Applied Mathematics
Vol.4 No.4(2013), Article ID:30780,7 pages DOI:10.4236/am.2013.44101

A Note on the Guignard Constraint Qualification and the Guignard Regularity Condition in Vector Optimization

Giorgio Giorgi

Faculty of Economics, University of Pavia, Via S. Felice, Pavia, Italy


Copyright © 2013 Giorgio Giorgi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Received December 12, 2012; revised February 14, 2013; accepted February 21, 2013

Keywords: Constraint Qualifications; Regularity Conditions; Optimality Conditions; Vector Optimization Problems


Some remarks are made on the use of the Abadie constraint qualification, the Guignard constraint qualifications and the Guignard regularity condition in obtaining weak and strong Kuhn-Tucker type optimality conditions in differentiable vector optimization problems.

1. Introduction

In discussing a gap between multiobjective optimization and scalar optimization (a gap first pointed out by Wang and Yang [1]), Aghezzaf and Hachimi [2] state that “in multiobjective optimization problems, many authors have derived the first-order and second-order necessary conditions under the Abadie constraint qualification, but never under the Guignard constraint qualification”. This deserves some comments. Indeed, some authors have proposed a suitable Guignard-Gould-Tolle constraint qualification (it would be better to speak of “GuignardGould-Tolle regularity condition”, as it involves also the objective function, besides the constraint functions) in order to obtain a Karush-Kuhn-Tucker type multiplier rule for a Pareto optimization problem.

For example, Maeda [3] considers the following Pareto optimization problem

where, are differentiable functions, and introduces the following “generalized Guignard constraint qualification” for this problem

where is a feasible vector,

is the Bouligand tangent cone or contingent cone to at (see Definition 2) and Indeed, in the above constraint qualification (better: regularity condition) the inclusion means in fact equality.

Jimenez and Novo [4], Giorgi, Jimenez and Novo [5], [6], Giorgi and Zuccotti [7] have given similar, but more general results. What is true is that the GuignardGould-Tolle theory cannot be transferred “sic et simpliciter” from the scalar to the vector case. Indeed, it is wellknown that if we have a scalar optimization problem

with, f differentiable, and is a local solution of the said problem, then we have


(A* is the negative polar cone of the cone A). The result obtained by Guignard [8] seems at first sight more general, as Guignard claims that

where is the so-called pseudotangent cone to at. However, this greater generality is only apparent, as it is true that for any cone, it holds

so we obtain

Relation (1) obviously is equivalent to the inconsistency of the inequality

for or, equivalently, for

Now, consider a vector optimization problem (vop)

where is differentiable. When, as in the present paper, the ordering cone is (vop) is also known as Pareto optimization problem. We recall some basic notions and definitions.

Definition 1.

A vector is said to be a weak Pareto optimal point for (vop) if there does not exist another vector such that for all A vector is said to be a Pareto optimal point for (vop) if there does not exist another vector such that for all with for at least one index. A vector is a local weak Pareto optimal point (respectively, a local Pareto optimal point) if the above definitions hold with respect to where is a suitable neighborhood of

Definition 2.

Let The contingent cone at or Bouligand tangent cone at is:

or, equivalently,

It is well-known that this cone is closed, but not necessarily convex (see, e.g., Aubin and Frankowska [9], Bazaraa and Shetty [10]).

It can be proved that if is a local weak optimal point for (vop), then we have the relation (see, e.g., Bigi ([11], Corley [12], Giorgi and Zuccotti [13], Staib [14])


i.e. the system


has no solution for, i.e. it holds

One may wonder if the system (3) (for) is also inconsistent for, as it holds for. The answer is: no, as shown by Wang and Yang [1] with a numerical example (a misprint in the example has been corrected by Castellani and Pappalardo [15]). This is the “gap”, with reference to a result of Guignard to which the title of the paper of Wang and Yang in its turn makes reference.

This note is organized as follows.

In Section 2 we give short proofs of the weak KuhnTucker type necessary optimality conditions, for a Pareto optimization problem with both inequality and equality constraints, under the Abadie constraint qualification, and of the strong Kuhn-Tucker type conditions, for the same problem, under a Guignard regularity condition.

In Section 3 we make some further comments on the said “gap” between scalar optimization problems and vector optimization problems.

The conclusions are briefly expounded in Section 4.

2. First Order Necessary Conditions: Abadie Constraint Qualification, Guignard Regularity Condition

The feasible set of (vop) is, from now on, specified by inequality and equality constraints. More precisely, we consider the problem



are differentiable at least in a neighborhood of the feasible point x0, and is continuously differentiable at least in the same neighborhood of x0. We denote by the index set of the active constraints at, i.e.

Let; the cone

is called the linearizing cone at for (vop)1.

The cone

is called the weak linearizing cone at or cone of decreasing directions at for (vop)1.

Lemma 1. (see Giorgi [16])

Let and let f, g and h verify the previous differentiability assumptions. Then we have:


2) if the Jacobian matrix has full rank, it holds

3) if, moreover, then it holds

We are now ready to prove in a short way a Fritz John-type optimality condition for (vop)1.

Lemma 2.

Suppose that the Jacobian matrix has full rank. Let be a local weak Pareto optimal point for (vop)1. Then the system


has no solution


Apply Lemma 1 to the optimality condition (3).   

Theorem 1.

If is a local weak Pareto optimal point for (vop)1, then there exist vectors and not all zero, such that





If the gradients are linearly dependent, just set and choose a nonzero vector such that

If the above gradients are linearly independent, the thesis follows from Lemma 2, applying the Motzkin alternative theorem (see, e. g., Mangasarian [17]) and setting for all                   

A Karush-Kuhn-Tucker-type optimality conditions for (vop)1, by means of the Abadie constraint qualification (acq), has been obtained by Lin [18] and by Singh [19]; however, their proofs work only if is a weak Pareto optimal point for (vop)1, and not a local weak Pareto optimal point. The flaw is corrected in Marusciac [20]; see also the errata corrige of Singh [19], who, however, does not justify his rectification; see also the paper of Wang [21], more specific on this point. We give here a simple proof of the result of Wang.

Definition 3.

The constraint set M satisfies the Abadie constraint qualification (acq) at if

Due to relation 1) of Lemma 1, the (acq) can be written also as an inclusion

Singh [19] claims that his version, as an inclusion, of the (acq) is more general than the one proposed by Marusciac [20] as an equality.

Lemma 3.

Let be a local weak Pareto optimal point for (vop)1, and suppose that the (acq) holds at. Then the system


has no solution


Suppose, on the contrary, that the above system has a solution. Then, the (acq) implies that; thanks to relation (3) it will hold for at least one index k, contradicting the relations of the system.                                        

Applying to system (8) the Motzkin theorem of the alternative, we get the following (weak) Karush-KuhnTucker-type multiplier rule for (vop)1.

Theorem 2.

Let be a local weak Pareto optimal point for vop)1 and let the (acq) hold at. Then there exist vectors and, with, such that (5), (6) and (7) hold.

As the cone is polyhedric, the (acq) implies that is a convex cone. If we substitute with the closure of its convex hull, we obtain the Guignard-Gould-Tolle constraint qualification (Guignard [8] Gould and Tolle [22])


but, as already remarked in the Introduction, this more general constraint qualification does not enable to obtain, for, the multiplier rule of Theorem 2, unless to make further assumptions on or on the objective function. See the next Section 3.

If we want to obtain a strong Karush-Kuhn-Tucker type optimality condition for (vop)1, i.e. a multiplier rule (5), (6) and (7), where in (5), we have to impose some condition, where also the objective function is considered. We prefer, in this case, to speak of regularity conditions, instead of constraint qualifications. The condition considered by Maeda [3] and reported in the Introduction of the present paper, is therefore a regularity condition. For other regularity conditions for (vop)1 and their relationships, see also Jimenez and Novo [4] and Giorgi and Zuccotti [7].

We now consider a slightly modified version of the Guignard regularity condition proposed by the above authors. See also Bigi [11].

Definition 4.

Let; then the set

is the cone of descent directions for f at. Given any, the set

is the set of all feasible points, which do not allow to be a weak minimum point for (vop)1 when the component is dropped from f.

Definition 5.

Let Then the (modified) Guignard-GouldTolle regularity condition (ggtrc) holds at if

Lemma 4.

Suppose that is a local weak Pareto optimal point for (vop)1 and that (ggtrc) holds at Then for each the following system


has no solution


On the contrary suppose that there exists such that (10) has a solution. Therefore, (ggtrc) implies that Since the definition of local weak Pareto optimal point for (vop)1 implies that is a local minimum point of the scalar function over, then we get the inequality, in contradiction with the assumption.                   

The previous lemma, which states the impossibility of p systems, enables us to obtain a strong Karush-KuhnTucker-type multiplier rule for (vop)1.

Theorem 3.

Suppose that is a local weak Pareto optimal point for (vop)1 and that (ggtrc) holds at Then there exists vectors and with, for each such that (5), (6) and (7) hold.


The proof is immediate, by applying the Motzkin theorem of the alternative to system (10), for each and summing up the resulting multipliers.          

We note that Maeda [3] has proposed a slightly weaker condition than (ggtrc), generalized to (vop)1 by Jimenez and Novo [4], by Giorgi, Jimenez and Novo [5,6], and by Giorgi and Zuccotti [7], but this weaker regularity condition “works” for local Pareto optimal points and not for the weak ones. Finally, we remark that (generalizing some approaches of Bigi and Pappalardo [23]) Maciel, Santos and Sottosanto [24] and Giorgi and Zuccotti [7] have studied the following question: which regularity condition for (vop)1 is both necessary and sufficient to obtain that, for all triplets of multipliers which satisfy relations (5), (6) and (7)? Let us denote by the above set of multipliers, i.e. the set of all Fritz John multipliers for (vop)1, and let us introduce the following generalization of the Mangasarian-Fromovitz constraint qualification.

Definition 6.

Let, then the Mangasarian-Fromovitz regularity condition (mfrc) for (vop)1 is satisfied at if:

1) the vectors are linearly independent;

2) for all the system

has solution

It can be proved the following result (see Maciel, Santos and Sottosanto [24], Giorgi and Zuccotti [7]).

Theorem 4.

Let and let. Then in (vop)1 we have with for all, if and only if (mfrc) holds at

In this section we have given a simple and short proof of the Fritz John type and of the weak Kuhn-Tucker type necessary optimality conditions for the problem (vop)1, under the Abadie constraint qualification. This completes and improves what previously proved by Singh [19]. We give also a short proof of the strong Kuhn-Tucker type conditions for (vop)1, under a Guignard regularity condition. This improves what previously proved by Maeda [3].

3. Again on the “Gap” between Scalar Problems and Vector Problems

We have described in the Introduction the “gap” occurring between scalar and vector optimization problems, generated by the use of the classical Guignard-GouldTolle constraint qualification. We have also mentioned this “gap” in the previous section. An obvious sufficient condition to remove this “gap” is that the cone is convex; this condition has been envisaged by Wang and Yang [1], who, however, did not go further in the discussion. It is well-known that is convex when M is a convex set. As the structure of the contingent cone, as of all the other first-order local cone approximations, depends only from the structure of M arouund (see, e.g., Elster and Thierfelder [25]), we can state that is convex also when M is locally convex at, i.e. there exists a neighborhood of, , such that is a convex set. This is no longer true when M is only star-shaped at, i.e. contrary to what stated in Giorgi and Guerraggio [26] and to what seems stated in Palata [27] and in Staib [14].

If M is star-shaped at it holds that where

is the cone of the attainable directions or Kuhn-Tucker cone at So, when is star-shaped at, the (acq) and the Kuhn-Tucker constraint qualification (ktcq)

coincide (see Bazaraa and Shetty [10], who, however, do not recognize that the cone is closed).

It is also well-known that if equals the Clarke tangent cone

then is convex, being always closed and convex. In this case Penot [28] qualifies the set M as tangentially regular at We follow the treatment of Aubin and Frankowska [9].

Definition 7.

We say that a closed subset is sleek at if the multivalued map is lower semicontinuous at.

Theorem 5. (Aubin and Frankowska [9])

Let M be a closed subset of and. If is sleek at, then the contingent cone and the Clarke tangent cone coincide and consequently is convex.

Another possibility to remove the “gap” is suggested by Castellani and Pappalardo [15], who impose that the objective function f is convexlike ad refer a result of Li and Wang [29]. This can be proved directly in the following way.

Definition 8.

A function is convexlike on the nonempty set if for any and any there exists such that

Note that in the above definition which usually depends from and is not required to be the convex combination of and Note, moreover, that if, then any real-valued function is convexlike. Obviously, all convex functions are convexlike; this is only a sufficient condition. For other sufficient conditions see Elster and Nehse [30]. See also Hayashi and Komiya [31] and Jeyakumar [32] for applications of convexlike functions to optimization problems.

A straightforward consequence of Definition 8 is the following characterization.

Theorem 6.

The function is convexlike on if and only if the set is convex.

Theorem 7.

Suppose that f is convexlike on If is a weak Pareto optimal point for (vop), then there exists a nonnegative nonzero vector such that is a minimum point of the scalar problem


By assumption The convexlikeness assumption on f implies that is convex; therefore the usual separation theorem implies the existence of a nonzero vector and of a scalar such that

for all and for all Therefore, considering, we obtain; given any

and considering td as we get

and therefore the thesis follows.                  

We note that in the proof of the above ressult we use the property that is a convex set; this weaker condition is equivalent that is subconvexlike on M (see Li and Wang [29]). The “scalarization theorem” just proved allows us to state the following result (recall that for the Guignard-GouldTolle theory is consistent).

Theorem 8.

Suppose that is convexlike (or subconvexlike) on M. If is a weak Pareto optimal point for (vop), then

Similarly, with reference to (vop)1, we can assert the following result.

Theorem 9.

Suppose that is a weak Pareto optimal point for (vop)1 and that is convexlike (or subconvexlike) on M. Suppose that the Guignard-GouldTolle constraint qualification (9) holds at. Then, there exist multipliers and such that (5), (6) and (7) hold.

Another condition which allows to apply to (vop)1 the (ggtcq) and which entails the objective function f is given in the following theorem.

Theorem 10.

Let be a weak Pareto optimal point for (vop)1; suppose that x0 verifies the (ggtcq) and that there exists a nonnegative vector, such that

Then verifies the conditions (5), (6) and (7).


The (ggtcq) can be equivalently written in the form

or On the other hand, being a polyhedral cone, determined by the vectors, and, , its polar will be given by

Therefore, being, we can write

i.e. the conditions (5), (6) and (7), by choosing for                                

We remark that if is a weak Pareto opyimal point for (vop)1, then, for each convex cone S, with, there exists a nonnegative vector, , such that, The proof is left to the reader (apply the classical theorem on separation of convex sets). Note that when we obtain the result already observed at the beginning of this Section and obtain also the result stated in the previous theorem.

In this section we have investigated the reasons for the existence of a gap between a scalar programming problem and a multiobjective programming problem (vop)1, gap which has its origins in the use of the classical Guignard-Gould-Tolle constraint qualification. We have given some proposals to remove the said gap.

4. Conclusions

The aim of the present paper is twofold. On one side we present simple and brief optimality conditions of the Fritz John type and of the Kuhn-Tucker type (both weak and strong) for a Pareto multiobjective programming problem with both inequality and equality constraints. On the other side we investigate the existence of a gap, originated by the use of the classical Guignard constraint qualification, between a scalar nonlinear programming problem and the Pareto problem described above.

The author thanks an anonymous referee for his suggestions.


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