Abstract
In this paper, we design two inertial iterative methods involving one and two inertial steps for investigating a general quasi-variational inequality in a real Hilbert space. We establish an existence result and a non-trivial example is furnished to substantiate our theoretical findings. We discuss the convergence of the inertial iterative algorithms to approximate the solution of a general quasi-variational inequality. Finally, we apply an inertial iterative scheme with two inertial steps to investigate a delay differential equation. The results presented herein can be seen as substantial generalizations of some known results.
1. Introduction
Let be a non-empty closed convex set in a real Hilbert space and be a non-linear mapping in . The variational inequality problem is to find a point , such that:
It is well documented that the study of variational inequality, which was initiated by Stampacchia [] becomes a very productive and fruitful tool to examine several problems arising in the natural sciences. Due to an application oriented nature, this field of research has been expanded and generalized in several directions, see [,,,,,,]. One of the pronounced generalizations of variational inequality is quasi-variational inequality (QVI) which is to find , such that:
where is a closed convex-valued set in . The QVI (2) was coined for the first time by Bensoussan and Lions [] to deal with impulse control problems. The quasi-variational inequalities are variational inequalities in which the admissible space or the involved potentials depend on the solution of the problem. Quasi-variational inequalities bring forth a consolidated platform for variation inequalities, as well as integrated modelling of various physical problems of significance. The resulting applications of quasi-variational inequalities include game theory [], continuum and solid mechanics [,,], transportation [,], superconductivity, thermoplasticity, or electrostatics [,,].
It is well known that numerous physical problems occurring in non-linear analysis and related fields can be represented in the template of fixed point problem. Let be a non-linear mapping. The fixed point of T is to locate a point , such that . The set of fixed points is denoted as . One of the most abundantly studied techniques for figuring out fixed points of non-expansive mappings in Banach and Hilbert spaces is known as Mann iterative technique, proposed by Mann [] as follows:
where and is a nonexpansive mapping on a closed convex subset of a given Banach space. For some recently developed iterative methods, we refer [,,,,,,,]. Recently, Ali et al. [] have constructed a new scheme. For initial and in , the sequence generated by this scheme is defined as:
The authors demonstrated that their scheme converges faster than some noted iterative methods, such as S, Picard-S, Gursoy and Karakaya, and M-iteration schemes. To achieve augmented convergence rate of iterative methods for non-linear problems is fascinating for researchers. So far, numerous iterative techniques have been explored and examined for obtaining an incremental convergence rate. In this progression, many multi-step iterative algorithms are studied by adding initial term, see [,,,]. The inertial term is derived from the heavy ball with friction method due to Polyak [] to examine optimization problems which were obtained by the discretizing of second order dynamical system for an oscillator with damping and conservative restoring force:
where and represent time continuous trajectory, external gravitational field and friction, respectively, and is differentiable. In fact, inertial type iterative methods are generalization of proximal point algorithm as they are produced by discretization of a second-order-in-time dissipative dynamical system. Alvarez [] have shown that is a smooth convex function then each trajectory converges weakly to a minimizer of . The relaxation method introduced by Richardson [] for solving linear systems is also a technique for augmentation of convergence rate. Eckstein and Bertsekas [] designed a relaxed proximal point scheme to accelerate the proximal point algorithm. They reported that the rate of convergence is enhanced by adding relaxation parameter. Alvarez [] proposed an iterative scheme by combining relaxation techniques and inertial term to examine monotone inclusion and convex optimization problems. Maigne [] added an inertial term to Krasnoselskii–Mann iteration and designed the inertial Mann iterative method for calculating fixed points of a non-expansive mapping in Hilbert spaces as following:
where is a damping type term and is a relaxation factor.
Inspired and persuaded by the acknowledged facts in the above-mentioned references, we introduce two inertial iterative methods. The first is based on (4) which includes one inertial step and is defined as:
where is a sequence in . The second inertial scheme contains two inertial steps which is to define the sequence with initial points as below:
where is a constant and are sequences in . We deal with a class of general quasi-variational inequalities (GQVI) by implementing newly established inertial iterative methods. We prove an existence result and theoretical claims are verified by a non-trivial example. Additionally, we establish the convergence of inertial iterative algorithms involving one and two inertial steps. Finally, as an application of our proposed inertial method, we investigate a delay differential equation.
2. Preliminaries
Let be a Hilbert space over the real numbers with norm and inner-product , and let denote the collection of non-empty closed convex subsets of . In addition, let be not necessarily linear mappings in , and suppose that the set-valued mapping assigns to every a closed convex subset of .
We consider the problem of finding , such that:
called the general quasi-variational inequality (GQVI). It is chronicle that quasi-variational inequalities are desperately applications oriented field of research. Several problems of practical applications, such as modeling of stochastic impulsive control problems, free boundary problems, mechanics, and economy, have been framed as a model of quasi-variational inequalities, see [,,,]. We take into account the following third order implicit obstacle boundary value problem () of finding p, such that:
where is a continuous function and stands for the cost (obstacle) function. A typical form of this function in (10) is:
where s represents switching cost and the cost function C provides the coupling between unknowns . It is positive or zero, if the unit is turned on or off, respectively. To exhibit (10) as a quasi-variational inequality, we define:
where is a Sobolev space, see [] and is a closed convex set in . The (10) can be imitated as the following energy functional:
where:
Note that specified in (13) is linear, nonsymmetric, and g-positive. By implementing the approach as in [], we see that the minimum of energy functional on can be represented as:
which is indeed GQVI (9), for more detail see []. The problem described in (9) is a unification of several others. Some special cases of GQVI (9) are listed below.
- 1.
- For , GQVI (9) reduces to the general variational inequality introduced by Noor [] which is to find , such that:
- 2.
- Let for , the dual cone of be represented byThen problem (15) becomes a general complementarity problem, that is, to find , such that:
- 3.
- 4.
Next, we list some handy tools to accomplish our results.
Definition 1.
A single-valued mapping is called:
- (i)
- η-strongly monotone if for some
- (ii)
- relaxed -cocoercive if for some
- (iii)
- σ-Lipschitz continuous if for some
- (iv)
- g-positive if, and only if,
Lemma 1
([]). Let and be sequences of non-negative real numbers, such that there exists some with the property that for each the inequality is satisfied. If , then .
Lemma 2
([]). Let be a sequence of non-negative real numbers, such that
where the sequences and accomplish the following conditions:
- (i)
- the sequence is in such that ;
- (ii)
- ;
- (iii)
- for all , such that .
Then, .
Let be a closed convex subset of . It is known that for each there exists a unique point in , such that
Then, by definition, the surjective mapping , is the metric projection from onto .
The following lemma is essential and plays a central role in achieving our goal.
Lemma 3.
For any given and implicit projection of onto , we have if
Note that the implicit projection mapping is non-expansive, that is,
Assumption 1
([]). For any , the implicit projection mapping satisfies following characteristic condition
where is a constant.
Next, we remodel GQVI (9) into a fixed point problem by using the projection.
Lemma 4.
The function is a solution of GQVI (9) if, and only if, is a fixed point of , i.e.,
where is the projection of onto and is a constant.
3. Existence Result
Following theorem ensures the existence of unique solution of GQVI (9) which is followed by a demonstrative numerical example to verify our theoretical claims.
Theorem 1.
Let be a projection and be non-linear mappings, such that θ is relaxed -cocoercive and ς-Lipschiz continuous and ϕ is γ-Lipschitz continuous and η-strongly monotone. Suppose that assumption C holds and there exists complying with the following condition
where . Then, GQVI (9) admits a unique solution.
Proof.
Using the -Lipschitz continuity and the -strongly monotone property of , we obtain
which turns into
Additionally, from the relaxed -cocoercivity and -Lipschiz continuity of , we obtain:
which leads to:
4. Convergence Results
Now, we inspect the convergence of inertial iterative methods to figure out the approximate solution of GQVI (9). By means of (19), we can redesign (18) as below:
Next, we prove the following lemma, which plays a deciding role in establishing the convergence.
Lemma 5
([]). Under the assumptions of the Theorem 1, the sequence norm convergence in the Hilbert space to 0. Here, the sequence is given by:
for all and , such that .
Proof.
We give a proof of Lemma 5 by considering four different cases.
Case . If , then there is nothing to show because is zero.
Next, we consider the cases, when .
Case . Suppose that for all , , then from (25), we acquire and hence .
Case . Suppose that for all , , then from (25), we obtain: and hence .
Case . Suppose that for some , , then from (25), we obtain .
Thus, for all , we have . Since and, hence, converges to 0. □
Theorem 2.
Suppose that mappings and are the same and comply with all the assumptions of Theorem 1. Suppose the sequence initiated by Algorithm 1 with the updating parameter represented by (25) with , for all and is in . Let be a sequence given in (25), such that . Then converges strongly to the unique solution of GQVI (9).
Algorithm 1.
Define the sequence with arbitrary initial point as below:
where is a sequence in .
Proof.
Utilizing the -Lipschitz continuity and -strongly monotone property of , we obtain:
It follows from the relaxed -cocoercivity and -Lipschitz continuity of that:
From (20), we know that and from Lemma 5, as . Hence, from Lemma 1, we obtain that . □
Next, we put forward a more prevalent inertial iterative algorithm for approximating GQVI (9), which contains two inertial terms. By making use of (19), (8) can be redesigned as follows:
Theorem 3.
Suppose that the mappings and are the same and comply with all the assumptions of Theorem 1. Suppose that the sequence initiated by Algorithm 2 with the updating parameter represented by (24) with , for all and the sequences are in , such that , for all . Let be a sequence given in (25), such that . Then converges strongly to the unique solution of GQVI (9).
Algorithm 2.
Define the sequence with initial points as below:
where is a constant and are sequences in .
Proof.
It is proved in Theorem 1 that GQVI (9) has a unique solution . Next, it remains to substantiate that the sequence converges to under the assumption of Algorithm 2. It follows from (17) that:
By utilizing assumption C, the non-expansiveness of the mapping and following the steps as in (21), we obtain:
Since is -Lipschitz continuous, -strongly monotone and is relaxed -cocoercive, -Lipschitz continuous, then adopting the approach as in (22)–(24), we can write:
Since is in , then for all , one can find a constant , such that . It follows from (20) that and utilizing (43), (44), we can write:
From (20), we know that and, hence, and from Lemma 5, as . Hence, from Lemma 1, we conclude that . □
Remark 1.
It can be perceived that under the assumptions of Theorem 3, by following the approach as in Lemma 5, is convergent.
Corollary 1.
Suppose that the mappings and are the same and comply with all the assumptions of Theorem 1. Suppose that the sequence initiated by Algorithm 2 with the updating parameter represented by (25) with , for all and the sequences are in , such that , for all and . Let be a sequence given in (25) such that . Then, the sequence converges strongly to the unique solution of GQVI (9).
Proof.
It emanates from Remark 1 that and . From the assumption, we can write . Hence, from Lemma 2, we have . □
Example 1.
Let be a real Hilbert space equipped with norm . Define the mappings by
Then, for all , one can observe that:
Thus, the mapping ϕ is -strongly monotone and -Lipschitz continuous. Additionally,
Thus, the mapping θ is relaxed -cocoercive and -Lipschitz continuous. Next, we define a set-valued mapping by
We claim that is a closed convex set. Indeed, for any arbitrary and , we have and, hence, is a convex set. Now, we define by . Evidently, g is well defined. In point of fact, for distinct , we have , i.e., g is injective. It is easy to see that there exists an so that for each , i.e., g is surjective. Let and be usual metric spaces, then for each , we obtain:
Thus g is continuous. Moreover, is also continuous and bijective and hence g is a homeomorphism. Being homeomorphic to a closed set , is also closed. Define metric projection by:
To show that the projection satisfies the assumption C, we discuss the following cases.
Case . For arbitrary , suppose that .
- 1.
- If , then and, hence:
- 2.
- If and , then either or . For , we have:For , we have:
- 3.
- If and , then either or and or . For , we have . Therefore, we can conclude thatFor and , we haveFor and , we have
Case . Similarly, for arbitrary with , we can verify that
Thus, we conclude that the projection satisfies the assumption C with constant . Additionally, the condition (20) is satisfied for , and . Finally, we shall find a unique point which solves GQVI (9). Consider , then for , we have:
However, for , we have:
Hence, is a unique solution of GQVI (9). Consider a sequence and for all , take with initial points in Algorithm 2. Table 1 shows that the scheme 2 converges faster with initial terms than that of with .
Table 1.
Numerical comparison of iterative Algorithm 2 with inertial and non-inertial terms.
5. Application to Delay Differential Equation
In this section, we make use of the inertial iterative Algorithm 2 to find an approximate solution of the delay differential equation stated as under:
Let denotes the space of all continuous real-valued functions defined on equipped with the Chebyshev norm From classical analysis, observe that is a Banach space. Suppose the following assumptions are fulfilled.
- (C1)
- ;
- (C2)
- ;
- (C3)
- ;
- (C4)
- there exists so that
- (C5)
- 2.
Suppose that the solution of the problem (51) exists. Then it can be modelled as being a solution of the following integral equation.
Next, we present convergence of our inertial iterative algorithm to look over the solution of Problem (51).
Theorem 4.
Proof.
Define the operator where and are sequences in (0,1), such that . From (17) and (19), it follows that is a fixed point of . In addition, let be a sequence generated by (45). Then, for , we have:
Since are sequences in and from assumption (), we know that . Then, by utilizing (43) and (44), (53) becomes:
From (), we know that and hence and from Lemma 5, as . Hence, from Lemma 1, we conclude that . □
6. Concluding Remarks
In this paper, new inertial iterative algorithms have been constructed and their convergence analysis is considered in order to approximate solutions of general quasi-variational inequalities. The existence result of vectors satisfying a general quasi-variational inequality is proved and verified by illustrative example. Finally, as an application of the two steps inertial iterative algorithm, we examined a delay differential equation.
Author Contributions
Conceptualization, M.A.; methodology, M.A.; validation, M.D.; formal analysis, M.D.; investigation, M.A. and M.D.; writing—original draft preparation, M.A.; writing—review and editing, M.D. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Informed Consent Statement
Not applicable.
Data Availability Statement
Not applicable.
Acknowledgments
The authors thank to the referees for their valuable suggestions and comments which bring the manuscript in the present form. The researchers wish to extend their sincere gratitude to the Deanship of Scientific Research at the Islamic University of Madinah for the support provided to the Post-Publishing Program 1.
Conflicts of Interest
Regarding the publication of this article, no conflicts of interest are reported by the authors.
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