- Open Access
Total Lagrange duality for DC infinite optimization problems
Fixed Point Theory and Applications volume 2013, Article number: 269 (2013)
We present some total Lagrange duality results for inequality systems involving infinitely many DC functions. By using properties of the subdifferentials of involved functions, we introduce some new notions of constraint qualifications. Under the new constraint qualifications, we provide necessary and/or sufficient conditions for the stable total Lagrange duality to hold.
Let X be a real locally convex Hausdorff topological vector space, whose dual space is endowed with the weak∗ topologies . Let T be an arbitrary (possibly infinite) index set, C be a nonempty convex subset of X, and let , , be proper convex functions. Consider the following optimization problem (cf. [1–13] and the references therein):
and its Lagrange dual problem
where is the cone consisting of vector with nonnegative and only finitely many nonzero coordinates, that is,
The optimal values of problems and are denoted by and , respectively.
Usually, there is a so-called duality gap between the optimal values of primal problem and its Lagrange dual problem . A challenge in convex analysis is to give sufficient conditions which guarantee the strong Lagrange duality, that is, and the dual problem has an optimal solution. Several sufficient and/or necessary conditions were given in the past in order to eliminate the above-mentioned duality gap, see, for example, [1–3, 5] and the references therein. In particular, the authors in  established a complete characterization for the strong Lagrange duality under assumption that f and are not necessarily convex, and in , the authors considered the optimization problem , but with and , being DC (difference of two convex functions) functions, and they obtained some complete characterizations for the weak and strong Lagrange dualities. As pointed in , problems of DC programming are highly important from both viewpoints of optimization theory and applications, and they have been extensively studied in the literature (cf. [15–24] and the references therein).
Inspired by the works mentioned above, we continue to study the optimization problem which was studied in , that is,
where T, C are as in (1.1), , , are proper convex functions. Throughout this paper, we assume that
Following , we define the Lagrange function for the DC optimization problem (1.3) by
for any with and . Then the Lagrange dual problem for the DC optimization problem (1.3) is defined by
where and throughout the whole paper, following [, p.39], we adopt the convention that and . Then, for any two proper convex functions , we have that
As mentioned in , in the case when g and , are lsc, then the dual problem (1.5) is equivalent to the following problem
However, without assuming the lower semicontinuity of g and , problems (1.8) and are in general not equivalent.
The present paper is centered around the total Lagrangian duality for the DC problem and its dual problem . For the problem of total Lagrange duality, one seeks conditions ensuring that the following implication holds for :
Clearly, the strong Lagrange duality ensures the total Lagrange duality, but the converse does not necessarily hold in general. To our knowledge, not many results are known to provide complete characterizations for the total Lagrangian duality for the DC optimization problem (1.3). Except the works in paper  by Fang et al., where, assuming in addition that , , a complete characterization was established for the stable total Lagrangian duality for problem (1.3), that is, the characterization for (1.9) to hold for in place of f with any . However, the approaches in  do not work for the DC optimization problem (1.3).
In this paper, we do not impose any topological assumption on C or on f, g, and , that is, C is not necessarily closed, and f, g, , are not necessarily lsc, and , are necessarily differentiable. One of our main aims in the present paper is to use these constraint qualifications (or their variations) involving subdifferentials, which have been studied and extensively used, see, for example, [2, 3, 6, 12, 26], to provide characterizations for the total Lagrangian duality. Most of results obtained in the present paper seem new and are proper extensions of the results in  in the special case when , . In particular, both our dual problem and the regularity conditions introduced here are defined in terms of subdifferential of the convex functions f, g, and rather than those of the DC functions and , which are different from the consideration in .
The paper is organized as follows. The next section contains the necessary notations and preliminary results. In Section 3, we provide some characterizations for the weak Lagrange dualities and the total Lagrangian dualities to hold.
2 Notations and preliminaries
The notations used in this paper are standard (cf. ). In particular, we assume throughout the whole paper that X is a real locally convex space, and let denote the dual space of X. For and , we write for the value of at x, that is, . Let Z be a set in X. The closure of Z is denoted by clZ. If , then clW denotes the weak∗ closure of W. For the whole paper, we endow with the product topology of and the usual Euclidean topology.
Following , we use to denote the space of real tuples with only finitely many , and let denote the nonnegative cone in , that is,
The normal cone of the nonempty set Z at is denoted by and is defined by
and the indicator function of Z is defined by
Let f be a proper function defined on X. We use domf, epif and to denote respectively the effective domain, the epigraph and the conjugate function of f, that is,
Let . The subdifferential of f at x is defined by
if , and , otherwise. Then by definition,
By [, Theorems 2.3.1 and 2.4.2(iii)], the Young-Fenchel inequality below holds
and the Young equality holds
Furthermore, if g, h are proper functions, then
The closure of f is denoted by clf, which is defined by
Then (cf. [, Theorems 2.3.1]),
By [, Theorem 2.3.4], if clf is proper and convex, then the following equality holds:
Moreover, by [, Theorem 2.4.1], if , then
Finally, note that an element can be naturally regarded as a function on X in such way that
Then the following facts are clear for any and a real-valued proper function f:
3 The total Lagrange dualities
Unless explicitly stated otherwise, let f, g, T, C, and A be as in Section 1, namely, T is an index set, is a convex set, f, g, , , are proper convex functions on X such that and , are proper, and A is the solution set of the following system:
Then by (1.7), we have that
To avoid the triviality, we always assume that . For simplicity, we denote
To make the dual problem considered here well defined, we further assume that clg and , are proper. Then . For the whole paper, any elements and are understood as and , respectively.
Replacing f by all of its linear perturbed functions , where , we consider the following DC infinite optimization problem
and its dual problem
where the Lagrange function is defined by
for any with and . In particular, in the case when , problem , as well as its dual problem , are reduced to problem , and its dual problem as defined in (1.3) and (1.5), respectively.
Let and denote the optimal values of and , respectively. For each , we use to denote the optimal solution set of . In particular, we write for . Obviously, for each , . This section is devoted to the study of characterizing the total Lagrange dualities. Unlike the convexity case, the cases for DC optimization problems are more complicated. We begin with the following definition, where the notations of the weak Lagrange duality and the stable weak Lagrange duality were introduced in .
Definition 3.1 Let be a subset of X. Between problems and , we say that
the weak Lagrange duality holds if ;
the stable weak Lagrange duality holds if for each ;
the stable -total Lagrange duality holds if, for each , and problem has an optimal solution provided that . In particular, in the case when , the stable -total duality is called the stable total duality.
Unlike the convexity case, the weak Lagrange duality does not necessarily hold in general as shown in [, Example 3.1]. In order to provide some sufficient conditions ensuring the weak Lagrange duality, we consider the following optimization problem, which plays a bridging role for our study:
where . Let denote the solution set of the system , that is,
Then, . As usual, we use to define the optimal value of problem . Then,
Moreover, by [, (1.5)], we see that
Thus, if g and , , are lsc, then the weak Lagrange duality holds. The following proposition provides a weaker condition for the weak Lagrange duality to hold.
Proposition 3.1 Let . Suppose that g and each are lsc at . Then the weak Lagrange duality holds.
Proof Since , it follows that
Note that g and each are lsc at . Then for each ,
the last inequality holds because and . This implies that . Hence, by (3.11), one gets and the proof is complete. □
Let denote the set of all points such that . Below we will make use of the subdifferential for a general proper function (not necessarily convex) ; see (2.1). Clearly, the following equivalence holds:
Form (2.9), if , then g and each are lsc at . Hence, the following corollary follows from Proposition 3.1 directly.
Corollary 3.1 Let . If , then .
Motivating by , we introduce the following condition (LSC) to characterize the relationships between and and the weak Lagrange duality.
Definition 3.2 The family is said to satisfy the lower semi-continuity closure (LSC) if
Since and , it follows that . Hence, by (2.5), the family satisfies the (LSC) if and only if(3.15)
Obviously, if g and , are lsc, then the (LSC) holds. But the converse is not true, in general, as to be shown by Example 3.1 below.
Example 3.1 Let , and let . Let be defined respectively by
and . Then f, g, , are proper convex functions and
Moreover, it is easy to see that and
This implies that the (LSC) holds. However, the function g is not lsc at .
The following proposition gives an equivalent condition to ensure that
in terms of the (LSC). For this purpose, we first give the following lemma by the definition of conjugate functions. The proof is standard (cf. [, Lemma 4.1]), and so we omit it.
Lemma 3.1 Let , and let . Then the following statements hold:
Proposition 3.2 The family satisfies the (LSC) if and only if (3.16) holds. Consequently, if the (LSC) holds, then the weak Lagrange duality holds.
Proof Suppose that the (LSC) holds. Then (3.14) holds. Let . To show that , it suffices to show that by (3.10). To do this, suppose, on the contrary, that . Then there exists such that . Thus, by (3.18), , and so by (3.14). It follows from (3.17) that . This contradicts and completes the proof of the inequality .
Conversely, suppose that (3.16) holds. By Remark 3.1(b), it suffices to show that (3.15) holds. To do this, let . Then, by (3.18), , and so , thanks to (3.16). Hence, by (3.17), . Therefore, (3.15) is proved. The proof is complete. □
The remainder of this paper is devoted to studying the stable total Lagrange duality between and . For each , let be the active index set of system (3.1), that is,
For simplicity, we define by
where, following [, p.2], we adapt the convention that . Then for each ,
The following proposition provides an estimate for the subdifferential of the DC function in terms of the subdifferentials of the convex functions involved.
Proposition 3.3 Suppose that the family satisfies the (LSC). Then for each ,
Proof Let and . Then there exists such that for each ,
Let . Then for each ,
Taking the infimum over , we get that
Since , it follows from (2.9) that and for each . Note that and for each . Then, by (3.22), one has that for each ,
Moreover, by Proposition 3.2, the (LSC) implies that . This together with (3.23) implies that for each ,
Hence, , and inclusion (3.21) holds. □
Considering the possible inclusions among , and , we introduce the following definition.
Definition 3.3 The family is said to satisfy
the quasi weakly basic constraint qualification (the quasi (WBCQ)) at if(3.24)
the weakly basic constraint qualification (the (WBCQ)) at if(3.25)
We say that the family satisfies the quasi (WBCQ) (resp. the (WBCQ)) if it satisfies the quasi (WBCQ) (resp. the (WBCQ)) at each point .
The following implication holds:
In the special case, when , , the quasi (WBCQ) and (WBCQ) are reduced to the (WBCQ) f for the family introduced in .
For our main theorems in this section, the following lemma is helpful.
Lemma 3.2 Let and with . If , then there exists such that for each ,
Proof Let . Then there exists with
such that for each ,
Let . Then there exists such that
By the Young equality (2.6),
and by the Young-Fenchel inequality (2.3),
Combining (3.28), (3.29) with (3.30), we have
where the last equality holds because of (3.27) and . Since , it follows that (3.26) holds. The proof is complete. □
The following theorem provides a sufficient condition and a necessary condition for the stable -total Lagrange duality.
Theorem 3.1 Consider the following assertions:
The family satisfies the (WBCQ).
The stable -total Lagrange duality holds between and .
The family satisfies the quasi (WBCQ).
Then (i) ⇒ (ii) ⇒ (iii).
Proof (i) ⇒ (ii) Suppose that (i) holds. Let be such that . Take . Then
thanks to the assumed (WBCQ). Thus, by Lemma 3.2, we get that there exists such that (3.26) holds for each . Moreover, we have that by Corollary 3.1. Thus, and is an optimal solution of . This implies that the stable -total Lagrange duality holds.
(ii) ⇒ (iii) Suppose that (ii) holds. Let . Obviously, if , then the quasi (WBCQ) holds trivially because . Below, we assume that . Let . Then by (3.13), we have that , and hence . By the assumed -total Lagrange duality, there exists such that for each ,
Noting that , we have
Since by the Young-Fenchel inequality (2.3),
and by the Young equality (2.6),
it follows that
where the last inequality holds because . Thus,
Moreover, by (3.31) and (3.32), we have
where the last equality holds by (3.33) and (3.34). This implies that
Hence, by the Young equality (2.4),
as is arbitrary. Hence, . Therefore, (3.24) holds, and the proof is complete. □
Theorem 3.2 below provides sufficient conditions ensuring the stable total Lagrange duality.
Theorem 3.2 Suppose that the family satisfies the (WBCQ), and that the stable weak Lagrange duality holds between and . Then the stable total Lagrange duality holds.
Proof Let . Suppose that . Let . Then and hence by the assumed (WBCQ). Thus, Lemma 3.2 is applied to get that there exists such that (3.26) holds for each . This together with the stable weak Lagrange duality implies that , and is an optimal solution of . Thus, the stable total Lagrange duality holds, and the proof is complete. □
In the case when , , by Theorem 3.1, we have the following corollary, which was given in [, Theorem 5.2].
Corollary 3.2 The family satisfies the (WBCQ) f if and only if the following formula holds for each satisfying :
Dinh N, Goberna MA, López MA: From linear to convex systems: consistency, Farkas’ lemma and applications. J. Convex Anal. 2006, 13: 279–290.
Dinh N, Goberna MA, López MA, Song TQ: New Farkas-type constraint qualifications in convex infinite programming. ESAIM Control Optim. Calc. Var. 2007, 13: 580–597. 10.1051/cocv:2007027
Goberna MA, Jeyakumar V, López MA: Necessary and sufficient conditions for solvability of systems of infinite convex inequalities. Nonlinear Anal. 2008, 68: 1184–1194. 10.1016/j.na.2006.12.014
Goberna MA, López MA: Linear Semi-Infinite Optimization. Wiley, Chichester; 1998.
Fang DH, Li C, Ng KF: Constraint qualifications for extended Farkas’s lemmas and Lagrangian dualities in convex infinite programming. SIAM J. Optim. 2009, 20: 1311–1332.
Fang DH, Li C, Ng KF: Constraint qualifications for optimality conditions and total Lagrange dualities in convex infinite programming. Nonlinear Anal. 2010, 73: 1143–1159. 10.1016/j.na.2010.04.020
Jeyakumar V, Mohebi H: Limiting ϵ -subgradient characterizations of constrained best approximation. J. Approx. Theory 2005, 135: 145–159. 10.1016/j.jat.2005.04.004
Li C, Ng KF: Constraint qualification, the strong CHIP and best approximation with convex constraints in Banach spaces. SIAM J. Optim. 2003, 14: 584–607. 10.1137/S1052623402415846
Li C, Ng KF: On constraint qualification for infinite system of convex inequalities in a Banach space. SIAM J. Optim. 2005, 15: 488–512. 10.1137/S1052623403434693
Li C, Ng KF: Strong CHIP for infinite system of closed convex sets in normed linear spaces. SIAM J. Optim. 2005, 16: 311–340. 10.1137/040613238
Li C, Ng KF, Pong TK: The SECQ, linear regularity and the strong CHIP for infinite system of closed convex sets in normed linear spaces. SIAM J. Optim. 2007, 18: 643–665. 10.1137/060652087
Li C, Ng KF, Pong TK: Constraint qualifications for convex inequality systems with applications in constrained optimization. SIAM J. Optim. 2008, 19: 163–187. 10.1137/060676982
Li W, Nahak C, Singer I: Constraint qualifications for semi-infinite systems of convex inequalities. SIAM J. Optim. 2000, 11: 31–52. 10.1137/S1052623499355247
Fang, DH, Lee, GM, Li, C, Yao, JC: Extended Farkas’s lemmas and strong Lagrange dualities for DC infinite programming. J. Nonlinear Convex Anal. (to appear)
Dinh N, Mordukhovich BS, Nghia TTA: Subdifferentials of value functions and optimality conditions for some classes of DC bilevel infinite and semi-infinite programs. Math. Program., Ser. B 2010, 123: 101–138. 10.1007/s10107-009-0323-4
An LTH, Tao PD: The DC (difference of convex functions) programming and DCA revisited with DC models of real world non-convex optimization problems. Ann. Oper. Res. 2005, 133: 23–46. 10.1007/s10479-004-5022-1
Dinh N, Mordukhovich BS, Nghia TTA: Qualification and optimality conditions for convex and DC programs with infinite constraints. Acta Math. Vietnam. 2009, 34: 125–155.
Dinh N, Nghia TTA, Vallet G: A closedness condition and its applications to DC programs with convex constraints. Optimization 2008, 1: 235–262.
Dinh N, Vallet G, Nghia TTA: Farkas-type results and duality for DC programs with convex constraints. J. Convex Anal. 2008, 2: 235–262.
Fang DH, Li C, Yang XQ: Stable and total Fenchel duality for DC optimization problems in locally convex spaces. SIAM J. Optim. 2011, 21: 730–760. 10.1137/100789749
Horst R, Thoai NV: DC programming: overview. J. Optim. Theory Appl. 1999, 103: 1–43. 10.1023/A:1021765131316
Martinez-Legaz JE, Volle M: Duality in DC programming: the case of several constraints. J. Math. Anal. Appl. 1999, 237: 657–671. 10.1006/jmaa.1999.6496
Sun XK, Li SJ, Zhao D: Duality and Farkas-type results for DC infinite programming with inequality constraints. Taiwan. J. Math. 2013, 17: 1227–1244.
Toland JF: Duality in nonconvex optimization. J. Math. Anal. Appl. 1978, 66: 399–415. 10.1016/0022-247X(78)90243-3
Zǎlinescu C: Convex Analysis in General Vector Spaces. World Scientific, Singapore; 2002.
Li C, Fang DH, López G, López MA: Stable and total Fenchel duality for convex optimization problems in locally convex spaces. SIAM J. Optim. 2009, 20: 1032–1051. 10.1137/080734352
Kortanek KO: Constructing a perfect duality in infinite programming. Appl. Math. Optim. 1977, 3: 357–372.
The first author was supported in part by the National Natural Science Foundation of China (grant 11101186) and supported in part by the Scientific Research Fund of Hunan Provincial Education Department (grant 13B095). The second author was supported in part by the National Natural Science Foundation of China (11001289) and the Key Project of Chinese Ministry of Education (211151).
The authors declare that they have no competing interests.
DF studied and researched the Total Lagrange duality for DC programming and also wrote this article. ZC participated in the process of the study and helped to draft the manuscript. All authors read and approved the final manuscript.