- Research Article
- Open Access
Tightly Proper Efficiency in Vector Optimization with Nearly Cone-Subconvexlike Set-Valued Maps
© Y. D. Xu and S. J. Li. 2011
Received: 26 September 2010
Accepted: 7 January 2011
Published: 11 January 2011
A scalarization theorem and two Lagrange multiplier theorems are established for tightly proper efficiency in vector optimization involving nearly cone-subconvexlike set-valued maps. A dual is proposed, and some duality results are obtained in terms of tightly properly efficient solutions. A new type of saddle point, which is called tightly proper saddle point of an appropriate set-valued Lagrange map, is introduced and is used to characterize tightly proper efficiency.
One important problem in vector optimization is to find efficient points of a set. As observed by Kuhn, Tucker and later by Geoffrion, some efficient points exhibit certain abnormal properties. To eliminate such abnormal efficient points, there are many papers to introduce various concepts of proper efficiency; see [1–8]. Particularly, Zaffaroni  introduced the concept of tightly proper efficiency and used a special scalar function to characterize the tightly proper efficiency, and obtained some properties of tightly proper efficiency. Zheng  extended the concept of superefficiency from normed spaces to locally convex topological vector spaces. Guerraggio et al.  and Liu and Song  made a survey on a number of definitions of proper efficiency and discussed the relationships among these efficiencies, respectively.
Recently, several authors have turned their interests to vector optimization of set-valued maps, for instance, see [13–18]. Gong  discussed set-valued constrained vector optimization problems under the constraint ordering cone with empty interior. Sach  discussed the efficiency, weak efficiency and Benson proper efficiency in vector optimization problem involving ic-cone-convexlike set-valued maps. Li  extended the concept of Benson proper efficiency to set-valued maps and presented two scalarization theorems and Lagrange mulitplier theorems for set-valued vector optimization problem under cone-subconvexlikeness. Mehra , Xia and Qiu  discussed the superefficiency in vector optimization problem involving nearly cone-convexlike set-valued maps, nearly cone-subconvexlike set-valued maps, respectively. For other results for proper efficiencies in optimization problems with generalized convexity and generalized constraints, we refer to [24–26] and the references therein.
In this paper, inspired by [10, 21–23], we extend the concept of tight properness from normed linear spaces to locally convex topological vector spaces, and study tightly proper efficiency for vector optimization problem involving nearly cone-subconvexlike set-valued maps and with nonempty interior of constraint cone in the framework of locally convex topological vector spaces.
The paper is organized as follows. Some concepts about tightly proper efficiency, superefficiency and strict efficiency are introduced and a lemma is given in Section 2. In Section 3, the relationships among the concepts of tightly proper efficiency, strict efficiency and superefficiency in local convex topological vector spaces are clarified. In Section 4, the concept of tightly proper efficiency for set-valued vector optimization problem is introduced and a scalarization theorem for tightly proper efficiency in vector optimization problems involving nearly cone-subconvexlike set-valued maps is obtained. In Section 5, we establish two Lagrange multiplier theorems which show that tightly properly efficient solution of the constrained vector optimization problem is equivalent to tightly properly efficient solution of an appropriate unconstrained vector optimization problem. In Section 6, some results on tightly proper duality are given. Finally, a new concept of tightly proper saddle point for set-valued Lagrangian map is introduced and is then utilized to characterize tightly proper efficiency in Section 7. Section 8 contains some remarks and conclusions.
Definition 2.1 (see ).
Cheng and Fu in  discussed the propositions of , and the following remark also gives some propositions of .
Remark 2.4 (see ).
Definition 2.5 (see ).
Now, we give the following example to illustrate Definition 2.7.
but, in general, the converse is not valid. The following example illustrates this case.
Definition 2.11 (see ).
Lemma 2.13 (see ).
3. Tightly Proper Efficiency, Strict Efficiency, and Superefficiency
In [11, 12], the authors introduced many concepts of proper efficiency (tightly proper efficiency except) for normed spaces and for topological vector spaces, respectively. Furthermore, they discussed the relationships between superefficiency and other proper efficiencies. If we can get the relationship between tightly proper efficiency and superefficiency, then we can get the relationships between tightly proper efficiency and other proper efficiencies. So, in this section, the aim is to get the equivalent relationships between tightly proper efficiency and superefficiency under suitable assumption by virtue of strict efficiency.
Let , (see Figure 2) and .
Proposition 3.6 (see ).
From Propositions 3.2, 3.3, and 3.6, we can get immediately the following corollary.
Lemma 3.9 (see ).
From Corollary 3.7 and Lemma 3.9, we can get the following proposition.
4. Tightly Proper Efficiency and Scalarization
The fundamental results characterize tightly properly efficient solution of (VP) in terms of the solutions of ( ) are given below.
Now, we give the following example to illustrate Theorem 4.2.
From Theorem 4.2, we can get immediately the following corollary.
5. Tightly Proper Efficiency and the Lagrange Multipliers
In this section, we establish two Lagrange multiplier theorems which show that tightly properly efficient solution of the constrained vector optimization problem (VP), is equivalent to tightly properly efficient solution of an appropriate unconstrained vector optimization problem.
Definition 5.1 (see ).
Therefore, the proof is completed.
6. Tightly Proper Efficiency and Duality
We now can establish the following dual theorems.
Theorem 6.4 (weak duality).
This completes the proof.
Theorem 6.5 (strong duality).
Let be a closed convex pointed cone with a bounded base in and be a closed convex pointed cone with in . Let , , be nearly -subconvexlike on . Furthermore, let (VP) satisfy the generalized Slater constraint qualification. Then, and if and only if is an efficient point of (VD).
7. Tightly Proper Efficiency and Tightly Proper Saddle Point
that is condition (i) holds.
In this paper, we have extended the concept of tightly proper efficiency from normed linear spaces to locally convex topological vector spaces and got the equivalent relations among tightly proper efficiency, strict efficiency and superefficiency. We have also obtained a scalarization theorem and two Lagrange multiplier theorems for tightly proper efficiency in vector optimization involving nearly cone-subconvexlike set-valued maps. Then, we have introduced a Lagrange dual problem and got some duality results in terms of tightly properly efficient solutions. To characterize tightly proper efficiency, we have also introduced a new type of saddle point, which is called the tightly proper saddle point of an appropriate set-valued Lagrange map, and obtained its necessary and sufficient optimality conditions. Simultaneously, we have also given some examples to illustrate these concepts and results. On the other hand, by using the results of the Section 3 in this paper, we know that the above results hold for superefficiency and strict efficiency in vector optimization involving nearly cone-convexlike set-valued maps and, by virtue of [12, Theorem 3.11], all the above results also hold for positive proper efficiency, Hurwicz proper efficiency, global Henig proper efficiency and global Borwein proper efficiency in vector optimization with set-valued maps under the conditions that the set-valued and is closed convex and the ordering cone has a weakly compact base.
The authors would like to thank the anonymous referees for their valuable comments and suggestions, which helped to improve the paper. This research was partially supported by the National Natural Science Foundation of China (Grant no. 10871216) and the Fundamental Research Funds for the Central Universities (project no. CDJXS11102212).
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