Abstract
In this paper, a class of controlled variational control models is studied by considering the notion of -invexity. Our aim is to investigate a solution set in the considered interval-valued controlled models. To achieve this, we establish some characterization results of solutions in the controlled interval-valued variational models. More precisely, necessary and sufficient conditions of optimality are highlighted as part of a feasible solution. To prove that the optimality conditions are sufficient, we impose generalized invariant convexity hypotheses for the involved multiple integral functionals. Finally, a duality result is provided in order to better describe the problem under study. The methodology used in this paper is a combination of techniques from the Lagrange–Hamilton theory, calculus of variations, and control theory. This study could be immediately improved by including an analysis of this class of optimization problems by using curvilinear integrals instead of multiple integrals. The independence of path imposed to these functionals and their physical significance would increase the applicability and importance of the paper.
Keywords:
controlled variational models; optimal pair; (q,w) − π-invexity; dual problem; feasible solution MSC:
90C26; 90C30; 49N15; 90C46
1. Introduction
Considering interval-valued functions or functionals and the corresponding analysis may result in easier models which will provide satisfactory accuracy results in practical situations. In this regard, we mention the research works of Charnes et al. [1], Steur [2], and Urli and Nadeau [3]. Hanson and Mond [4] established the necessary and sufficient conditions of optimality in constrained optimization. Chanas and Kuchta [5] considered multiobjective programming with interval-valued objective functions. Stancu-Minasian [6,7] studied multiobjective programming models with inexactness in the objective functions and, also, various approaches in multiple-objective fractional programming governed by set coefficients in the objectives. Jayswal et al. [8] formulated sufficiency and duality theorems in some programming problems that had interval values. Along the same lines, Treanţă [9] studied the efficiency of multi-dimensional variational control problems with uncertain data. Also, Treanţă [10] obtained results on solutions in interval-valued extremization problems with equality and inequality constraints. For other connected ideas, the reader can read the works of Peng et al. [11], Wang et al. [12], Xi et al. [13], Saeed and Treanţă [14], Treanţă and Saeed [15].
Convexity and its generalizations play an important role in various aspects of mathematical programming, including sufficient optimality conditions and duality results (see Hanson [16], Mond and Hanson [17], Arrow and Enthoven [18], Craven [19], Jeyakumar [20], Antczak [21]). Later, Wu [22,23] studied Wolfe-type duality theorems associated with an interval-valued programming model. Mandal and Nahak [24] established duality theorems (weak and strong) under invexity assumptions. Zhang et al. [25] analyzed optimality conditions of KKT type for generalized convex optimization with interval-valued objectives. For further information on the various different ideas explored in this research area, the reader is directed to [26,27].
In this paper, based on the works of Mandal and Nahak [24], Ahmad et al. [28], and Treanţă and Ciontescu [29], we establish necessary and sufficient optimality conditions for a class of multi-dimensional interval-valued controlled models involving generalized invex multiple integral functionals. Moreover, a duality theorem is presented in order to connect the studied model with a new variational problem. The study of such classes of interval-valued control problems, by considering generalized invex multiple integral functionals, represents the main novelty element. The main theorems derived in the paper are new in the specialized literature. The limitations associated with the existing papers and the principal novelties of this study are (i) the appearance of mixed-type constraints formulated in terms of partial differential equations; (ii) the appearance of control variables in the objective and constraint-type functionals; (iii) the use of Lagrange–Hamilton techniques to investigate the considered variational control models.
The rest of the current article is divided into the following sections: Section 2 provides the notations and basic elements such as the notion of generalized invex multiple integral functionals and the formulation of the controlled model. Also, the associated necessary Karush–Kuhn–Tucker-type optimality conditions are formulated. In Section 3, we provide some characterization results of solutions for the considered controlled model, under generalized invexity assumptions of the involved functionals. In Section 4, we formulate the final conclusions.
2. Preliminaries
Here, we establish the notations and elements used to formulate the main results stated in the present study.
Let I be the family of closed bounded real intervals. If , then , with and as the lower and upper bounds for V, respectively. For , we obtain as a real scalar. If , , we define
- (i)
- and ,
- (ii)
- .
We notice that . We also have
- (i)
- ,
- (ii)
- where .
Let denote the real n-dimensional Euclidean space. The function is an interval-valued continuously differentiable functional; that is, is a closed bounded real interval, for each piecewise smooth state function , and piecewise continuous control function . The interval-valued functional can be formulated as , with , two real-valued functionals defined on and satisfy the condition , for each and , with . Let be the space of piecewise smooth state functions such that and consider it is equipped with the norm . Also, let be the space of piecewise continuous control functions , endowed with the uniform norm, as well.
Next, for and , we use the partial ordering if and only if and . Also, we write if and only if but . This means that if and only if
Next, in accordance with Mandal and Nahak [24], we introduce the following definitions. We use the following notations: , , with .
The following definitions will be used in future work in order to formulate and prove the main results derived in the present paper.
Definition 1.
Consider are two real scalars and is a smooth function. If there exist the functions , , and the real scalar , with , such that the following inequalities
are satisfied, then the real-valued functional is named (strictly) -invex at on with respect to and σ.
Definition 2.
Consider are two real scalars and is a smooth function. If there exist the functions , , and the real scalar , with , such that the following inequalities
are satisfied, then the real-valued functional is named (strictly) -pseudoinvex at on with respect to and σ.
Definition 3.
Consider are two real scalars and is a smooth function. If there exist the functions , , and the real scalar , with , such that the following inequalities
are satisfied, then the real-valued functional is called -quasi-invex at on with respect to and σ.
Remark 1.
The exponentials used in the above-mentioned inequalities are considered component-wise. Also, without loss of generality, in the rest of this paper, we assume that .
Example 1.
Consider , , , , , . By direct computation, we obtain the real-valued functional is -invex at with respect to and σ.
In this study, we investigate the primal optimization problem (see also Treanţă [9]) driven by an interval-valued cost functional:
where is a -class interval-valued functional,
with , a differentiable interval-valued function, , some given differentiable real-valued functions.
Let be the set of all feasible solutions of (P).
Definition 4.
(Treanţă [9]). The pair is an LU-optimal pair of problem (P) if there exists no satisfying .
The following result presents the Karush–Kuhn–Tucker-type necessary optimality conditions associated with (P).
Theorem 1.
If is an LU-optimal pair of primal variational control problems (P) and the constraint functions satisfy constraint qualification at , then there exist multipliers (functions on Θ) , , , and , , , such that the next relations
are fulfilled for all , except at discontinuities.
Proof.
The proof follows in the same manner as in Theorem 6.1 of Treanţă [30]. □
3. On Solution Set for (P)
In the present section, we formulate and prove some characterization results of solutions for (P).
Next, by considering only the -invexity assumptions of the involved functionals, we state sufficient conditions of optimality for (P).
Theorem 2.
Let , the constraint functions satisfy the suitable Kuhn–Tucker constraint qualification at , and the relations (1)–(3) from Theorem 1 are satisfied for , except at discontinuities. Moreover, we assume that and are -invex and -invex at with respect to and σ, respectively, and and are -invex and -invex at with respect to and σ, respectively. If , then is an LU-optimal pair to the problem (P).
Proof.
We assume that is not an LU-optimal pair of (P). This fact implies there exists , such that
that is,
Since we have , by using the properties of exponential functions, we get
Using the -invexity property of and the -invexity property of with respect to and at at , the above inequalities imply
or
or
Since and , from the above inequalities, we get
Next, by considering the feasibility property of to (P), we obtain
Since we have , by using the properties of exponential functions, we obtain
and by considering that is -invex at , with respect to and , we get
In a similar way, by considering the assumption that is -invex at , with respect to and , we get
The next result, by considering the -pseudoinvexity and quasi-invexity assumptions of the involved functionals, introduces sufficient conditions of optimality for (P).
Theorem 3.
Let , the constraint functions satisfy the suitable Kuhn–Tucker constraint qualification at , and the relations (1)–(3) from Theorem 1 are satisfied for , except at discontinuities. Moreover, we consider is -pseudoinvex at with respect to and σ, and and are -quasi-invex and -quasi-invex at with respect to and σ, respectively. If , then is an LU-optimal pair for the problem (P).
Proof.
Consider is not an LU-optimal pair of (P) and, consequently, there exists , such that
that is,
Since and , from the above inequalities, we have
or, equivalently,
Since we have , by using the properties of exponential functions, it follows
which together with the assumption that is -pseudoinvex at , with respect to and , gives
Next, by considering the feasibility property of to (P), we get
Since we have , by using the properties associated with exponential functions, we get
which, together with the assumption that is -quasi-invex at , with respect to and , involves
In a similar way, by considering the assumption that is -quasi-invex at , with respect to and , we get
Next, we consider the following dual problem associated with the primal optimization problem (P):
}
subject to
where are the same functionals defined in the previous section.
Definition 5.
Let be a feasible solution to the dual problem (D-W). We say that is an LU-optimal point of the dual problem (D-W) if there exists no , a feasible solution of the dual problem (D-W), such that .
Theorem 4.
Suppose that and are some feasible points for (P) and (D-W), respectively. Also, consider that and , such that the functional is -invex at with respect to and σ, with . Then,
Proof.
Suppose, contrary to the result, that
that is,
or
or
Since we have and , the feasibility property of to (P) and the above inequalities imply
From the assumption that is -invex at with respect to and , we have
Since we have , by using the properties associated with exponential functions, we get
which is a contradiction with (13). □
Remark 2.
We could mention, as potential extensions of the proposed method for various types of extremization models or domains, the study of posedness and efficiency criteria for similar families of extremization problems governed by path-independent curvilinear integral functionals (which are crucial in applications due to their physical meaning (mechanical work)). This is a specific research question or unresolved issue that could be addressed in future studies to build upon the current findings. Therefore, we note the applicability of the proposed approach to larger and more complex extremization problems.
4. Conclusions and Further Developments
A class of controlled variational control models has been studied by considering the notion of -invexity and its associated variants. In particular, we have established some characterization results of solutions in the considered controlled interval-valued variational models. In this regard, necessary and sufficient conditions of optimality have been stated for a feasible solution. The latter have been formulated and proved by imposing generalized invariant convexity hypotheses for the involved multiple integral functionals. In addition, in the end of the paper, a duality theorem was presented in order to connect the studied model with a new variational problem. Related to some future research directions of the current work, let us consider, for instance, a situation in which the partial derivatives of second-order are included in the studied model.
Author Contributions
Conceptualization, S.T. and O.M.A.; formal analysis, S.T. and O.M.A.; funding acquisition, S.T. and O.M.A.; investigation, S.T. and O.M.A.; methodology, S.T. and O.M.A.; validation, S.T. and O.M.A.; visualization, S.T. and O.M.A.; writing—original draft, S.T. and O.M.A.; writing—review and editing, S.T. and O.M.A. All authors have read and agreed to the published version of the manuscript.
Funding
The research was funded by TAIF University, TAIF, Saudi Arabia, Project No. (TU-DSPP-2024-258).
Data Availability Statement
The original data presented in the study are included in the article; further inquiries can be directed to the corresponding author.
Acknowledgments
The authors extend their appreciation to TAIF University, Saudi Arabia, for supporting this work through project number TU-DSPP-2024-258.
Conflicts of Interest
The authors declare no conflicts of interest.
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