A Unified Iterative Treatment for Solutions of Problems of Split Feasibility and Equilibrium in Hilbert Spaces
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, ℋ denotes a real Hilbert space with inner product 〈·, ·〉 and the norm ∥·∥, I the identity mapping on ℋ, ℕ the set of all natural numbers, and ℝ the set of all real numbers. For a self-mapping T on ℋ, Fix (T) denotes the set of all fixed points of T. If M : ℋ → 2ℋ is a set-valued mapping; then 𝒟(M) denotes its domain, that is, 𝒟(M) = {x ∈ ℋ : M(x) ≠ ∅}.
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].
- (A1)
f(x, x) = 0, for all x ∈ C;
- (A2)
f is monotone, that is, f(x, y) + f(y, x) ≤ 0, for all x ∈ C;
- (A3)
for all x, y, z ∈ C, limsup t↓0f((1 − t)x + tz, y) ≤ f(x, y);
- (A4)
for all x ∈ C, f(x, ·) is convex and lower semicontinuous.
- (a)
is single-valued;
- (b)
is firmly nonexpansive;
- (c)
;
- (d)
EP (f) is closed and convex.
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.
- (i)
limn→∞an = limn→∞(dn/an) = 0, , ;
- (ii)
, limn→∞infbncn > 0;
- (iii)
there are two nonnegative real-valued functions κ1 and κ2 on ℕ 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
- (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
()
We need some lemmas that will be quoted in the sequel.
Lemma 1 (see [4].)If S is a self-mapping on ℋ, then the following assertions hold.
- (a)
S is nonexpansive if and only if the complement I − S 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 I − S is (1/2α)-ism.
- (d)
If S is α1-averaged and T is α2-averaged, then the composite ST is α1 + α2 − α1α2-averaged.
- (e)
If S and U are averaged on ℋ so that Fix (S)∩Fix (U) ≠ ∅, then
()
For α > 0, the resolvent of a maximal monotone operator A on ℋ has the following properties.
Lemma 2. Let A be a maximal monotone operator on ℋ. 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 [14–24] for maximal monotone operators and their related algorithms.
Lemma 3. Let x, y, z ∈ ℋ. Then,
- (a)
∥x+y∥2 ≤ ∥x∥2 + 2〈y, x + y〉;
- (b)
for any λ ∈ ℝ,
() - (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 ℋ and suppose that {xn} is a sequence in ℋ such that {xn} converges weakly to some z ∈ ℋ and lim n→∞∥xn − Sxn∥ = 0. Then, Sz = z.
Lemma 5 (see [23].)Let {sn} be a sequence of nonnegative real numbers satisfying
- (i)
{αn}⊆[0,1], ;
- (ii)
limsup n→∞μn ≤ 0;
- (iii)
{νn}⊆[0, ∞) and .
Lemma 6 (see [25].)Let {sn} be a sequence in ℝ that does not decrease at infinity in the sense that there exists a subsequence such that
3. Weak Convergence Theorems
In this section, we at first transform SFFP(5) into a fixed point problem for the operator S(I − γA*(I − T)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 ∈ ℕ (resp., T and Tn, n ∈ ℕ) are firmly nonexpansive self-mappings on ℋ1 (resp., ℋ2), and A denotes a bounded linear operator from ℋ1 into ℋ2 with adjoint A*.
Lemma 7. Let S be a firmly nonexpansive self-mapping on ℋ with Fix (S) ≠ ∅. Then, for any x ∈ ℋ, one has
Proof. Since v ∈ Fix (S) and S is firmly nonexpansive, we have
Although 〈x − Sx, v − Sx〉 ≤ 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 ∈ ℝ, which is obviously firmly nonexpansive with Fix (S) = {0}. Thus, PFix (S)(x) = 0 for all x ∈ ℝ, while Sx ≠ 0 for all x ≠ 0.
Proposition 8. For any γ ∈ (0, 2/∥A∥2), the operator I − γA*(I − T)A is γ∥A∥2/2-averaged and S[I − γA*(I − T)A] is (2 + γ∥A∥2)/4-averaged.
Proof. Using the fact that T is firmly nonexpansive, it is routine to show that A*(I − T)A is 1/∥A∥2-ism, and so γA*(I − T)A is 1/γ∥A∥2-ism by Lemma 1(b). Thus, Lemma 1(c) shows that I − γA*(I − T)A is γ∥A∥2/2-averaged. As S is firmly nonexpansive, it is 1/2-averaged. Therefore, S[I − γA*(I − T)A] is (1/2) + (γ∥A∥2/2) − (γ∥A∥2/4) = ((2 + γ∥A∥2)/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/∥A∥2), let U = I − γA*(I − T)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
For the inverse inclusion, let x* be any member of Fix (S)∩Fix (U) and pick v ∈ Ω. It is readily seen from x* ∈ Fix (U) that
Proposition 10 (see [2], [4].)If G is an averaged self-mapping on ℋ with Fix (G) ≠ ∅, then for any x ∈ ℋ, 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/∥A∥2) and starting with any point x1 ∈ ℋ, the sequence {xn} generated by
Proposition 12 (see [2].)Let G be a α-averaged self-mapping on ℋ with Fix (G) ≠ ∅ and assume that {αn} is a sequence in [0, 1/α] such that
Applying Propositions 8, 9, and 12, we have the following result.
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/∥A∥2) and all x, y ∈ ℋ1, one has
Proof. Since T is firmly nonexpansive, so is I − T. Hence, for all x, y ∈ ℋ1, we have
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 ∈ ℕ. Let {γn} be a sequence in (0, 2/∥A∥2) and let {en} be a bounded sequence in ℋ1. Suppose that the solution set Ω of SFFP(16) is nonempty. For any u ∈ ℋ1, start with any x1 ∈ ℋ1 and define a sequence {xn} by
- (i)
lim n→∞an = lim n→∞(dn/an) = 0, , ;
- (ii)
, lim n→∞inf bncn > 0;
- (iii)
there are two nonnegative real-valued functions κ1 and κ2 on ℕ with
()
Proof. Put p = PΩu. For simplicity, put Gn = Sn(I − γnA*(I − Tn)A). In view of Proposition 9, Gn is nonexpansive, so
Case I. Suppose that {∥xn − p∥} is eventually decreasing; that is, there is n0 ∈ ℕ such that is decreasing. In this case, lim n→∞∥xn − p∥ exists in ℝ. From inequality (52) we have
Case II. Suppose that {∥xn − p∥} is not eventually decreasing. In this case, by Lemma 6, there exists a nondecreasing sequence {mk} in ℕ such that mk → ∞ and
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 ∈ ℕ. Let {γn} be a sequence in (0, 2/∥A∥2) and let {en} be a bounded sequence in ℋ1. Suppose that the solution set Ω of SFFP(16) is nonempty. For any u ∈ ℋ1, start with any x1 ∈ ℋ1 and define a sequence {xn} by
- (i)
lim n→∞an = lim n→∞(dn/an) = 0, , ;
- (ii)
, lim n→∞inf bncn > 0;
- (iii)
there are two nonnegative real-valued functions κ1 and κ2 on ℕ with
()() - (iv)
either lim n→∞(∥en∥/an) = 0 or .
Proof. Put p = PΩu and Gn = Sn[I − γnA*(I − Tn)A]. Let z1 = x1 and define a sequence {zn} iteratively by
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 ∈ ℕ. Let {γn} be a sequence in (0, 2/∥A∥2) and let {en} be a bounded sequences in ℋ1. Assume that the solution set Ω of SFFP(5) is nonempty. For any u ∈ ℋ1, start with an arbitrary x1 ∈ ℋ1 and define the sequence {xn} by
- (i)
lim n→∞an = lim n→∞(dn/an) = 0, , ;
- (ii)
, lim n→∞inf bncn > 0.
When the sequence {γn} is taken to be a constant γ ∈ (0, 2/∥A∥2), then because S[I − γnA*(I − T)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 ∈ ℕ. Suppose that γ ∈ (0, 2/∥A∥2) and suppose that {en} is a bounded sequence in ℋ1. Assume that the solution set Ω of SFFP(5) is nonempty. For any u ∈ ℋ1, start with an arbitrary x1 ∈ ℋ1 and define the sequence {xn} by
- (i)
lim n→∞an = lim n→∞(dn/an) = 0, , ;
- (ii)
lim n→∞inf bn > 0, lim n→∞inf cn > 0.
Since both condition (ii) of Corollary 17 and Theorem 18 are equivalent provided that γn = γ for every n ∈ 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/∥A∥2) and assume that the solution set Ω of SFFP(5) is nonempty. Start with any x1 ∈ ℋ1 and define a sequence {xn} by
- (i)
lim n→∞an = 0;
- (ii)
;
- (iii)
either or lim n→∞(|an+1 − an|/an) = 0.
Proof. Put U = I − γA*(I − T)A, G = SU, and Gn = S[(1 − an)]U for all n ∈ ℕ. Then,
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:
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 ℋ1 and ℋ2, respectively, and suppose that A : ℋ1 → ℋ2 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 ∈ ℕ. Let {αn} and {βn} be sequences in (0, ∞), {γn} a sequence in (0, 2/∥A∥2), and {en} a bounded sequence in ℋ1. Assume that the solution set Ω of the problem
- (i)
lim n→∞an = lim n→∞(dn/an) = 0, , ;
- (ii)
, lim n→∞inf bncn > 0;
- (iii)
lim n→∞inf αn > 0, lim n→∞inf βn > 0.
Proof. For any n ∈ ℕ, letting and , then we have by Lemma 2(b) that M−10 = Fix (Sn) and N−10 = Fix (Tn) for all n ∈ 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 n→∞αn = 0, liminf n→∞βn = 0 by assumption, we may assume that there is τ ∈ (0,1) so that τ < αn and τ < βn for all n ∈ 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 ∈ ℕ that
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 ℋ1 and ℋ2, respectively, and suppose that A : ℋ1 → ℋ2 is a bounded linear operator with adjoint A*. Let {an} be a sequence in (0,1), γ ∈ (0, 2/∥A∥2), and α, β ∈ (0, ∞) and assume that the solution set Ω of problem (94) is nonempty. Start with any x1 ∈ ℋ1 and define a sequence {xn} by
- (i)
lim n→∞an = 0;
- (ii)
;
- (iii)
either or lim n→∞(|an+1 − an|/an) = 0.
Theorem 22. Suppose that C and Q are two nonempty closed convex subsets of ℋ1 and ℋ2, respectively, and suppose that A : ℋ1 → ℋ2 is a bounded linear operator with adjoint A*. Let f : C × C → ℝ and g : Q × Q → ℝ 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 ∈ ℕ. Let {αn} and {βn} be sequences in (0, ∞), {γn} a sequence in (0, 2/∥A∥2), and {en} a bounded sequence in ℋ1. Assume that the solution set Ω of the problem
- (i)
lim n→∞an = lim n→∞(dn/an) = 0, , ;
- (ii)
, lim n→∞inf bncn > 0;
- (iii)
lim n→∞inf αn > 0, lim n→∞inf βn > 0.
Proof. Define two set-valued mappings Mf and Ng on ℋ1 and ℋ2, respectively, by
If ℋ1 = ℋ2 = ℋ, C = Q, f = g, and A is the identity transformation on ℋ, 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 ℋ and f : C × C → ℝ 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 ∈ ℕ. Let {αn} and {βn} be sequences in (0, ∞), and let {γn} be a sequence in (0, 2/∥A∥2) and suppose that {en} is a bounded sequence in ℋ1. Assume that the solution set Ω of the problem
- (i)
lim n→∞an = lim n→∞(dn/an) = 0, , ;
- (ii)
, lim n→∞inf bncn > 0;
- (iii)
lim n→∞inf αn > 0, lim n→∞inf 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.