Abstract
In this paper, for the considered multi-cost variational problem (P), we associate a dual model (D) in order to study and state the connections between the solution sets of these control problems. Thus, under S-type I assumptions associated with the integral functionals involved, we formulate and prove various reciprocal results, such as weak, strong, and converse-type dualities.
MSC:
58E25; 90C30; 49K20; 58E17
1. Introduction
Over time, many researchers have been interested in studying and solving an optimization problem by transforming it into a simpler one, both from a computational point of view and from the point of view of the equivalence of the solution sets associated with the two models. In this regard, duality theory has gained momentum among scientists. Recently, Abdulaleem and Treanţă [1] formulated optimality conditions and a duality theory for E-differentiable multiobjective programming problems involving V-E-type I functions. Aghezzaf and Hachimi [2] used generalized invexity and duality in some classes of multi-objective programming problems. Antczak [3,4] studied the G-invex and G-type I variational optimization problems that provide sufficient optimality criteria and duality results. Also, considering various types of invexity, several authors (see Bhatia and Mehra [5], Hachimi and Aghezzaf [6], Khazafi and Rueda [7,8], Kim and Kim [9]) have investigated some families of optimization problems and provided optimality conditions and duality connections. Very recently, Marghescu and Treanţă [10] investigated a class of control problems driven by approximately pseudo-convex multiple integral functionals. Mishra et al. [11,12] involved vanishing constraints and tangential subdifferentials in the considered multiobjective semi-infinite programs. In addition, approximate solutions associated with non-smooth infinite optimization problems have been studied by Pham [13]. Infinite interval constraints via integral-type penalty function have been included by Qian et al. [14] in the analysis of some vector interval-valued optimization problems. Sadeghieh et al. [15] investigated stationarity in non-smooth multiple-objective problems involving vanishing constraints. Saeed et al. [16] presented optimality criteria in multiobjective models with semi-infinite constraints and some characterization theorems for new classes of multi-objective control models. Very recently, Treanţă et al. [17] formulated necessary and sufficient efficiency criteria for multi-cost models with H-type I functionals. Tung [18] presented Karush–Kuhn–Tucker-type optimality criteria and duality outcomes for some multiobjective semi-infinite models with vanishing constraints. Yadav and Gupta [19,20] formulated an optimality and duality analysis in conic and interval-valued semi-infinite programs having vanishing constraints. In 2012, Zhang et al. [21] established sufficiency and duality theorems for multi- objective control models implying G-invexity.
In this study, based on the above research papers, keeping in mind the results established in Treanţă et al. [17], we associate a dual model (D) for the considered multi-cost variational problem (P) in order to study and state the connections between the solution sets of these control problems. Thus, under S-type I assumptions associated with the integral functionals involved, we formulate and prove various reciprocal results, such as weak, strong, and converse-type dualities. The novelty elements of our study are given by the use of S-type I functionals for the analysis of the reciprocal outcomes established in this article. Compared with other papers in the specialized area, this is the principal new tool for the duality study of the considered family of constrained optimization problems. The tool used in this paper is more effective or different from previous methods in terms of efficiency or coverage. More precisely, the S-type I assumptions associated with the multiple integral functionals involved cover broader and more general classes of problems, where the convexity of the functionals is not fulfilled or the functionals considered are not of simple integral type.
2. Preliminary Elements
In the following, according to Treanţă et al. [17], we state various definitions and the following convention for equalities and inequalities: for any two vectors, , we assume
- (i)
- ] for ;
- (ii)
- for ;
- (iii)
- for ;
- (iv)
- and .
Consider to be compact and . Suppose is a smooth (piecewise) mapping of , and . Also, consider to be a smooth (piecewise) mapping of , and . Consider R to be the family of functions (state variables) with , and Q is the family of functions (control variables) with the same norm, as well.
In the following, we investigate the multi-cost model defined as:
with , as -class functionals, and as the volume element in .
Also, we consider the notations and for and , respectively, and for q and , respectively, and
Consider U, defined as
Definition 1.
The feasible point is weakly efficient point for (P) if there exists no satisfying
Definition 2.
The feasible point is efficient point for (P) if there exists no satisfying
Definition 3.
The feasible point is properly efficient point for (P) if there exists such that
is satisfied, for every , and for k, fulfilling
whenever and .
Definition 4.
A mapping is said to be strictly increasing if
Consider , as the range for
where , and , be the range of
where .
Definition 5
(Treanţă et al. [17]). For , if there exists a differentiable functional with as a real-valued strictly increasing mapping, a differentiable functional with as a real-valued strictly increasing mapping, with , and with , such that [see ]
and
hold, for all , then is called S-type I at on (related to and ψ).
If (1) and (2) are valid for each , then is called S-type I on related to , ϕ and ψ.
Definition 6
(Treanţă et al. [17]). For , if there exists a differentiable functional with as a real-valued strictly increasing mapping, a differentiable functional with as a real-valued strictly increasing mapping, with , and with , such that the inequalities
and
hold, for all , then is called strictly-S-type I at on related to and ψ.
If (3) and (4) are valid for each , then is called strictly-S-type I on related to and ψ.
Definition 7
(Treanţă et al. [17]). For , if there exists a differentiable functional with as a real-valued strictly increasing mapping, a differentiable functional with as a real-valued strictly increasing mapping, with , and with , such that
and
hold, for all , then is called pseudo-quasi-S-type I at on (related to and ψ).
If (5) and (6) are valid for each , then is called pseudo-quasi-S-type I on related to and ψ.
Definition 8
(Treanţă et al. [17]). For , if there exists a differentiable functional with as a real-valued strictly increasing mapping, a differentiable functional with as a real-valued strictly increasing mapping, with , and with , such that
and
hold, for all , then is called strictly-pseudo-quasi-S-type I at on related to and ψ.
If (7) and (8) are valid for each , then is called strictly-pseudo-quasi-S-type I on related to and ψ.
Definition 9
(Treanţă et al. [17]). For , if there exists a differentiable functional with as a real-valued strictly increasing mapping, a differentiable functional with as a real-valued strictly increasing mapping, with , and with , such that
and
hold, for all , then is called weak-pseudo-quasi-S-type I at on related to and ψ.
If (9) and (10) are valid for each , then is called weak-pseudo-quasi-S-type I on related to and ψ.
Definition 10
(Treanţă et al. [17]). For , if there exists a differentiable functional with as a real-valued strictly increasing mapping, a differentiable functional with as a real-valued strictly increasing mapping, with , and with , such that
and
hold, for all , then is called strong-pseudo-quasi-S-type I at on related to and ψ.
If (11), (12) and (13) are valid for each , then is called strong-pseudo-quasi-S-type I on related to and ψ.
Definition 11
(Treanţă et al. [17]). For , if there exists a differentiable functional with as a real-valued strictly increasing mapping, a differentiable functional with as a real-valued strictly increasing mapping, with , and with , such that
and
hold, for all , then is called weak-strictly-pseudo-quasi-S-type I at on related to and ψ.
If (14) and (15) are valid for each , then is called weak-strictly-pseudo-quasi-S-type I on related to and ψ.
3. Reciprocal Theorems
In this section, for the considered multi-cost variational problem (P), we associate a dual model (D) in order to study and investigate the connections between the solution sets of these two variational control problems. Thus, we introduce the following multiple objective variational control problem:
with and defined as above.
Consider B is the set of all feasible solutions for (D), that is, the set
The following result formulates a weak-type reciprocal outcome associated with the considered variational problems.
Theorem 1
(Weak duality). Consider and and suppose that one of the following assumptions is valid:
- (a)
- is strictly S-type I at with respect to and ψ;
- (b)
- is strictly-pseudo-quasi-S-type I at with respect to and ψ;
- (c)
- is strong-pseudo-quasi-S-type I at with respect to and ψ.
Then, the relations
and
cannot hold.
Proof.
Let and . We proceed by contradiction and consider that we are in hypothesis a) (for instance). It follows
and
Since every , is a strictly increasing functional on its domain, the inequalities (16) and (17) yield
and
By (18), (20) and (21), it follows that
Multiplying by , and then adding, it results
Multiplying each inequality
by , and then adding, we obtain
Using the feasibility of in (D) together with (24), we get
Adding (23) and (25), we obtain that
holds, contradicting the feasibility of in (D). This completes the proof. □
Under weaker generalized invexity assumptions, the next result is true.
Theorem 2
(Weak duality). Consider and and suppose that one of the following assumptions is valid:
- (a)
- is S-type I at with respect to , and ψ;
- (b)
- is pseudo-quasi-S-type I at with respect to and ψ;
- (c)
- is weak-strictly-pseudo-quasi-S-type I at with respect to and ψ.
Then, the relation
cannot hold.
The next two results formulate strong-type reciprocal outcomes associated with the considered variational problems.
Theorem 3
(Strong duality). Consider is an (weakly efficient) efficient point for (P), implying
are satisfied at this point. Then, is a feasible point for (D) and, in addition, if Theorem 1 (or Theorem 2) is true, then is an (weakly efficient) efficient solution for (D).
Theorem 4
(Strong duality). Consider is a properly efficient point for (P), involving
are valid, and assume the assumptions in Theorem 1 (or Theorem 2) hold. Then, is a feasible point for (D) and, in addition, is a properly efficient solution for (D) and the cost values are equal at these points.
Proof.
As is a properly efficient solution for (P), there exist and such that the conditions mentioned abobe are satisfied. Thus, is feasible point for (D). By using Theorem 1 (or Theorem 2), it results that is an efficient solution for (D). Now, by considering the method of contradiction, we prove that is a properly efficient solution for (D). Therefore, there exists and such that
is valid for every scalar and all k, satisfying
We consider the set of indexes , with as the set of indexes for the cost functionals that satisfy (27). Define as follows . Relation in (26) is valid for all . We set , where denotes the number of elements in the set . Thus, (26) and (27) yield
By the definition of the set , (26)–(28), it follows that
This is a contradiction to the weak duality theorem (see Theorem 1). Hence, is a properly efficient solution for (D), and the cost values are equal at these points. □
The last result derived in the current paper is provided by the following theorem. It establishes a strict converse duality associated with the two considered variational models.
Theorem 5
(Strict converse duality). Let and such that
Further, assume that is strictly-S-type I at with respect to and ψ. Then .
Proof.
holds, which is a contradiction to the feasibility of in (D). □
Contrary to the result, we assume that . By assumption, is strictly-S-type I at with respect to and . Then, we have
Multiplying each inequality (30) by , then (29) gives
Adding both sides of the above inequalities, we get
Multiplying each inequality (31) by , and then adding both sides of the obtained inequalities, we obtain
Hence, the feasibility of in (D) implies
Adding both sides of (32) and (34), we get that the following inequality
Illustrative Example.
We formulate the following constrained multi-objective nonlinear control problem:
where
Let be a feasible solution to the problem (P). The dual problem associated with (P) is defined as follows:
subject to
We note that is the feasible solution set to (D). Let us consider . Then is a feasible solution to (D). Further, it can be easily verified that all the involved functionals are strictly S-type I at with respect to and . Also, the following inequality
shows that the duality gap is positive. In consequence, Theorem 1 (Weak duality) is verified.
4. Conclusions and Further Developments
In this study, new results and characterizations of the set of solutions associated with a family of constrained optimization problems have been stated. Namely, a new family of multi-cost models has been introduced and, under S-type I hypotheses of the involved multiple integral-type functionals, various connections were highlighted between the solution sets of these control problems. We have formulated and proved various reciprocal results, such as weak, strong, and converse-type dualities. More specifically, firstly, under suitable hypotheses, we have established that the value of the cost functional associated with the original (primal) model can not be greater than the value of the cost functional associated with the dual model. Then, the results presented a strong and converse-type duality between the considered variational models. The limitations of the presented results could be, for instance, the appearance of (path-dependent or path-independent) curvilinear integrals as functionals instead of multiple integrals. This situation would add additional conditions to the study of this class of problems. In addition, as for issues that remain unsolved, we can mention the analysis of well-posedness associated with this type of extremization model and, also, establish new efficiency criteria based on saddle-points or a modified cost functional approach. Such research directions are still unexplored and, due to their importance and efficiency, deserve to be studied as soon as possible. Consequently, we note the applicability of the proposed technique to larger or more complex optimization problems.
Author Contributions
Conceptualization, S.T., V.C. and O.M.A.; formal analysis, S.T., V.C. and O.M.A.; funding acquisition, S.T., V.C. and O.M.A.; investigation, S.T., V.C. and O.M.A.; methodology, S.T., V.C. and O.M.A.; validation, S.T., V.C. and O.M.A.; visualization, S.T., V.C. and O.M.A.; writing—original draft, S.T., V.C. and O.M.A.; writing—review and editing, S.T., V.C. 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). The authors would like to thank the reviewers for their constructive remarks and suggestions.
Conflicts of Interest
The authors declare no conflicts of interest.
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