Abstract
In this paper, we study a schematic approximation of solutions of a split null point problem for a finite family of maximal monotone operators in real Hilbert spaces. We propose an iterative algorithm that does not depend on the operator norm which solves the split null point problem and also solves a generalized mixed equilibrium problem. We prove a strong convergence of the proposed algorithm to a common solution of the two problems. We display some numerical examples to illustrate our method. Our result improves some existing results in the literature.
Keywords:
split feasibility problem; null point problem; generalized mixed equilibrium problem; monotone mapping; strong convergence; Hilbert space MSC:
47H06; 47H09; 47H10; 46N10; 47J25
1. Introduction
Let C be a nonempty, closed and convex subset of a real Hilbert space The Equilibrium Problem (EP) in the sense of Blum and Oettli [1] is to find a point such that
where is a bifunction. The EP unify many important problems, such as variational inequalities, fixed point problems, optimization problems, saddle point (minmax) problems, Nash equilibria problems and complimentarity problems [2,3,4,5,6,7]. It also finds applications in other fields of studies like physics, economics, engineering and so on [1,2,8,9,10]. The Generalized Mixed Equilibrium Problem (GMEP) (see e.g., [11]) is to find such that
where is a nonlinear mapping and is a proper lower semicontinuous convex function. The solution set of (2) will be denoted
The GMEP includes as special cases, minimization problem, variational inequality problem, fixed point problem, nash equilibrium etc. GMEP (2) and these special cases have been studied in Hilbert, Banach, Hadamard and p-uniformly convex metric spaces, see [11,12,13,14,15,16,17,18,19,20,21].
For a real Hilbert space H, the Variational Inclusion Problem (VIP) consists of finding a point such that
where is a multivalued operator. If A is a maximal monotone operator, then the VIP reduces to the Monotone Inclusion Problem (MIP). The MIP provides a general framework for the study of many important optimization problems, such as convex programming, variationa inequalities and so on.
For solving Problem (3), Martinet [22] introduced the Proximal Point Algorithm (PPA), which is given as follows: and
where and is the resolvent of the maximal monotone operator A corresponding to the control sequence Several iterative algorithms have been proposed by authors in the literature for solving Problem (3) and related optimization problems, see [23,24,25,26,27,28,29,30,31,32,33,34,35,36,37].
Censor and Elfving [38] introduced the notion of Split Feasibility Problem (SFP). The SFP consists of finding a point
where C and Q are nonempty closed convex subsets of and respectively and L is an matrix. The SFP has been studied by researchers due to its applications in various field of science and technology, such as signal processing, intensity-modulated radiation therapy and medical image construction, for details, see [39,40]. In solving (5), Byrne [39] introduced the following iterative algorithm: let be arbitrary,
where is the transpose of the matrix and are nearest point mappings onto C and Q respectively. Lopez et al. [41] suggested the use of a stepsize in place of in Algorithm (6), where the stepsize does not depend on operator The stepsize is given as:
where and They proved a weak convergence theorem of the proposed algorithm. The authors in [41] noted that for L with higher dimensions, it may be hard to compute the operator norm and this may have effect on the iteration process. Instances of this effect can be observed in the CPU time. The algorithm with stepsizes improves the performance of the Byrne algorithm.
The Split Null Point Problem (SNPP) was introduced in 2012 by Byrne et al. [42]. These authors combined the concepts of VIP and SFP and defined SNPP as follows: Find such that
where are maximal monotone operators, and are real Hilbert spaces. For solving (8), Byrne et al. [42] proposed the following iterative algorithm: For and an arbitrary
where They prove a weak convergence of (9) to a solution of (8).
One of our aim in this work is to consider a generalization of Problem (3) in the following form: Find such that
where is a finite family of maximal monotone operators. There have been some iterative algorithms for approximating the solution of (10) in the literature, (see [37] and the references therein).
In this study, we consider the problem of finding the common solution of the GMEP (2) and the SNPP for a finite family of intersection of maximal monotone operator in the frame work of real Hilbert spaces. We consider the following generalization of the SNPP: Find such that and
In our quest to obtain a common element in the solution set of problems (2) and (11), the following two research questions arise.
2. Preliminaries
In this section, we give some important definitions and Lemmas which are useful in establishing our main results.
From now, we denote by H a real Hilbert space, C a nonempty closed convex subset of H with inner product and norm denoted by and respectively. We denote by and respectively the weak and strong convergence of a sequence to a point
The nearest point mapping is defined by where is the distance function of The mapping is known to satisfy the inequality
see e.g., [9,10] for details.
A point is said to be a fixed point of a mapping if We denote by the set of fixed point of A mapping is said to be a contraction, if there exists a constant such that
If then f is called nonexpansive.
A mapping is said to be firmly nonexpansive if, for all the following holds
where I is an identity mapping on
Lemma 1
([43]). Let be a mapping. Then the following are equivalent:
- (i)
- T is firmly nonexpansive,
- (ii)
- is firmly nonexpansive,
- (iii)
- is nonexpansive,
- (iv)
- ,
- (v)
A multivalued mapping is called monotone if for all and we have
A monotone mapping A is said to be maximal if its graph is not properly contained in the graph of any other monotone operator.
Let be a single-valued mapping, then for a positive real number A is said to be -inverse strongly monotone (-ism), if
This class of monotone mapping have been widely studied in literature (see [44,45]) for more details. If A is a monotone operator, then we can define, for each a nonexpansive single-valued mapping by which is generally known as the resolvent of (see [46,47]). It is also known that where and
Lemma 2
([6,48]). Let H be a real Hilbert space. Then the following hold:
- (i)
- (ii)
- (iii)
- and
The bifunction will be assumed to admit the following restrictions:
- ()
- for all
- F is monotone, i.e., for all
- ()
- for each
- ()
- for each is convex and lower semicontinuous.
Lemma 3
([11]). Let C be a nonempty closed convex subset of real Hilbert space H. Let F be a real valued bifunction on admitting restrictions , be a nonlinear mapping and let be a proper lower senicontinuous convex function. For any given and define a mapping as
for all Then the following conclusions hold:
- (i)
- for each
- (ii)
- is single valued,
- (iii)
- is firmly nonexpansive, i.e., for any
- (iv)
- ,
- (v)
- is closed and convex.
Lemma 4
([49,50]). Let be a sequence of nonnegative real numbers satisfying the following relation:
where and are sequences of real numbers satisfying
- (i)
- (ii)
- (iii)
Then,
3. Main Result
Throughout, we let where Define the stepsize by
where depends on and is any nonnegative number.
Lemma 5.
Let H be a real Hilbert space and be a monotone mapping. Then for we have
Proof.
Notice that and Using the monotonicity of A, we have
That is
which implies that
Using Lemma 2 (ii), we obtain
that is
and
Since we obtain
which implies
Now, since by (20), we obtain
□
Lemma 6.
Let C and Q be nonempty, closed and convex subsets of real Hilbert spaces and respectively and be a bounded linear operator. Assume F is a real valued bifunction on which admits condition - Let be a proper, lower semicontinuous convex function, g be a β-inverse strongly monotone mapping and be a differentiable function, such that is a contraction with coefficient . For let and be finite families of monotone mappings. Assume where For an arbitrary let be a sequence defined iteratively by
where is a nonnegative sequence of real numbers, and are sequences in is a nonnegative sequence defined by (19), satisfying the following restrictions:
- (i)
- (ii)
- (iii)
Then and are bounded.
Proof.
Observe that can be rewritten as for each Fix Since g is -inverse strongly monotone and for any we have from (21) and Lemma 2 (ii) that
Also by Lemma 2, we have
Using the definition of we obtain
hence,
Further, we obtain that
Let We show that for all Indeed, we see that Now suppose for some Then, we have that
By induction, we obtain that for all Therefore is bounded, consequently and are bounded. □
Theorem 1.
Let C and Q be nonempty, closed and convex subsets of real Hilbert spaces and respectively and be a bounded linear operator. Assume F is a real valued bifunction on which admits condition - Let be a proper, lower semicontinuous function, g be a β-inverse strongly monotone mapping and be a differentiable function, such that is a contraction with coefficient . For let and be finite families of monotone mappings. Assume where For an arbitrary let be a sequence defined iteratively by (21) satisfying the conditions of Lemma 6. Then converges strongly to where
Proof.
Case 1: Suppose that there exists such that is not monotonically increasing. Then by Lemma 6, we have that is convergent. From (21), we have by Lemma 2 that
Thus,
From (23), we have that
by using restriction (i) in Lemma 6, we have
Using (19), we have that
thus by (31), we obtain
Therefore, since we obtain
Notice that which implies
by (33), we obtain
consequently,
From (21), we see that
By (35), we get that
Furthermore, we have from (21),
Observe from (21), that
using the nonexpansivity of , we obtain that
Using restriction (i) in Lemma 6, the boundedness of and the convergence of we have that as Thus by the strong nonexpansivity of we get that
Using this and restriction (i) of Lemma 6 in (38), we get
Observe from (28), that
since using (39), we have that
thus, by restriction (i) in Lemma 6, we obtain
Combining (36) and (40), we obtain
Moreover, since
we have that
Furthermore,
but
Hence, by substituting this, (41) and (42) into (43), we obtain
Since we have by Lemma 5, that
Now, since is bounded in there exists a subsequence of such that First, we show that Consider for each
Since as a consequence of (44), we have
Therefore, using and (46) in (45), we have
Thus, and hence
Secondly, we show that Consider again for each
observe that,
which by (34) and (36), implies
Again, since , we have by Lemma 5, that
So for any subsequence we also have that
Thus, by the linearity and continuity of as and as implies as Hence from (49), we have
Therefore, that is Further, we show that From (40), we have Since for any , we have
It follows from condition of the bifunction that
Replacing n by we have
Let for all and Then we have So from (53), we have
Since we obtain as Moreover, since g is monotone, we have Therefore by of the bifunction F and the weak lower semicontinuity of taking the limit of (54), we obtain
Using of bifunction F and (55), we obtain
this implies that
By letting we have
which implies
Finally we show that Let be subsequence of , such that and
since as and it follows that Consequently, we obtain by (12), that
By using Lemma 4 in (28), we conlude that as Thus, as ditto for both and
Case 2: Let be monotonically nondecreasing. Define for all (for some large enough) by
Clearly, is nondecreasing, as and
By using similar argument as in Case 1, we make the following conclusions
and
Using the boundedness of we can obtain a subsequence of which converges weakly to and Therefore, it follows from (28), that
Since we obtain Thus, from (61), we obtain
We note that then from (62), we get
This implies
hence
Using this and , we obtain
Further, for we clearly observe that if (i.e., ). Since for Consequently, for all
Using (63), we conclude that that is □
The following are some consequences of our main theorem.
Let in (21), we have the following corollary:
Corollary 1.
Let C and Q be nonempty, closed and convex subsets of real Hilbert spaces and respectively and be a bounded linear operator. Assume F is a real valued bifunction on which admits condition - Let be a proper, lower semicontinuous function, g be a β-inverse strongly monotone mapping. For let and be finite families of monotone mappings. Assume where For an arbitrary let be a sequence defined iteratively by
where is a nonnegative sequence of real numbers, and are sequences in is a nonnegative sequence defined by (19), satisfying the following restrictions:
- (i)
- (ii)
- (iii)
Then converges strongly to where
For we obtain the following result for approximation a common solution of a split null point for a sum of monotone operators and generalized mixed equilibrium problem.
Corollary 2.
Let C and Q be nonempty, closed and convex subsets of real Hilbert spaces and respectively and be a bounded linear operator. Assume F is a real valued bifunction on which admits condition - Let be a proper, lower semicontinuous function, g be a β-inverse strongly monotone mapping. For let and be finite families of monotone mappings. Assume where For an arbitrary let be a sequence defined iteratively by
where is a nonnegative sequence of real numbers, and are sequences in is a nonnegative sequence defined by (19), satisfying the following restrictions:
- (i)
- (ii)
- (iii)
Then converges strongly to where
4. Numerical Example
In this section, we provide some numerical examples. The algorithm was coded in MATLAB 2019a on a Dell i7 Dual core 8.00 GB(7.78 GB usable) RAM laptop.
Example 1.
Let be the linear spaces of 2-summable sequences of scalars in , that is
with the inner product defined by and the norm by where Let be given by for all then for each
Let it is easy to that f is differentiable with For each define and by and respectively for all
For each define the bifunction by the function by and by for each For each we have the following steps to get Find u such that
for all Hence, by Lemma 3 (2), it follows that Therefore,
For choose the sequences and We obtain the graph of errors against the number of iterations for different values of The following cases are presented in Figure 1 below:
Figure 1.
Case 1 (top); Case 2 (middle); Case 3 (bottom).
- Case 1
- Case 2
- Case 3
Example 2.
Let be endowed with an inner product where and the euclidean norm. Let be defined by and For each define and by and respectively, where Let Define and By simple calculation, we obtain that
Choose the sequences and For (21) becomes
We make different choices of our initial value as follow:
We use as our stopping criterion and plot the graphs of errors against the number of iterations. See Figure 2.

Figure 2.
Case 1 (top); Case 2 (middle); Case 3 (bottom).
5. Conclusions
This paper considered the approximation of common solutions of a split null point problem for a finite family of maximal monotone operators and generalized mixed equilibrium problem in real Hilbert spaces. We proposed an iterative algorithm which does not depend on the prior knowledge of the operator norm as being used by many authors in the literature [39,42]. We proved a strong convergence of the proposed algorithm to a common solution of the two problems. We displayed some numerical examples to illustrate our method. Our result improves some existing results in the literature.
Author Contributions
Conceptualization of the article was given by O.K.O. and O.T.M., methodology by O.K.O., software by O.K.O., validation by O.T.M., formal analysis, investigation, data curation, and writing–original draft preparation by O.K.O. and O.T.M., resources by O.K.O. and O.T.M., writing–review and editing by O.K.O. and O.T.M., visualization by O.K.O. and O.T.M., project administration by O.T.M., Funding acquisition by O.T.M. All authors have read and agreed to the published version of the manuscript.
Funding
O.K.O is funded by Department of Science and Innovation and National Research Foundation, Republic of South Africa Center of Excellence in Mathematical and Statistical Sciences (DSI-NRF COE-MaSS) and O.T.M. is funded by National Research Foundation (NRF) of South Africa Incentive Funding for Rated Researchers (grant number 119903).
Acknowledgments
The authors sincerely thank the reviewers for their careful reading, constructive comments and fruitful suggestions that substantially improved the manuscript. The first author acknowledges with thanks the bursary and financial support from Department of Science and Innovation and National Research Foundation, Republic of South Africa Center of Excellence in Mathematical and Statistical Sciences (DST-NRF COE-MaSS) Doctoral Bursary. The second author is supported by the National Research Foundation (NRF) of South Africa Incentive Funding for Rated Researchers (Grant Number 119903). Opinions expressed and conclusions arrived are those of the authors and are not necessarily to be attributed to the CoE-MaSS and NRF.
Conflicts of Interest
The authors declare that they have no competing interests.
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