1. Introduction

Convex semidefinite optimization problem is to optimize an objective convex function over a linear matrix inequality. When the objective function is linear and the corresponding matrices are diagonal, this problem becomes a linear optimization problem.

For convex semidefinite optimization problem, Lagrangean duality without constraint qualification [1, 2], complete dual characterization conditions of solutions [1, 3, 4], saddle point theorems [5], and characterizations of optimal solution sets [6, 7] have been investigated.

To get the -approximate solution, many authors have established -optimality conditions, -saddle point theorems and -duality theorems for several kinds of optimization problems [1, 8–16].

Recently, Jeyakumar and Glover [11] gave -optimality conditions for convex optimization problems, which hold without any constraint qualification. Yokoyama and Shiraishi [16] gave a special case of convex optimization problem which satisfies -optimality conditions. Kim and Lee [12] proved sequential -saddle point theorems and -duality theorems for convex semidefinite optimization problems which have not conic constraints.

The purpose of this paper is to extend the -duality theorems by Kim and Lee [12] to convex semidefinite optimization problems with conic constraints. We formulate a Wolfe type dual problem for the problem for its -approximate solutions, and then prove -weak duality theorem and -strong duality theorem for the problem and its Wolfe type dual problem, which hold under a weakened constraint qualification. Moreover, we give an example illustrating the duality theorems.

2. Preliminaries

Consider the following convex semidefinite optimization problem:

where is a convex function, is a closed convex cone of , and for , where is the space of real symmetric matrices. The space is partially ordered by the Lwner order, that is, for if and only if is positive semidefinite. The inner product in is defined by , where is the trace operation.

Let . Then is self-dual, that is, Let , , Then is a linear operator from to and its dual is defined by for any Clearly, is the feasible set of SDP.

Definition 2.1. Let be a convex function.(1)The subdifferential of at where , is given by where is the scalar product on .(2)The -subdifferential of at is given by

Definition 2.2. Let Then is called an -approximate solution of SDP, if, for any ,

Definition 2.3. The conjugate function of a function is defined by

Definition 2.4. The epigraph of a function , , is defined by

If is sublinear (i.e., convex and positively homogeneous of degree one), then , for all . If , , , then . It is worth nothing that if is sublinear, then

Moreover, if is sublinear and if , , and , then

Definition 2.5. Let be a closed convex set in and .(1)Let . Then is called the normal cone to at .(2)Let . Let . Then is called the -normal set to at .(3)When is a closed convex cone in , we denoted by and called the negative dual cone of .

Proposition 2.6 (see [17, 18]). Let be a convex function and let be the indicator function with respect to a closed convex subset C of , that is, if , and if . Let . Then

Proposition 2.7 (see [7]). Let be a continuous convex function and let be a proper lower semicontinuous convex function. Then

Following the proof of Lemma in [1], we can prove the following lemma.

Lemma 2.8. Let . Suppose that Let and . Then the following are equivalent:

3. -Duality Theorem

Now we give -duality theorems for SDP. Using Lemma 2.8, we can obtain the following lemma which is useful in proving our -strong duality theorems for SDP.

Lemma 3.1. Let . Suppose that is closed. Then is an -approximate solution of SDP if and only if there exists such that for any ,

Proof. () Let be an -approximate solution of SDP. Then , for any . Let . Then , for any . Thus we have, from Proposition 2.7, and hence, . So there exists such that and hence there exists such that for any . Since , for any ; and hence it follows from Lemma 2.8 that Thus there exist , and such that This gives for any . Thus we have for any .
() Suppose that there exists such that
for any . Then we have for any Thus , for any . Hence is an -approximate solution of SDP.

Now we formulate the dual problem SDD of SDP as follows:

We prove -weak and -strong duality theorems which hold between SDP and SDD.

Theorem 3.2 (-weak duality). For any feasible solution of SDP and any feasible solution of SDD,

Proof. Let and be feasible solutions of SDP and SDD respectively. Then and there exist and such that . Thus, we have Hence .

Theorem 3.3 (-strong duality). Suppose that is closed. If is an -approximate solution of SDP, then there exists such that is a -approximate solution of SDD.

Proof. Let be an -approximate solution of SDP. Then for any By Lemma 3.1, there exists such that for any . Letting in (3.14), . Since and , .
Thus from (3.14),
for any . Hence is an -approximate solution of the following problem: and so, , and hence, by Proposition 2.6, there exist , such that and So, is a feasible solution of SDD. For any feasible solution of SDD, Thus is a 2-approximate solution to SDD.

Now we characterize the -normal set to .

Proposition 3.4. Let and Then where

Proof. Let and . Then Let (where is at the th position in ) Thus, we have

From Proposition 3.4, we can calculate .

Corollary 3.5. Let and Then following hold. (i)If , then (ii)If and , then (iii)If and , then (iv)If and and , then

Now we give an example illustrating our -duality theorems.

Example 3.6. Consider the following convex semidefinite program. Let , and 0. Let and Then is the set of all feasible solutions of SDP and the set of all -approximate solutions of SDP is . Let . Then is the set of all feasible solution of SDD. Now we calculate the set .