Duality for Composite Optimization Problem within the Framework of Abstract Convexity
Abstract
We study conjugate and Lagrange dualities for composite optimization problems within the framework of abstract convexity. We provide conditions for zero duality gap in conjugate duality. For Lagrange duality, intersection property is applied to obtain zero duality gap. Connection between Lagrange dual and conjugate dual is also established. Examples related to convex and weakly convex functions are given.
keywords:
Abstract convexity; -convexity; -subdifferentials; Conjugate dual; Lagrange dual; Zero duality gap; Nonconvex programming; Global Optimization49J27; 49J35; 49N15; 90C46; 90C26
1 Introduction
During the last eighty years, convexity has become an essential part in the development of optimization, nonlinear analysis etc. With the surge of computational power, interests in machine learning and data science have risen, which make convexity the backbone of many theories and applications. Together with convexity, there are also many works that try to go beyond convexity which include functions like: strongly convex [1], quasi-convex, pseudo-convex, strongly para-convex and para-convex [2], delta-convex [3], approximately convex [4] etc…
Abstract convexity encompasses many of the above mentioned classes of functions. Abstract convexity, presented in the monographs of Rubinov [5], Pallaschke and Rolewicz [6] is based on the idea to bring convexity outside the range of linearity, by introducing the nonlinear environment where a function becomes an upper envelope of a subset of functions with specific rules. The term “abstract convexity” was used by Rubinov [5] to describe functions which are upper envelopes of a given class of function , i.e.
where is a nonempty set.
Consequently, the concept of abstract convexity works well with classical convex functions. As the supremum operation is retained, many global properties of convex analysis are still in effect for abstract convexity. Till now, the books of Pallaschke and Rolewicz [6], and the monograph of Singer [7] gathered many results about abstract convexity, especially about conjugation, subdifferential and duality. They also have historical results of the main ideas of abstract convexity and its applications. In [5], Rubinov presented basic notions of abstract convexity and applications to global optimization problems.
It has been fifty years since the theory of abstract convexity started. It has made great progress in many disciplines with applications in both theory and computation. In [8], the authors investigated the class of increasing star-shaped functions and constructed an algorithm to solve the global optimization problem using abstract convexity. They also generalized the cutting plane algorithm for nonconvex global minimization problems [8, 9]. While in [10, 11, 12], a general version of monotone operator is developed based on abstract convexity.
In this paper, we pay special attention to duality theory for composite minimization problem by using abstract convexity. We mainly study the conjugate and Lagrangian dualities within the abstract convexity for the composite minimization problem,
(CP) |
where and are the domain sets (spaces) of the functions and , the mapping maps into .
The composite optimization problem is taken as our main interest as it is a general problem of many optimization formulation. One can think of (CP) as nonlinear programming problem, min-max optimization, constrained and unconstrained problem. These problems have been studied extensively in [13, 14]. On the other hand, composite problem can also be considered as a regularized problem by treating the as a penalty term. Such problems are knows as total variation model in image deblurring and denoising [15].
Several efforts have been made for the last twenty years to investigate dualities for optimization problem within the framework of abstract convexity. In [16] strong duality is proved for infimal convolution of Fenchel’s duality. In [17], zero duality gap and exact multiplier of augmented Lagrangian are investigated by using the framework of abstract convexity. While in [18, 19] necessary and sufficient conditions are given to achieve minimax equality from a general Lagrangian using the definition of abstract convexity. In the works of Dolgopolik [20], Gorokhovik and Tykoun [21, 22], the authors applied abstract convexity to approximate the class of nonsmooth functions and formulate necessary optimal conditions for global nonsmooth nonconvex problem. The authors in [23] investigated the problem of minimizing the finite sum of arbitrary functions and provided conditions for zero duality gap through infimal convolution dual. On the other hand, in [24], the problem of zero duality gap is studied with the help of perturbation function.
Our contribution is as follow. Inspired by Moreau’s general subdifferential and conjugation [25], we utilize the framework of abstract conjugation and abstract subdifferential [16] and construct conjugate dual problem to (CP). We derive conditions for zero duality gap and strong duality. As we have different variable spaces of and , the conditions we propose reduce to the ones proposed in e.g. [23] when is the identity mapping. In deriving the Lagrangian dual problem we make use of the intersection property for abstract convex functions, [24, 19] to obtain Lagrange zero duality gap. We also discuss the relationship between conjugate and Lagrangian duals.
The structure of paper is as follows. In Section 2, we state the framework of abstract convexity with conjugation and subdifferential as well as some basic properties for abstract convex functions. Section 3 contains the definition of the composite minimization problem and way to construct the conjugate dual problem. Section 4 contains our main result about zero duality gap given in Theorem 4.3. In Section 5, we provide conditions for strong duality in Theorem 5.2 and Corollary 5.3. In Section 6, we prove Lagrange zero duality gap in Theorem 6.5. The equivalence of conjugate dual and Lagrange dual is examined under some conditions in Corollary 6.8.
2 Preliminaries
Let be a nonempty set. A function is proper if its domain, denoted by is nonempty.
Let be a collection of real-valued functions, which is closed under addition of a constant. The support set of with respect to is defined as
For , we write for all . Elements of are called elementary functions.
Note that when a function is -convex, then is nonempty, since otherwise, .
Definition 2.2.
As in convex analysis, the following result holds.
In view of this theorem, we say that is -convex at a point if .
Example 2.4.
Let be a Banach space with the topological dual and
(5) |
If a function is -convex, then it is proper, convex and lower semi-continuous. When is a locally convex space, [26], then is -convex if and only if is proper, convex and lower semi-continuous.
Example 2.5.
Let be a Hilbert space with the inner product and the norm . Let , take as a collection of quadratic functions
(6) |
A function is -convex, if for every , we have
Since consists of continuous functions, is lower semi-continuous on . Depending upon the sign of , we get the following.
Definition 2.6.
Let be a normed space with the norm . A function is weakly convex with modulus or -weakly convex if is a convex function. When , becomes a convex function.
Note that many authors consider the class
(7) |
where is a Hilbert space, see e.g. [5, Example 6.2]. For this class of elementary functions, the set -convex functions coincides with the set of all lower semi-continuous functions defined on and minorized by a function from , see [5, Proposition 6.3]. Quadratically minorized functions have also been investigated in [28]. The starting point of numerous variant concepts of convex functions stems from the work of Hyers and Ulam [3], where they defined -convex function for , next approximately convex functions were investigated in e.g. [29, 4, 30] and the references therein. Rolewicz has coined the term ”paraconvexity”, [2], by turning into a non-negative function. The definitions of strong and weak convexity were also given in [1] by Vial.
Definition 2.7.
[5, 6] Let be a nonempty set and . A -subgradient of at a point is any element such that
(8) |
The collection of all -subgradients of at is called the -subdifferentials of at and is denoted by ,
(9) |
Furthermore, for , the -subdifferentials of at , , is defined as
(10) |
an elements of are called -subgradients of at .
The following properties follow directly from the definitions.
Proposition 2.8.
Let be a nonempty set and .
-
(i)
For all and , an element is a -subgradient of at if and only if
(11) -
(ii)
.
3 Construction of the conjugate dual
We consider the composite minimization problem
(CP) |
where is a nonempty set and is a vector space, , and is a mapping from to . Note that here we do not assume that is linear and continuous.
Let be a set of elementary functions and let be a set of elementary functions . Assume that both classes are closed under addition of constants and .
We introduce the conjugate dual of (CP) by considering perturbed minimization problems of the form
(12) |
Following the classical ideas coming from convex analysis, see e.g. [31, 14], we calculate the conjugate of the function , where
Note that, in the convex case, conjugation is taken with respect to linear functionals, while here we are considering elementary functions from given classes and , respectively, see e.g. [25].
Now, We define the conjugate of with respect to the coupling function
(13) |
For other types of coupling functions defined on Cartesian products of elementary function sets, see e.g., [32].
The c-conjugate of , , is defined as
(14) |
The conjugate dual to problem (CP) is defined as
(15) |
Investigation of the relationship between (CP) and problem (15) is the main focus of the paper. To write (15) in a more explicit form, observe that the objective function of (15) (the dual objective) is equal to
Hence, the conjugate dual problem takes the form
(DCP) |
Notice that we have kept the formulation of in the dual problem (DCP), since we do not know if belongs to the class in general. Below, we give some examples where we further investigate the dual problem.
Example 3.1.
In some cases, we can rewrite the conjugate dual (DCP) in an equivalent and more convenient way.
- •
-
•
If is symmetric, and for any , is -convex, we have
The optimal value of the dual problem (DCP) (obtained with the help of the coupling function from (13)) satisfies
(20) Clearly, the -convexity of is more general than the condition . However, assuming the latter, we obtain, from (DCP) i.e.
If are symmetric, and is such that , for any then
and the conjugate dual (DCP) takes the form
(21) If is the identity operator, then (CP) becomes the minimization problem
for which the conjugate dual (21) has been discussed in [24, 23].
3.1 Conjugate dual for specific classes
Now, we discuss the conjugate dual problem (DCP) when are the sets of linear and quadratic functions i.e. and given by (5) and (6) respectively.
Example 3.2.
Let and be Banach spaces with the topological duals and their bilinear forms , respectively. Let be a linear continuous operator with the conjugate . We define the sets and of elementary functions as follows.
-
(i)
For the case of affine elementary functions, (cf. (5) above),
we have which leads to the coupling function as defined in the convex case [14]. We have
Since , we have so
and the dual problem
(22) Note that the condition is satisfied thanks to the form of , , and (22) coincides with the classical Fenchel dual investigated e.g. in [31, Chapter 1, Section 2].
- (ii)
-
(iii)
In the construction of dual problem (15), the roles of and are not symmetric. We can see this by considering the following pair of elementary functions,
In this case, since is an affine function we have
and the dual problem has the same form as (22)
(26) However, when reversing the roles of and i.e. by taking
we cannot write the dual problem in the form (22), but it is possible to write, for any ,
where and the dual problem takes the form
(27)
Unlike the dual problems, considered in [24, 23] where , the appearance of general operators in the minimization problem (CP) makes it more difficult to present the dual problem without imposing additional assumptions.
Remark 1.
It is clear from the formula (3) and the above examples that the presence of constants in the definition of elementary functions is inessential in the -conjugate of . Thus, in the sequel, we consider classes and of functions in which constants are omitted. For the general consideration related to this fact, see [5, formula 1.4.4].
4 Zero Duality Gap for Conjugate Dual
Having defined the dual problem (DCP), now, we discuss conditions for weak duality and zero duality gap. As in (CP), we assume that the sets and of elementary functions are defined on and , respectively, and both contain zeros, i.e., .
Let be a nonempty set and let be a vector space. Let be a mapping from to . The dual problem (DCP) can be equivalently rewritten in the form
(28) |
For the problem (CP), we have
(29) |
Weak duality, i.e. the inequality means that
(30) |
In fact, a more general inequality holds true.
Theorem 4.1.
For any , it holds
Proof.
For any and , we have
Since is a lower bound of for arbitrary , we get
which completes the proof. ∎
By taking in the above theorem, we obtain the weak duality (30) between (CP) and (DCP). Note that no additional assumptions are imposed on the functions and . However, when zero duality gap is considered, we need to assume a relationship between and .
Our first result of zero duality gap is related to [23, Theorem 3.5].
Theorem 4.2.
Let be a nonempty set and be a vector space. Let , , and be a mapping. Assume that . Suppose and . The following are equivalent.
-
(i)
For every , there exist such that .
-
(ii)
.
Proof.
(i) (ii): Thanks to Theorem 4.1, we only need to prove that
Let . From (i), there exist and such that
From the definition of infimum, we have
(31) |
By using inequality (11) applied to and taking into account , we obtain
(32) | ||||
Finally, by using (31) and (32),
As both sides of the above inequality do not depend on or , we can let and obtain zero duality gap
from the assumption of and . Thus .
(ii) (i): From the definition of supremum, for every there exists an such that
Also, there exists such that
Zero duality gap gives us
After rearranging both sides, we get
By the definition of conjugate function, each of the two terms is non-negative, and so each of them has to be smaller than . Thus
which implies that . Hence, (i) holds. ∎
Remark 2.
Observe that, in some cases, we cannot find such that (this latter condition is used in [23, Theorem 3.5]). In the case and is symmetric (for any ), Theorem (4.2)-(i) means that, for ,
i.e. . This means that , which reduces to the respective condition used in [23, Theorem 3.5-(i)] for proving zero duality gap.
Condition (i) of Theorem 4.2, could be replaced by conditions which are easier to be checked, when we introduce the following assumption: for given ,
(33) |
Note that condition (34) below will be used in the sequel to check zero duality gap.
Remark 3.
When is a convex cone, then (33) is satisfied when where is the recession cone of and is a linear space of all functions defined on .
Theorem 4.3.
Let be such that . Let be a mapping from into . Suppose that and . Consider the following conditions.
-
(i)
For every , there exist such that
(34) -
(ii)
.
We have (i) (ii). If, for every , there exists such that
and , then (i)(ii).
Proof.
(i) (ii). For any
By using inequality (11), for
where in the third estimation, we use (34) to obtain .
By letting we obtain zero duality gap. We can use the same argument as in Theorem 4.2 to prove that
(ii) (i). We have
so for every , there exists such that
and . Moreover, there exists an such that
Combining the two inequalities gives us
By adding and substracting , we get
Since each term is nonnegative, we obtain
Since , and thus . By the above inequalities, and
which is equivalent to . Hence, (i) is proved. ∎
Theorem 4.4.
Let be such that and let be a mapping from into . Suppose that and . Consider the following conditions.
-
(i)
For all , there exist such that
-
(ii)
and is an optimal solution to (CP).
We have (i) (ii). For every , if there exists such that , then the two statements are equivalent.
Proof.
The lines of the proof coincide with the ones of Theorem 4.3. We only need to prove that is an optimal solution to (CP). Now as (i) holds for all , we have
Letting in the above inequality as it holds for all . We obtain zero duality gap
Notice that
As , the primal problem (CP) is finite with as an optimal solution. ∎
Remark 4.
111We thank an annonymuous referee for this observation4.1 Zero duality gap for specific classes
Consider the classes of elementary functions as in (6). i.e. and defined in (23) and (24) for . We let because they do not affect the dual problem and the condition for zero duality gap. The dual problem (25) takes the form
(36) |
Proposition 4.5.
Let and be Hilbert spaces with inner products, and , respectively. Let be a continuous linear operator from to , and be a weakly convex function with modulus . Then is weakly convex with modulus .
Proof.
By Definition 2.6, a function is weakly convex on with modulus if , is a convex function, or equivalently for any and ,
Now we analyze the weak convexity of the function , where . We have
Denote for . Therefore,
As is linear and continuous, we obtain
where . This means is weakly convex with modulus . ∎
The following corollary follows from Theorem 4.3.
Corollary 4.6.
Proof.
Observe that the above corollary does not hold for with due to formula (37).
We give simple examples illustrating Theorem 4.3.
Example 4.7.
-
•
The conjugates of and are given as
The -subdifferentials of and at are given as
To verify condition (i) of Theorem 4.3, we find such that
Now consider the first inequality
which gives , so the second inequality i.e. , holds for . Hence, Theorem 4.3-(i) holds.
Moreover, as , we have
Let , we obtain . Since , we get . With calculated above we get
and
Hence, we achieve zero duality gap.
-
•
Now we reverse the roles of and i.e.
Then the conjugates of and are
The -subdifferentials of and are
We need to find such that
The first inequality, for all ,
also implies , so the second inequality is automatically satisfied. Repeating the same steps as before, we achieve zero duality gap at value which is the same in the previous case. However, this time condition (33) does not hold for any and , because there is no such that
unless .
In some cases, if Theorem 4.3-(i) is not satisfied, we need to check zero duality gap in a another way. Let us consider the following example.
Example 4.8.
We have
One option is to find, for , such that
(38) |
The first inequality is
which gives us , so the second inequality of (38) holds for any . However, there are no element in such that , and we cannot applied condition (i) of Corollary 4.3 to determine zero duality gap.
One option is that instead of solving (38), we take and calculate . We consider the simplest case where and we calculate
where , for . We examine whether by checking the inequality
Substituting or , we have
This inequality has solution as the left hand side is a parabola with minimum value . Thus, condition (i) of Theorem 4.2 is satisfied, and we have zero duality gap for the conjugate duality.
5 Strong Duality for Conjugate Dual
In this section, we investigate strong duality for the conjugate dual (DCP), i.e., we provide conditions for zero duality gap and the existence of solution to the dual problem (DCP); the respective conditions are expressed in terms of additivity of epigraphs of the conjugate functions.
Definition 5.1.
The epigraph of a function is a subset of defined as
(39) |
We have
(40) |
In consequence, if , then . To investigate strong duality, together with (33), we need the following condition: for a given and
(41) |
Remark 5.
Note that (41) is satisfied when where is the linear space generated by .
Theorem 5.2.
Let be a nonempty set and be a vector space. Let and be proper functions. Let and be sets of elementary functions defined on and , respectively. Let be a mapping from to . Assume that and consider the following conditions.
-
(i)
Every , can be expressed as , where and .
-
(ii)
For any , it holds and the infimum is attained.
We have (i) (ii). Moreover, for any , if is a solution to the problem
(42) |
Proof.
(i) (ii): If , then and (ii) holds. Consider the case . By Theorem 4.1, for every , we have
(43) |
We start by proving the opposite inequality. Since , by (i), there exist and such that
Thus
and we have (ii). The infimum is attained from the above inequality, as
so solves problem (42).
Remark 6.
- •
-
•
The assumption (i) in Theorem 5.2 does not coincide with the following
(see also [16, Theorem 3.1]), because we consider the set instead of , where . Then the assumption (i) of Theorem 5.2 is
whenever holds true for any . Examples of classes of elementary functions, where condition is satisfied, can be found in Example 3.2-3.
Corollary 5.3.
Let be a nonempty set and be a vector space. Let and be a mapping from to . Let be sets of elementary functions on and , respectively. Assume that . Let
-
(i)
,
-
(ii)
For , and the infimum is attained.
Consider the following conditions.
-
a.
For any pair , there exists such that and
-
b.
There exists such that and .
-
c.
For any pair and a given , there exist such that and
-
d.
, for any and a given , there exist such that
If or hold, then (i) (ii). If or hold for at which the infimum in (ii) is attained, then (ii) (i).
Proof.
(i) (ii): For any . There exist two pairs and such that and . Thanks to Theorem 4.1, for any , we have
so we need to prove the reverse inequality. Consider
(44) |
Assume . We can find such that and . Note that
Hence,
(45) |
Inequality (45) gives us
and the infimum in (ii) is attained at .
Now let hold. There exists such that and . We have
(46) |
where we have used in the second inequality. The attainment of the infimum of (ii) at follows in the same way as in the proof with condition (a).
(ii)(i): Let we have
so , which means
We want to prove the reverse inclusion. Take . From (ii), there exists which is a solution to the problem,
(47) |
We assume holds. There exist such that , , so that and
(48) |
Combining this with (47) and (48), we obtain
By taking , we can make . This means
Thus, we have
(49) |
Now, let hold. Let be a solution to the infimum problem in (ii). For every , there exist , such that
We have
(50) |
Remark 7.
-
•
In Theorem 5.2 and Corollary 5.3, to obtain condition (ii), the assumptions mostly rely on and the set of elementary functions . Thus, with appropriate choices of , we can guarantee the attainment of the infimum in (ii). The motivation comes from the fact that in the construction of the conjugate dual, we perturb but not .
-
•
If , then the condition implies symmetry of the set . Because we can take for any .
-
•
It is true that . To see this, let us take , then for all . As is closed under addition of constant, we have and . Conversely, if then (see [16]).
- •
-
•
In the classical convex analysis, when and are separated locally convex spaces. For lower semicontinuous proper convex functions and a continuous linear mapping , [31, Theorem 14.1], condition ensuring strong duality are expressed with the help of regularity condition as defined in [31, Chapter 4, Section 14] while in Theorem 5.2 and Corollary 5.3, we are using assumptions related to the additivity of epigraph of the conjugate. We refer the reader to Section 16 in [31] for the discussion of relationship between different regularity conditions ensuring strong duality.
We give an example of a convex problem satisfying the assumption (i) of Theorem 5.2.
Example 5.4.
Let and
Condition (41), , always holds for all (see Example 3.2-3). Let us denote
We want to calculate
Let us take then we want decompose and where and . We have the following system
We can choose so and . Taking and assumption (i) of Theorem 5.2 is satisfied. Thus, we have strong duality for conjugate duality. Note that if is composed of affine functions only, we arrive at the same conclusion.
6 Lagrange Dual
6.1 Construction of Lagrangian Primal-Dual Problems
Of equal importance is a problem to restate the results from [24], which connects the Conjugate Duality with Lagrange Duality. In the case we have an operator in the formulation of problem (CP). For other construction of Lagrangian dual proposed recently, see e.g. [34] and the references therein.
In this Section, we give new results for zero duality gap for Lagrange dual of composite problems. To construct Lagrangian dual, we introduce the Lagrangian function as follow
(51) |
where is a set of elementary functions defined on . In the case is a convex set then is concave with respect to . The Lagrangian dual is
(LD) |
and the corresponding Lagrangian primal
(LP) |
Let us state the weak duality for Lagrangian duality.
Theorem 6.1.
Let and be a mapping. Let be sets of elementary functions defined on and , respectively. It holds
This result holds true for any functions not necessarily of the form (51). First we notice that Lagrangian primal (LP) is not equivalent to the composite problem (CP) as
Even though the Lagrange dual and conjugate dual are the same, the primal problems are different. Since our main focus is (CP), we discuss the assumptions which make these two primal problems equivalent. Clearly, we can assume
(52) |
Usually, in the classical convex approach, is lsc and convex iff so condition (52) holds. Conversely, we have the following.
Proposition 6.2.
Let where is a nonempty set and is a vector space. Let be a mapping. Let be the sets of elementary functions defined on and , respectively. Assume (52) holds, if there exists an such that
then is -convex at .
Proof.
We have
From (52) we have
which implies . From the definition of biconjugate function, , so we have . Thus, is -convex at . ∎
6.2 Lagrange Zero Duality Gap
In the present subsection, we discuss zero duality gap for Lagrange duality. We follow the result of [24] and exploit the intersection property to prove zero duality gap. In our context, Theorem 6.1 in [24] takes the following form.
Theorem 6.3.
The proof can be found in [19].
Remark 8.
In the case where for every , we have , i.e. belong to the same support set for some , the assumption of concavity of the Lagrangian can be removed. (cf. [18]).
For convenience, we state a result from [24].
Lemma 6.4.
(Lemma 6.2 [24]) Let be a set, and let . Two functions have the intersection property at level if and only if there exists such that for all .
Observe that in Theorem 6.3, the class of elementary functions is arbitrary. Below, we relate the intersection property to the lower semi-continuity of the optimal value function, , in the case where elementary functions satisfy additional conditions. For other results along this line, see e.g. [35, 36].
Let us define the optimal value function as follow
(53) |
where the function is defined at the beginning of Section 3. Similar to (52), we also define the following condition for any
(54) |
A function is lower semi-continuous at a point , if for every , there exists a neighborhood such that
(55) |
The following theorem below connects Theorem 4.2, the intersection property and the lower semi-continuity of the objective function at .
Theorem 6.5.
Let . Let be a mapping from to with . Let be sets of elementary functions defined on and , respectively. Let be the Lagrangian defined in (51). Assume is convex, and . Consider the following.
-
(i)
For every , there exist and such that .
-
(ii)
For every , there exists , , such that the intersection property holds for at level .
-
(iii)
The function is lower semi-continuous at 0.
We have (i)(ii). If condition (52) holds, then (ii)(i). Moreover, if (52) holds and is composed of lower semi-continuous functions, then (ii)(iii). (iii)(ii) when is a collection of continuous functions and (54) holds.
Proof.
(i) (ii): For every , let . For every , we can find and such that ,
(56) |
We also have so . Putting this into (56),
so
(57) |
As is arbitrary, we choose so that . Pick and such that . By choosing , we have . From Lemma 6.4 with we have
so the intersection property holds for at level . As is arbitrary, we have (ii).
(ii) (i): As (52) holds, we have . From the primal problem (CP), for every , there exists an such that
Denoting , we have . There exist and such that and the intersection property holds at level . Lemma 6.4 gives us a such that
(58) |
Because , we have
As is a convex set, . Moreover, is concave in the second variable, i.e. for all and
(59) |
where the inequality holds due to the concavity of . Combining inequality (59) with (58), we get
We obtain
By denoting , we get
(60) |
Rearranging both sides, we obtain
We have for all and . From the previous inequality,
Because, in general, does not belong to , we cannot claim that , but we have
which means . On the other hand, from (60), for any , we have
By choosing , we can write
or
This gives . Thus, (i) holds.
(ii) (iii): As the intersection property holds for every , for every , we can take . Now we can find such that for all , and satisfy the intersection property at level . Using Lemma 6.4, there exists such that for all ,
(61) |
On the other hand, we have
where . Denoting , as is a convex set, (61) gives us
for any . Since is lower semi-continuous, the function is also lower semi-continuous for all . There exists a neighborhood such that
Thus,
for all . The inequality holds for all , we can take the infimum with respect to on both sides and obtain
for all . Hence, is lower semi-continuous at .
(iii)(ii): Conversely, for every , we choose . There exists a neighborhood such that
Now considering
We can find such that
In the last inequality, we can find a neighborhood such that is continuous at . Hence, for all , we have
Finally, for all , we can take and . Then has the intersection property at level with any , for . ∎
Now we can state zero duality gap for Lagrange dual problem.
Proposition 6.6.
Let . Let be a mapping from to with . Let be sets of elementary functions defined on and , respectively. Let be the Lagrangian defined by (51). Assume is convex, and (52) holds. We further assume . The following are equivalent.
-
(i)
For every , there exists and such that have the intersection property at level .
-
(ii)
Remark 9.
-
•
In order to work with the intersection property, we need to find and . Because , so for any . We can set , and the intersection property always holds for any , as .
-
•
Let be symmetric, and for every , there exist , for , such that . We have, for all
so is a -approximate solution to the problem .
We can replace assumption (52) with a stronger one by the following lemma.
Lemma 6.7.
Proof.
If condition (52) holds i.e.,
then for every , one can find an such that . Hence,
Conversely, assume that
We have
Recall that,
Because for all , we can write
Both sides do not depend on , we can let and get the equality. ∎
In fact, condition (62) is stronger than intersection property, as we can see in the corollary below.
Corollary 6.8.
Let and let be a mapping from to with . Let be sets of elementary functions defined on and , respectively. Let the Lagrangian function be defined by (51). Assume is convex, , . The following are equivalent.
-
(i)
For every , there exists such that .
-
(ii)
For every , there exist and such that .
-
(iii)
Proof.
Thanks to Theorem 4.2, Theorem 6.5 and Proposition 6.6, we have (ii) (iii). We only need to prove (i)(ii).
(i) (ii): For every , we have
There also exists such that
Thus, for all . While , we have
(63) |
Let and we obtain
so . On the other hand, by using the property of -conjugate,
By putting this in (63), we have . Hence, we prove (ii).
The right-hand side does not depend on , taking the infimum with respect to gives us
We have proved (i). ∎
Having the class of elementary functions, we recall the notion of -convexity in [6, Proposition 1.2.3], by using fomula for . Together with the intersection property, we can prove Langrage zero duality gap as follow.
Proposition 6.9.
Assume that for every , there exist , such that . If , then we have .
Proof.
Let be such that
Then . On the other hand, means
Moreover, and
And,
Combining the two above inequalities, we obtain
∎
Remark 10.
We complete this Section with two examples which illustrate Proposition 6.9.
Example 6.10.
Consider the functions and . Let
is our sets of elementary functions. We want to find , i.e. the set of all such that
This gives us where or . Consider , for every such that
We need to find such that , we can let . There are two cases
-
•
or , we cannot find such that as .
-
•
we have to solve for . We arrive at the following system
By direct calculations, there are no solution for the above system of inequalities.
For , and this turns into
which is zero duality gap.
Example 6.11.
Now consider the functions with , and the set of elementary functions is
Calculating gives us and . Now we find
We also calculate
for . Observe that
while
Thus we have zero duality gap.
Acknowledgments
The authors are thankful to the anonymous referees for their constructive comments and remarks which improve the quality of the paper.
This work has been supported by the ITN-ETN project TraDE-OPT funded by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No.861137
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