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Convergence analysis of an iterative algorithm for monotone operators
Journal of Inequalities and Applications volume 2013, Article number: 199 (2013)
Abstract
In this paper, an iterative algorithm is proposed to study some nonlinear operators which are inverse-strongly monotone, maximal monotone, and strictly pseudocontractive. Strong convergence of the proposed iterative algorithm is obtained in the framework of Hilbert spaces.
MSC:47H05, 47H09.
1 Introduction
Splitting methods have recently received much attention due to the fact that many nonlinear problems arising in applied areas such as image recovery, signal processing, and machine learning are mathematically modeled as a nonlinear operator equation, and this operator is decomposed as the sum of two nonlinear operators. Study of fixed (zero) point approximation algorithms for computing fixed (zero) points constitutes now a topic of intensive research efforts. Many well-known problems can be studied by using algorithms which are iterative in their nature. As an example, in computer tomography with limited data, each piece of information implies the existence of a convex set in which the required solution lies. The problem of finding a point in the intersection of these convex sets is then of crucial interest, and it cannot be usually solved directly. Therefore, an iterative algorithm must be used to approximate such a point. The well-known convex feasibility problem which captures applications in various disciplines such as image restoration and radiation therapy treatment planning is to find a point in the intersection of common fixed (zero) point sets of a family of nonlinear mappings; see, for example, [1–16].
In this paper, we will investigate the problem of finding a common solution to inclusion problems and fixed point problems based on an iterative algorithm. Strong convergence of the proposed iterative algorithm has been obtained in the framework of Hilbert spaces.
The organization of this paper is as follows. In Section 2, we provide some necessary preliminaries. In Section 3, an iterative algorithm is proposed and analyzed. Some subresults of the main results are also discussed in this section.
2 Preliminaries
From now on, we always assume that H is a real Hilbert space with the inner product and the norm , respectively. Let C be a nonempty closed convex subset of H.
Let be a mapping. stands for the fixed point set of S; that is, .
Recall that S is said to be nonexpansive iff
S is said to be asymptotically nonexpansive iff there exists a sequence with such that
Recall that S is said to be strictly pseudocontractive iff there exits a positive constant κ such that
S is said to be asymptotically strictly pseudocontractive iff there exits a positive constant κ and a sequence with such that
Let be a mapping. Recall that A is said to be monotone iff
A is said to be inverse-strongly monotone iff there exists a constant such that
For such a case, A is also said to be α-inverse-strongly monotone. It is not hard to see that inverse-strongly monotone mappings are Lipschitz continuous.
A multivalued operator with the domain and the range is said to be monotone if for , , and , we have . A monotone operator T is said to be maximal if its graph is not properly contained in the graph of any other monotone operator. Let I denote the identity operator on H and be a maximal monotone operator. Then we can define, for each , a nonexpansive single-valued mapping by . It is called the resolvent of T. We know that for all and is firmly nonexpansive; see [17–23] and the references therein.
Recently, many authors have investigated the solution problems of nonlinear operator equations or inequalities based on iterative methods; see, for instance, [24–33] and the references therein. In [19], Kamimura and Takahashi investigated the problem of finding zero points of a maximal monotone operator via the following iterative algorithm:
where is a sequence in , is a positive sequence, is a maximal monotone and . They showed that the sequence generated in (2.1) converges weakly to some provided that the control sequence satisfies some restrictions.
Recall that the classical variational inequality is to find an such that
In this paper, we use to denote the solution set of (2.2). It is known that is a solution to (2.1) iff x is a fixed point of the mapping , where is a constant, I stands for the identity mapping, and stands for the metric projection from H onto C. If A is α-inverse-strongly monotone and , then the mapping is nonexpansive; see [28] for more details. It follows that is closed and convex.
In [28], Takahashi an Toyoda investigated the problem of finding a common solution of variational inequality problem (2.1) and a fixed point problem involving nonexpansive mappings by considering the following iterative algorithm:
where is a sequence in , is a positive sequence, is a nonexpansive mapping and is an inverse-strongly monotone mapping. They proved that the sequence generated in (2.3) converges weakly to some provided that the control sequence satisfies some restrictions.
In [29], Tada and Takahashi investigated the problem of finding a common solution of an equilibrium problem and a fixed point problem involving nonexpansive mappings by considering the following iterative algorithm:
for each , where is a sequence in , is a positive sequence, is a nonexpansive mapping and is a bifunction. They showed that the sequence generated in (2.4) converges weakly to some , where stands for the solution set of the equilibrium problem, provided that the control sequence satisfies some restrictions.
In [30], Manaka and Takahashi introduced the following iteration:
where is a sequence in , is a positive sequence, is a nonexpansive mapping, is an inversely-strongly monotone mapping, is a maximal monotone operator, is the resolvent of B. They showed that the sequence generated in (2.5) converges weakly to some provided that the control sequence satisfies some restrictions.
In this paper, motivated by the above results, we consider the problem of finding a common solution to the zero point problems involving two monotone operators and fixed point problems involving asymptotically strictly pseudocontractive mappings based on a one-step iterative method. Weak convergence theorems are established in the framework of Hilbert spaces.
In order to obtain our main results in this paper, we need the following lemmas.
Recall that a space is said to satisfy Opial’s property [34] if, for any sequence with , where ⇀ denotes the weak convergence, the inequality
holds for every with . Indeed, the above inequality is equivalent to the following:
Lemma 2.1 [20]
Let C be a nonempty, closed, and convex subset of H, be a mapping, and be a maximal monotone operator. Then .
Lemma 2.2 Let H be a real Hilbert space. For any and , the following holds:
Lemma 2.3 [35]
Let , , and be three nonnegative sequences satisfying the following condition:
where is some nonnegative integer, and . Then the limit exists.
Lemma 2.4 [36]
Let C be a nonempty closed convex subset of H and S be an asymptotically κ-strictly pseudocontractive mapping. Then we have
-
(a)
S is uniformly Lipschitz continuous;
-
(b)
is demiclosed at zero, that is, if is a sequence in C with and , then .
The following lemma can be obtained from [37] immediately.
Lemma 2.5 Let H be a real Hilbert space. The following holds:
where denotes some positive integer, are real numbers with in and .
3 Main results
Theorem 3.1 Let C be a nonempty closed convex subset of H. Let be some positive integer and be an asymptotically strictly pseudocontractive mapping with the constant κ and the sequence . Let be an inverse-strongly monotone mapping with the constant and be a maximal monotone operator on H such that the domain of is included in C for each . Assume . Let and are real number sequences in . Let  , and be positive real number sequences. Let be a sequence in C generated in the following iterative process:
where is the resolvent of . Assume that the sequences , , , and satisfy the following restrictions:
-
(a)
and , ;
-
(b)
;
-
(c)
, ;
-
(d)
,
where a, b, c, and d are positive real numbers. Then the sequence generated in (3.1) converges weakly to some point in ℱ.
Proof First, we show is nonexpansive. In view of the restriction (c), we find that
This proves that is nonexpansive. Let . In view of Lemma 2.1, we find that
Putting , we find that
In view of Lemma 2.2, we find from the restriction (b) that
From (3.2) and (3.3), we have
We draw the conclusion that exists with the aid of Lemma 2.3. This implies that the sequence is bounded. In view of Lemma 2.5, we find that
which yields
In view of the restriction (a), we find that
On the other hand, we have
It follows that
This in turn implies that
It follows from the restrictions (b) and (d) that
Notice that
It follows that
This implies that
which finds that
In view of the restriction (a), we find from (3.8) that
Notice that
From (3.6) and (3.10), we obtain that
On the other hand, we have
which yields
This implies from the restriction (c) and (3.11) that
Notice that
This implies from (3.10) and (3.11) that
On the other hand, we have
Since S is uniformly continuous, we obtain from (3.12) and (3.13) that
Since is bounded, there exists a subsequence of such that . We find that with the aid of Lemma 2.4.
Next, we show for every . In view of (3.10), we can choose a subsequence of such that . Notice that
This implies that
That is,
Since is monotone, we get for any that
Replacing n by and letting , we obtain from (3.10) that
This means , that is, . Hence we get for every . This completes the proof that .
Suppose there is another subsequence of such that . Then we can show that in the same way. Assume . Since exits for any . Put . Since the space satisfies Opial’s condition, we see that
This is a contradiction. This shows that . This proves that the sequence converges weakly to . This completes the proof. □
If , then we have the following.
Corollary 3.2 Let C be a nonempty closed convex subset of H. Let be an asymptotically strictly pseudocontractive mapping with the constant κ and the sequence . Let be an inverse-strongly monotone mapping with the constant α, and B be a maximal monotone operator on H such that the domain of B is included in C. Assume . Let , , and be real number sequences in . Let be a positive real number sequence. Let be a sequence in C generated in the following iterative process:
where is the resolvent of B. Assume that the sequences , , , , and satisfy the following restrictions:
-
(a)
and , ;
-
(b)
;
-
(c)
;
-
(d)
,
where a, b, c, and d are positive real numbers. Then the sequence converges weakly to some point in ℱ.
If S is asymptotically nonexpansive, then we find from Theorem 3.1 the following by letting .
Corollary 3.3 Let C be a nonempty closed convex subset of H. Let be some positive integer and be an asymptotically nonexpansive mapping with the sequence . Let be an inverse-strongly monotone mapping with the constant and let be a maximal monotone operator on H such that the domain of is included in C for each . Assume . Let , and be real number sequences in . Let  , and be positive real number sequences. Let be a sequence in C generated in the following iterative process:
where is the resolvent of . Assume that the sequences , , , and satisfy the following restrictions:
-
(a)
and , ;
-
(b)
, ;
-
(c)
,
where a, b and c are positive real numbers. Then the sequence converges weakly to some point in ℱ.
If S is the identity mapping, then we draw from Theorem 3.1 the following.
Corollary 3.4 Let C be a nonempty closed convex subset of H. Let be some positive integer. Let be an inverse-strongly monotone mapping with the constant and let be a maximal monotone operator on H such that the domain of is included in C for each . Assume . Let  , and be real number sequences in . Let  , and be positive real number sequences. Let be a sequence in C generated in the following iterative process:
where is the resolvent of . Assume that the sequences ,  , and satisfy the following restrictions:
-
(a)
and , ;
-
(b)
, ,
where a, b, and c are positive real numbers. Then the sequence converges weakly to some point in ℱ.
Let be a proper lower semicontinuous convex function. Define the subdifferential
for all . Then ∂f is a maximal monotone operator of H into itself; see [38] for more details. Let C be a nonempty closed convex subset of H and be the indicator function of C, that is,
Furthermore, we define the normal cone of C at v as follows:
for any . Then is a proper lower semicontinuous convex function on H and is a maximal monotone operator. Let for any and . From and , we get
where is the metric projection from H into C. Similarly, we can get that .
Corollary 3.5 Let C be a nonempty closed convex subset of H. Let be some positive integer and be an asymptotically strictly pseudocontractive mapping with the constant κ and the sequence . Let be an inverse-strongly monotone mapping with the constant for each . Assume . Let , and be real number sequences in . Let  , and be positive real number sequences. Let be a sequence in C generated in the following iterative process:
Assume that the sequences , , , and satisfy the following restrictions:
-
(a)
and , ;
-
(b)
;
-
(c)
, ;
-
(d)
,
where a, b, c, and d are positive real numbers. Then the sequence converges weakly to some point in ℱ.
Proof Putting for every , we see . We can immediately draw from Theorem 3.1 the desired conclusion. □
If S is the identity mapping, then we find from Corollary 3.5 the following.
Corollary 3.6 Let C be a nonempty closed convex subset of H. Let be some positive integer. Let be an inverse-strongly monotone mapping with the constant for each . Assume . Let  , and be real number sequences in . Let  , and be positive real number sequences. Let be a sequence in C generated in the following iterative process:
Assume that the sequences , , , and satisfy the following restrictions:
-
(a)
and , ;
-
(b)
, ;
-
(c)
,
where a, b, and c are positive real numbers. Then the sequence converges weakly to some point in ℱ.
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Cho, S.Y., Li, W. & Kang, S.M. Convergence analysis of an iterative algorithm for monotone operators. J Inequal Appl 2013, 199 (2013). https://doi.org/10.1186/1029-242X-2013-199
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DOI: https://doi.org/10.1186/1029-242X-2013-199