Abstract
In this paper, we formulate and prove necessary conditions of efficiency for a new class of multiobjective variational models governed by higher order partial derivatives. More precisely, we consider a multiobjective optimization model of minimizing a vector of multiple integral functionals subject to certain higher order differential equations and/or inequations. The main results are derived by applying suitable techniques coming from variational calculus. The current contribution lies in vector-valued functionals given by multiple integrals, constraint coupling, and the characterization of efficiency criteria.
MSC:
90C29; 65K10; 26B25
1. Framework and Problem Description
Over time, several authors have been interested in the investigation of vector (multiobjective) variational problems by considering generalized convexity (see, for instance, Mititelu and Udrişte [1], Mititelu [2], Nahak and Mohapatra [3], and Preda and Gramatovici [4]). Moreover, many studies extend this notion of convexity and develop a multitime variational theory, sometimes using a geometrical language (see, for instance, Treanţă [5,6,7], Vetro and Zeng [8]). For other ideas related to this topic, we address the readers to Chinchuluun and Pardalos [9], Geoffrion [10], Jagannathan [11], Treanţă [12], and Cen et al. [13,14,15]. Sadek et al. [16,17] extended the classical framework to conformal and trigonometric fractional calculus and provided new properties and applications. Moreover, the Galerkin Bell method to solve the fractional optimal control problems with inequality constraints, and the extended block Arnoldi method to solve generalized differential Sylvester equations have been considered in recent studies (see Sadek et al. [18,19]).
In this work, we extend and further develop some optimization outcomes in connection with the efficiency of admissible solutions associated with a family of multiple objective non-fractional programming problems implying higher order partial derivatives. Thus, we introduce and formulate a study on multiobjective (vector) variational model consisting in extremizing a vector made up of multiple integral functionals constrained by partial differential equations and inequations of higher order. The main novelty element associated with this paper is the presence of higher order partial derivatives. Also, as a natural consequence, the corresponding context is dominated by new and more complex techniques and methods. Moreover, the current paper is motivated by various applications in wide areas of research and in natural phenomena, where partial derivatives (of higher order) are requested.
The proper motivation for investigating such issues is, more precisely, the following:
(i) Pointwise state-constrained optimization problem:
subject to
studied by Udrişte and Matei [20], as well, by considering a simplified multi-time maximum principle.
(ii) Neumann boundary control:
where is a bounded set in having the boundary of -class, the pair satisfies in and on . The above-mentioned function f is a source term, and the function is a control variable. Since (with ) is proportional with the consumed energy, the extremizing of the functional is a compromise between finding such that x is close to the desired profile and the energy consumption. The control variable is named boundary control because it acts on the boundary .
The above optimization problems (implying second-order partial derivatives) were taken from the specialized literature. Other illustrative examples and new points of view, can be read in Udrişte and Matei [20] and Raymond [21].
The passing from the first-order partial derivatives to the higher-order partial derivatives is a very complex task because it requests specific techniques and an appropriate mathematical framework. Studies regarding higher-order variational calculus and optimal control theory have been formulated until now by certain researchers. However, the current contribution lies in vector-valued functionals given by multiple integrals, constraint coupling, and the characterization of efficiency criteria.
Before presenting our results, for the completeness of the exposition, we set the following notations.
Consider the real multi-interval (hyper-parallelepiped determined by the diagonal corners and ) and
a -class functional, where , with a fixed natural number, and . Also, let , with , and , with , two -class functionals. Assume that the previous -class Lagrangians,
generate the multiple integral functionals
where (volume element).
Let be the space of all functions of -class, with the norm
or, for generality, we can assume the Sobolev-type spaces for our framework.
As usual, two vectors, in are related via
Also, we underline that the argument of our Lagrangians is denoted as follows
Using these ingredients, we formulate the multiobjective variational problem (MVP) (higher-order PDE constrained optimization problem),
where the set (domain) of all feasible solutions is
with .
In this paper, we are looking for necessary efficiency conditions for the foregoing multiobjective variational problem (MVP). The next section introduces the necessary mathematical elements which will be used to state the main results.
2. Preliminaries
Let us start with the case of one functional, considering the next scalar problem (SP),
Consider the auxiliary Lagrange functional , defined as
(with summation over the repeated indices) that allows us to establish necessary conditions of efficiency for (SP). We must note that the next theorem includes functional perturbation, first variation derivation, and integration by parts associated with higher-order derivatives.
Theorem 1.
Consider that is an optimal solution and are -class functions. Then, there exist and the following functions (piecewise smooth) and , fulfilling
The previous result represents a generalization of Valentine’s necessary conditions (see [22]).
Definition 1.
The optimal solution of problem (SP) is called normal optimal solution if .
Further, we can assume that .
In this work, we are interested in finding the necessary conditions of efficiency of the considered problem (MVP) in . In this respect, we give the following definition.
Definition 2.
A feasible solution is named efficient solution in (MVP) if there exists no other satisfying .
3. Main Results
To develop our theory, we establish the following results. The next lemma is a connection of the weighted-sum scalarization and the Geoffrion efficiency concept (see Definition 2).
Lemma 1.
The feasible solution is an efficient solution of the problem (MVP) if and only if is an optimal solution of every ,
for .
Proof.
“” Consider an efficient solution of (MVP). Let us suppose there exists such that is not an optimal solution of . Thus, there exists fulfilling
for , and
This contradicts the efficiency of the function in (MVP). Consequently, we proved the direct implication.
“” Let be an optimal solution of . Assume that is not an efficient solution of (MVP). Thus, there exists fulfilling
and there exists such that
But, minimizes on the feasible solution set of problem . The proof is complete. □
The next lemma, assuming the necessary constraint qualifications, takes into account the Lagrange multipliers. Similar arguments are considered for Pontryagin’s Principle in PDE control systems (see Udrişte and Matei [20]).
Lemma 2.
Consider fixed and an optimal solution for . Then there exist and , (piecewise smooth functions) such that
Proof.
Consider , . Define the -class functions, , as follows
Thus, the problem , fixed, becomes
or
or, equivalently,
Let consider the following Lagrangian,
where , and , are piecewise smooth functions. Since is an optimal solution in , the following Valentine’s necessary conditions hold
Concretely, we have
or, equivalently,
Next, we write the conditions: , for any , . By considering , it follows
and the proof is complete. □
Definition 3.
The point is called a normal efficient solution in (MVP) if it is a normal optimal solution for at least one .
Now, we establish the main result of this study, namely, the normal necessary efficiency conditions of the program (MVP). The presence of higher-order derivatives introduces additional boundary terms and complexity. This result can be reduced to the classical form, as well.
Theorem 2
([Normal] necessary efficiency conditions for (MVP)). If is a [normal] efficient solution in (MVP), then there are , and fulfilling the relations:
Proof.
By using Lemma 1, we get is an optimal solution in . Thus, if is [normal] optimal solution of fixed, then Lemma 2 holds []. By summation over of all condition given in Lemma 2 and by setting
we obtain
By dividing with and denoting , , we obtain
and the proof is complete. □
Application.
Find the extremals of the following functional
subject to and the boundary conditions , where .
Solution. Consider the following Lagrangian
Since
the extremals are described by the following system of PDEs
The first-order PDE admits the general solution , where f is an arbitrary -class function. Replacing in the second-order PDE, we find a first-order PDE with as unknown.
Remark 1.
The higher-order Euler–Lagrange conditions have, of course, an impact on solution smoothness. Moreover, computational challenges (e.g., solving high-order PDE systems) inevitably appear (see, for instance, Treanţă [5]).
4. Conclusions
In this paper, the authors investigated a new family of optimization models involving higher order partial derivatives. Concretely, necessary efficiency criteria have been stated and proved for this class of variational problems. In this regard, a specific and complex framework has been adopted. As further developments associated with the current paper, the author would recommend the extension of these results for curvilinear type functionals which are path-independent. Another new research direction is the study of saddle-point criteria for this class of extremization models.
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 authors would like to acknowledge the Deanship of Graduate Studies and Scientific Research, Taif University, for funding this work.
Data Availability Statement
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.
Acknowledgments
The authors would like to thank the referees for their important suggestions that contributed to the current form of this article. Also, the authors would like to acknowledge the Deanship of Graduate Studies and Scientific Research, Taif University, for funding this work.
Conflicts of Interest
The authors declare no conflicts of interest.
References
- Mititelu, Ş.; Udrişte, C. Vector fractional programming with quasiinvexity on Riemannian manifolds. In Proceedings of the 12th WSEAS CSCC Multiconference, Heraklion, Greece, 22–25 July 2008; New Aspects of Computers, Part III; Association for Computing Machinery: New York, NY, USA, 2008; pp. 1107–1112. [Google Scholar]
- Mititelu, Ş. Efficiency conditions for multiobjective fractional variational problems. Appl. Sci. 2008, 10, 162–175. [Google Scholar]
- Nahak, C.; Mohapatra, R.N. Nonsmooth ρ − (η, θ)-invexity in multiobjective programming problems. Optim. Lett. 2012, 6, 253–260. [Google Scholar] [CrossRef] [Scilit]
- Preda, V.; Gramatovici, S. Some sufficient optimality conditions for a class of multiobjective variational problems. An. Univ. Bucur. Mat.-Inform. 2002, 61, 33–43. [Google Scholar]
- Treanţă, S. On a new class of vector variational control problems. Numer. Funct. Anal. Optim. 2018, 39, 1594–1603. [Google Scholar] [CrossRef] [Scilit]
- Treanţă, S. KT-geodesic pseudoinvex control problems governed by multiple integrals. J. Nonlinear Convex Anal. 2019, 20, 73–84. [Google Scholar]
- Treanţă, S. Efficiency in generalized V-KT-pseudoinvex control problems. Int. J. Control 2020, 93, 611–618. [Google Scholar] [CrossRef] [Scilit]
- Vetro, C.; Zeng, S.D. Regularity and Dirichlet Problem for Double-Phase Energy Functionals of Different Power Growth. J. Geom. Anal. 2024, 34, 105. [Google Scholar] [CrossRef] [Scilit]
- Chinchuluun, A.; Pardalos, P.M. A survey of recent developments in multiobjective optimization. Ann. Oper. Res. 2007, 154, 29–50. [Google Scholar] [CrossRef] [Scilit]
- Geoffrion, M.A. Proper efficiency and the theory of vector minimization. J. Math. Anal. Appl. 1968, 22, 618–630. [Google Scholar] [CrossRef] [Scilit]
- Jagannathan, R. Duality for nonlinear fractional programming. Z. Oper. Res. 1973, 17, 1–3. [Google Scholar]
- Treanţă, S. Characterization of efficient solutions for a class of PDE-constrained vector control problems. Numer. Algebr. Control. Optim. 2020, 10, 93–106. [Google Scholar] [CrossRef] [Scilit]
- Cen, J.X.; Khan, A.A.; Motreanu, D.; Zeng, S.D. Inverse problems for generalized quasi-variational inequalities with application to elliptic mixed boundary value systems. Inverse Probl. 2022, 38, 065006. [Google Scholar] [CrossRef] [Scilit]
- Cen, J.X.; Haddad, T.; Nguyen, V.T.; Zeng, S.D. Simultaneous distributed-boundary optimal control problems driven by nonlinear complementarity systems. J. Glob. Optim. 2022, 84, 783–805. [Google Scholar] [CrossRef] [Scilit]
- Cen, J.X.; Paczka, D.; Yao, J.-C.; Zeng, S.D. Asymptotic Analysis of Double Phase Mixed Boundary Value Problems with Multivalued Convection Term. J. Geom. Anal. 2023, 33, 287. [Google Scholar] [CrossRef] [Scilit]
- Sadek, L.; Akgül, A. New properties for conformable fractional derivative and applications. Progr. Fract. Differ. Appl. 2024, 10, 335–344. [Google Scholar]
- Sadek, L.; Algefary, A. On quantum trigonometric fractional calculus. Alex. Eng. J. 2025, 120, 371–377. [Google Scholar] [CrossRef] [Scilit]
- Sadek, L.; Ounamane, S.; Abouzaid, B.; Sadek, E.M. The Galerkin Bell method to solve the fractional optimal control problems with inequality constraints. J. Comput. Sci. 2024, 77, 102244. [Google Scholar] [CrossRef] [Scilit]
- Sadek, L.; Alaoui, H.T. The extended block Arnoldi method for solving generalized differential Sylvester equations. J. Math. Model. 2020, 8, 189–206. [Google Scholar]
- Udrişte, C.; Matei, L. Lagrange-Hamilton Theories; Monographs and Textbooks 8; Geometry Balkan Press: Bucharest, Romania, 2008. (In Romanian) [Google Scholar]
- Raymond, J.P. Optimal Control of Partial Differential Equations; Université Paul Sabatier: Toulouse, France, 2010. [Google Scholar]
- Valentine, F.A. The problem of Lagrange with differentiable inequality as added side conditions. In Contributions to the Calculus of Variations; University of Chicago Press: Chicago, IL, USA, 1937; pp. 407–448. [Google Scholar]
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