1. Introduction
Set-valued optimization is a kind of extension of vector optimization, which has become a flourishing branch of applied mathematics due to the application of set-valued optimization problems in many fields [
1,
2,
3]. It is widely known that the analysis of optimality conditions for various types of set optimization problems and their solutions strongly depends on the features of set-valued maps and their derivatives, or epiderivatives, see [
4,
5,
6]. Based on a unique concept of the difference of sets, Jahn [
7] presented the idea of the directional derivative of a set-valued map and used the derivative to derive the optimality conditions for a set optimization problems. In order to figure out the optimality conditions of the
ℓ-minimal solution for a set optimization problem, Durea and Strugariu [
8] proposed the concept of the directional derivative of set-valued maps. Using the modified Demyanov difference and the derivative, Dempe and Pilecka [
9] defined the directional derivative of the set-valued maps and developed the optimality condition for the set optimization problem. Since the radial set of a set contains global information of the set [
10,
11,
12], radial derivatives [
13] have drawn a lot of attention, see [
14,
15]. For constrained set-valued optimization problem, Yu [
14] proposed the the higher-order radial derivative of set-valued maps, by means of the derivative, they developed optimality conditions for lower weak minimal solution.
In practical application, the mathematical programming models are usually not accurate enough, so the solutions of the model are generally approximate rather than exact. Meanwhile, approximate solutions can approximate exact solutions for mathematical programming problems. It is important to note that most of the real-world optimization issues, including economic analysis and traffic optimization, ecological planning, etc., have approximate solutions, which are highly helpful in the analysis and treatment of set-valued optimization problems; see [
16,
17,
18] for details. Therefore, the approximate solution of optimization problems has attracted much attention from many scholars, see [
19,
20]. The idea of
-quasi solutions to vector optimization problems was first suggested by Loridan [
21]. For a set-valued optimization problem, by combining vector and set criteria, Dhingra and Lalitha [
22] introduced concepts of approximative solutions. In a locally convex Hausdorff topological vector space, Hu et al. [
20] proposed an approximative Benson proper effective solution to the set-valued equilibrium problem and explored the relationship between the Benson effective solution and the approximate one. The Painlevé-Kuratowski lower and upper convergence of the approximation solution for set optimization problems under continuity and convexity are derived by Han et al. [
23]. To gain the sufficient conditions of minimal solution sets, Gupta and Srivastava [
24] introduced a novel concept of approximation weak minimal solution for a set optimization problem. Without employing the convexity, Han [
25] obtained two scalarization theorems for the connectedness of the weak
l-minimal approximate solutions for set optimization problems.
To our knowledge, there is relatively little literature on the second-order optimal conditions for approximate solutions of set optimization problems with the Minkowski difference. Motivated by the the derivatives in [
14,
15] and approximate solutions in [
20,
24], we introduce the generalized second-order lower radial epiderivative for set-valued maps, approximate Benson proper effective solutions and approximate weakly minimal solutions of the set optimization problem based on the Minkowski difference. By the second-order lower radial epiderivative, we establish the necessary optimality conditions and sufficient optimality conditions of approximate Benson solutions and approximate weakly minimal solutions for unconstrained set optimization problems, respectively.
The article is organized as follows. We recall some preliminaries and establish a few features of the Minkowski difference for sets in
Section 2. We firstly propose the generalized second-order lower radial epiderivative for set-valued maps and discuss some properties of the epiderivative in
Section 3. We discuss the second-order necessary and sufficient conditions for approximate Benson proper efficient solutions and approximate weakly minimal solutions of the unconstrained set optimization problems in
Section 4. The brief conclusion of the paper is given in
Section 5.
2. Preliminaries and Definitions
Throughout the paper, unless otherwise specified, let V and P be two normed spaces, be the topological dual space of P, be the family of all nonempty subsets of V. We denote by and , the interior and closure of a set , respectively. The generated cone of A is defined by . In the sequel, C is a solid pointed closed convex cone in P. We have , and for all . The dual cone of C is .
Let
be a set-valued map. The domain, graph, epigraph and profile map of
S are defined, respectively, by
Clearly, .
Definition 1 ([
26]).
Let . The Minkowski difference of I and W is defined as By the definition of Minkowski difference, the following results obviously hold.
Proposition 1. Let . Then
(a) if and only if
(b) .
(c) .
(d) .
Proposition 2. Let , , and . Then Proof. (i) (⇒) Let
. Then, it follows from Proposition 1
that
Therefore,
that is,
which implies
(ii) (⇐) Let
Then, from Proposition 1
, we get
Therefore,
then
which implies
The proof is complete. □
Lemma 1 ([
26]).
Let . If I is a convex set, then for any , the Minkowski difference is a convex set. Lemma 2 ([
27]).
If I, and then(a)
(b)
(c) If I is closed, then is also closed.
Definition 2 ([
15]).
Let and .(i)The generalized radial set of G on H is defined by (ii) The generalized second-order radial set of G on H with respect to t is defined by Inspired by the mth-order lower radial set in [
11] and the generalized second-order radial set in [
15], we propose the notion of the generalized lower radial set and the generalized second-order lower radial set.
Definition 3. Let and .
(i)The generalized lower radial set of G on H is defined by (ii) The generalized second-order lower radial set of G on H with respect to t is defined by Remark 1. If G is convex, then is convex.
Remark 2. if and only if
Remark 3. If the set H is a singleton and , then the generalized radial set reduces to the closed radial cone introduced in [13], the generalized second-order radial set reduces to second-order upper radial set introduced in [11] and the generalized second-order lower radial set reduces to second-order lower radial set introduced in [11]. Remark 4 ([
15]).
Let be two nonempty sets.(i) .
(ii) .
(iii) If , then is a nonempty closed cone, is a nonempty closed set such that . is not a cone in general.
By Definitions 2 and 3, the following results naturally hold.
Proposition 3. Let be three nonempty sets and let . Then
(i) .
(ii)
(iii) and
Note that the inverse inclusion of Proposition 3 may not hold by the following example.
Example 1. Let , and . By calculating, we obtainandThus, . 3. Generalized Second-Order Lower Radial Epiderivatives for Set-Valued Maps
In this section, by virtue of the Minkowski difference, we introduce generalized second-order lower radial epiderivatives for set-valued maps, and then investigate some characteristics of the epiderivative and generalized second-order radial epiderivatives. Firstly, we recall two concepts in [
15].
Definition 4 ([
15]).
Let be a set-valued map, and (i) The generalized radial derivatives of S at is the set-valued map defined by (ii) The generalized second-order radial derivatives of S at with respect to is the set-valued map defined by Next, we introduce the generalized lower radial epiderivative and the generalized second-order lower radial epiderivative of a set-valued map.
Definition 5. Let be a set-valued map, and
(i) The generalized lower radial epiderivatives of S at is the set-valued map defined by (ii) The generalized second-order lower radial derivatives of S at with respect to is the set-valued map defined by (iii) The generalized second-order lower radial epiderivatives of S at with respect to is the set-valued map defined by Remark 5. If , then reduces to the mth-order lower radial derivative introduced in [13]. Proposition 4. Let be a set-valued map, . Then is strictly positive homogeneous, i.e., Proof. Let , .
Let
. Then
Thus, for any sequence
with
, there exists a sequence
with
such that
Then
Naturally,
. It follows from (
1) that
. Therefore,
In this way,
(ii) Next, we prove that
The relationship of can be proved according to the same proof idea as (i).
So is strictly positive homogeneous. This completes the proof. □
Remark 6. It is clear thatHowever, the converse inclusions of (
2)
may not hold. The following example show the case. Example 2. Consider set optimization problem with , and . Let It is obvious to get that . So, for every and , we calculate thatandThus, Remark 7. By Definitions 4 and 5, we getand Proposition 5. Let be a set-valued map, and Then
Proof. Let
. Then there exist
and
such that
Since
, for any sequence
with
, there exists a sequence
with
such that
that is,
Set
. Then
and
which implies that
Hence,
(2) We now prove that
Since
, one gets
From (
3) and (
4), we have
This proof is complete. □
Proposition 6. Let be a set-valued map, and Then Proof. Let
. Then, for any sequence
with
, there exists a sequence
with
such that
that is,
Then
Set
. Then
. So
Hence,
This completes the proof. □
Proposition 7. Let be a nonempty subset, be a set-valued map and Then Proof. It follows from Remark 4
that
Let
and
. Then it follows from Proposition 2 that for any
, we get
Therefore,
In combination with (
6), we have
which implies that
Hence,
This completes the proof. □
Remark 8. Proposition 7 is established without any assumption of convexity.
Proposition 8. Let be a nonempty subset, be a set-valued map and Then Proof. From Proposition 7, we derive
Since
, for any
, one has
This completes the proof. □
4. Optimality Conditions for Approximate Solutions of Set Optimization Problems
In this section, we discuss optimality conditions of approximate Benson proper efficient solution and approximate weakly minimal solutions for unconstrained set optimization problems by using the generalized second-order radial derivatives and the generalized second-order lower radial epiderivatives.
Let
be a set-valued map,
, we consider a set optimization problem
as follows:
Next, we consider the following definitions for set optimization problem with the Minkowski difference.
Definition 6 ([
24]).
Let and . A vector is said to be a -weak minimal solution of , denoted by -WMin , if Remark 9. (i) If , then -weak minimal solution reduces to m-weak minimal solution considered in [24] for . (ii) If S is single-valued, then Definition 6 of -weak minimal solution reduces to the weak -efficient solution for the vector optimization problems introduced in [28]. Inspired by the Definition 6, we define MBenson proper efficient solution and -MBenson proper efficient solution with the Minkowski difference.
Definition 7. Let , , and .
(i) is said to be a MBenson proper efficient solution of , denoted by MBenson , if (ii) is said to be a -MBenson proper efficient solution of , denoted by -MBenson , if Remark 10. (i) If , then -MBenson proper efficient solution reduces to MBenson proper efficient solution for .
(ii) For every , we have
(iii) For every , we have
Firstly, we derive the optimality conditions of -weak minimal efficient solution for .
Theorem 1. Let
,
and
. If
is a
-weak minimal solution of
, then
where
.
Proof. Suppose that (
7) dose not hold. Then, there exists
and
such that
By the definition of the generalized second-order lower radial epiderivatives, for a sequence
with
, there exists
with
such that
Then, for every
and
, we get
Therefore,
In combination with
, one gets
Since
and
, one has
Obviously,
. It follows from (
8) that there exists a natural number
N such that
In combination with (
9), it follows from Proposition 1
that
Therefore,
which contradicts that
is a
-weak minimal solution of problem
. The proof is complete. □
According to Proposition 6, we get that the following corollary.
Corollary 1. Let
,
and
. If
is a
-weak minimal solution of problem
, then
where
.
Now we give an example to explain Theorem 1.
Example 3. Consider set optimization problem with , , , . Let It is easy to check that
is a
-weak minimal solution of the problem
. Let
and
. Then, by directly calculating, we get
Then
Remark 11. The condition of Theorem 1 is also a second-order necessary condition for m-weak minimal solution in [24]. Theorem 2. Let
and
. If there exists
such that
then
is a
-weak minimal solution of
Proof. From (
10), we derive
Suppose that (
11) dose not hold. Then there exist
,
and
such that
Since
, one has
Obviously,
. Therefore,
which contradicts with (
10). Hence (
11) holds.
As
and
, it follows from Proposition 7 that
Combining with (
11), we have
Therefore
is a
-weak minimal solution of
The proof is complete. □
Now we give an example to show Theorem 2.
Example 4. Consider with , , and . LetTake . Let and . It is easy to caculate thatThenThus, is a -weak minimal solution. Since the -weak minimal solution is not always the -MBenson proper efficient solution for , we next provide optimality conditions of the -MBenson proper efficient solution .
Theorem 3. Let
,
,
and
. If
is a
-MBenson proper efficient solution of problem
, then
where
.
Proof. Suppose to the contrary that there exists some
such that (
12) does not hold. Then there exists some
such that
and
By the definition of the generalized second-order lower radial epiderivatives, for a sequence
with
, there exists
with
such that
Then, for every
and
, we get
Therefore,
Combining with
and Proposition 1
, one gets
Since
and
, one has
So
Combining with (
13), we get
which contradicts that
is a
-MBenson proper efficient solution of problem
.The proof is complete. □
According to Proposition 6, we get that the following corollary.
Corollary 2. Let
,
,
and
. If
is a
-MBenson proper efficient solution of problem
, then
where
.
Now we provide an example to illustrate Theorem 3.
Example 5. Consider set optimization problem with , , , . Let It is easy to check that
is a
-MBenson proper efficient solution of the problem
. Let
and
. Then, by directly calculating, we get
Then
Remark 12. If and in Theorem 3, it follows from Remark 10 (i) that becomes a MBenson proper efficient solution of problem , and (
12)
becomes the necessary condition for MBenson proper efficient solution. Remark 13. As from Theorem 3, so the condition of Theorem 3 is also a second-order necessary condition for MBenson proper efficient solution.
Remark 14. If the condition of is not satisfied in Theorem 3, then Theorem 3 may not hold. The following example explains the case.
Example 6. Consider Example 5. Then is a -MBenson proper efficient solution of the problem . Take andThus, Theorem 4. Let
,
,
and
. If
then
is a
-MBenson proper efficient solution of problem
.
Proof. By Proposition 7, we have
Since
, one has
Then, from Proposition 1
, we have
Therefore, combining with
, we get
Then it follows from (
14) that
which implies that
is a
-MBenson proper efficient solution of problem
. This completes the proof. □
Now we give an example to illustrate Theorem 4.
Example 7. Consider with , , and . LetTake . Let and , then by calculating, we getThus,which implies that is a -MBenson proper efficient solution. Remark 15. if we replace the -MBenson proper efficient solution with the MBenson proper efficient solution in Theorems 3 and 4, then the corresponding conclusions are still valid.