Volume 2013, Issue 1 613928
Research Article
Open Access

A Unified Iterative Treatment for Solutions of Problems of Split Feasibility and Equilibrium in Hilbert Spaces

Young-Ye Huang

Young-Ye Huang

Department of Accounting Information, Southern Taiwan University of Science and Technology, 1 Nantai Street, Yongkang District, Tainan 71005, Taiwan stust.edu.tw

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Chung-Chien Hong

Corresponding Author

Chung-Chien Hong

Department of Industrial Management, National Pingtung University of Science and Technology, 1 Shuefu Road, Neipu, Pingtung 91201, Taiwan npust.edu.tw

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First published: 28 October 2013
Citations: 3
Academic Editor: Simeon Reich

Abstract

We at first raise the so called split feasibility fixed point problem which covers the problems of split feasibility, convex feasibility, and equilibrium as special cases and then give two types of algorithms for finding solutions of this problem and establish the corresponding strong convergence theorems for the sequences generated by our algorithms. As a consequence, we apply them to study the split feasibility problem, the zero point problem of maximal monotone operators, and the equilibrium problem and to show that the unique minimum norm solutions of these problems can be obtained through our algorithms. Since the variational inequalities, convex differentiable optimization, and Nash equilibria in noncooperative games can be formulated as equilibrium problems, each type of our algorithms can be considered as a generalized methodology for solving the aforementioned problems.

1. Introduction

Throughout this paper, denotes a real Hilbert space with inner product 〈·, ·〉 and the norm ∥·∥, I the identity mapping on , the set of all natural numbers, and the set of all real numbers. For a self-mapping T on , Fix (T) denotes the set of all fixed points of T. If M : → 2 is a set-valued mapping; then 𝒟(M) denotes its domain, that is, 𝒟(M) = {x : M(x) ≠ }.

Let C and Q be nonempty closed convex subsets of two Hilbert spaces 1 and 2, respectively, and let A : 12 be a bounded linear mapping. The split feasibility problem (SFP) is the problem of finding a point with the property
()
The SFP was first introduced by Censor and Elfving [1] for modeling inverse problems which arise from phase retrievals and medical image reconstruction. Recently, it has been found that the SFP can also be used to model the intensity-modulated radiation therapy. For details, the readers are referred to Xu [2] and the references therein.

Assume that the SFP has a solution. There are many iterative methods designed to approximate its solutions. The most popular algorithm is the CQ algorithm introduced by Byrne [3, 4].

Start with any x11 and generate a sequence {xn} through the iteration
()
where γ ∈ (0, 2/∥A2), A* the adjoint of A, and PC and PQ are the metric projections onto C and Q, respectively.
The sequence {xn} generated by the CQ algorithm (2) converges weakly to a solution of SFP(1), cf. [24]. Under the assumption that SFP(1) has a solution, it is known that a point x*1 solves SFP(1) if and only if x* is a fixed point of the operator
()
cf. [2], where Xu also proposed the regularized method,
()
and proved that the sequence {xn} converges strongly to the minimum norm solution of SFP(1) provided that the parameters {αn} and {γn} verify some suitable conditions. This regularized method was further investigated by Yao et al. [5] and Yao et al. [6].
Putting S = PC and T = PQ, SFP(1) is of the forms:
()
As a metric projection is firmly nonexpansive, it is reasonable to require the S and T in (5) to be firmly nonexpansive only and call it a split feasibility fixed point problem (SFFP). Many interesting problems in the literature can be described as SFFP.
(i) A set-valued map M : → 2 is called monotone if
()
for all x, y𝒟(M) and for any uM(x), vM(y). M is said to be maximal monotone if its graph {(x, u) : x,   uM(x)} is not properly contained in the graph of any other monotone operator. A point v is called a zero point of a maximal monotone operator M if 0 ∈ M(v). The set of all zero points of M is denoted by M−10, which is equal to for any α > 0, where denotes the resolvent of a monotone operator M; that is, for any x. It is known that for any α > 0, is firmly nonexpansive. Now, let M and N be two maximal monotone operators on 1 and 2, respectively. Replacing C and Q with and , respectively, in (1), the SFP becomes a SFFP:
()
Putting A = I, the previous SFFP is reduced to the common zero point problem of two maximal monotone operators:
()
(ii) Let f : C × C. An equilibrium problem is the problem of finding such that
()
whose solution set is denoted by EP (f). For solving an equilibrium problem, we usually assume that the function f satisfies the following conditions:
  • (A1)

      f(x, x) = 0,  for all  xC;

  • (A2)

      f  is monotone, that is,  f(x, y) + f(y, x) ≤ 0,  for all  xC;

  • (A3)

      for all  x, y, zC,  limsup t↓0f((1 − t)x + tz, y) ≤ f(x, y);

  • (A4)

    for all  xC, f(x, ·)  is convex and lower semicontinuous.

Blum and Oettli [7] and Aoyama et al. [8] showed that there exists a unique zC such that
()
Moreover, For r > 0, define by
()
for all x. Combettes and Hirstoaga [9] showed that there hold
  • (a)

      is single-valued;

  • (b)

      is firmly nonexpansive;

  • (c)

    ;

  • (d)

    EP (f)  is closed and convex.

Now, let f : C × C and g : Q × Q be two functions satisfying conditions (A1)–(A4). Replacing C and Q with and , respectively, in (1), the SFP becomes a SFFP:
()
Putting 1 = 2, C = Q, and A = I, the previous SFFP is reduced to the common equilibrium problem:
()
(iii) When 1 = 2 = , and the bounded linear operator A is the identity mapping, SFP(1) is reduced to the convex feasibility problem (CFP):
()
which in turn can be described as a SFFP:
()
where S = PC and T = PQ. Although SFFP(5) contains CFP as a special case, it cannot cover the multiple-set split convex feasibility problem described in [10].

In this paper, we are concerned with iterative methods for SFFP(5). We derive some weak convergence theorems for SFFP(5) in Section 3. In Section 4, we describe SFFP(5) in a more general form.

Let  {Sn}  and  {Tn}  be two families of firmly nonexpansive self-mappings on  1  and 2,  respectively, so that    and  
()
and obtain the following main result.
Let  {an},  {bn},  {cn},  and  {dn}  be sequences in  [0,1]  with  an + bn + cn + dn = 1  and  an ∈ (0,1)  for all  n. Let {γn} be a sequence in (0, 2/∥A2) and let {en} be a bounded sequence in 1. Suppose that the solution set Ω of  SFFP(16)  is nonempty. For any u1, start with an arbitrary x11 and define a sequence {xn} by
()
Then, the sequence {xn} converges strongly to PΩu provided that the following conditions are satisfied:
  • (i)

    limnan = limn(dn/an) = 0,  ,  ;

  • (ii)

    ,  limninfbncn > 0;

  • (iii)

    there are two nonnegative real-valued functions  κ1  and  κ2  on    with

    ()

Based on the concept of using contractions to approximate nonexpansive mappings, another type of algorithms for SFFP(5) is also introduced, and the corresponding strong convergence theorem for the sequence generated by such algorithm is given too.

In Section 5, since resolvents of monotone operators are firmly nonexpansive, we replace the sequences {Sn} and {Tn} of firmly nonexpansive mappings in the previous condition (iii) by two sequences of resolvents of maximal monotone operators. Then, the proposed algorithm becomes a scheme to approach the minimum norm solution of zero point problem of maximal monotone operators and the equilibrium problem. It is worth noting that as Blum and Oettli [7] showed that the variational inequalities, convex differentiable optimization, and Nash equilibria in noncooperative games can be formulated as equilibrium problems, the proposed algorithm can be considered as a generalized methodology for solving all aforementioned problems.

2. Preliminaries

In order to facilitate our investigation in this paper, we recall some basic facts. A mapping S : is said to be
  • (i)

    nonexpansive if

    ()

  • (ii)

    firmly nonexpansive if

    ()

  • (iii)

    λ-averaged by G if

    ()
    for some λ ∈ (0,1) and some nonexpansive mapping G;

  • (iv)

    ν-inverse strongly monotone (ν-ism), with ν > 0, if

    ()

If S is nonexpansive, then the fixed point set Fix (S) of S is closed and convex, cf. [11]. If S = (1 − λ)I + λG is averaged, then S is nonexpansive with Fix (S) = Fix (G). It is well known that S is firmly nonexpansive if and only if it is 1/2-averaged, cf. [11], and so is IS. Here we would like to mention that the term “averaged mapping” originated in [12, 13]. In [12], Baillon el at. showed that if S is a λ-averaged mapping by G on a nonempty closed convex subset C of a uniformly convex Banach space, then Fix (G) = ϕ if and only if lim nSnx∥ = for all x in C. Moreover, in [13], Bruck and Reich showed that if the above C satisfies C = −C and S is odd, then {Snx} converges strongly to a fixed point of G.
Let C be a nonempty closed convex subset of . The metric projection PC from onto C is the mapping that assigns each x the unique point PCx in C with the property
()
It is known that PC is firmly nonexpansive and characterized by the inequality: for any x,
()

We need some lemmas that will be quoted in the sequel.

Lemma 1 (see [4].)If S is a self-mapping on , then the following assertions hold.

  • (a)

    S is nonexpansive if and only if the complement IS is 1/2-averaged.

  • (b)

    If S is ν-ism and γ > 0, then γS is (ν/γ)-ism.

  • (c)

    S is α-averaged if and only if IS is (1/2α)-ism.

  • (d)

    If S is α1-averaged and T is α2-averaged, then the composite ST is α1 + α2α1α2-averaged.

  • (e)

    If S and U are averaged on so that Fix (S)∩Fix (U) ≠ , then

    ()

For α > 0, the resolvent of a maximal monotone operator A on has the following properties.

Lemma 2. Let A be a maximal monotone operator on . Then,

  • (a)

    is single-valued and firmly nonexpansive;

  • (b)

    and ;

  • (c)

    (the resolvent identity) for μ, λ > 0, the following identity holds:

    ()

We referred readers to [1424] for maximal monotone operators and their related algorithms.

Lemma 3. Let x, y, z. Then,

  • (a)

    x+y2 ≤ ∥x2 + 2〈y, x + y〉;

  • (b)

    for any λ,

    ()

  • (c)

    for a, b, c ∈ [0,1] with a + b + c = 1,

    ()

Lemma 4 (see [11] demiclosedness principle.)Let S be a nonexpansive self-mapping on and suppose that {xn} is a sequence in such that {xn} converges weakly to some z and lim nxnSxn∥ = 0. Then, Sz = z.

Lemma 5 (see [23].)Let {sn} be a sequence of nonnegative real numbers satisfying

()
where {αn}, {μn}, and {νn} verify the following conditions:
  • (i)

    {αn}⊆[0,1], ;

  • (ii)

    limsup nμn ≤ 0;

  • (iii)

    {νn}⊆[0, ) and .

Then, lim nsn = 0.

Lemma 6 (see [25].)Let {sn} be a sequence in that does not decrease at infinity in the sense that there exists a subsequence such that

()
For any k, define mk = max {jk : sj < sj+1}. Then, mk as k and , for all k.

3. Weak Convergence Theorems

In this section, we at first transform SFFP(5) into a fixed point problem for the operator S(IγA*(IT)A), where γ is any positive real number. And then use fixed point algorithms to solve SFFP(5). From now on until the end of this paper, unless we state specifically, S and Sn, n (resp., T and Tn, n) are firmly nonexpansive self-mappings on 1 (resp., 2), and A denotes a bounded linear operator from 1 into 2 with adjoint A*.

Lemma 7. Let S be a firmly nonexpansive self-mapping on with Fix (S) ≠ . Then, for any x, one has

()

Proof. Since v ∈ Fix (S) and S is firmly nonexpansive, we have

()
and hence,
()

Although 〈xSx, vSx〉 ≤ 0, for all v ∈ Fix (S) is similar to the characterization inequality (24) for the metric projection PFix (S), as Sx needs not to be in Fix (S), it is in general different from PFix (S)(x). For example, let Sx = 3x/4 for all x, which is obviously firmly nonexpansive with Fix (S) = {0}. Thus, PFix (S)(x) = 0 for all x, while Sx ≠ 0 for all x ≠ 0.

Proposition 8. For any γ ∈ (0, 2/∥A2), the operator IγA*(IT)A is γA2/2-averaged and S[IγA*(IT)A] is (2 + γA2)/4-averaged.

Proof. Using the fact that T is firmly nonexpansive, it is routine to show that A*(IT)A is 1/∥A2-ism, and so γA*(IT)A is 1/γA2-ism by Lemma 1(b). Thus, Lemma 1(c) shows that IγA*(IT)A is γA2/2-averaged. As S is firmly nonexpansive, it is 1/2-averaged. Therefore, S[IγA*(IT)A] is (1/2) + (γA2/2) − (γA2/4) = ((2 + γA2)/4)-averaged by Lemma 1(d).

Proposition 9. Let Ω be the solution set of SFFP(5); that is, Ω = Fix (S)∩A−1(Fix (T)). For any γ ∈ (0, 2/∥A2), let U = IγA*(IT)A. Suppose that Ω. Then, Fix (SU) = Fix (S)∩Fix (U) = Ω.

Proof. If x* solves SFFP(5), we have x* ∈ Fix (S) and Ax* ∈ Fix (T). Now, note that Ax* ∈ Fix (T) implies

()
and so
()
which means that x* ∈ Fix (U). Consequently, x* ∈ Fix (S)∩Fix (U). This shows that Ω⊆Fix (S)∩Fix (U).

For the inverse inclusion, let x* be any member of Fix (S)∩Fix (U) and pick vΩ. It is readily seen from x* ∈ Fix (U) that

()
Since Av ∈ Fix (T), an application of Lemma 7 yields
()
which together with (36) implies that
()
This comes to conclude that Ax* ∈ Fix (T), and hence x*Ω once we note that x* ∈ Fix (S). Finally, since Ω by assumption, we have Fix (S)∩Fix (U) = Ω. Thus, Fix (SU) = Fix (S)∩Fix (U) follows from Lemma 1(e).

Proposition 10 (see [2], [4].)If G is an averaged self-mapping on with Fix (G) ≠ , then for any x, the sequence {Gnx} converges weakly to a fixed point of G.

An immediate consequence of Propositions 8, 9, and 10 is the following convergence result.

Theorem 11. Assume that SFFP(5) has a solution. Then, for any γ ∈ (0, 2/∥A2) and starting with any point x1, the sequence {xn} generated by

()
converges weakly to a solution of SFFP(5).

Proposition 12 (see [2].)Let G be a α-averaged self-mapping on with Fix (G) ≠ and assume that {αn} is a sequence in [0, 1/α] such that

()
Then, for any x1, the sequence {xn} generated by the Mann′s algorithm
()
converges weakly to a fixed point of G.

Applying Propositions 8, 9, and 12, we have the following result.

Theorem 13. Assume that SFFP(5) has a solution and γ ∈ (0, 2/∥A2). Let {αn} be a sequence in [0, 4/(2 + γA2)] with

()
Then, for any x1, the sequence {xn} generated by the Mann′s algorithm
()
converges weakly to a solution of SFFP(5).

4. Strong Convergence Theorems

In this section, we devise two algorithms, one for SFFP(16) and the other for SFFP(5). We deal with SFFP(16) firstly. To begin with, we need a lemma.

Lemma 14. For any γ ∈ (0, 2/∥A2) and all x, y1, one has

()

Proof. Since T is firmly nonexpansive, so is IT. Hence, for all x, y1, we have

()
Consequently,
()

Theorem 15. Let {an}, {bn}, {cn}, and {dn} be sequences in [0,1] with an + bn + cn + dn = 1 and an ∈ (0,1) for all n. Let {γn} be a sequence in (0, 2/∥A2) and let {en} be a bounded sequence in 1. Suppose that the solution set Ω of SFFP(16) is nonempty. For any u1, start with any x11 and define a sequence {xn} by

()
Then the sequence {xn} converges strongly to PΩu provided that the following conditions are satisfied:
  • (i)

    lim nan = lim n(dn/an) = 0,  ,  ;

  • (ii)

    ,  lim ninf bncn > 0;

  • (iii)

    there are two nonnegative real-valued functions κ1 and κ2 on with

    ()

Proof. Put p = PΩu. For simplicity, put Gn = Sn(IγnA*(ITn)A). In view of Proposition 9, Gn is nonexpansive, so

()
from which follows that {xn} is a bounded sequence. Taking into account Lemma 3, we get
()
Meanwhile, we have by Lemma 14 that
()
Therefore, we deduce that
()
We now carry on with the proof by considering the following two cases: (I) {∥xnp∥} is eventually decreasing and (II) {∥xnp∥} is not eventually decreasing.

Case I. Suppose that {∥xnp∥} is eventually decreasing; that is, there is n0 such that is decreasing. In this case, lim nxnp∥ exists in . From inequality (52) we have

()
which together with the boundedness of {xn} and conditions (i) and (ii) implies
()
Then, an application of condition (iii) follows that for all m,
()
Since {xn} is bounded, it has a subsequence such that converges weakly to some z and
()
where the last inequality follows from (24) since zΩ by Lemma 4. Choose M > 0 so that . From (52) we have
()
Accordingly, because of (56) and condition (i), we can apply Lemma 5 to inequality (57) with , αn = an + bn, μn = 2〈up, xn+1p〉, and νn = dnM to conclude that
()

Case II. Suppose that {∥xnp∥} is not eventually decreasing. In this case, by Lemma 6, there exists a nondecreasing sequence {mk} in such that mk and

()
Then, it follows from (52) and (59) that
()
Therefore,
()
and then proceeding just as in the proof in Case I, we obtain
()
which in conjunction with condition (iii) shows that for all mj
()
and then follows that
()
From (60) we have
()
and thus,
()
Letting k and using (64) and condition (i), we obtain
()
Also, since
()
which together with (62) and condition (i) implies that , and so
()
by virtue of (67). Consequently, we conclude that lim kxkp∥ = 0 via (59) and (69). This completes the proof.

This theorem says that the sequence {xn} converges strongly to a point of Ω which is the nearest to u. In particular, if u is taken to be 0, then the limit point v of the sequence {xn} is the unique minimum solution of SFFP(16).

Corollary 16. Let {an}, {bn}, and {cn} be sequences in [0,1] with an + bn + cn = 1 and an ∈ (0,1) for all n. Let {γn} be a sequence in (0, 2/∥A2) and let {en} be a bounded sequence in 1. Suppose that the solution set Ω of SFFP(16) is nonempty. For any u1, start with any x11 and define a sequence {xn} by

()
Then, the sequence {xn} converges strongly to PΩu provided that the following conditions are satisfied:
  • (i)

    lim nan = lim n(dn/an) = 0,  ,  ;

  • (ii)

    ,  lim ninf bncn > 0;

  • (iii)

    there are two nonnegative real-valued functions κ1 and κ2 on with

    ()
    ()

  • (iv)

    either lim n(∥en∥/an) = 0 or .

Proof. Put p = PΩu and Gn = Sn[IγnA*(ITn)A]. Let z1 = x1 and define a sequence {zn} iteratively by

()
We have lim nzn = p by Theorem 15. Since
()
the limit lim nxnzn∥ = 0 follows by applying Lemma 5 to (74), and thus,
()

If the sequence {Sn} (resp., {Tn}) of firmly nonexpansive mappings consists of a single mapping S (resp., T), then {Sn} and {Tn} obviously verify condition (iii), and hence, we have the following corollary.

Corollary 17. Let {an}, {bn}, {cn}, and {dn} be sequences in [0,1] with an + bn + cn + dn = 1 and an ∈ (0,1) for all n. Let {γn} be a sequence in (0, 2/∥A2) and let {en} be a bounded sequences in 1. Assume that the solution set Ω of SFFP(5) is nonempty. For any u1, start with an arbitrary x11 and define the sequence {xn} by

()
Then, {xn} converges strongly to PΩu provided that the following conditions are satisfied:
  • (i)

    lim nan = lim n(dn/an) = 0,  ,  ;

  • (ii)

    ,  lim ninf bncn > 0.

When the sequence {γn} is taken to be a constant γ ∈ (0, 2/∥A2), then because S[IγnA*(IT)A] is an averaged mapping, we can apply Corollary 3.4 of Huang and Hong [26] to obtain the following result.

Theorem 18. Let {an}, {bn}, {cn}, and {dn} be sequences in [0,1] with an + bn + cn + dn = 1 and an ∈ (0,1) for all n. Suppose that γ ∈ (0, 2/∥A2) and suppose that {en} is a bounded sequence in 1. Assume that the solution set Ω of SFFP(5) is nonempty. For any u1, start with an arbitrary x11 and define the sequence {xn} by

()
Then, {xn} converges strongly to PΩu provided that the following conditions are satisfied:
  • (i)

    lim nan = lim n(dn/an) = 0,  ,  ;

  • (ii)

    lim ninf bn > 0,  lim ninf cn > 0.

Since both condition (ii) of Corollary 17 and Theorem 18 are equivalent provided that γn = γ for every n, Theorem 18 also follows from Corollary 17.

We now turn to SFFP(5) for another algorithm, which essentially follows the argument of Wang and Xu [27]. For the sake of completeness, we still give a detailed proof.

Theorem 19. Let {an} be a sequence in (0,1). Suppose that γ ∈ (0, 2/∥A2) and assume that the solution set Ω of SFFP(5) is nonempty. Start with any x11 and define a sequence {xn} by

()
Then, the sequence {xn} converges strongly to the minimum norm solution of SFFP(5) provided that the following conditions are satisfied:
  • (i)

    lim nan = 0;

  • (ii)

    ;

  • (iii)

    either   or  lim n(|an+1an|/an) = 0.

Proof. Put  U = IγA*(IT)A, G = SU, and Gn = S[(1 − an)]U for all n. Then,

()
Take pΩ. From Proposition 9, we have
()
Hence,
()
from which follows that {xn} is bounded and so is {Uxn}. Choose M > 0 so that ∥Uxn∥ ≤ M for all n. We have
()
In view of conditions (i), (ii), and (iii), we can apply Lemma 5 to (82) to get
()
and then from
()
we see that
()
Consequently, the demiclosedness principle ensures that each weak limit point of {xn} is a fixed point of the averaged mapping SU. And then we conclude from Proposition 9 that each weak limit point of {xn} lies in Ω.

Let be the minimum norm element of Ω; that is, . We shall show that {xn} converges strongly to . To see this, we compute as follows:

()
If , then an application of Lemma 5 to (86) yields that . Hence, to complete the proof, it suffices to show that . For this, taking into account Proposition 8, we can write U = (1 − β)I + βV for some β ∈ (0,1) and some nonexpansive mapping V. Then, from
()
we obtain
()
which ensures that lim nxnVxn∥ = 0, and hence,
()
once we note that IU = β(IV). Since {xn} is bounded, we can take a subsequence so that it is weakly convergent to x*Ω and
()
where the last inequality comes from the characterization inequality of a metric projection. Now, applying (89) and (90) to the equality
()
we obtain
()
and thus,
()
This completes the proof.

5. Applications

In this section, we shall apply the results in Section 4 to approximate zeros of maximal monotone operators and solutions of equilibrium problems.

Theorem 20. Suppose that M and N are two maximal monotone operators on 1 and 2, respectively, and suppose that A : 12 is a bounded linear operator with adjoint A*. Suppose further that {an}, {bn}, {cn}, and {dn} are sequences in [0,1] with an + bn + cn + dn = 1 and an ∈ (0,1) for all n. Let {αn} and {βn} be sequences in (0, ), {γn} a sequence in (0, 2/∥A2), and {en} a bounded sequence in 1. Assume that the solution set Ω of the problem

()
is nonempty. For any u1, start with an arbitrary x1 and define a sequence {xn} by
()
Then, the sequence {xn} converges strongly to PΩu provided that the following conditions are satisfied:
  • (i)

    lim nan = lim n(dn/an) = 0, , ;

  • (ii)

    , lim ninf bncn > 0;

  • (iii)

    lim ninf αn > 0, lim ninf βn > 0.

Proof. For any n, letting and , then we have by Lemma 2(b) that M−10 = Fix (Sn) and N−10 = Fix (Tn) for all n, and hence, as shown in Section 1, the problem (94) becomes SFFP(16). Since all the requirements of Theorem 15 are satisfied except condition (iii), we have to check this condition. Because liminf nαn = 0, liminf nβn = 0 by assumption, we may assume that there is τ ∈ (0,1) so that τ < αn and τ < βn for all n. Let κ1(n) = 2 + (αn/τ) and κ2(n) = 2 + (βn/τ). Then, by virtue of the resolvent identity and the nonexpansiveness of , one has for all m that

()
and thus,
()
The same argument shows for all m that
()
Therefore, condition (iii) of Theorem 15 is true for and , and then the desired conclusion follows from Theorem 15.

Replacing S and T with and , respectively, in Theorem 19, we obtain the following result.

Theorem 21. Suppose that M and N are two maximal monotone operators on 1 and 2, respectively, and suppose that A : 12 is a bounded linear operator with adjoint A*. Let {an} be a sequence in (0,1), γ ∈ (0, 2/∥A2), and α, β ∈ (0, ) and assume that the solution set Ω of problem (94) is nonempty. Start with any x11 and define a sequence {xn} by

()
Then, the sequence {xn} converges strongly to the minimum norm solution of problem (94) provided that the following conditions are satisfied:
  • (i)

    lim nan = 0;

  • (ii)

    ;

  • (iii)

    either or lim n(|an+1an|/an) = 0.

Recall some facts in Section 1. For a nonempty closed convex subset C of , let f : C × C be a function satisfying conditions (A1)–(A4), which are described in Section 1. The solution set of the equilibrium problem
()
is denoted by EP (f), which is equal to for any α > 0, where is a function on into C defined by
()
for all x. is a single-valued firmly nonexpansive mapping.

Theorem 22. Suppose that C and Q are two nonempty closed convex subsets of 1 and 2, respectively, and suppose that A : 12 is a bounded linear operator with adjoint A*. Let f : C  ×  C and g : Q  ×  Q be functions satisfying conditions (A1)–(A4). Suppose further that {an}, {bn}, {cn}, and {dn} are sequences in [0,1] with an + bn + cn + dn = 1 and an ∈ (0,1) for all n. Let {αn} and {βn} be sequences in (0, ), {γn} a sequence in (0, 2/∥A2), and {en} a bounded sequence in 1. Assume that the solution set Ω of the problem

()
is nonempty. For any u1, start with an arbitrary x1 and define a sequence {xn} by
()
Then, the sequence {xn} converges strongly to PΩu provided that the following conditions are satisfied:
  • (i)

    lim nan = lim n(dn/an) = 0, , ;

  • (ii)

    , lim ninf bncn > 0;

  • (iii)

    lim ninf αn > 0, lim ninf βn > 0.

Proof. Define two set-valued mappings Mf and Ng on 1 and 2, respectively, by

()
As shown in Takahashi et al. [21], the set-valued mapping Mf (resp., Ng) is a maximal monotone operator with 𝒟(Mf)⊆C (resp., 𝒟(Ng)⊆Q), and for any α > 0 (resp., for any β > 0). With M = Mf and N = Ng in Theorem 20, the desired conclusion follows.

If 1 = 2 = , C = Q, f = g, and A is the identity transformation on , then (102) is reduced to the usual equilibrium problem, and we have the following corollary.

Corollary 23. Suppose that C is a nonempty closed convex subsets of and f : C × C is a function satisfying conditions (A1)–(A4). Let {an}, {bn}, {cn}, and {dn} be sequences in [0,1] with an + bn + cn + dn = 1 and an ∈ (0,1) for all n. Let {αn} and {βn} be sequences in (0, ), and let {γn} be a sequence in (0, 2/∥A2) and suppose that {en} is a bounded sequence in 1. Assume that the solution set Ω of the problem

()
is nonempty. For any u1, start with an arbitrary x1 and define a sequence {xn} by
()
Then, the sequence {xn} converges strongly to PΩu provided that the following conditions are satisfied:
  • (i)

    lim nan = lim n(dn/an) = 0, , ;

  • (ii)

    , lim ninf bncn > 0;

  • (iii)

    lim ninf αn > 0, lim ninf βn > 0.

As shown in Blum and Oettli [7] that the variational inequalities, convex differentiable optimization, Nash equilibria in noncooperative games and so on can be formulated as equilibrium problems, we see that the pervious corollary may be applied to approximate solutions of all aforementioned problems. The readers may readily write down the details.

Just as Theorem 21 to Theorem 20, there are similar results corresponding to Theorem 22 and Corollary 23. We leave the work to readers.

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