Abstract
In this paper, by using the invexity (or pseudoinvexity) and Fréchet differentiability of some integral functionals of curvilinear type, we state some relations between the solutions of a new non-linear optimization problem and the associated variational inequality. In order to prove the results derived in this paper, we use the new notion of invex set by considering some given functions. To justify the effectiveness and outstanding applicability of this work, some illustrative examples are provided.
Keywords:
multiple objective optimization; invex set; integral functional; Fréchet differentiability; invexity; pseudoinvexity MSC:
49J21; 49K20
1. Introduction
As is well known, by multi-objective optimization problems, we understand some optimization problems having more than one objective function, that need to be optimized simultaneously. Since there exist rarely feasible points that simultaneously extremize (maximize or minimize) all the objective functions, it is necessary to introduce some concepts of efficient solutions. In this direction, Geoffrion [1] proposed the notion of proper efficient solutions. Furthermore, Klinger [2] investigated improper solutions for the multi-objective maximum problem, and Kazmi [3] proved the existence results of a weak minimum for constrained vector optimization problems via vector variational-like inequalities. Ghaznavi-ghosoni and Khorram [4] established efficiency conditions associated with (weakly, properly) approximating efficient points for multi-objective optimization problems via the approximate solutions of scalarized problems.
On the other hand, convexity is a concept almost inevitable in control and optimization theory in order to formulate the optimality conditions. However, convexity is not sufficient in many concrete problems in applied sciences, its generalization is necessary. Therefore, Hanson [5] formulated the notion of invexity. Of course, various extensions of convexity have been defined, namely, preinvexity, univexity, approximate convexity, quasiinvexity, pseudoinvexity, and so on (Antczak [6,7], Mishra et al. [8], Arana-Jiménez et al. [9]). Moreover, these concepts were translated into the multi-dimensional context with multiple or curvilinear integrals (Treanţă [10,11], Mititelu and Treanţă [12]).
Since many problems in physics, mechanics, traffic analysis, and engineering are modelled as variational inequalities, these mathematical tools have been thoroughly studied. In this sense, the vector case of variational inequalities was developed, with remarkable results, by Giannessi [13]. As is well-known, under some convexity or generalized convexity assumptions, variational inequalities of vector type provide some existence results of solutions for the corresponding multi-objective optimization problems. Over time, several papers have studied the connections between the solution sets of vector variational inequalities and the associated multi-objective variational problems (Ruiz-Garzón et al. [14,15], Jayswal et al. [16]). Moreover, Treanţă [17] recently introduced and analysed the variational control inequalities given by integral functionals of curvilinear type.
Variational problems are structured as follows: variational problems of classic type, and (multi-objective) variational problems with continuous-time. In 2004, Kim [18] stated some relations between multiple objective programs with continuous-time and variational-type inequalities of vector type. The problems dependant on control, seen as variational problems with continuous-time, are a powerful tool in the study of various engineering problems, game theory, economics, and operations research. Following this, Jha et al. [19] and Treanţă [20,21] have contributed by formulating and proving well-posedness and existence results for classes of multi-dimensional optimization problems given by functionals of various types.
Despite all previously mentioned advances, we present this paper in which we define (weak) variational control inequalities of vector type, and multiple objective variational control problems defined by curvilinear integral-type functionals (independent of the path). More precisely, we establish some relations between the solution sets of the considered multi-dimensional variational control problems. The presence of the control function in this context is an element of novelty. Furthermore, in order to prove the theoretical results derived in the paper, we use the new notion of invex set by considering some given functions ( and , see Definition 7) For similar studies in this area, the reader is directed to [10,11]. In [10], the authors considered the case of multiple integral functionals instead of the curvilinear ones, and in [11] the authors dealt with the well-posedness study of a class of variational inequality constrained control problems driven also by multiple integrals. To justify the effectiveness and outstanding applicability of this paper, some illustrative examples are provided.
Further, the paper continues with preliminaries and formulation of the considered problem. In Section 3, some characterization results of the solution sets for the given variational control problems are established. Section 4 contains an illustrative application. In Section 5, we conclude this study and provide a further research direction.
2. Preliminaries
In this paper, consider as a compact set in , and . Let be a curve (piece-wise smooth) linking the points and in . Consider is the space of state functions (piece-wise smooth) , and is the space of control functions (piece-wise continuous) . Define on the following scalar product
for all , and the associated induced norm.
Consider the -class functions , in order to define the functional
Further, we assume that , is the derivative operator, and the 1-forms
are closed (. Furthermore, the following rules are used:
for in .
At this moment, we introduce the following constrained multiple objective optimization problem:
where
and
In the above context, we consider the -class functions , to generate the PDEs
satisfying , and are functions of -class.
A solution of means a feasible pair that minimizes (simultaneously) all objective functions . Thus, the following kinds of solutions of are needed.
Definition 1
(see Mititelu and Treanţă [12]). The pair is an efficient solution to if there exists no other with , or , with a strict inequality for at least one l.
Definition 2
is true for some , such that
whenever and
(see Geoffrion [1]). The pair is a proper efficient solution to if is an efficient solution in and there exists such that, for all , the inequality
Definition 3.
The pair is a weak efficient solution to if there exists no other such that , or .
Taking into account Treanţă [11], in order to introduce the invexity and pseudoinvexity, we consider the following functional (which is independent of the path):
Definition 4.
of -class with , satisfying
for any .
The functional is called invex at with respect to and if there exist
of -class with , and
Definition 5.
If we replace with , , we say that is called strictly invex at with respect to and .
Definition 6.
of -class with , and
of -class with , satisfying
or, in an equivalent manner,
for any .
The functional is said to be pseudoinvex at with respect to and if there exist
For invex and pseudoinvex integral functionals of curvilinear type, the reader can consult Treanţă [11] (Examples 2.1 and 2.2).
Definition 7.
The subset is called invex with respect to and if
for and .
Now, for establishing the existence results of solution sets for , we consider (weak) variational control inequalities of vector type:
- Find such that there exists no with
- Find such that there exists no with
The class of (weak) variational control inequalities of vector type is solvable for a given point (see Example 1).
Example 1.
as
Further, we can easily notice that is a solution of . Indeed, it results
for all functions (piece-wise differentiable).
For , and a curve (differentiable) linking and , assuming that are functions (piece-wise differentiable), and are defined by: , and and for , we define the 1-forms
3. Main Results
In this section, of the present paper, we will state some existence results and connections between the solution sets of the mentioned (weak) variational control inequalities of vector type and the associated multiple objective optimization problem .
Theorem 1.
Consider is an invex set, is a proper efficient solution to , and the integral functionals
are Fréchet differentiable at . Then solves .
Proof.
Consider, in contrast, that is a proper efficient solution to and it does not solve . Thus, there exists , for all , such that
and
for . As is an invex set, we consider the pair
where is a sequence of positive real numbers, with as . Now, since each integral functional , is Fréchet differentiable at , it results
where the continuous function is defined on a neighbourhood of , and fulfils . Dividing Relation (3) with and by considering the limit, it follows
By Integrals (1) and (4), we obtain
for (see N as a natural number).
Next, considering the non-empty set (since is a proper efficient solution to )
The Fréchet differentiability property of at , for , gives
where the continuous function is defined on a neighbourhood of , and fulfils . Dividing the Relation (5) with and bay considering the limit, it follows
In the following, for , by considering the set , it results
By Integrals (2) and (6), we obtain that
for (see N as a natural number), and .
By computing the limit, for ,
we obtain it as ∞ as . This contradicts that is a proper efficient solution of . □
Now, by using the variational control inequality of vector type, we formulate a characterization of efficient solutions to .
Theorem 2.
Consider is a solution to and the integral functionals , are invex and Fréchet differentiable at . Then the pair is an efficient solution to .
Proof.
Consider, in contrast, the pair is a solution to and it is not an efficient solution to . Thus, there exists , for all , such that
with < for at least one l. As the integral functionals , are invex and Fréchet differentiable at , we obtain
for and . By Relations (7) and (8), for all , there exists satisfying
with < for at least one l. This contradicts as a solution to . □
Further, a sufficient condition of in order to become a solution to is presented.
Theorem 3.
Consider is an invex set, is a weak efficient solution to , and the integral functionals , are Fréchet differentiable at . Then solves .
Proof.
By hypothesis, we have that there exists no other such that , equivalent with
As is an invex set, for , we obtain
Therefore, by Relation (9), we obtain there exists no other such that
Further, since the integral functionals , are Fréchet differentiable at , following the same manner as in Theorem 1 and by considering Relation (10), it results that there exists no other such that
for . □
The next theorem uses the weak variational control inequality of vector type to provide a description of weak efficient solutions to .
Theorem 4.
Consider is a solution to , and the integral functionals , are pseudoinvex and Fréchet differentiable at . Then is a weak efficient solution to .
Proof.
Consider, in contrast, that is a solution to and it is not a weak efficient solution to . Thus, there exists , for all , such that
As the integral functionals , are pseudoinvex and Fréchet differentiable at , we obtain
for and . This is in contradiction with as a solution to . □
A sufficient condition for a weak efficient solution to become an efficient solution of is provided below.
Theorem 5.
Consider is a weak efficient solution of , the integrals , are strictly invex and Fréchet differentiable at , and W is an invex set. Then is an efficient solution to .
Proof.
Assume, in contrast, that is a weak efficient solution to and it is not an efficient solution. Therefore, there exists such that
with < for at least one l. Since the integrals , are strictly invex and Fréchet differentiable at , we have
for and . By Relations (11) and (12), for all , there exists such that
Thus, is not a solution to and, by Theorem 3, we get is not a weak efficient solution to . □
4. Application
The next illustrative application uses the theoretical results established in this paper.
Example 2.
Now, we formulate the associated constrained multi-objective optimization problem
with
and subject it to the constraints mentioned above. We find that the integral functional
is Fréchet differentiable at and each integral functional
is invex at (related to ϑ and υ). Furthermore, we note that is a solution to :
for all (piece-wise differentiable functions). As a consequence, by using Theorem 2, it follows that is an efficient solution to . Moreover, by computation, it results in
for all (piece-wise differentiable functions). In addition,
for all (piece-wise differentiable functions). Since, for and , the following inequality
holds, we find is a proper efficient solution to .
Find the extremized mechanical work provided by the following forces and in order to move its application point along the curve (piece-wise differentiable), included in connecting and , in a such way the next dynamic control system
to be fulfilled related to and for , and and for .
For solving this concrete problem, we consider , is a curve that connects and , are functions (piece-wise differentiable) with , and are defined by: and for , and and for . Furthermore, we introduce the next closed 1-forms
defined as
5. Conclusions
In this paper, we have established some connections between the solution sets of a new non-linear optimization problem and the associated variational inequality. More precisely, to establish the principal results, we have used the notions of invex set, invexity (or pseudoinvexity), and Fréchet differentiability of some integral functionals of curvilinear type. In addition, to justify the effectiveness of this work, illustrative examples have been presented. As a further research direction, we mention the reformulation of these results by taking into account the notion of variational derivatives.
Author Contributions
Conceptualization, S.T., T.A. and T.S.; methodology, S.T., T.A. and T.S.; software, S.T., T.A. and T.S.; validation, S.T., T.A. and T.S.; formal analysis, S.T., T.A. and T.S.; investigation, S.T., T.A. and T.S.; resources, S.T., T.A. and T.S.; data curation, S.T., T.A. and T.S.; writing—original draft preparation, S.T., T.A. and T.S.; writing—review and editing, S.T., T.A. and T.S.; visualization, S.T., T.A. and T.S.; supervision, S.T., T.A. and T.S.; project administration, S.T., T.A. and T.S.; funding acquisition, T.S. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
Not applicable.
Acknowledgments
This research work was funded by Institutional Fund Projects under grant no. (IFPIP:0323-130-1443). The authors gratefully acknowledge the technical and financial support provided by the Ministry of Education and King Abdulaziz University, DSR, Jeddah, Saudi Arabia.
Conflicts of Interest
The authors declare no conflict of interest.
References
- Geoffrion, A.M. Proper efficiency and the theory of vector maximization. J. Math. Anal. Appl. 1968, 22, 618–630. [Google Scholar] [CrossRef]
- Klinger, A. Improper solutions of the vector maximum problem. Oper. Res. 1967, 15, 570–572. [Google Scholar] [CrossRef]
- Kazmi, K.R. Existence of solutions for vector optimization. Appl. Math. Lett. 1996, 9, 19–22. [Google Scholar] [CrossRef]
- Ghaznavi-ghosoni, B.A.; Khorram, E. On approximating weakly/properly efficient solutions in multi-objective programming. Math. Comput. Model. 2011, 54, 3172–3181. [Google Scholar] [CrossRef]
- Hanson, M.A. On sufficiency of the Kuhn-Tucker conditions. J. Math. Anal. Appl. 1981, 80, 545–550. [Google Scholar] [CrossRef]
- Antczak, T. (p,r)-Invexity in multiobjective programming. Eur. J. Oper. Res. 2004, 152, 72–87. [Google Scholar] [CrossRef]
- Antczak, T. Exact penalty functions method for mathematical programming problems involving invex functions. Eur. J. Oper. Res. 2009, 198, 29–36. [Google Scholar] [CrossRef]
- Mishra, S.K.; Wang, S.Y.; Lai, K.K. Nondifferentiable multiobjective programming under generalized d-univexity. Eur. J. Oper. Res. 2005, 160, 218–226. [Google Scholar] [CrossRef]
- Arana-Jiménez, M.; Blanco, V.; Fernández, E. On the fuzzy maximal covering location problem. Eur. J. Oper. Res. 2019, 283, 692–705. [Google Scholar] [CrossRef]
- Treanţă, S.; Guo, Y. The study of certain optimization problems via variational inequalities. Res. Math. Sci. 2023, 10, 7. [Google Scholar] [CrossRef]
- Treanţă, S.; Antczak, T.; Saeed, T. On some variational inequality constrained control problems. J. Ineq. Appl. 2022, 2022, 156. [Google Scholar] [CrossRef]
- Mititelu, Ş.; Treanţă, S. Efficiency conditions in vector control problems governed by multiple integrals. J. Appl. Math. Comput. 2018, 57, 647–665. [Google Scholar] [CrossRef]
- Giannessi, F. Theorems of the alternative quadratic programs and complementarity problems. In Variational Inequalities and Complementarity Problems; Cottle, R., Giannessi, F., Lions, J., Eds.; Wiley: Chichester, UK, 1980; pp. 151–186. [Google Scholar]
- Ruiz-Garzón, G.; Osuna-Gxoxmez, R.; Rufián-Lizana, A. Relationships between vector variational-like inequality and optimization problems. Eur. J. Oper. Res. 2004, 157, 113–119. [Google Scholar] [CrossRef]
- Ruiz-Garzón, G.; Osuna-Gxoxmez, R.; Rufián-Lizana, A.; Hernxaxndez-Jiménez, B. Optimality in continuous-time multiobjective optimization and vector variational-like inequalities. Top 2015, 23, 198–219. [Google Scholar] [CrossRef]
- Jayswal, A.; Choudhury, S.; Verma, R.U. Exponential type vector variational-like inequalities and vector optimization problems with exponential type invexities. J. Appl. Math. Comput. 2014, 45, 87–97. [Google Scholar] [CrossRef]
- Treanţă, S. On Controlled Variational Inequalities Involving Convex Functionals. In Optimization of Complex Systems: Theory, Models, Algorithms and Applications. WCGO 2019; Le Thi, H., Le, H., Pham Dinh, T., Eds.; Advances in Intelligent Systems and Computing; Springer: Cham, Swithzerland, 2020; Volume 991, pp. 164–174. [Google Scholar]
- Kim, M.H. Relations between vector continuous-time program and vector variational-type inequality. J. Appl. Math. Comput. 2004, 16, 279–287. [Google Scholar] [CrossRef]
- Jha, S.; Das, P.; Bandhyopadhyay, S.; Treanţă, S. Well-posedness for multi-time variational inequality problems via generalized monotonicity and for variational problems with multi-time variational inequality constraints. J. Comput. Appl. Math. 2022, 407, 114033. [Google Scholar] [CrossRef]
- Treanţă, S. On some vector variational inequalities and optimization problems. AIMS Math. 2022, 7, 14434–14443. [Google Scholar] [CrossRef]
- Treanţă, S. Results on the Existence of Solutions Associated with Some Weak Vector Variational Inequalities. Fractal Fract. 2022, 6, 431. [Google Scholar] [CrossRef]
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