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Ergodicity of the implicit midpoint rule for nonexpansive mappings

Journal of Inequalities and Applications20152015:4

https://doi.org/10.1186/1029-242X-2015-4

  • Received: 24 October 2014
  • Accepted: 8 December 2014
  • Published:

Abstract

We prove a mean ergodic theorem for the implicit midpoint rule for nonexpansivemappings in a Hilbert space. We obtain weak convergence for the general case andstrong convergence for certain special cases.

MSC: 47J25, 47N20, 34G20, 65J15.

Keywords

  • ergodic
  • implicit midpoint rule
  • nonexpansive mapping
  • projection
  • Hilbert space

1 Introduction

The first mean ergodic theorem for nonlinear noncompact operators was proved byBaillon [1]. Let C be a closed convex subset of a Hilbert space Hand let be a nonexpansive mapping (i.e., for all ) with fixed points. Then, for each , the Cesàro means

converge weakly to a fixed point of T. This mean ergodic theorem wasextended by Bruck [2] to the setting of Banach spaces that are uniformly convex and have aFréchet differentiable norm. Baillon and Clement [3] also investigated ergodicity of the nonlinear Volterra integral equationsin Hilbert spaces.

It is quite natural to consider ergodic convergence of iterative algorithms in thecase where the sequences generated by the algorithms either are not guaranteed toconverge or not convergent at all. For instance, the double-backward method ofPassty [4] generates a sequence in the recursive manner:
(1.1)
where A and B are maximal monotone operators in a Hilbert spacesuch that is also maximal monotone and the inclusion is solvable, and and are the resolvents of A and B,respectively, that is, and . It is well known [5] that the sequence generated by the double-backward method (1.1) failsto converge weakly, in general. However, Passty [4] showed that if the sequence of parameters, , is in , then the averages
(1.2)

converge weakly to a solution to the inclusion .

The implicit midpoint rule (IMR) for nonexpansive mappings in a Hilbert spaceH, inspired by the IMR for ordinary differential equations [612], was introduced in [13]. This rule generates a sequence via the semi-implicit procedure:
(1.3)

where the initial guess is arbitrarily chosen, for all n, and is a nonexpansive mapping with fixed points.

The IMR (1.3) is proved to converge weakly [13] in the Hilbert space setting provided the sequence satisfies the two conditions:

(C1) for all and some , and

(C2) .

However, this algorithm may fail to converge weakly without the assumption (C2). Wetherefore turn our attention to the ergodic convergence of the algorithm. We willshow that for any sequence in the interval , the mean averages as defined by (1.2) will always converge weakly to afixed point of T as long as is an approximate fixed point of T(i.e., ). We will also show that under certain additionalconditions the means can converge in norm to a fixed point of T.This paper is organized as follows. In the next section we introduce the concept ofnearest point projections and properties of nonexpansive mappings. The main resultsof this paper (i.e., weak and strong ergodicity of the IMR (1.3)) arepresented in Section 3.

2 Preliminaries

Let C be a nonempty closed convex subset of a Hilbert space H.Recall that the nearest point projection from H to C, , is defined by
(2.1)

We need the following characterization of projections.

Lemma 2.1LetCbe a nonempty closed convex subset of a Hilbert spaceH. Given and , then if and only if any one of the following properties is satisfied:
  1. (i)

    for all ;

     
  2. (ii)

    for all ;

     
  3. (iii)

    for all .

     
Recall that a mapping is said to be nonexpansive if
A point such that is said to be a fixed point of T. The set ofall fixed points of T is denoted by , namely,

In the rest of this paper we always assume .

We need the demiclosedness principle of nonexpansive mappings as described below.

Lemma 2.2[14]

LetCbe a closed convex subset of a Hilbert spaceHand let be a nonexpansive mapping. Then the mapping is demiclosed in the sense that, for any sequence ofC, the following implication holds:

Next we need the following lemma (not hard to prove).

Lemma 2.3[15]

For each integer , such that , points , and any nonexpansive mapping , we have
(2.2)
Recall also that the implicit midpoint rule (IMR) [13] generates a sequence by the recursion process
(2.3)

where for all n, and is a nonexpansive mapping.

The following properties of the IMR (2.3) are proved in [13].

Lemma 2.4Let be any sequence in and let be the sequence generated by the IMR (2.3). Then
  1. (i)
    for all and . In particular, is bounded, and moreover, we have
    (2.4)
     
  2. (ii)

    .

     
  3. (iii)

    .

     

The convergence of the IMR (2.3) is proved in [13].

Theorem 2.5LetCbe a nonempty closed convex subset of a Hilbert spaceHand be a nonexpansive mapping with . Assume is generated by the IMR (2.3) where the sequence of parameters satisfies the conditions (C1) and (C2) in theIntroduction. Then converges weakly to a fixed point ofT.

3 Ergodicity

In this section we discuss the ergodic convergence of the sequence generated by the IMR (2.3), that is, the convergenceof the means
(3.1)
where is a sequence of positive numbers such that
(3.2)

Set and let be the nearest point projection from H toF.

Lemma 3.1The sequence is convergent in norm.

Proof First observe that
(3.3)
As a matter of fact, we get for , by Lemma 2.1(i) and Lemma 2.4(i),

That is, is decreasing and (3.3) is proven.

Applying the inequality (Lemma 2.1(iii))
(3.4)
to the case where and (with ) together with Lemma 2.4(i), we get

The strong convergence of follows immediately from the fact(3.3). □

Remark 3.2 The limit of , which we denote by , can also be identified as the asymptotic center ofthe sequence with respect to the fixed point set F ofT. In other words,
(3.5)
As a matter of fact, by (3.4) we get, for any ,
Upon taking limsup we immediately obtain

Hence, (3.5) holds.

Theorem 3.3LetCbe a closed convex subset of a Hilbert spaceHand let be a nonexpansive mapping such that . Assume is any sequence of positive numbers in the unit interval and let be the sequence generated by the IMR (2.3). Define the means by (3.1), where the weights are all positive and satisfy the condition (3.2). Assume, inaddition, . Then converges weakly to a pointz, where (in norm).

Proof Let which is well defined by Lemma 3.1. ByLemma 2.1(ii), we have, for each k,
It turns out that, for ,

(Here M is a constant such that for all k.)

By multiplying by and then summing up from to n, we conclude
(3.6)
We now claim that
(3.7)

Consequently, by Lemma 2.2, each weak cluster point of falls in F.

To see (3.7), we will prove that
(3.8)
for all n big enough, where as . For the sake of simplicity, we may, due to theassumption , assume that
(3.9)

for all n.

Let for and let M be a constant such that . For each n, we put for and apply (2.2) to get
(3.10)
Combining (3.9) and (3.10), we derive that
(3.11)

It turns out that (3.8) with .

Now since in norm, we see that the means in norm, as well. Consequently, if is a subsequence weakly converging to some point , it follows from (3.6) that
(3.12)

This together with the fact that implies that . That is, z is the only weak cluster pointof the sequence and therefore, we must have weakly. □

Remark 3.4 In Theorem 3.3 we assumed that . This assumption is guaranteed if the sequence satisfies the condition (C2) in the Introduction,that is, . Indeed, by (C2) and Lemma 2.4(ii), we find
(3.13)
Since the definition of IMR (2.3) yields
we also have
(3.14)
Combining (3.13) and (3.14), we infer that
Remark 3.5 If we assume (3.9) holds for all , then we need some more delicate technicalitiesdealing with (3.10). We may proceed as follows. Decompose (for ) as
where
As , we may assume . Repeating the argument for (3.10) and (3.11), we get
Let . We finally obtain, for ,

Next we show that in some circumstances, the sequence can converge strongly.

Theorem 3.6Let the assumptions of Theorem 3.3 holds. Then thesequence converges in norm to the point if, in addition, any one of the following conditions issatisfied:
  1. (i)

    The fixed point setFofThas nonempty interior.

     
  2. (ii)
    Tis a contraction, that is,
     
where is a constant. In this case, the sequence generated by the IMR (2.3) converges in norm to the unique fixed pointofT.
  1. (iii)

    Tis compact, namely, Tmaps bounded sets to relatively norm-compact sets.

     

Proof (i) By assumption, we have and such that

  • for all such that .

Therefore, upon substituting for u in (3.6) we obtain
(3.15)

for all such that .

Taking the supremum in (3.15) over such that immediately yields
This verifies that in norm.
  1. (ii)
    Since T is a contraction, T has a unique fixed point which is denoted by p. By (2.3) we deduce that (noticing )
     
It turns out that
and hence
Since , we must have . However, since the sequence is decreasing, we must have . Namely, in norm, and so in norm.
  1. (iii)

    Since T is compact and since is weakly convergent, is relatively norm-compact. This together with (3.7) evidently implies that is relatively norm-compact. Therefore, must converge in norm to . □

     

Declarations

Acknowledgements

The authors are grateful to the anonymous referees for their helpful comments andsuggestions, which improved the presentation of this manuscript. This projectwas funded by the Deanship of Scientific Research (DSR), King AbdulazizUniversity, under grant No. (49-130-35-HiCi). The authors, therefore,acknowledge technical and financial support of KAU.

Authors’ Affiliations

(1)
Department of Mathematics, School of Science, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, China
(2)
Department of Mathematics, King Abdulaziz University, P.O. Box 80203, Jeddah, 21589, Saudi Arabia
(3)
Department of Mathematics, Sciences Faculty for Girls, King AbdulazizUniversity, P.O. Box 4087, Jeddah, 21491, Saudi Arabia

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Copyright

© Xu et al.; licensee Springer. 2015

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative CommonsAttribution License (http://creativecommons.org/licenses/by/4.0), which permitsunrestricted use, distribution, and reproduction in any medium, provided theoriginal work is properly credited.

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