Hence .
Now, for all , we have from Assertion (A) that
Consequently, using condition (3.1.6), Assertion (B) and , we get .
-
Assertion (D): .
Using (5), it follows from (8), (9) and (11) that
Hence, on account of for all , we have
which together with Assertions (B) and (C) implies that .
-
Assertion (E): .
From
condition (3.1.6) and Assertions (B) and (C), we conclude that .
As is firmly nonexpansive, one has
and so
In addition, since A is α-inverse strongly monotone, we see from (10) that
Then (13), (14) and (15) give us that
Therefore
By Assertions (C), (D) and condition (3.1.6), we obtain
-
Assertion (F): Ω is a nonempty closed convex subset of ℋ.
Since Ω is nonempty by assumption, it suffices to show that Ω is closed and convex. From Lemma 2.4, we have that for any ,
In case , we have that is nonexpansive by Lemma 2.5. Then is nonexpansive, and so is closed and convex. In the like manner, for any , is closed and convex. Besides, it is shown in [2] that is closed and convex. Hence comes the conclusion for Ω.
-
Assertion (G): has a unique fixed pointin Ω, and thisis also a unique solutionto the hierarchical variational inequality (7)
Taking into account that Ω is a nonempty closed convex subset, the conclusion follows from Lemma 2.6.
-
Assertion (H): Letbe the unique solution to the hierarchical variational inequality (7). Then
Choose a subsequence of so that
and converges weakly to . In view of Assertion (E), we see that both of the sequences and converge weakly to w. We at first show that . Since T is λ-hybrid, one has for all . Express λ as . Then, for all , we have
that is,
In particular, for all , all and all ,
Summing these inequalities from to and dividing by n, we obtain
Replacing n with in (16) and letting , we get via Assertion (B) that
Putting in (17), we arrive at . This shows that .
Since , has a convergent subsequence. For simplicity, we assume that converges to a number . Note that for all ,
where . Furthermore, we have for all ,
and
Hence, is bounded. Now, replace n with in (18) and note that both and are bounded. Letting , we get that
Applying Lemma 2.1 to the nonexpansive mapping , we conclude that , that is, .
We now show that . Since G is a maximal monotone operator, the Yosida approximation of G for is in . So, for any , one has
Since , converges weakly to w and , we have
and then the maximality of T shows that , that is, .
In summary, we have shown that , and so, by Assertion (G), we conclude that
-
Assertion (I): The sequenceconverges strongly to.
Replacing p with in (13), we have
Hence
where . Since , and since
by Assertion (H), it follows from Lemma 2.7 that converges strongly to . This completes the proof. □
When T is the identity mapping, the theorem reduces to the following corollary.
Corollary 3.2 Suppose that
(3.2.1) and are two maximal monotone operators with and ;
(3.2.2) is a k-contraction, is an α-inverse strongly monotone mapping, and is a -strongly monotone and L-Lipschitzian mapping with and ;
(3.2.3) ;
(3.2.4) μ and γ are two real numbers satisfying and .
Start with any and define a sequence iteratively by
where the sequences , and verify the following conditions:
(3.2.5) is a sequence in with and ;
(3.2.6) and are sequences in so that and there are with for all .
Then has a unique fixed point in Ω, and this is also a unique solution to the hierarchical variational inequality
Here we would like to remark that Corollary 3.2 is related to Theorem 8 in Lin and Takahashi [3], although our conditions (3.2.5) and (3.2.6) are different from the corresponding ones in [3].
4 Applications
In this section, we shall apply Theorem 3.1 to study the related equilibrium problem. Let and . Then a generalized equilibrium problem is the problem of finding such that
The solution set for Eq. (19) is denoted by , that is,
In case , problem (19) reduces to the equilibrium problem of finding such that
whose solution set is denoted by . When , the generalized equilibrium problem becomes the variational problem of finding such that
whose solution set is denoted by . For solving an equilibrium problem, we assume that the function f satisfies the following conditions:
-
(A1)
, ;
-
(A2)
f is monotone, that is, , ;
-
(A3)
for all , ;
-
(A4)
for all , is convex and lower semicontinuous.
The following Lemma 4.1 appears implicitly in Blum and Oettli [11] and is proved in detail by Aoyama et al. [12], while Lemma 4.2 is Lemma 2.12 of Combettes and Hirstoaga [13].
Let be a function satisfying conditions (A1)-(A4), and let and . Then there exists a unique such that
Lemma 4.2 [13]
Let be a function satisfying conditions (A1)-(A4). For , define by
for all . Then the following hold:
-
(a)
is single-valued;
-
(b)
is firmly nonexpansive;
-
(c)
;
-
(d)
is closed and convex.
We call the resolvent of f for . Using Lemmas 4.1 and 4.2, Takahashi et al. [9] established the lemma below.
Lemma 4.3 [9]
Let be a function satisfying conditions (A1)-(A4) and define a set-valued mapping of ℋ into itself by
Then the following hold:
-
(a)
is a maximal monotone operator with ;
-
(b)
;
-
(c)
for all .
Theorem 4.4 Suppose that
(4.4.1) is a function satisfying conditions (A1)-(A4) and is a maximal monotone operator with ;
(4.4.2) is a k-contraction, is an α-inverse strongly monotone mapping, and is a -strongly monotone and L-Lipschitzian mapping with and ;
(4.4.3) is a λ-hybrid mapping;
(4.4.4) ;
(4.4.5) μ and γ are two real numbers satisfying and .
Start with any and define a sequence iteratively by
where the sequences , and verify the following conditions:
(4.4.6) is a sequence in with and ;
(4.4.7) and are sequences in so that and there are two with for all .
Then the sequence constructed by algorithm (20) converges strongly to a point , where is the unique fixed point of , and this point is also a unique solution of the hierarchical inequality
Proof The set-valued mapping associated with f defined in Lemma 4.3 is a maximal monotone operator with , and it follows from Lemmas 4.2 and 4.3 that for any . Putting in Theorem 3.1, we see that , and so the conclusion follows from Theorem 3.1. □
Since C is a nonempty closed convex subset of ℋ, the indicator function defined by
is a proper lower semicontinuous convex function, and its subdifferential defined by
is a maximal monotone operator, cf. Rockafellar [14]. As shown in Lin and Takahashi [3] the resolvent of for is the same as the metric projection .
Theorem 4.5 Suppose that
(4.5.1) is a function satisfying conditions (A1)-(A4);
(4.5.2) is a k-contraction, is an α-inverse strongly monotone mapping, and is a -strongly monotone and L-Lipschitzian mapping with and ;
(4.5.3) is a λ-hybrid mapping;
(4.5.4) ;
(4.5.5) μ and γ are two real numbers satisfying and .
Start with any and define a sequence iteratively by
where the sequences , and verify the following conditions:
(4.5.6) is a sequence in with and ;
(4.5.7) is a sequence in so that there are two with for all .
Then the sequence constructed by algorithm (21) converges strongly to a point , where is the unique fixed point of , and this point is also a unique solution of the hierarchical inequality
Proof Put in Theorem 3.1. Then, for and , we have that . Furthermore, as shown in Theorem 12 in Lin and Takahashi [3], we have
Thus we obtain the desired results from Theorem 3.1. □
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Hong, CC. A general iterative algorithm for monotone operators with λ-hybrid mappings in Hilbert spaces. J Inequal Appl 2014, 264 (2014). https://doi.org/10.1186/1029-242X-2014-264
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DOI: https://doi.org/10.1186/1029-242X-2014-264