Weak convergence theorems for split feasibility problems on zeros of the sum of monotone operators and fixed point sets in Hilbert spaces
 Montira Suwannaprapa^{1},
 Narin Petrot^{1}Email author and
 Suthep Suantai^{2}
DOI: 10.1186/s1366301705997
© The Author(s) 2017
Received: 20 December 2016
Accepted: 20 April 2017
Published: 28 April 2017
Abstract
In this paper, we consider a type of split feasibility problem by focusing on the solution sets of two important problems in the setting of Hilbert spaces that are the sum of monotone operators and fixed point problems. By assuming the existence of solutions, we provide a suitable algorithm for finding a solution point. Some important applications and numerical experiments of the considered problem and constructed algorithm are also discussed.
Keywords
split feasibility problems maximal monotone operators inverse strongly monotone operator fixed point problems weak convergence theoremsMSC
26A18 47H04 47H05 47H10 54A201 Introduction
One may note that finding the zeros of maximal monotone operator can be solved via a fixed point of its resolvent operator. This is because \(0\in Bx^{\ast}\) if and only if \(J_{\lambda}^{B}x^{\ast}=x^{\ast}\), when \(B:H\rightarrow2^{H}\) is a maximal monotone operator and \(\lambda >0\). Thus the problem of type (1.6) contains problem SCNPP as a special case in some sense.
2 Preliminaries
Throughout this paper, we denote by \(\mathbb{N}\) the set of positive integers, and by \(\mathbb{R}\) the set of real numbers. Let H be a real Hilbert space with the inner product \(\langle\cdot,\cdot\rangle\) and the norm \(\Vert\cdot\Vert\), respectively. When \(\{x_{n}\}\) is a sequence in H, we denote the weak convergence of \(\{x_{n}\}\) to x in H by \(x_{n}\rightharpoonup x\).
We now collect some important properties, which are needed in this work.
Lemma 2.1
 (i)
The composite of finitely many averaged mappings is averaged. In particular, if \(T_{i}\) is \(\alpha_{i}\)averaged, where \(\alpha_{i}\in(0,1)\) for \(i=1,2\), then the composite \(T_{1}T_{2}\) is αaveraged, where \(\alpha=\alpha_{1}+\alpha_{2}\alpha _{1}\alpha_{2}\).
 (ii)
If A is βism and \(r\in(0,\beta]\), then \(T:=IrA\) is firmly nonexpansive.
 (iii)
A mapping \(T:H\rightarrow H\) is nonexpansive if and only if \(IT\) is \(\frac{1}{2}\)ism.
 (iv)
If A is βism, then, for \(\gamma>0\), γA is \(\frac{\beta}{\gamma}\)ism.
 (v)
T is averaged if and only if the complement \(IT\) is βism for some \(\beta>\frac{1}{2}\). Indeed, for \(\alpha\in (0,1)\), T is αaveraged if and only if \(IT\) is \(\frac {1}{2\alpha}\)ism.
The following result can be found in [27], but here we modify the presentation for showing a finer conclusion of the considered mapping T.
Lemma 2.2
[27]
Let \(T=(1\alpha)A+\alpha N\) for some \(\alpha\in(0,1)\). If A is βaveraged and N is nonexpansive then T is \(\alpha +(1\alpha)\beta\)averaged.
Proof
We use the following lemmas for proving the main result.
Lemma 2.3
[16]
 (i)
\(L^{\ast}(IT)L\) is \(\frac{1}{2\Vert L\Vert^{2}}\)ism,
 (ii)for \(0< r<\frac{1}{\Vert L\Vert^{2}}\),
 (iia)
\(IrL^{\ast}(IT)L\) is \(r\Vert L\Vert ^{2}\)averaged,
 (iib)
\(J_{\lambda}^{B}(IrL^{\ast}(IT)L)\) is \(\frac {1+r\Vert L\Vert^{2}}{2}\)averaged, for \(\lambda>0\),
 (iia)
 (iii)
if \(r=\Vert L\Vert^{2}\), then \(IrL^{\ast}(IT)L\) is nonexpansive.
Lemma 2.4
[29]
Lemma 2.5
[30]
Let C be a closed convex subset of a Hilbert space H and let T be a nonexpansive mapping of C into itself. Then \(U:=IT\) is demiclosed, i.e., \(x_{n}\rightharpoonup x_{0}\) and \(Ux_{n}\rightarrow y_{0}\) imply \(Ux_{0}=y_{0}\).
Lemma 2.6
[16]
 (i)
for every \(x^{\ast}\in C\), \(\lim_{n\rightarrow\infty }\Vert x_{n}x^{\ast}\Vert\) exists;
 (ii)
if a subsequence \(\{x_{n_{j}}\}\subset\{x_{n}\}\) converges weakly to \(x^{\ast}\), then \(x^{\ast}\in C\).
3 Main results
We start by considering an equivalence theorem.
Theorem 3.1
 (i)
\(z\in\Omega_{L,T}^{A+B}\),
 (ii)
\(z=J_{\lambda}^{B} ((I\lambda A)\gamma L^{\ast }(IT)L )z\),
 (iii)
\(0\in L^{\ast}(IT)Lz+(A+B)z\),
Proof
Since \(\Omega_{L,T}^{A+B}\neq\emptyset\), there exists \(z_{0}\in D(B)\) such that \(0\in(A+B)z_{0}\) and \(Lz_{0}\in F(T)\). Let us put \(S=\frac{1}{2}(I+T)\). It follows that S is a firmly nonexpansive mapping and \(F(T)=F(S)\). Moreover, we have \(L^{\ast}(IT)L=2L^{\ast}(IS)L\).
Now, in view of Theorem 3.1, we are in a position to present our main algorithm and show its convergence theorem.
Theorem 3.2
 (i)
\(0< a\leq\lambda_{n}\leq b_{1}< \frac{\beta}{2}\),
 (ii)
\(0< a\leq\gamma_{n}\leq b_{2}< \frac{1}{2\L\ ^{2}}\),
Proof
 (a)
for each \(x^{\ast}\in\Omega_{L,T}^{A+B}\), \(\lim_{n\rightarrow\infty}\x_{n}x^{\ast}\\) exists;
 (b)
\(\sum_{n=1}^{\infty}(12\gamma_{n}\L\^{2})\ x_{n}T_{n}x_{n}\^{2}<\infty\).
Next, we will denote \(\omega_{w}(x_{n})\) for the set of all weak cluster points of \(\{x_{n}\}\). Let \(\{x_{n_{j}}\}\) be a subsequence of \(\{x_{n}\}\) and \(x_{n_{j}}\rightharpoonup\hat{x}\), for some \(\hat {x}\in\omega_{w}(x_{n})\). Also, we assume that \(\lambda_{n_{j}}\rightarrow\hat{\lambda}\in (0,\frac{\beta}{2})\) and \(\gamma_{n_{j}}\rightarrow\hat{\gamma }\in (0,\frac{1}{2\Vert L\Vert^{2}})\).
Remark 3.3
We will discuss more applications of our main Theorem 3.2 in the next section.
4 Applications
In this section, we will show some applications of the problem (1.9) and Theorem 3.2.
4.1 Variational inequality problem
Theorem 4.1
 (i)
\(0< a\leq\lambda_{n}\leq b_{1}< \frac{\beta}{2}\),
 (ii)
\(0< a\leq\gamma_{n}\leq b_{2}< \frac{1}{2\L\ ^{2}}\).
4.2 Convex minimization problem
Theorem 4.3
 (i)
\(0< a\leq\lambda_{n}\leq b_{1}< \frac{1}{2\alpha }\),
 (ii)
\(0< a\leq\gamma_{n}\leq b_{2}< \frac{1}{2\L\ ^{2}}\).
4.3 Split common fixed point problem
Theorem 4.5
 (i)
\(0< a\leq\lambda_{n}\leq b_{1}< \frac{1}{4}\),
 (ii)
\(0< a\leq\gamma_{n}\leq b_{2}< \frac{1}{2\L\ ^{2}}\).
Proof
We consider \(B:=0\), the zero operator. The required result follows from the fact that the zero operator is monotone and continuous, hence it is a maximal monotone. Moreover, in this case, we see that \(J_{\lambda}^{B}\) is the identity operator on \(H_{1}\), for each \(\lambda>0\). Thus the algorithm (3.4) reduces to (4.6), by setting \(A:=IV\) and \(B:=0\). □
5 Numerical experiments
In this section, we will show some numerical results and discuss on the possible good choices of step size parameters \(\lambda_{n}\) and \(\gamma _{n}\), those satisfy the control conditions in Theorem 3.2.
 Case 1.:

\(\lambda_{n}=0.25\), \(\gamma_{n}=0.14\).
 Case 2.:

\(\lambda_{n}=1.0e^{04}+\frac{1}{10n}\), \(\gamma _{n}=1.0e^{04}+\frac{1}{10n}\).
 Case 3.:

\(\lambda_{n}=1.0e^{04}+\frac{1}{10n}\), \(\gamma _{n}=0.2799\frac{1}{10n}\).
 Case 4.:

\(\lambda_{n}=0.4999\frac{1}{10n}\), \(\gamma _{n}=1.0e^{04}+\frac{1}{10n}\).
 Case 5.:

\(\lambda_{n}=0.4999\frac{1}{10n}\), \(\gamma _{n}=0.2799\frac{1}{10n}\).
Influence of the step size parameters \(\pmb{\lambda_{n}}\) and \(\pmb{\gamma_{n}}\) for the initial vector \(\pmb{(1,1)}\) with the 4 decimal places
Case →  1  2  3  4  5  

#(Iters) ↓  \(\boldsymbol{x_{\mathrm{Iter}}}\)  Errors  \(\boldsymbol{x_{\mathrm{Iter}}}\)  Errors  \(\boldsymbol{x_{\mathrm{Iter}}}\)  Errors  \(\boldsymbol{x_{\mathrm{Iter}}}\)  Errors  \(\boldsymbol{x_{\mathrm{Iter}}}\)  Errors 
50  \(\begin{pmatrix} 0.4924\\0 \end{pmatrix} \)  0.0076  \(\begin{pmatrix} 0.0343\\0.5622 \end{pmatrix} \)  0.7300  \(\begin{pmatrix} 0.0343\\0.5622 \end{pmatrix} \)  0.7300  \(\begin{pmatrix} 0.4999\\0 \end{pmatrix} \)  \(1.0e^{04}\)  \(\begin{pmatrix} 0.4999\\0 \end{pmatrix} \)  \(1.0e^{04}\) 
60  \(\begin{pmatrix} 0.4966\\0 \end{pmatrix} \)  0.0034  \(\begin{pmatrix} 0.0420\\0.5505 \end{pmatrix} \)  0.7161  \(\begin{pmatrix} 0.0420\\0.5505 \end{pmatrix} \)  0.7161  \(\begin{pmatrix} 0.5000\\0 \end{pmatrix} \)  0  \(\begin{pmatrix} 0.5000\\0 \end{pmatrix} \)  0 
120  \(\begin{pmatrix} 0.5000\\0 \end{pmatrix} \)  0  \(\begin{pmatrix} 0.0708\\0.5073 \end{pmatrix} \)  0.6645  \(\begin{pmatrix} 0.0708\\0.5073 \end{pmatrix} \)  0.6645  \(\begin{pmatrix} 0.5000\\0 \end{pmatrix} \)  0  \(\begin{pmatrix} 0.5000\\0 \end{pmatrix} \)  0 
250,000  \(\begin{pmatrix} 0.5000\\0 \end{pmatrix} \)  0  \(\begin{pmatrix} 0.4999\\0 \end{pmatrix} \)  \(1.0e^{04}\)  \(\begin{pmatrix} 0.4999\\0 \end{pmatrix} \)  \(1.0e^{04}\)  \(\begin{pmatrix} 0.5000\\0 \end{pmatrix} \)  0  \(\begin{pmatrix} 0.5000\\0 \end{pmatrix} \)  0 
275,000  \(\begin{pmatrix} 0.5000\\0 \end{pmatrix} \)  0  \(\begin{pmatrix} 0.5000\\0 \end{pmatrix} \)  0  \(\begin{pmatrix} 0.5000\\0 \end{pmatrix} \)  0  \(\begin{pmatrix} 0.5000\\0 \end{pmatrix} \)  0  \(\begin{pmatrix} 0.5000\\0 \end{pmatrix} \)  0 
Influence of the step size parameters \(\pmb{\lambda_{n}}\) and \(\pmb{\gamma_{n}}\) for the initial vector \(\pmb{(0,0)}\) with the 4 decimal places
Case →  1  2  3  4  5  

#(Iters) ↓  \(\boldsymbol{x_{\mathrm{Iter}}}\)  Errors  \(\boldsymbol{x_{\mathrm{Iter}}}\)  Errors  \(\boldsymbol{x_{\mathrm{Iter}}}\)  Errors  \(\boldsymbol{x_{\mathrm{Iter}}}\)  Errors  \(\boldsymbol{x_{\mathrm{Iter}}}\)  Errors 
50  \(\begin{pmatrix} 0.4901\\0 \end{pmatrix} \)  0.0099  \(\begin{pmatrix} 0.0654\\0 \end{pmatrix} \)  0.4346  \(\begin{pmatrix} 0.0654\\0 \end{pmatrix} \)  0.4346  \(\begin{pmatrix} 0.4998\\0 \end{pmatrix} \)  \(2.0e^{04}\)  \(\begin{pmatrix} 0.4998\\0 \end{pmatrix} \)  \(2.0e^{04}\) 
60  \(\begin{pmatrix} 0.4956\\0 \end{pmatrix} \)  0.0044  \(\begin{pmatrix} 0.0680\\0 \end{pmatrix} \)  0.4320  \(\begin{pmatrix} 0.0680\\0 \end{pmatrix} \)  0.4320  \(\begin{pmatrix} 0.5000\\0 \end{pmatrix} \)  0  \(\begin{pmatrix} 0.5000\\0 \end{pmatrix} \)  0 
120  \(\begin{pmatrix} 0.5000\\0 \end{pmatrix} \)  0  \(\begin{pmatrix} 0.0779\\0 \end{pmatrix} \)  0.4221  \(\begin{pmatrix} 0.0779\\0 \end{pmatrix} \)  0.4221  \(\begin{pmatrix} 0.5000\\0 \end{pmatrix} \)  0  \(\begin{pmatrix} 0.5000\\0 \end{pmatrix} \)  0 
275,000  \(\begin{pmatrix} 0.5000\\0 \end{pmatrix} \)  0  \(\begin{pmatrix} 0.4999\\0 \end{pmatrix} \)  \(1.0e^{04}\)  \(\begin{pmatrix} 0.4999\\0 \end{pmatrix} \)  \(1.0e^{04}\)  \(\begin{pmatrix} 0.5000\\0 \end{pmatrix} \)  0  \(\begin{pmatrix} 0.5000\\0 \end{pmatrix} \)  0 
300,000  \(\begin{pmatrix} 0.5000\\0 \end{pmatrix} \)  0  \(\begin{pmatrix} 0.5000\\0 \end{pmatrix} \)  0  \(\begin{pmatrix} 0.5000\\0 \end{pmatrix} \)  0  \(\begin{pmatrix} 0.5000\\0 \end{pmatrix} \)  0  \(\begin{pmatrix} 0.5000\\0 \end{pmatrix} \)  0 
Influence of the step size parameters \(\pmb{\lambda_{n}}\) and \(\pmb{\gamma_{n}}\) for the initial vector \(\pmb{(1,1)}\) with the 4 decimal places
Case →  1  2  3  4  5  

#(Iters) ↓  \(\boldsymbol{x_{\mathrm{Iter}}}\)  Errors  \(\boldsymbol{x_{\mathrm{Iter}}}\)  Errors  \(\boldsymbol{x_{\mathrm{Iter}}}\)  Errors  \(\boldsymbol{x_{\mathrm{Iter}}}\)  Errors  \(\boldsymbol{x_{\mathrm{Iter}}}\)  Errors 
50  \(\begin{pmatrix} 0.6997\\0.1331 \end{pmatrix} \)  0  \(\begin{pmatrix} 0.8477\\0.0097 \end{pmatrix} \)  0.1848  \(\begin{pmatrix} 0.8477\\0.0097 \end{pmatrix} \)  0.1848  \(\begin{pmatrix} 0.6758\\0.1172 \end{pmatrix} \)  0  \(\begin{pmatrix} 0.6758\\0.1172 \end{pmatrix} \)  0 
60  \(\begin{pmatrix} 0.6997\\0.1331 \end{pmatrix} \)  0  \(\begin{pmatrix} 0.8457\\0.0126 \end{pmatrix} \)  0.1813  \(\begin{pmatrix} 0.8457\\0.0126 \end{pmatrix} \)  0.1813  \(\begin{pmatrix} 0.6758\\0.1172 \end{pmatrix} \)  0  \(\begin{pmatrix} 0.6758\\0.1172 \end{pmatrix} \)  0 
120  \(\begin{pmatrix} 0.6997\\0.1331 \end{pmatrix} \)  0  \(\begin{pmatrix} 0.8384\\0.0236 \end{pmatrix} \)  0.1681  \(\begin{pmatrix} 0.8384\\0.0236 \end{pmatrix} \)  0.1681  \(\begin{pmatrix} 0.6758\\0.1172 \end{pmatrix} \)  0  \(\begin{pmatrix} 0.6758\\0.1172 \end{pmatrix} \)  0 
50,000  \(\begin{pmatrix} 0.6997\\0.1331 \end{pmatrix} \)  0  \(\begin{pmatrix} 0.7455\\0.1629 \end{pmatrix} \)  \(6.3791e^{04}\)  \(\begin{pmatrix} 0.7455\\0.1629 \end{pmatrix} \)  \(6.3791e^{04}\)  \(\begin{pmatrix} 0.6758\\0.1172 \end{pmatrix} \)  0  \(\begin{pmatrix} 0.6758\\0.1172 \end{pmatrix} \)  0 
75,000  \(\begin{pmatrix} 0.6997\\0.1331 \end{pmatrix} \)  0  \(\begin{pmatrix} 0.7452\\0.1634 \end{pmatrix} \)  0  \(\begin{pmatrix} 0.7452\\0.1634 \end{pmatrix} \)  0  \(\begin{pmatrix} 0.6758\\0.1172 \end{pmatrix} \)  0  \(\begin{pmatrix} 0.6758\\0.1172 \end{pmatrix} \)  0 
Remark 5.1
6 Concluding remarks
This paper can be considered as a refinement of work by Takahashi et al. [16], by providing an algorithm for finding a solution of the main problem (1.9), which is a generalization of the problem that was considered in [16]. Some sufficient conditions for the weak convergence of such introduced algorithm are given. Also, in order to show the significance of the considered problem, some important applications are discussed. Since in this paper we are considering and focusing on the weak convergent type of the constructive algorithm, it should be a natural direction for the next research to study the algorithms and sufficient conditions and focus on strong convergence type.
Declarations
Acknowledgements
The authors are thankful to the referees and the editor for their constructive comments and suggestions which have been useful for the improvement of the paper. This research has been funded by Naresuan University and the Thailand Research Fund under the project RTA5780007.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Authors’ Affiliations
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