A regularization algorithm for zero points of accretive operators
© Qing and Cho; licensee Springer. 2013
Received: 10 November 2013
Accepted: 3 December 2013
Published: 13 December 2013
A regularization algorithm with a computational error for treating accretive operators is investigated. A strong convergence theorem for zero points of accretive operators is established in a reflexive Banach space.
In this paper, we are concerned with the problem of finding zero points of a mapping ; that is, finding a point x in the domain of A such that . The domain of a mapping A is defined by the set . Many important problems have reformulations which require finding zero points, for instance, evolution equations, complementarity problems, mini-max problems, variational inequalities and optimization problems. It is well known that minimizing a convex function f can be reduced to finding zero points of the subdifferential mapping . One of the most popular techniques for solving the inclusion problem goes back to the work of Browder . One of the basic ideas in the case of a Hilbert space H is reducing the above inclusion problem to a fixed point problem of the operator defined by , which is called the classical resolvent of A. If A has some monotonicity conditions, the classical resolvent of A is with full domain and firmly nonexpansive, that is, , . The property of the resolvent ensures that the Picard iterative algorithm converges weakly to a fixed point of , which is necessarily a zero point of A. Rockafellar introduced this iteration method and called it the proximal point algorithm; for more detail, see [2–4] and the references therein. Methods for finding zero points of monotone mappings in the framework of Hilbert spaces are based on the good properties of the resolvent , but these properties are not available in the framework of Banach spaces.
In this paper, we study a viscosity algorithm with a computational error. A strong convergence theorem for zero points of accretive operators is established in a reflexive Banach space. The organization of this paper is as follows. In Section 2, we provide some necessary preliminaries. In Section 3, a strong convergence theorem is established in a reflexive Banach space. Two applications of the main results are also discussed in this section.
In what follows, we always assume that E is a Banach space with the dual . Let . E is said to be smooth or is said to have a Gâteaux differentiable norm if the limit exists for each . E is said to have a uniformly Gâteaux differentiable norm if for each , the limit is attained uniformly for all . E is said to be uniformly smooth or is said to have a uniformly Fréchet differentiable norm if the limit is attained uniformly for . Let denote the pairing between E and . The normalized duality mapping is defined by , . In the sequel, we use j to denote the single-valued normalized duality mapping. It is known that if the norm of E is uniformly Gâteaux differentiable, then the duality mapping j is single-valued and uniformly norm to weak∗ continuous on each bounded subset of E.
Let C be a nonempty closed convex subset of E. Let be a mapping. In this paper, we use to denote the set of fixed points of T. Recall that T is said to be α-contractive if there exits a constant such that , . T is said to be nonexpansive if . T is said to be pseudocontractive if there exists some such that , .
Recall that a closed convex subset C of a Banach space E is said to have normal structure if for each bounded closed convex subset K of C which contains at least two points, there exists an element x of K which is not a diametral point of K, i.e., , where is the diameter of K. Let D be a nonempty subset of C. Let . Q is said to be contraction if ; sunny if for each and , we have ; sunny nonexpansive retraction if Q is sunny, nonexpansive, and contraction. K is said to be a nonexpansive retract of C if there exists a nonexpansive retraction from C onto D; for more details, see  and the references therein.
Let I denote the identity operator on E. An operator with domain and range is said to be accretive if for each and , , there exists such that . An accretive operator A is said to be m-accretive if for all . In a real Hilbert space, an operator A is m-accretive if and only if A is maximal monotone. In this paper, we use to denote the set of zero points of A. For an accretive operator A, we can define a nonexpansive single-valued mapping by for each , which is called the resolvent of A.
One of classical methods of studying the problem , where is an accretive operator, is the proximal point algorithm (PPA) which was initiated by Martinet  and further developed by Rockafellar . It is known that PPA is only weakly convergent; see Güler . In many disciplines, including economics, image recovery, quantum physics, and control theory, problems arise in infinite dimension spaces. In such problems, strong convergence (norm convergence) is often much more desirable than weak convergence, for it translates the physically tangible property that the energy of the error between the iterate and the solution x eventually becomes arbitrarily small. The importance of strong convergence is also underlined in , where a convex function f is minimized via the proximal-point algorithm: it is shown that the rate of convergence of the value sequence is better when converges strongly than when it converges weakly. Such properties have a direct impact when the process is executed directly in the underlying infinite dimensional space.
Regularization methods recently have been investigated for treating zero points of accretive operators; see [8–22] and the references therein. In this paper, zero points of m-accretive operators are investigated based on a viscosity iterative algorithm with a computational error. A strong convergence theorem for zero points of m-accretive operators is established in a reflexive Banach space.
In order to state our main results, we need the following lemmas.
Lemma 2.1 
Let and be bounded sequences in a Banach space E. Let be a sequence in with . Suppose that , and . Then .
Lemma 2.2 
Let E be a real reflexive Banach space with the uniformly Gâteaux differentiable norm and the normal structure, and let C be a nonempty closed convex subset of E. Let be a nonexpansive mapping with a fixed point, and let be a fixed contraction with the coefficient . Let be a sequence generated by the following , where . Then converges strongly as to a fixed point of T, which is the unique solution in to the following variational inequality , .
Lemma 2.3 
Let E be a Banach space, and let A be an m-accretive operator. For , , and , we have , where and .
Lemma 2.4 
Let be a sequence of nonnegative numbers satisfying the condition , , where is a number sequence in such that and , is a number sequence such that , and is a positive number sequence such that . Then .
3 Main results
Then the sequence converges strongly to , which is the unique solution to the following variational inequality , .
Hence, for any , there exists such that the following inequality holds . Taking in the above inequality, we find that . Since ϵ is arbitrary, we obtain from (3.7) that .
In view of Lemma 2.4, we find the desired conclusion immediately. □
In this section, we give two applications of our main result in the framework of Hilbert spaces.
First, we consider, in the framework of Hilbert spaces, solutions of a Ky Fan inequality, which is known as an equilibrium problem in the terminology of Blum and Oettli; see  and  and the references therein.
for all ;
F is monotone, i.e., for all ;
for each , ;
for each , is convex and lower semi-continuous.
The following lemma can be found in .
for all and . Then (1) is single-valued and firmly nonexpansive; (2) is closed and convex.
Lemma 4.2 
where is defined as in (4.2).
where and are real number sequences in , is a sequence in H, is a positive real number sequence, and . Assume that is not empty and the above control sequences satisfy the restrictions (a), (b), (c) and (d) in Theorem 3.1. Then the sequence converges strongly to , which is the unique solution to the following variational inequality , .
Next, we consider the problem of finding a minimizer of a proper convex lower semicontinuous function.
Rockafellar  proved that ∂g is a maximal monotone operator. It is easy to verify that if and only if .
where and are real number sequences in , is a sequence in E, and is a positive real number sequence. Assume that the above control sequences satisfy the restrictions in Theorem 3.1. Then the sequence converges strongly to , which is the unique solution to the following variational inequality , .
Following the proof in Theorem 3.1, we draw the desired conclusion immediately. □
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