Abstract
In this paper, an iterative scheme for finding common solutions of the set of fixed points for a pair of asymptotically quasi-nonexpansive mapping and the set of minimizers for the minimization problem is constructed. Using the idea of the jointly demicloseness principle, strong convergence results are achieved without imposing any compactness condition on the space or the operator. Our results improve, extend and generalize many important results in the literature.
1. Introduction
Over the years, fixed point theory has been revealed to be a powerful and all- encompassing tool in the study of nonlinear phenomena. In this direction, several researchers have obtained different results relating to practical consequences of this field of study in areas such as tomography, quantize design, signal enhancement, signal and image reconstruction, signal and filter synthesis, telecommunications, interpolation, extrapolation, and several others. An instance of the above applications can be seen in [1]. It is a known fact that many interesting problems that dwell in physical systems could be recast as fixed-point problems. For instance, let be a signal of interest and let be its accompanying distorted version. Suppose, in addition, that and are related by the operator equation
where we have assumed that is the signal measured at the reception point of the transmission system ð and is the transmitted signal. The problem of interest is how to approximate given and the model ð of the distortion that underwent. Assume satisfies the constraint equation
Then, the identity
holds. Under the seemingly general condition, the solution to the equation will give the unknown signal .
Recently, the concept of symmetry, a congenial characteristic of a Banach space, which is closely connected to fixed point problems [2], has drawn the attention of renowned mathematicians worldwide. This unwavering interest has been known to stem from the practical application of this subject to different fields. Recall that a symmetry is a mapping of the object X, considered to be structured onto itself such that the structure is preserved. Saleem et al. [3] and Sain [4], illustratively gave different ways this mapping could occur. Neugebaner [2], using the concept of symmetry, obtained several applications of a layered compression–expansion fixed-point theorem in the existence of solutions of a second-order difference equation with Dirichlet boundary conditions. Let be a real Hilbert space with inner product and induced norm . In this paper, denotes the nonempty, close and convex subset of and by , the set of natural numbers. Additionally, if is any sequence in then we shall adopt the following notations:
- (a)
- means that converges strongly to
- (b)
- means that converges weakly to
Let be a nonlinear mapping with domain and range . The set of fixed points of will be denoted by , while the set of common fixed points of the mappings will be denoted by .
Definition 1.
The mapping is:
- (a)
- Lipschitizian if there exists a constant L, such thatwhere L is the Lipschitizian constant of It is worth noting that if in (1) then Δ is a contraction, while Δ is called nonexpansive if in (1)
- (b)
- asymptotically nonexpansive (see [5]) if for all there exists a sequence with such that
- (c)
- uniformly Lipschitizian if there exists a constant L such that
- (d)
- Asymptotically quasi-nonexpansive if and is satisfied; that is, if and for all there exists a sequence with such that
Remark 1.
A uniformly Lipschitizian mapping is a superclass of the classes of nonexpansive mappings (which is a subclass of the class of asymptotically nonexpansive mapping) and asymptotically nonexpansive mappings, while the class of asymptotically quasi-nonexpansive mappings is a superclass of the classes of asymptotically nonexpansive mappings and quasi-nonexpansive mappings (recall that a nonlinear mapping is called quasi-nonexpansive if and , we have ) (see examples 4.1, 4.3 and 4.9 in [6] for more details).
Definition 2.
A nonlinear mapping with domain and range is called:
- (e)
- A strongly positive bounded linear operator if there exists a constant , such that inequalityholds.
- (f)
- Monotone if
- (g)
- α-strongly monotone if there exists , such that
- (h)
- Let , then δ-inverse strongly monotone (for short δ-ism), such that
Remark 2.
The map is monotone if Δ is nonexpansive. The projection operator is inverse strongly monotone. It is a common knowledge that inverse strongly monotone operators are indispensable in solving practical problems of traffic assignment problems (see [7,8] for more details).
Approximating a fixed point for single-valued mappings is an active area of research and has attracted many established mathematicians worldwide. Since exact solutions for practical problems in different fields of human endeavor are difficult to attain, approximation via iteration scheme has become a vital tool for solving fixed-point problems. Let f be a contraction map on . Starting from an initial point , define the sequence iteratively, as follows
where is a sequence in The iteration sequence (9) was first introduced by Moudafi [9] and has been positively used to approximate fixed points of different nonlinear mappings in recent times (see [10,11] and the reference therein for further study).
The iteration scheme for the fixed point of non-expansive mappings has been extensively investigated, mainly because of the intimate connection between non-expansive mappings and monotonicity methods. In this direction, Marino and Xu [12] and Xu [11] discovered that the iterative method for non-expansive mappings could be used to solve the convex minimization problem. More precisely, it was shown in [11] that a typical minimization problem of a quadratic function of the form:
over the set of fixed points for nonexpansive mappings in a real Hilbert space could be solved using the iteration scheme
where is a nonexpansive mapping and is a strongly positive bounded linear operator (Recall (e) and (g) from Definition 2 that a strongly bounded linear operator is a –Lipschitizian and -strongly monotone operator).
Motivated by the results in [9], Xu [10] generalized (9) as follows: let f be a contraction on and be a strongly positive bounded linear operator. Let be the sequence generated from an arbitrary point , such that
where is a sequence in He showed that (11) converges strongly to the fixed point of which at the same time serves as the solution to the variational inequality problem below:
Despite the practical value of some crucial fixed-point results obtained for the case of single-valued mappings, there have been a concentrated efforts in evaluating fixed points for nonlinear multivalued mappings. This special interest is believed to have come from the various practical applications of multivalued mappings. For instance, a monotonic operator in optimization theory is the multivalued mapping of the subdifferential of the function g, , and is defined by
and satisfies the condition
In particular, if is a convex, continuously differentiable function, then , the gradient is a subdifferential, which is a single-valued mapping, and the condition is an operator equation and is variational inequalities and both conditions are closely related to optimality conditions. Hence, finding fixed points or common fixed points for multivalued mapping is an important area in applications. However, we have noticed (with concern) that fewer iteration schemes exist, especially in the direction of asymptotically nonlinear multivalued mappings.
Let be a metric space, D a nonempty subset of E and a multivalued mapping. A point is said to be a fixed point of if . The fixed-point set of is denoted by . Let and represent the family of closed and bounded subset of E, the family of nonempty compact and convex subset of E and the family of proximinal subset of E, respectively. A subset D of E is called proximinal if, for each , there exists a point for which (13) holds.
where It is known that every nonempty closed and convex subset of a real Hilbert is proximinal.
Let . The Pompieu Hausdorff metric induced by the metric is defined as
Definition 3.
A multivalued mapping is called:
- (1)
- β-Lipschitizian if there exists such thatNote that in (14), Δ is a contraction if and nonexpansive if .
- (2)
- Uniformly Lipschitizian if there exists , such that
- (3)
- asymptotically nonexpansive if, for all , there exists a sequence with , such that
- (4)
- asymptotically quasi-nonexpansive mapping in [13], if and holds; that is, if and for all , there exists a sequence with such that
Remark 3.
The class of asymptotically quasi-nonexpansive multivalued mapping is a superclass of the classes of asymptotically nonexpansive multivalued mappings and quasi-nonexpansive multivalued mappings (where a multivalued mapping is called quasi-nonexpansive (a superclass of the class of nonspreading-type multivalued mapping) if and for all , we have . Also, V is called nonspreading-type if the inequality holds Every nonspreading-type multivalued mapping with a nonempty fixed point set is quasi-nonexpansive).
The minimization problem, an invaluable problem in application, especially in the area of nonlinear analysis and optimization theory, is defined as follows: find , such that
where is a proper convex and lower semicontinuous function. Note that problem (18) is consistent if it has a solution. The set of all solutions (minimizers) of g on is defined as
Let be a proper convex and lower semicontinuous function. Starting from an arbitrary point , define the iteration scheme as follows:
The fixed-point algorithm (19) for solving problem (18) was first introduced in 1970 by Martinet [14], and was called a proximinal point algorithm (for short, PPA). In recent times, many researchers have studied and generalized (19), and many interesting results have been obtained for different classes of nonlinear single-valued and multivalued mappings: Rockfeller [15] solved problem (18) using (19); Marino and Xu [12], and subsequently Phuengrattan and Lerkchaiyaphum [16], obtained weak and strong convergence to the common solution of the minimization problem and fixed-point problem using the modified version of (19) in the setting of real Hilbert spaces.
More recently, Chang et al. [17] used the scheme
where is a closed and convex subset of a real Hilbert space and is a proper convex and lower semicontinuous function, in order to prove that (20) converges weakly and strongly to the common solutions of the minimization problem of (18) and fixed point problem of nonspreading-type multivalued mapping in the framework of a real Hilbert space
Most recently, El-Yekheir et al. [18] introduced and studied the following modified PPA: Let be a closed and convex subset of a real Hilbert space , a proper convex and lower semicontinuous function and a multivalued quasi-nonexpansive mapping. The PPA-Ishikawa iteration method is defined as follows:
where and are sequences in . Using (21), they proved strong convergence results under mild conditions on the control sequences. More precisely, they proved the following theorem:
Theorem 1.
Let Θ be a closed and convex subset of a real Hilbert space Υ and a proper convex and lower semicontinuous function. Let be a b-Lipschitizian mapping and be a multivalued quasi-nonexpansive mapping, such that and . Let A be a k–strongly monotone and L–Lipschitizian operator. Assume that where and is demiclosed at the origin. Let be as defined by (21), where and are sequences in such that and is a sequence such that for all and some λ. Then, the sequence generated by (21) converges strongly to the unique solution of the variational inequality problem
The demiclosedness principle, first studied by Opial [19], is one of the essential tools for proving weak and strong convergence theorems for both single-valued and multivalued nonlinear mappings. It is on record that the theory of fixed points with the associated mappings satisfying demiclosedness principle due to Opial [20] has been extensively studied for the past 40 years or so, and much more intensively recently (see, e.g., [19,20,21,22,23] and the references therein). Although some invaluable results have been obtained, it is worth mentioning that, in some cases, the mapping of the class of nonexpansive mappings defined in the setting of a real Hilbert space does not necessarily satisfies the demiclosedness principle due to Opial [20] (see example 2.1 in reference [19] for more details). Consequently, it is natural to ask:
Question 1.
Is there any way one can obtain strong convergence theorems of Halpern’s type for such mappings that fail to satisfy the original demiclosedness principle due to Opial in the setting of Banach spaces?
Naraghrad [19] answered the above question in the affirmative, satisfying the jointly demiclosedness principle (if C is a nonempty subset of a Banach space E, then a pair satisfies jointly demiclosedness principle if converges weakly to a point and , then and ; that is, is jointly demiclosed at zero) which they introduced. Later, Agwu [24] extended the ideas of the jointly demiclosedness principle to a more general class of multivalued mappings and gave the following definition.
Definition 4
([24]). Let D be a nonempty closed convex subset of a Banach space E. A pair of multivalued mappings satisfies the jointly demiclosedness principle, in the sense of Naraghirad [19], if for any sequence converging weakly to a point and there exist and with such that as then and that is, is jointly demiclosed at zero. In particular, if where I is the identity mapping on E, then is demiclosed at zero.
Inspired and motivated by the results in [12,18,24], it is natural to ask the following question:
Question 2.
Can we construct a modified proximinal point algorithm that is independent of (21)? If so, can the proposed modified PPA be used to achieve convergence results for a larger class of asymptotically quasi-nonexpansive multivalued mappings that fail to satisfy the original demiclosedness principle due to Opial in the setting of real Hilbert spaces?
It is our purpose in this paper to give an affirmative answer to Question 2. Let be a closed and convex subset of a real Hilbert space , a proper convex and lower semicontinuous function and be an asymptotically quasi-nonexpansive multivalued mappings. Then, the modified PPA iteration scheme generated by for the above mentioned mappings is as follows:
where and are sequences in
Remark 4.
Observe that if:
- (I)
- in (22), we get
Note that (23) generalizes (19), (20) and many other iteration schemes in this direction.
2. Preliminaries
For the sake of convenience, we restate the following concepts and results:
Assumption V
Assume that:
- (a)
- is a real Hilbert space, is closed and convex and is a projection operator;
- (b)
- , and are multivalued mapping and proper, convex and lower semicontinuous function and multivalued nonspreading mapping, respectively;
- (c)
- and are set of fixed points of the resolvent of g and set of minimizers of g and
- (d)
- Is a set of real numbers and be a k-strongly monotone and L-Lipschitizian operator with
Let be as described in Assumption V. Then, , assigns to each the unique point of , , such that
It has been established that for every
Definition 5.
Let Υ and S be as described in Assumption V. Then, the mapping is known to be demiclosed at the origin if for any sequence , such that and ; then , where I is the identity map of
Lemma 1.
Let be as described in Assumption V. Given any define the Moreau–Yosida resolvent of f in Υ as
Then,
- coincides with (see [25]), and for any the resolvent of g is firmly nonexpansive mapping, and hence is nonexpansive in [26].
- Since is firmly nonexpansive mapping, if then we have
Lemma 2
([27]). Let be as described in Assumption V. Then, is demiclosed at zero.
Lemma 3
([28]). Let Υ be as in Assumption V. Then, for every and for every the following inequalities hold
- (i)
- (ii)
Lemma 4
([11]). Let with where and such that and Then, .
Lemma 5
([29]). Let be as described in Assumption V. Assume that and Then, we have
Lemma 6
([30]). be as described in Assumption V. Given and , the identity below holds:
Lemma 7
([31]). be as described in Assumption V. Then, for every and the following subdifferential inequality holds
Lemma 8
([32]). Let and which does not increase at infinity in the sense that there exists a subsequence of , such that for For (sufficiently large), let be the sequence of integers defined as follows
Then, as and
Agwu and Igbokwe [33] worked on a two-step extragradient-viscosity algorithm for the common fixed-point problem of two asymptotically nonexpansive mappings and variational inequality problems in Banach spaces. Khatoon et al. [34,35] carried out great work on a modified proximal point algorithm for nearly asymptotically quasi-nonexpansive mappings. Khunpanuk et al. [36] worked on a proximal point algorithm for solving common fixed-point problems and convex minimization problems in Geodesic spaces with positive curvature. Garodia et al. [37] worked on constrained minimization, variational inequality and a split feasibility problem via a new iteration scheme in Banach spaces. Abass et al. [38] worked on a common solution of the family of minimization problems and fixed-point problems for multivalued type-one demicontractive mappings. Ezeora et al. [39] established a strong convergence of an inertial-type algorithm to a common solution of a minimization problem and a fixed-point problem.
3. Main Results
Assumption Z
Assume that:
- (I)
- is a real Hilbert space and is closed and convex;
- (II)
- R is a set of real numbers, and are proper convex and lower semicontinuous function and -Lipschitizian mappings, respectively. For a given , we define the Moreau–Yosida resolvent of in as
- (III)
- are two asymptotically quasi-nonexpansive multivalued mappings with and , such that are jointly demiclosed at the origin.
- (IV)
- Let be an –strongly monotone and L–Lipschitizian operator.
Algorithm Q
Let and be as described in Assumption Z. Then, is a sequence generated as follows:
Further, let and retain their usual meaning. A multivalued mapping is called asymptotically -nonspreading if there exists such that
Note that a multivalued mapping is called asymptotically nonspreading-type if ; that is,
Again, if is an asymptotically nonspreading-type and then is asymptotically quasi-nonexpansive mapping. Indeed, and , we have
Thus, it follows that
Theorem 2.
Let and be as described in Assumption Z. Suppose ; ;
where and is jointly demiclosed at the origin. Let be a sequence generated by Algorithm Q, such that satisfy the conditions:
- (i)
- and
- (ii)
- for each
- (iii)
- is such that and for some
Then, strongly converges to , which simultaneously serves as a unique solution to the problem:
Proof.
Firstly, we prove that the solution to problem (31) is unique. To achieve this, we assume for contradiction that there exist two points which are solutions of (31) and . Then, using the same argument as in the proof of the uniqueness of (31) in [18], we have as required.
Again, we note that the operator is a contraction. Now, for any two fixed numbers such that are in and for all , we get, using Lemma 5, and , that
According to the Banach contraction principle, the mapping has a fixed point, say , which is equivalent to the problem:
The rest of the proof of Theorem 2 will be presented in stages:
Stage 1: We show that the sequence and are bounded. Let and . Then, , and for all Hence, for all
Since is firmly nonexpansive (and hence nonexpansive), we have
Also, from (30), we have
According to condition (i), there exists a constant with and . Moreover, from (30), (32), (33) and Lemma 5, we get
By induction, it is easy to see that
Hence, is bounded, and so are the sequences , and
Stage 2: We prove that the sequence converges strongly to
From (30) and Lemma 3 (ii), we obtain
Set Then, from (30), (31), (35) Lemmas 3(i) and 5, we get
Set so that the last inequality becomes:
To show that is convergent, we consider the following two cases:
Case 1: Assume that the sequence is monotonically decreasing. Then, is convergent. Indeed, we have
Thus, by (36), condition [(i) and (ii)] and the fact that , we have
Since, for we get
Additionally, using (30), we get
(40) and condition (i) imply that
It follows from (41) that
Again, for any , using Lemma 7 and the fact that , we obtain
Furthermore, from (30), (34), (42), (43) and Lemma 3(i), we obtain
(44) implies that
The last inequality and condition (i) yields
Since
it follows from (43) and (45) that
In addition, using (30) and Lemma 6, we get
(45) and (47) imply that
Additionally, by the boundedness of the sequence , there exists a subsequence of , such that . Again, from (39), we get
Moreover, since by assumption, the pair jointly satisfy the demiclosedness principle, it follows from (49) that Assume that another subsequence of exists, such that , where Then, following the same argument as in [24] with and , we get that .
Next, we show that . Now, since is a Hilbert space and the sequence is bounded, it follows that there exists a subsequence of that converges weakly to and
Following the information above, with , we have Additionally, since is single-valued and nonexpansive, using (48), we get Therefore, Thus,
Lastly, we prove that From (30) and (35) and Lemma 5, we have
From (51), we obtain
where
Put
and
Then, from condition (i), we get
Thus, using Lemma 4 and (52), the result follows as required (i.e., as ).
Case 2:
Suppose is a monotonically increasing sequence. Set and the mapping , for all (for some large enough), by Then, is a nondecreasing sequence, such that as and for From (36), we have
where Moreover, we have
Following a similar argument to the one above in Case 1, we conclude that
Hence, for all we have
from which we get
Consequently, we have
and
Therefore, we conclude, using Lemma 8, that
Hence, that is, . This ends the proof Theorem 2. □
Now, in application, we employ Theorem 2 to estimate a point without any compactness assumptions on either the space or the operators, where is a pair of asymptotically nonspreading-type mappings.
Theorem 3.
Let and be as described in Assumption Z. Let be two asymptotically nonspreading-type multivalued mappings, such that ; . Assume that where and is jointly demiclosed at the origin. Let be a sequence generated by Algorithm Q, such that satisfy conditions and of Theorem 2. Then, strongly converges to , which, at the same time, serves as a unique solution to the problem:
Proof.
Since every asymptotically nonspreading-type mapping with a nonempty fixed point set is asymptotically quasi nonexpansive multivalued mapping, the proof of Theorem 3 immediately follows from Lemma 2 and Theorem 2. □
Again, if are asymptotically quasi-nonexpansive single-valued mapping and is a strongly positive bounded linear operator, then the following theorem can be obtained from Theorem 3.
Theorem 4.
Let and be as described in Assumption Z. Let be two asymptotically quasi-nonexpansive multivalued mappings, such that ; . Assume that where and is jointly demiclosed at the origin. Let be a sequence generated by Algorithm Q, such that satisfy conditions and of Theorem 3.1. Then, strongly converges to , which satisfies the optimality condition of the minimization problem
where h is a potential function for (i.e., on
4. Conclusions
In this paper, a modified proximinal point algorithm used to approximate a common element of the set of solutions of a fixed-point problem for a pair of asymptotically quasi-nonexpansive multivalued mappings and a minimization problem of (18) is introduced and studied. Under appropriate conditions on the control sequences, strong convergence results were obtained using the algorithm so introduced, providing an affirmative answer to Question 1.2 raised in the paper. Since asymptotically quasi-nonexpansive multivalued mapping is much more general than asymptotically nonexpansive multivalued mapping, asymptotically nonspreading-type multivalued mapping, quasi-nonexpansive multivalued mapping, nonexpansive multivalued mapping, nonspreading-type multivalued mapping, the problem studied in our paper is quite general and includes problems with optimization, variational inequality and fixed point as its special cases. Thus, it follows that Theorem 2 in our paper improves, extends and generalizes the results obtained in [10,12,14,15,16,17,18,33], and many more others currently existing in the literature.
Author Contributions
Conceptualization, N.S., I.K.A., M.W. and U.I.; formal analysis, N.S., M.W. and I.K.A.; investigation, I.K.A. and N.S.; writing—original draft preparation, I.K.A., U.I. and N.S.; writing—review and editing, N.S. and U.I. All authors have read and agreed to the published version of the manuscript.
Funding
This research is partially supported by the scientific research start-up project of Mianyang Techer’s College (Grant No. QD2019A08).
Data Availability Statement
Not applicable.
Conflicts of Interest
The authors declare that there is no conflict of interest.
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