- Research Article
- Open Access
Approximating Curve and Strong Convergence of the CQ Algorithm for the Split Feasibility Problem
© F.Wang and H.-K. Xu. 2010
- Received: 14 December 2009
- Accepted: 14 January 2010
- Published: 14 February 2010
Using the idea of Tikhonov's regularization, we present properties of the approximating curve for the split feasibility problem (SFP) and obtain the minimum-norm solution of SFP as the strong limit of the approximating curve. It is known that in the infinite-dimensional setting, Byrne's CQ algorithm (Byrne, 2002) has only weak convergence. We introduce a modification of Byrne's CQ algorithm in such a way that strong convergence is guaranteed and the limit is also the minimum-norm solution of SFP.
- Minimization Problem
- Weak Convergence
- Nonexpansive Mapping
- Strong Convergence
- Bounded Linear Operator
Let and be nonempty closed convex subsets of real Hilbert spaces and , respectively. The problem under consideration in this article is formulated as finding a point satisfying the property:
where is a bounded linear operator. Problem (1.1), referred to by Censor and Elfving  as the split feasibility problem (SFP), attracts many authors' attention due to its application in signal processing . Various algorithms have been invented to solve it (see [2–7] and reference therein).
In particular, Byrne  introduced the so-called algorithm. Take an initial guess arbitrarily, and define recursively as
where and where denotes the projector onto and is the spectral radius of the self-adjoint operator Then the sequence generated by (1.2) converges strongly to a solution of SFP whenever is finite-dimensional and whenever there exists a solution to SFP (1.1).
However, the algorithm need not necessarily converge strongly in the case when is infinite dimensional. Let us mention that the algorithm can be regarded as a special case of the well-known Krasnosel'skii-Mann algorithm for approximating fixed points of nonexpansive mappings . This iterative method is introduced in  and defined as follows. Take an initial guess arbitrarily, and define recursively as
where satisfying If is nonexpansive with a nonempty fixed point set, then the sequence generated by (1.3) converges weakly to a fixed point of . It is known that Krasnosel'skii-Mann algorithm is in general not strongly convergent (see [9, 10] for counterexamples) and neither is the algorithm.
It is therefore the aim of this paper to construct a new algorithm so that strong convergence is guaranteed. The paper is organized as follows. In the next section, some useful lemmas are given. In Section 3, we define the concept of the minimal norm solution of SFP (1.1). Using Tikhonov's regularization, we obtain a continuous curve for approximating such minimal norm solution. Together with some properties of this approximating curve, we introduce, in Section 4, a modification of Byrne's algorithm so that strong convergence is guaranteed and its limit is the minimum-norm solution of SFP (1.1).
Throughout the rest of this paper, denotes the identity operator on , the set of the fixed points of an operator and the gradient of the functional The notation " " denotes strong convergence and " " weak convergence.
Recall that an operator from into itself is called nonexpansive if
contractive if there exists such that
Obviously, contractions are nonexpansive, and if is nonexpansive, then is monotone (see ).
Let denote the projection from onto a nonempty closed convex subset of ; that is,
It is well known that is characterized by the inequality
Consequently, is nonexpansive.
The lemma below is referred to as the demiclosedness principle for nonexpansive mappings (see ).
Lemma 2.1 (demiclosedness principle).
Let be a nonempty closed convex subset of and a nonexpansive mapping with If is a sequence in weakly converging to and if the sequence converges strongly to , then In particular, if , then .
Let be a a functional. Recall that
(ii) is strictly convex if the strict less than inequality in (2.6) holds for all distinct .
(iv) is coercive if whenever It is easily seen that if is strongly convex, then it is coercive. See  for more details about convex functions.
The following lemma gives the optimality condition for the minimizer of a convex functional over a closed convex subset.
Lemma 2.2 (see ).
Moreover, if is, in addition, strictly convex and coercive, then problem (2.8) has a unique solution.
The following is a sufficient condition for a real sequence to converge to zero.
Lemma 2.3 (see ).
where the sequences and satisfy the conditions:
where . A classical way to deal with such a possibly ill-posed problem is the well-known Tikhonov regularization, which approximates a solution of problem (3.1) by the unique minimizer of the regularized problem
where is known as the regularization parameter.
We now try to transfer this idea of Tikhonov's regularization method for solving the constrained linear inverse problem (3.1) to the case of SFP (1.1).
It is not hard to find that SFP (1.1) is equivalent to the minimization problem
Motivated by Tikhonov's regularization, we consider the minimization problem
where is the regularization parameter. Denote by the unique solution of (3.4); namely,
Thus, applying Lemma 2.2 gets the assertion (3.6), as desired.
The next result collects some useful properties of .
The following assertions hold.
(a) is decreasing for .
(b) is increasing for .
(c) defines a continuous curve from to
which implies that . Hence (a) holds.
and therefore (b) holds.
Thus (c) holds.
Let , where . In what follows, we assume that that is, the solution set of SFP (1.1) is nonempty. The fact that is nonempty closed convex set thus allows us to introduce the concept of minimum-norm solution of SFP (1.1).
An element is said to be the minimal norm solution of SFP (1.1) if . In other words, is the projection of the origin onto the solution set of SFP (1.1). Thus the minimum-norm solution for SFP (1.1) exists and is unique.
Let be given as (3.5). Then converges strongly as to the minimum-norm solution of SFP (1.1).
and (3.20) is proven.
This shows that is also a point in which assumes minimum norm. Due to uniqueness of minimum-norm element, we must have .
The above argument shows that if the solution set of SFP (1.1) is empty, then the net of norms, , diverges to as .
It is a standard way to use contractions to approximate nonexpansive mappings. We follow this idea and use contractions to approximate the nonexpansive mapping in order to modify Byrne's algorithm. More precisely, we introduce the following algorithm which is viewed as a modification of Byrne's algorithm. The purpose for such a modification lies in the hope of strong convergence.
where is a sequence in such that
Note that a prototype of is for all .
To prove the convergence of algorithm (4.1) (see Theorem 4.3 below), we need a lemma below.
Set , where with being the spectral radius of the self-adjoint operator
(i) is an averaged mapping; namely, , where is a constant and is a nonexpansive mapping from into itself.
(ii) ; consequently, .
That which is averaged is actually proved in .
This shows that ; that is, .
Finally, since , and both and are averaged, we have .
The sequence generated by algorithm (4.1) converges strongly to the minimum-norm solution of SFP (1.1).
where is averaged by Lemma 4.2.
In particular, is bounded.
Therefore, the demiclosedness principle (Lemma 2.1) ensures that each weak limit point of is a fixed point of the nonexpansive mapping , that is, a point of the solution set of SFP (1.1).
Now since , (4.23) implies (4.20).
This is (4.21).
We therefore can apply Lemma 2.3 to (4.26) to conclude that . This completes the proof.
H. K. Xu was supported in part by NSC 97-2628-M-110-003-MY3, and by DGES MTM2006-13997-C02-01.
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