- S Xu
^{1}Email author and - SJ Li
^{1}

**2010**:154598

**DOI: **10.1155/2010/154598

© S. Xu and S. J. Li. 2010

**Received: **23 April 2010

**Accepted: **13 August 2010

**Published: **19 August 2010

## Abstract

## 1. Introduction

The notion of a weak sharp minimum in general mathematical program problems was first introduced by Ferris in [1]. It is an extension of sharp minimum in [2]. Weak sharp minima play important roles in the sensitivity analysis [3, 4] and convergence analysis of a wide range of optimization algorithms [5]. Recently, the study of weak sharp solution set covers real-valued optimization problems [5–8] and piecewise linear multiobjective optimization problems [9–11].

Most recently, Bednarczuk [12] defined weak sharp minima of order for vector-valued mappings under an assumption that the order cone is closed, convex, and pointed and used the concept to prove upper Hölderness and Hölder calmness of the solution set-valued mappings for a parametric vector optimization problem. In [13], Bednarczuk discussed the weak sharp solution set to vector optimization problems and presented some properties in terms of well-posedness of vector optimization problems. In [14], Studniarski gave the definition of weak -sharp local Pareto minimum in vector optimization problems under the assumption that the order cone is convex and presented necessary and sufficient conditions under a variety of conditions. Though the notions in [12, 14] are different for vector optimization problems, they are equivalent for scalar optimization problems. They are a generalization of the weak sharp local minimum of order .

In this paper, motivated by the work in [14, 15], we present a sufficient and necessary condition of which a point is a weak -sharp minimum for a vector-valued mapping in the infinite-dimensional spaces. In addition, we develop the characterization of weak -sharp minima in terms of a nonlinear scalarization function.

This paper is organized as follows. In Section 2, we recall the definitions of the local Pareto minimizer and weak -sharp local minimizer for vector-valued optimization problems. In Section 3, we present a sufficient and necessary condition for weak -sharp local minimizer of vector-valued optimization problems. We also give an example to illustrate the optimality condition.

## 2. Preliminary Results

Throughout the paper, and are normed spaces. denotes the open ball with center and radius . is the family of all neighborhoods of , and is the distance from a point to a set . The symbols , and denote, respectively, the complement, interior and boundary of .

Let be a convex cone (containing 0). The cone defines an order structure on , that is, a relation " " in is defined by . is a proper cone if .

In the sequel, we always assume that is a proper closed and convex cone.

Definition 2.1.

If one can choose , one will say that is a Pareto minimizer for (2.1), denoted by .

Note that (2.2) may be replaced by the simple condition if we assume that the cone is pointed.

Definition 2.2 (see [14]).

If one can choose , one says is a weak -sharp minimizer for (2.1), denoted by . In particular, let for Then, one says that is a weak -sharp local Pareto minimizer of order for (2.1) if , and one says that is a weak sharp Pareto minimizer of order for (2.1) if .

Remark 2.3.

Remark 2.4.

which is the well-known definition of a weak sharp minimizer of order for (2.1); see [16].

## 3. Main Results

In this section, we first generalize the result of Theorem in Studniarski [14] to infinite-dimensional spaces. Finally, we develop the characterization of weak -sharp minimizer by means of a nonlinear scalarization function.

The set is called a base for the dual cone . Recall that a point is an extremal point of a set if there exist no different points and such that .

Theorem 3.1.

Suppose that is a vector-valued map. Let be a proper closed convex cone with , , and .

Let be a fixed point. Set . Since is -compact, the infimum is attained at a point of . Namely, . Clearly, for any . Hence, .

and , which is a contradiction to (3.4). We have thus proved that covers .

Now, let and . From the procedure of the above proof, we see that . Hence, by (3.5), set , inequality (3.2) is true.

which is a contradiction to (3.19). So (3.6) holds and (3.3) is satisfied by the definition of .

which contradicts relation (3.23).

Remark 3.2.

By taking in part (i) (resp., (ii)) of Theorem 3.1, we obtain a necessary and sufficient condition for to be in (resp., ). In particular, if we choose and and , then, we obtain Theorem in [14].

Finally, we apply the nonlinear scalarization function to discuss the weak -sharp minimizer in vector optimization problems.

This function plays an important role in the context of nonconvex vector optimization problems and has excellent properties such as continuousness, convexity, and (strict) monotonicity on . More results about the function can be found in [17].

In what follows, we present several properties about the nonlinear scalarization function.

Lemma 3.3 (see [17]).

For any fixed , , and . One has

Next, we consider weak -sharp local minimizer for a vector-valued map through a weak sharp local minimizer of a scalar function .

Theorem 3.4.

Proof.

Let , by the definition of weak -sharp local minimizer, we have .

It is possible to illustrate Theorem 3.4 by means of adapting a simple example given in [14].

Example 3.5.

We choose . Using Definition 2.2, we derive that .

It is easy to verify that for all . Using relation (2.7), we show that . Hence, condition (3.28) with holds for .

## Declarations

### Acknowledgments

This paper was partially supported by the National Natural Science Foundation of China (Grant no. 10871216) and Chongqing University Postgraduates Science and Innovation Fund (Project no. 201005B1A0010338). The authors would like to thank the anonymous referees for their valuable comments and suggestions, which helped to improve the paper, and are grateful to Professor M. Studniarski for providing the paper [14].

## Authors’ Affiliations

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