1. Introduction
In optimization theory, bilevel optimization problems (BLPP) belong to a very interesting class of mathematical programming problems. Bilevel programming problems are hierarchical programming problems consisting of two decision makers (namely, the `leader’ and the `follower’), in which the two decision-makers target to obtain the best decision, with respect to different goals, in general. Moreover, these decision makers do not act independently, rather, they act according to a certain hierarchy, where based upon the choice of one decision maker (referred to as the ‘leader’), the second decision maker (referred to as the ‘follower’) has to react optimally.
The initial conceptualization and development of BLPP is attributed to the influential contributions of Von Stackelberg [
1].The first mathematical bilevel model was developed by Bracken and McGill [
2] in 1973. Various real-life practical problems arising in numerous areas of modern research, such as engineering, medicine, and economics, can be formulated in terms of BLPP. It is worthwhile to note that the investigation of BLPP has numerous benefits from both theoretical and practical standpoints (see, for instance, refs. [
3,
4,
5] and the references cited therein). As a result, in the last few decades, numerous researchers have studied BLPP extensively and explored various interesting theories, such as optimistic and pessimistic methods to solve BLPP, single-level reformulation of BLPP, optimality criteria, duality results, and algorithms for BLPP to solve practical BLPP problems arising in various applications.
Necessary optimality criteria for BLPP were investigated by Bard [
6]. Furthermore, Bard [
7,
8] discussed several interesting properties of BLPP and provided insights into its practical applications. Outrata [
9] derived the necessary optimality conditions for a class of two-level Stackelberg problems in which the followers’ lower-level problems are convex programs with unique solutions. Dempe [
10] derived necessary and sufficient optimality criteria for BLPP. Yezza [
11] established the first-order necessary optimality criteria for a general BLPP. Moreover, Yezza [
11] formulated the general multilevel programming problem and deduced the necessary conditions of optimality in the general case. Further, some rectifications of necessary optimality conditions for a general multilevel programming problem were discussed by Dempe [
12]. Specifically, Dempe [
12] has emphasized that upper semicontinuity should be used instead of lower semicontinuity to ensure the existence of optimal solutions. Furthermore, Dempe [
12] has rectified the assumption that the abnormal part of the directional derivative of the optimal value function reduces to zero, replacing it with the requirement that a nonzero abnormal Lagrange multiplier does not exist. The optimistic version of BLPP and its necessary optimality conditions were studied by Dempe [
13]. For further insights into BLPP, we refer to [
7,
14,
15,
16,
17,
18,
19,
20] and the references cited therein.
It has been observed by several researchers that the phenomenon of nonsmoothness arises frequently in various real-world problems (see, for instance, refs. [
21,
22] and the references cited therein). Rockafellar [
22] introduced the notion of convex subdifferentials for nonsmooth convex functions. Subsequently, Clarke [
21] introduced the notion of Clarke subdifferential for a class of local Lipschitz functions. However, the convexity of the Clarke subdifferential has led to various limitations. It has been observed that the generalized gradient may be too large for many important applications, particularly for necessary optimality conditions (see [
23]). For instance, minimizing
over
and
over
demonstrates that the Clarke subdifferential includes points that are not minimizers of these functions (see [
23]). Moreover, another significant drawback of the convex constructions of the Clarke subdifferentials concerns deficient conditions obtained in their terms for some fundamental properties in nonlinear analysis related to covering nonsmooth operators, metric regularity, open mapping theorems, and Lipschitzian stability (see, [
23]). To overcome these shortcomings stemming from its convexity, Mordukhovich [
23] proposed the concept of the limiting subdifferential. The Mordukhovich subdifferential is the smallest subdifferential among all nonconvex and robust subdifferential, and it has a better Lagrange multiplier rule than the Clarke subdifferential (see [
23,
24]). Moreover, for the class of local Lipschitz functions, the Mordukhovich subdifferential is contained in the Clarke subdifferential (see [
23]). Therefore, the results established in terms of limiting subdifferentials naturally sharpen the results which are derived in terms of the Clarke subdifferentials. Mordukhovich subdifferential plays an important role in variational analysis, economics, equilibrium problems, and optimization (see [
23,
25,
26] and the references cited therein).
In the realm of optimization theory, convexity assumes a pivotal role by ensuring that a stationary point acts as a global minimizer, and necessary optimality criteria are also sufficient for a point to be a global minimizer. In order to provide a much more accurate representation, modeling, and solutions to real-world problems, and numerous generalizations of convex functions have been introduced. Mangasarian [
27] generalized the notion of convex function by introducing the class of pseudoconvex functions. For a detailed study of generalized convex functions, we refer to [
28,
29,
30]. Ngai et al. [
31] introduced the concept of approximate convex functions, which are stable under finite sums and suprema. Additionally, most well-known subdifferential concepts such as Clarke [
21], Ioffe [
32], and Mordukhovich [
23] coincide for this class of functions. The main feature of this class of functions is that it includes the classes of convex functions, weakly convex functions, strongly convex functions of order
and strictly convex functions. Recently, several generalizations of approximate convex functions have been introduced, for instance, Bhatia et al. [
33] and Gupta et al. [
34].
The concept of variational inequality was introduced by Hartman and Stampacchia [
35]. Variational inequalities appear in the forms of Minty variational inequality [
36] and Stampacchia variational inequality [
37]. Variational inequalities have several applications in the fields of economics, game theory, and traffic analysis (see [
38,
39] and the references cited therein). Variational inequality problems have been studied by several scholars as tools for solving optimization problems, for more exposition (see, [
40,
41,
42,
43,
44,
45,
46,
47,
48,
49,
50,
51,
52] in the Euclidean space setting and [
53,
54,
55] on Hadamard manifolds). Komlósi [
56] derived the equivalence among the solutions of Minty and Stampacchia variational inequality and the optimal solution of the minimization problem. Kinderlehrer and Stampacchia [
39] studied the relations between the solutions of variational inequality and minimization problems. Crespi et al. [
57] investigated the relations between the solutions of Minty variational inequality and scalar optimization problems. Kohli [
58] studied the relations between variational inequalities and BLPP involving generalized convex functions in terms of convexificators. However, the investigation of the relationships between the solutions of approximate variational inequalities and the local
-quasi solutions of BLPP in the notion of limiting subdifferentials has not been explored yet. Consequently, this paper aims to address this specific research gap by establishing the relationships between the solutions of AMTVI, ASTVI, and the local
-quasi solutions of BLPP by employing the powerful tool of limiting subdifferentials.
The primary motivation for studying the relationships between the solutions of BLPP and approximate variational inequalities is that the BLPP is generally a non-convex problem, making it challenging to find the optimal solution (see [
15]). Jeroslow [
59] initially highlighted that the BLPP is a non-deterministic polynomial hard problem (NP-hard problem). Furthermore, Vicente et al. [
60] noted that finding a locally optimal solution to the BLPP also falls within the realm of NP-hard problems. To address this challenge, we delve into the concepts of AMTVI and ASTVI and investigate the relationships between the solutions of the BLPP and the considered approximate variational inequalities. By seeking solutions for AMTVI and ASTVI, one can typically identify local optimal solutions for the BLPP.
Motivated by the works of [
33,
57,
58,
61,
62,
63], in this paper we consider the BLPP and establish the relationships between the solutions of approximate variational inequalities, namely, AMTVI and ASTVI, and the local
-quasi solutions of BLPP. Moreover, we deduce some existing results for the solutions of AMTVI and ASTVI, by employing the assumption of generalized KKM-Fan’s lemma.
The novelty and contributions of this paper are twofold. Firstly, the results of this paper extend the analogous results presented in [
58] from smooth optimization problems to nonsmooth optimization problems and further generalize them from single-level optimization problems to bilevel optimization problems. Secondly, it is worthwhile to note that the limiting subdifferential is the smallest among all the known robust subdifferentials and offers a better Lagrange multiplier rule compared to the Clarke subdifferential (see [
23,
24]). Moreover, for a real-valued local Lipschitz function, the Mordukhovich limiting subdifferential is contained within its corresponding Clarke subdifferential (see [
23]). Therefore, the established results of this paper sharpen the corresponding results derived by [
33,
61,
63].
The organization of this article is as follows. In
Section 2 we recall some basic definitions and preliminaries. In
Section 3, employing the powerful tool of limiting subdifferential, we investigate the equivalence among the solutions of AMTVI and ASTVI, and the local
-quasi solution of nonsmooth BLPP. In
Section 4, a generalized KKM Fan’s lemma has been employed to establish the existence results for the solutions of approximate variational inequalities. In
Section 5, we conclude our discussions and provide some future research directions.
2. Definition and Preliminaries
Throughout this paper, we use the notation
to denote the Euclidean inner product in the
n-dimensional Euclidean space
. The empty set is denoted by the symbol ∅. Let
be a nonempty set in
, equipped with the Euclidean norm
The closure, interior, and the convex hull of
are denoted by the symbols
,
, and
, respectively. For any
, we use the notation
to denote an open ball centered at
, having radius
d. Let
. The following definitions will be employed in the sequel:
We recall the following definition from [
30].
Definition 1. The set Ω
is said to be convex, provided for all , we have: Now, we recall the following definitions related to nonsmooth analysis from [
23].
Definition 2. Let be a lower semi-continuous function. Then the Fréchet subdifferential of at , denoted by , is defined as follows: Definition 3. Let be a lower semi-continuous function. The limiting subdifferential of at , denoted by , is defined as follows:where lim sup is the Painlevé–Kuratowski outer limit. Remark 1. Let and be a local Lipschitz function at . Then, the the set valued map , defined by , is closed (see, for instance, [23]). In the following definition, we recall the notions of generalized directional derivatives and Clarke subdifferential (see, [
21]).
Definition 4. Let be a local Lipschitz function at the point . Then, the generalized directional derivative of at , in the direction , denoted by , is defined as follows: Definition 5. Let be a local Lipschitz function at the point . The Clarke subdifferential of at the point , denoted by , is defined as follows: Let
be a real-valued local Lipschitz function, defined on an open subset
U of
. Then, the classical Rademacher theorem (see, [
26]) states that the function
is differentiable almost everywhere on
U, in the sense of Lebesgue measure. Therefore, the Clarke subdifferential of
at the point
, can be represented as follows:
The following proposition from [
23] provides the relationships between the limiting subdifferential and the Clarke subdifferential.
Proposition 1. If is a local Lipschitz function at , then In the following definition, we recall the notion of the lower-regularity condition of an extended real-valued function (see, [
23]).
Definition 6. Let be finite at . Then is said to be lower-regular at , provided the following holds: The following definitions of convex functions and generalized convex functions will be employed in the sequel (see, [
28,
64]).
Definition 7. Let be a nonempty convex subset of .
- (i)
A function is said to be convex, provided the following inequality is satisfied for any and : - (ii)
A function is said to be strictly convex, provided the following inequality is satisfied for any with and :
Remark 2. From Definition 7, it follows that strict convexity of the function implies its convexity. However, the converse statement may not always be true. For instance, consider the function , defined aswhere and . The function is convex on , but not strictly convex on . From now onwards, we assume that is a non-empty open convex subset of , unless otherwise specified.
Definition 8. A function is said to be quasiconvex, provided the following inequality is satisfied for all and : Definition 9. A function is said to be pseudoconvex, provided for each satisfying:we have: The following propositions from Soleimani-damaneh [
64] provide the characterizations of quasiconvex functions in terms of limiting subdifferentials.
Proposition 2. Let be a local Lipschitz and quasiconvex function. Then, for each , satisfying:we have: Proposition 3. Let be a local Lipschitz function. Further, assume that for each and , we have , for all . Then, is a quasiconvex function.
Remark 3. (i) From Definitions 7–9, it follows that if is a convex function, then it is both quasiconvex and pseudoconvex.
- (ii)
If is a local Lipschitz pseudoconvex function on , then is quasiconvex (see, [64]). - (iii)
The class of convex functions is stable under the finite sum. However, the class of quasiconvex and pseudoconvex functions are not stable under the finite sum (see, [28]).
Now, we consider the following bilevel programming problem (BLPP):
where
,
, and
are real valued functions, and
Therefore, the basic idea is that based on the choice of the leader, the follower minimizes his objective function
, and the leader then uses the obtained solution
to minimize his objective function
. BLPP is said to be well-defined if we can uniquely determine the optimal solution of the lower-level problem for every
. In literature, two types of solution concepts have been studied for BLPPs having more than one optimal solution for the corresponding lower-level problems, namely, the optimistic solution and the pessimistic solution.
In the optimistic approach, the follower considers an optimal solution, which is the best from the leader’s perspective. As a result, one has the following optimistic bilevel programming problem (OBLPP):
and
is the set of all optimal solutions for the following lower-level problem:
Let
S be the set of all feasible solutions to the problem BLPP, that is,
Now, we define:
Moreover, for any fixed
, we define the function
, as follows:
Therefore, it is evident that
In the rest of the paper, we assume that
is a non-empty convex subset of
unless specified otherwise.
Remark 4. The relationships between BLPP and variational inequalities have been investigated by Kohli [58] via convexificators. It is worthwhile to note that the lower-level problem has been assumed to be continuous and convex by Kohli [58]. However, we emphasize that such convexity and continuity assumptions are not imposed by us on the lower-level problem. This observation highlights the broader scope of the findings presented in this paper compared to those established in [58]. In the following definition, we recall the notion of approximate convex functions, which is closed under finite sum and suprema (see, Ngai and Penot [
65]).
Definition 10. A function is termed as an approximate convex function around , provided for any , some exists, satisfying:for all and . We have the following characterization for lower semicontinuous approximate convex functions from Ngai and Penot [
65].
Proposition 4. A lower semicontinuous function is termed as an approximate convex function around , if and only if for any , some exists, satisfying:for any and any . Following the work of Bhatia et al. [
33] and Golestani et al. [
66], in the following definitions, we introduce the notions of approximate convex function and
-pseudoconvex functions of type
I and
in terms of limiting subdifferentials.
Definition 11. A function is termed as an approximate -pseudoconvex function of type I around , if for any , some exists, satisfying:for all , such that Definition 12. A function is termed as an approximate -pseudoconvex function of type II around , if for any some exists, satisfying:for all , such that Remark 5. It is evident from Definitions 11 and 12 that if is an approximate -pseudoconvex of type II around , then is also an approximate -pseudoconvex of type I around . However, the converse statement may not always be true. For example, let be defined as:Then, the limiting subdifferentiable of is given by:It can be verified that is the approximate -pseudoconvex function of type I around , but not the approximate -pseudoconvex function of type II around . Moreover, it is worthwhile to note that the function is not local Lipschitz at the point . Hence, the Clarke subdifferential of does not exist at . Remark 6. If is pseudoconvex at , then is also an approximate -pseudoconvex function of type I around . However, the converse statement may not always be true. For example, let be defined as:The Clarke and limiting subdifferentials of are given by:Let and , we observe thatHence, it can be verified that is an approximate -pseudoconvex function of type I around , but not a pseudoconvex function at . Following the work of Bhatia et al. [
33] and Golestani et al. [
66], in the following definitions, we introduce the notions of
-quasiconvex functions of type
I and
in terms of limiting subdifferentials.
Definition 13. A function is termed as an approximate -quasiconvex of type I around , if, for any some exists, satisfying:for all , such that Definition 14. A function is termed as an approximate -quasiconvex of type II around , if for any , some exists, satisfying:for all , such that Remark 7. From the above definitions, it follows that, if is the approximate -quasiconvex function of type II around , then is the approximate -quasiconvex function of type I around . However, the converse statement may not always be true. For example, let be given as:Then the Clarke and limiting subdifferentials of are given by:Therefore, one can conclude that is the approximate -quasiconvex function of type I around , but not the approximate -quasiconvex function of type II around . Definition 15. A function is termed as an approximate -quasiconvex function around , provided for any some exists, such that the following implication holds:for any and for all . Definition 16. A function is termed as an approximate -pseudoconvex function around , provided for any some exists, such that the following implication holds:for any and for all . Remark 8. It is worthwhile to mention that the limiting subdifferential of a local Lipschitz function at a point is contained in the corresponding Clarke subdifferential at that point. Therefore, if is local Lipschitz and generalized approximate convex function around in terms of Clarke subdifferential (see, Bhatia et al. [33]), then is also generalized -approximate convex function around . However, the converse statement may not always be true. To justify this fact, we consider the lower semicontinuous function , defined as:Then the limiting subdifferentiable of is given by:Evidently, is an approximate -pseudoconvex of type I around , but not an approximate pseudoconvex of type I around in terms of Clarke subdifferential. However, we can easily observe that is not local Lipschitz at . Consequently, the Clarke subdifferential does not exist at . Definition 17. A multi-valued mapping is termed as an approximate ϵ-pseudomonotone around , if there exists , such that for each , ifthenwhenever . The following mean value theorem for local Lipschitz functions from [
23], will be employed in the sequel.
Theorem 1. Let be a local Lipschitz function on an open set containing in Ω. Moreover, suppose that is lower regular on . Then, we havefor some . The following notions of
-quasi solution and local
-quasi solution for BLPP are adaptations of the notions of
-quasi solution and local
-quasi solution for scalar optimization problems from Loridan [
67].
Definition 18. Let be given. A point is termed as an ϵ-quasi solution to the BLPP if, for any , the following inequalities hold: Definition 19. Let be given. A point is termed as a local ϵ-quasi solution to BLPP if there exists , such that the following inequalities hold:for any . The following example illustrates the fact that the limiting subdifferential of a real-valued function may be strictly contained within its Clarke subdifferential.
Example 1. Consider the real-valued function , defined as follows:Then, the limiting subdifferential of is given as:and the Clarke subdifferential of is given by:In view of Proposition 1, one can observe that the limiting subdifferential for a real-valued local Lipschitz function is contained within its Clarke subdifferential. Furthermore, this example demonstrates that the limiting subdifferential of a real-valued local Lipschitz function , as defined above may be strictly contained within its Clarke subdifferential. Since the results of this paper are derived in terms of limiting subdifferentials, therefore, our findings naturally sharpen the analogous results of [33,61,63]. Now, we consider the following AMTVI and ASTVI in terms of limiting subdifferentials:
AMTVI: Find
, such that for any
, some
, such that for each
and all
and
, the following inequalities hold:
ASTVI: Find
, such that for any
, some
, such that for each
, there exists
and
such that the following inequalities hold:
3. Relationship among BLPP, ASTVI, and AMTVI
This section is devoted to the study of the equivalence relationships between the solutions of approximate variational inequalities, namely, AMTVI, ASTVI, and the local -quasi solutions of the BLPP by employing the powerful tool of limiting subdifferentials. In the remaining portion of this section, we suppose that is a preassigned positive real number and is a lower semicontinuous function unless otherwise specified.
Theorem 2. Let and be approximate convex functions around . If is local ϵ-quasi solution of BLPP, then solves AMTVI with respect to .
Proof. Let
is a local
-quasi solution of BLPP which does not solve AMTVI with respect to
. Then for all
, we can obtain
and
and
, such that one of the following holds:
Since
and
are approximate
-convex around
, therefore, for each
, some
, such that, for every
and
and
, we have:
From (
3), it follows that
From (
1), (
2), (
4), and (
5), it follows that for each
, one of the following inequalities holds true:
This contradicts the fact that
is a local
-quasi solution of BLPP. This completes the proof. □
Theorem 3. Let and be local Lipschitz lower-regular functions at . Moreover, assume that solves AMTVI with respect to and Φ, are approximate convex functions around . Then, is a local ϵ-quasi solution of BLPP.
Proof. On contrary, let us suppose that
solves AMTVI with respect to
, but
is not a local
-quasi solution of BLPP. Hence, for all
,
, such that one of the following holds:
Let
for all
. Since,
and
are approximate convex functions around
, hence, for each
,
, such that for all
, we have
Let
be arbitrary. Now in view of Theorem 1, there exist
and
and
such that
Combining (
8)–(
11), we have
From (
6), (
7), (
12), and (
13), it follows that one of the following inequalities holds true:
Since
, it follows that one of the following inequalities holds true:
where
This contradicts the fact that
solves AMTVI. This completes the proof. □
Theorem 4. Suppose that is a local ϵ-quasi solution of BLPP. Further, we assume that and are approximate -quasiconvex functions of type II around ). Then solves AMTVI with respect to ϵ.
Proof. Since
is a local
-quasi solution of BLPP, therefore,
, such that for all
, we have
Moreover, as
and
are approximate
-quasiconvex functions of type II around
. Hence, for any
,
, such that for each
, with
one has
and
Let
. Then from (
18) and in view of the
-quasiconvexity of type II of
and
around
, it follows that
and
are satisfied for every
. Therefore,
and
This completes the proof. □
Theorem 5. Let be a solution of ASTVI with respect to ϵ. Further, suppose that and are approximate -pseudoconvex functions around . Then is a local ϵ-quasi solution of BLPP.
Proof. On contrary, suppose that
solves ASTVI with respect to
, but not a local
-quasi solution of BLPP. Hence, for each
, some
exists, such that one of the following inequalities holds true:
Further,
and
are approximate
-pseudoconvex functions around
. Hence, from (
19) and (
20), it follows that for every
, there exists some
, such that for any
, one of the following inequalities holds true:
Evidently, this is a contradiction. This completes the proof. □
Theorem 6. Let be a solution of ASTVI with respect to ϵ. Further, suppose that and are approximate -pseudoconvex functions of type II around . Then is a local ϵ-quasi solution of BLPP.
Proof. Let
solves ASTVI with respect to
. Then we can obtain a
, such that for each
, there exist
and
, such that
Since
and
are approximate
-pseudoconvex functions of type II around
, then for every
,
, and for any
, with
we have
Let
. Then from (
21) and the
-pseudoconvexity of type II of
and
around
, it follows that
and
for every
. Hence,
is a local
-quasi solution of BLPP. This completes the proof. □
Theorem 7. Let and be local Lipschitz functions at . Moreover, assume that is a local ϵ-quasi solution of BLPP, and Φ and are approximate -quasiconvex functions of type II around (. Then is a solution of ASTVI with respect to ϵ.
Proof. Let
be a local
-quasi solution of BLPP. Then, some
exists, such that for each
, we have
Further,
and
are approximate
-quasiconvex functions of type II around
. Therefore, for all
, we can obtain a
such that for all
, if
then
Let
and
, such that
. Then from (
22), it follows that
Employing (
23) and the approximate
-quasiconvexity of type II of the functions
and
around
, one has
Hence, we arrive at the following:
From (
24), we have
Since,
and
are closed,
, and
as
, we have
and
. Therefore, for any
, there exist
and
, such that
This completes the proof. □
Remark 9. Theorem 3 extends Theorem 2.2 of [56] from smooth optimization problems to nonsmooth optimization problems and, moreover, generalizes them from single-level optimization problems to a more general class of optimization problems, namely, bilevel optimization problems. Theorem 8. Let be a solution of ASTVI with respect to ϵ. Further, suppose that and are approximate ϵ-pseudomonotone. Then solves AMTVI with respect to ϵ.
Proof. Let
be a solution of ASTVI with respect to
. Then, some
exists, such that for all
, there exists
and
, such that
Combining (
27) with the approximate
-pseudomonotonicity hypotheses on
,
, it follows that there exists
, such that for all
and all
,
, we have
Since
, it follows that
Hence,
is a solution of AMTVI with respect to
. This completes the proof. □
The following example illustrates the importance of the established results.
Example 2. We consider the following bilevel programming problem:where is the set of optimal solutions of the following optimization problem:where and . The set of optimal solutions for lower-level problem is given by:and Moreover, we haveLet denote the set of all feasible solutions of BLPP, that is, . Then, for , it can be verified that is a local ϵ-quasi solution of the problem. Moreover, we have:Further, it can be verified that Φ and are approximate -pseudoconvex and approximate -quasiconvex around . It is worthwhile to note that, Φ and are convex functions. Therefore, the limiting subdifferentials of Φ and coincide with the corresponding Clarke subdifferentials of Φ and .
Furthermore, we can verify that is a solution of with respect to ϵ, as for all , we haveMoreover, is also a solution of ASTVI with respect to the same ϵ, as for all , there exists and , such that 5. Conclusions and Future Directions
In this paper, we have investigated BLPP, AMTVI, and ASTVI in the framework of Euclidean spaces via limiting subdifferentials. In particular, we have derived the relationships among the solutions of AMTVI, ASTVI, and the local -quasi solution to the nonsmooth BLPP under suitable generalized approximate convexity hypotheses. Furthermore, existing results for the solution of AMTVI and ASTVI have been established by employing generalized KKM-Fan’s lemma. A non-trivial example has been furnished to illustrate the importance of the deduced results.
The results derived in this paper extend several interesting results available in the literature for a wider class of optimization problems. In particular, the results of this paper extend the corresponding results presented in [
56] from smooth optimization problems to nonsmooth optimization problems and further generalize it from single-level optimization problems into bilevel optimization problems. In [
58], the author has assumed that the lower-level problem of the BLPP is both continuous and convex. However, it is essential to emphasize that we did not impose any convexity and continuity assumptions on the lower-level problem. This observation highlights the broader scope of the findings presented in this paper compared to those in [
58]. Moreover, the author in [
58] has employed optimal value reformulation to convert the BLPP into single-level optimization problems. However, our work is distinctive in the sense that it directly establishes the connection between the two concepts without relying on any intermediate methods. Furthermore, to establish our results, we have employed the powerful tool of Mordukhovich limiting subdifferential, which is the smallest among all the known robust subdifferential, and it has a better Lagrange multiplier rule than the Clarke subdifferential (see, [
23,
24]). In addition, for a local Lipschitz real-valued function, the Mordukhovich limiting subdifferential may be strictly contained within its corresponding Clarke subdifferential (see, Example 1). Therefore, our findings naturally sharpen the analogous results of [
33,
61,
63]
Considering the contributions of Deb and Sinha [
14] and Oveisiha and Zafarani [
69], we aim to extend the results established in this paper to multiobjective bilevel programming problems and to a broader space, such as the Asplund space, in our future research endeavors.