Nguyen Thi

Hong Linh
email: honglinh98t1@yahoo.com

Jean-Paul Penot
email: jean-paul.penot@univ-pau.fr

Generalized a¢ ne maps and generalized convex functions

Keywords: colinvex, colin…ne, generalized derivative, mathematical programming, optimality conditions, pseudoconvex function, pseudolinear function, quasiconvex function. Mathematics Subject Classi…cation: 26B25, 46G05, 49K27, 90C26, 90C32

We study some classes of generalized convex functions, using a generalized derivative approach. We establish some links between these classes and we devise some optimality conditions for constrained optimization problems. In particular, we get Lagrange-Kuhn-Tucker multipliers for mathematical programming problems.

Introduction

Prompted by the needs of mathematical economics, some mathematicians have scrutinized several notions of generalized convexity or concavity. While some of them, such as quasiconvexity and pseudoconvexity are rather general, some others are rather peculiar. This is the case for functions which are both pseudoconvex and pseudoconcave. Initially, these functions have been called pseudomonotone (Martos [45]); nowadays these functions are usually called pseudolinear, but we prefer to call them pseudoa¢ ne as a function which is both convex and concave is a¢ ne; moreover, this class includes a¢ ne functions which are not linear. Albeit especial, this class of functions contains interesting, non trivial examples and enjoys striking properties, as shown in [START_REF] Aggarwal | Pseudolinearity and e¢ ciency via Dini derivatives[END_REF], [START_REF] Aussel | Subdi¤erential properties of quasiconvex and pseudoconvex functions: Uni…ed approach[END_REF], [START_REF] Bianchi | An extension of pseudolinear functions and variational inequalities problems[END_REF], [START_REF] Cambini | On generalized linearity of quadratic fractional functions[END_REF], [START_REF] Cambini | Generalized convexity and optimality conditions in scalar and vector optimization[END_REF], [START_REF] Chew | Pseudolinearity and e¢ ciency[END_REF], [START_REF] Dinh | Lagrange multiplier characterizations of solution sets of contrained pseudolinear optimizations problems, Optimization[END_REF], [START_REF] Ginchev | Second-order characterizations of convex and pseudoconvex function[END_REF], [START_REF] Jeyakuma | On characterizing the solution sets of pseudolinear programs[END_REF], [START_REF] Komlósi | First and second characterizations of pseudolinear functions[END_REF], [START_REF] Komlósi | Handbook of generalized convexity and generalized monotonicity[END_REF], [START_REF] Lalitha | Vector variational inequalities with cone-pseudomonotone bifunction[END_REF], [START_REF] Penot | Glimpses upon quasiconvex analysis[END_REF], [START_REF] Rapcsak | On pseudolinear functions[END_REF], [START_REF] Śmietański | Characterization of solution sets to pseudolinear programming problems[END_REF], [START_REF] Yang | Vector variational inequality and vector pseudolinear optimization[END_REF] among others. In all references dealing with this subject, pseudoa¢ ne functions are de…ned through the use of a directional derivative function or a Dini derivative. We also follow this line, taking a general bifunction h as a substitute for such a derivative. The use of subdi¤erentials for dealing with this class will be made elsewhere.

It is our purpose here to study related classes of functions, to give characterizations and to detect some relationships. In particular, we introduce and study properties which are related to some quasiconvexity, pseudoconvexity and invexity properties. We also present extensions to vectorvalued maps and study some composition properties (section 4).

Some relations between solutions of variational inequalities and optimization problems were discussed in the papers quoted above. We also tackle this question in section 5; in particular, we study necessary and su¢ cient optimality conditions in the form of a Karush-Kuhn-Tucker condition when the data belong to the class of functions under study. We provide examples allowing a comparison with previous results in [START_REF] Linh | Optimality conditions for quasiconvex programming[END_REF], [START_REF] Mangasarian | Nonlinear programming[END_REF], [START_REF] Martínez-Legaz | On lower subdi¤erentiable function[END_REF] and [START_REF] Ho¤manm | Martínez-Legaz and Romano-Rogríguez, -lower subdi¤erentiable function[END_REF].

Pseudoconvex, quasiconvex and protoconvex functions

In the sequel, X, Y are normed vector spaces (n.v.s.), C is a nonempty subset of X and f : C ! R; it is convenient to extend f by +1 outside C: We set It contains the radial tangent bundle of C which is the set

T r C := f(x; u) 2 C X : 9 (r n ) ! 0 + ; x + r n u 2 C 8ng = [ x2C fxg T r (C; x):
We also use the tangent bundle of C which is the set

T C := f(x; u) 2 C X : 9(r n ) 2 P N ; (u n ) ! u; x + r n u n 2 C 8ng = [ x2C fxg T (C; x):
Here T r (C; x) := fu 2 X : 9 (r n ) ! 0 + ; x + r n u 2 Cg is the radial tangent cone to C at x 2 C and T (C; x) := fu 2 X : 9 (r n ) ! 0 + ; (u n ) ! u; x + r n u n 2 C 8ng is the tangent cone to C at x: One has the obvious inclusions T r (C; x) T (C; x); T r (C; x) V (C; x): When C is starshaped at x 2 C; in the sense that [x; w] C for every w 2 C; one has V (C; x) = T r (C; x). When C is a convex subset of X; as it will be frequently the case in the sequel, T (C; x) is the closure of V (C; x) = T r (C; x). A bifunction h : V C ! R will stand for a generalized derivative of f . All the usual generalized directional derivatives have the common feature that they are positively homogeneous functions of the direction u. Thus, we assume that for all x 2 C, h(x; ) is positively homogeneous and h(x; 0) = 0. There are several possible choices for h: Among them are the Dini derivatives of f . The upper and the lower radial derivatives (or upper and lower Dini derivatives) of f at x 2 C, in the direction u 2 T r (C; x), are de…ned by

D + f (x; u) = lim sup t!0+; x+tu2C 1 t [f (x + tu) f (x)]; D + f (x; u) = lim inf t!0+; x+tu2C 1 t [f (x + tu) f (x)]:
If for some (x; u) 2 T r C the relation D + f (x; u) = D + f (x; u) holds, then the radial derivative of f at x in the direction u is the common value of the preceding limits:

f 0 (x; u) = D + f (x; u) = D + f (x; u):
This derivative is often called the directional derivative of f at x in the direction u; but we prefer to keep this term for the limit

f 0 (x; u) = lim (t;v)!(0 + ;u) x+tv2C 1 t [f (x + tv) f (x)] (x; u) 2 T C;
when it exists. Of course, when this directional derivative (also called the Hadamard derivative) exists for some (x; u) 2 T r C, the radial derivative exists and has the same value. Note that the limit in the de…nition of f 0 (x; u) is +1 if u does not belong to the tangent cone T (C; x) to C at x which is the set of limits of sequences of the form (t 1 n (x n x)) where (t n ) ! 0 + and x n 2 C for every n 2 N. Similarly, D + f (x; u) is +1 if u does not belong to the T r (C; x):

Other generalized derivatives can be used. For instance, one can use the lower directional derivative (or contingent derivative or lower Hadamard derivative) df given by df (x; u) := lim inf

(t;v)!(0 + ;u) x+tv2C 1 t [f (x + tv) f (x)]
and its upper version, or some epiderivative. One can also use the Clarke-Rockafellar derivative f of f ([10], [START_REF] Rockafellar | Generalized directional derivatives and subgradients of nonconvex functions[END_REF]) or the moderate derivative f } of ). Slightly modifying a notion introduced in [START_REF] Sach | Characterizations of generalized convexities via generalized directional derivatives[END_REF], we say that a mapping s :

X ! R is subodd on C X if the following condition is satis…ed (S) w; x 2 C =) s(w x) s(x w):
Clearly, if s is …nite valued and positively homogenous, s satis…es (S) if s is sublinear. We say that s : C C ! R reverses signs on C X if the following condition is satis…ed (R) w; x; y 2 C; x 2]w; y[=) s(w x)s(y x) 0:

Conditions (R) and (S) are independent, as the following simple examples show. Example 1. Let s 1 : R ! R be given by s 1 (u) = juj; then s 1 is subodd, but s 1 (u)s 1 ( u) > 0 for all u 2 Rnf0g: Example 2. Let s 2 : R ! R be given by s

2 (u) = 0 if u 0 and s 2 (u) = u if u < 0; then s 2 (u)s 2 ( u) 0 for all u 2 R but s 2 (1)
s 2 ( 1): Both conditions (R) and (S) imply the following condition: (T ) w; x; y 2 C; x 2]w; y[; s(w x) < 0 =) s(y x) 0:

Note that s satis…es condition (R) if, and only if s and s satisfy condition (T). On the other hand, s and s satisfy condition (S) if, and only if s is odd on C C:

A function f : C ! R is said to be is quasiconvex at x 2 C if for all w 2 C and x t := tx+(1 t)w 2 C for t 2 [0; 1] then f (x t ) maxff (x); f (w)g:
Let us recall the following de…nitions (see [START_REF] Ivanov | First order characterizations of pseudoconvex functions[END_REF], [START_REF] Komlósi | Generalized monotonicity in non-smooth analysis[END_REF], [START_REF] Komlósi | Generalized monotonicity and generalized convexity[END_REF], [START_REF] Lalitha | Vector variational inequalities with cone-pseudomonotone bifunction[END_REF] and [START_REF] Sach | Characterizations of generalized convexities via generalized directional derivatives[END_REF]).

De…nition 1 Given a bifunction

h : V C ! R; a function f : C ! R is said to be (a) h-pseudoconvex at x 2 C, if w 2 C; f (w) < f (x) ) h(x; w x) < 0: (b) h-quasiconvex at x 2 C, if w 2 C; f (w) < f (x) ) h(x; w x) 0: (c) h-protoconvex at x 2 C, if w 2 C; f (w) f (x) ) h(x; w x) 0: (d) h-pseudoconcave at x 2 C, if f is h-pseudoconvex at x 2 C: (e) h-quasiconcave at x 2 C, if f is h-quasiconvex at x 2 C: (f ) h-protoconcave at x 2 C, if f is h-protoconvex at x 2 C: We say that f is h-pseudoconvex on C or, in short, h-pseudoconvex (resp. h-quasiconvex, h- protoconvex, h-pseudoconcave, h-quasiconcave, h-protoconcave) if f is h-pseudoconvex (resp. h- quasiconvex, h-protoconvex, h-pseudoconcave, h-quasiconcave, h-protoconcave) at every x 2 C: Clearly, f is h-pseudoconcave (resp. h-quasiconcave, h-protoconcave) if, and only if, for any w; x 2 C; f (x) < f (w) ) h(x; w x) > 0 (resp. f (x) < f (w) ) h(x; w x) 0; (resp. f (x) f (w) ) h(x; w x) 0:) Clearly, f is h-pseudoconxex ) f is h-quasiconvex, f is h-protoconxex ) f is h-quasiconvex.
Let us observe that h-pseudoconvexity at x and h-protoconvexity at x are independent properties. Both properties are consequences of the following condition:

w 2 C; f (w) f (x) ) h(x; w x) < 0:
This rather stringent condition will not be considered here.

These de…nitions are closely related. Let us note that here the terminology slightly di¤ers from the one in [START_REF] Komlósi | Generalized monotonicity in non-smooth analysis[END_REF], [START_REF] Lalitha | A note of pseudolinearity in terms of bifunctions[END_REF] and [START_REF] Sach | Characterizations of generalized convexities via generalized directional derivatives[END_REF]. Such a change will allow a better concordance with the classical terminology for the case a subdi¤erential is used (see [START_REF] Aussel | Subdi¤erential properties of quasiconvex and pseudoconvex functions: Uni…ed approach[END_REF], [START_REF] Hadjisavvas | Handbook of generalized convexity and generalized monotonicity[END_REF], [START_REF] Linh | Generalized di¤erentials and generalized convex functions[END_REF], [START_REF] Penot | Generalized monotonicity of subdi¤erentials and generalized convexity[END_REF]). Here, we do not need any assumption on the bifunction h; but in some cases, we will require some comparisons with lower and upper Dini derivatives.

Proposition 2 If f has no local minimizer on f 1 (f (x)) and if h(x; ) is lower semicontinuous (l.s.c.) then h-quasiconvexity at x coincides with h-protoconvexity at x:

Proof. Let w 2 C be such that f (w) = f (x): Since f has no local minimizer on f 1 (f (x)); there exists (w n ) ! w such that f (w n ) < f (w) = f (x): Since f is h-quasiconvex at x; h(x; w n x) 0 hence, h(x; w x) 0 by lower semicontinuity of h(x; ): Thus, f is h-protoconvex at x:
Links with usual quasiconvexity are given in the next proposition. It shows that, under mild assumptions, quasiconvexity, h-quasiconvexity and h-protoconvexity coincide. We will use the following complement to the Three Points Lemma of [START_REF] Komlósi | Handbook of generalized convexity and generalized monotonicity[END_REF]; as there, we say that f satis…es the nonconstancy property, in short, the NC-property, if there is no line segment [a; b] in C on which f is constant.

Lemma 3 (Three Points Lemma)Let C be convex. Let f : C ! R be either radially continuous or radially lower semicontinuous and satisfying the NC-property, let w; z 2 C with w 6 = z and let y = z + t(w z), with t 2]0; 1[ be such that f (y) > f (w) f (z): Then there exist some x; x 0 2 [w; z] such that f (x 0 ) < f (x) and D + f (x; x 0 x) > 0:

Proof. If f is radially l.s.c. and satis…es the NC-property, the result is given in [START_REF] Komlósi | Handbook of generalized convexity and generalized monotonicity[END_REF]Lemma 10.1]. If f is radially continuous, by the intermediate value theorem, there exists some p 2]0; t[ such that f (w) < f (u) < f (y) for u := z + p(w z): Taking p close enough to t; we may assume that f (w) < f (v) for all v 2 [u; y]:

Let s := supfr 2 [p; t] : f (z + r(w z)) f (u)g and let v := z + s(w z): Since f is radially l.s.c., one has f (v) f (u) < f (y): By the mean value theorem [START_REF] Diewert | Alternative characterizations of six kinds of quasiconcavity in nondi¤erentiable case with applications to nonsmooth programming[END_REF], there exists some Proof. (a) Suppose f is quasiconvex and f (w) f (x) for some w; x 2 C: Then for t 2]0; 1[ we have f (x + t(w x)) f (x); hence D + f (x; w x) 0: Thus h(x; w x) 0 if h(x; ) D + f (x; ):

x 2 [v; y[ such that D + f (x; y v) f (y) f (v) > 0: Then D + f (x; w x) > 0 and f (w) < f (x) since f (z + t 0 (w z)) > f (w) for all t 0 2 [p; t]. Setting x 0 := w; one has the conclusion. Proposition 4 Let C be convex. (a) If f is quasiconvex and if h D + f; then f is h-protoconvex, hence h-quasiconvex. (b) Conversely, if f is h-quasiconvex, if h D + f ,
(b) Suppose that f is not quasiconvex. Then, there exist w; y; z such that f (y) > maxff (z); f (w)g =: f (w) where y = z + t(w z), t 2]0; 1[: By the preceding lemma, there exist x; x 0 2 [w; z] such that h(x; x 0 x) D + f (x; x 0 x) > 0 and f (x 0 ) < f (x) : a contradition with h-quasiconvexity of f:

(c) In view of the fact that a radially di¤erentiable function is radially continuous, the assertion follows from (a) and (b).

A comparison between h-pseudoconvexity and quasiconvexity is given in the next proposition. Note that its assertion (b) does not involve Dini derivatives; it has been proved in [START_REF] Sach | Characterizations of generalized convexities via generalized directional derivatives[END_REF]Theorem 5.1] when for all x 2 C; if h(x; ) is subodd. Its last assertion is well known.

Proposition 5 Suppose f is h-pseudoconvex on a convex set C. If one of the following two conditions is satis…ed, then f is quasiconvex:

(a) f is radially lower semicontinuous on C and h D + f . (b) for all x 2 C; h(x; ) satis…es (T).

In particular, if f is radially di¤ erentiable and f is h-pseudoconvex with h := f 0 (in short, f is pseudoconvex), then f is quasiconvex.

Proof. (a) It is proved in [START_REF] Komlósi | Handbook of generalized convexity and generalized monotonicity[END_REF]Theorem 10.5]. (b) Suppose h(x; ) satis…es (T) for all x 2 C and f is h-pseudoconvex. To prove that f is quasiconvex, it su¢ ces to show that if there exist w; x; y 2 C with x 2]w; y[ such that f (x) > f (w) and f (x) > f (y); one is led to a contradiction. By h-pseudoconvexity of f; one gets h(x; w x) < 0 and h(x; y x) < 0: Since h(x; ) satis…es (T), one obtains the required contradiction.

When f is both h-pseudoconvex and h-protoconvex, we get a special form of invexity. Here, in the relation 8w;

x 2 C f (w) f (x) + h(x; v(w; x))
which characterizes h-invexity of f on C; the vector v(w; x) is of the form (w; x)(w x); with (w; x) 2 P, i.e. v(w; x) is positively colinear to the vector w x: This fact explains the terminology chosen in the next statement. This terminology may also have an interest in the case one needs to avoid a possible confusion with the notion of semiconvexity used in [START_REF] Tanaka | Note on generalized convex function[END_REF], where semiconvexity is equivalent with quasiconvexity and invexity or a confusion with the notion used for the study of Hamilton-Jacobi equations [START_REF] Cannarsa | Semiconcave Functions, Hamilton-Jacobi Equations and Optimal Control[END_REF]. Proposition 6 For any function f : C ! R and any bifunction h : V C ! R, the following assertions are equivalent:

(a) f is h-semiconvex (i.e. f is h-pseudoconvex and h-protoconvex).

(b) f is h-colinvex: there exists : C C ! P such that for all w; x 2 C; f (w) f (x) + (w; x)h(x; w x):

Proof. (b))(a) is obvious. (a))(b) Let f be h-pseudoconvex and h-protoconvex. Let x; w 2 C: If h(x; w x) = 0 then one has f (w) f (x)
by h-pseudoconvexity of f and one can take (w; x) = 1 or any element in P:

If h(x; w x) > 0 then f (w) > f (x) by h-protoconvexity of f . Then one can take

(w; x) = [f (w) f (x)]=h(x; w x):
If h(x; w x) < 0 then one can take

(w; x) := maxf[f (w) f (x)]=h(x; w x); 0g + 1;
and then f (w) f (x) + (w; x)h(x; w x): Remark 1. When f is convex and if h is the radial derivative of f; then (b) is satis…ed with (w; x) = 1: As mentioned above, the speci…c choice of the function v given by v(w; x) := (w; x)(w x) as a vector colinear with w x pin points this class of functions among invex functions.

Pseudoa¢ ne, protoa¢ ne and colin…ne functions

In the present section we study classes of functions which are still more restrictive. Their interests lie in their striking properties. In particular, they enjoy nice composition properties with the classes studied in the preceding section. Their behaviors will be presented in Proposition 10 and their mutual relationships will be examined in Proposition 13 below.

The terminology we adopt in the following de…nition is new. It stems from the fact that a function which is both convex and concave is a¢ ne.

De…nition 7 Given a bifunction

h : V C ! R, a function f : C ! R is said to be (a) h-pseudoa¢ ne at x 2 C if f is h-pseudoconvex and h-pseudoconcave at x 2 C: (b) h-protoa¢ ne at x 2 C if f is h-protoconvex and h-protoconcave at x 2 C: (c) h-semia¢ ne at x 2 C if f is h-semiconvex and h-semiconcave at x 2 C: (d) h-colin…ne at x 2 C if there exists some function : C C ! P such that 8w 2 C f (w) f (x) = h(x; (w; x)(w x)): (1)
We say that

f is h-pseudoa¢ ne on C (in short, h-pseudoa¢ ne) (resp. h-protoa¢ ne, h-semia¢ ne, h-colin…ne) if f is h-pseudoa¢ ne (resp. h-protoa¢ ne, h-semia¢ ne, h-colin…ne) at every x 2 C: In the case h is the radial derivative of f , we simply say that f is pseudoa¢ ne (resp. protoa¢ ne, semia¢ ne, colin…ne) if f is h-pseudoa¢ ne (resp. h-protoa¢ ne h-semia¢ ne, h-colin…ne) on C.
Observe that a radially di¤erentiable function f is pseudoa¢ ne (resp. protoa¢ ne) if, and only if, f and f are pseudoconvex (resp. quasiconvex).

The …rst concept of the preceding de…nition has been introduced by Chew and Chow [START_REF] Chew | Pseudolinearity and e¢ ciency[END_REF] in the di¤erentiable case and has been used in [START_REF] Aggarwal | Pseudolinearity and e¢ ciency via Dini derivatives[END_REF], [START_REF] Cambini | On generalized linearity of quadratic fractional functions[END_REF], [START_REF] Cambini | Generalized convexity and optimality conditions in scalar and vector optimization[END_REF], [START_REF] Komlósi | First and second characterizations of pseudolinear functions[END_REF], [START_REF] Śmietański | Characterization of solution sets to pseudolinear programming problems[END_REF], [START_REF] Lalitha | Vector variational inequalities with cone-pseudomonotone bifunction[END_REF] [START_REF] Yang | Vector variational inequality and vector pseudolinear optimization[END_REF].

It is easy to show that f is h-colin…ne if, and only if,

f is h-colinvex and f is h-colinvex.
The following observation is immediate. We will prove a related property in Proposition 13.

Proposition 8 Given h : V C ! R, a function f : C ! R is h-colin…ne if, and only if, it is h-semia¢ ne.
Proof. Suppose that f is h-colin…ne. Then there exists some function :

C C ! P such that 8w; x 2 C f (w) f (x) = (w; x)h(x; w x): Hence, f is h-conlinvex and f is h-colinvex. By Proposition 6, one has that f is h-semiconvex and f is h-semiconvex. Thus, f is h-semia¢ ne.
Conversely, suppose that f is h-semia¢ ne. Using either Proposition 6, or the de…nition of a semia¢ ne function, we see that

f (w) > f (x) if, and only if, h(x; w x) > 0, f (w) < f (x) i¤ h(x; w x) < 0 and f (w) = f (x) i¤ h(x; w x) = 0: One can take any (w; x) 2 P if h(x; w x) = 0 and (w; x) = [f (w) f (x)]=h(x; w x) otherwise. Then f is h-colin…ne.
Now, let us give an answer to the question: when is a colin…ne function a convex function?

Proposition 9 Let f be h-colin…ne on a convex set C and let : C C ! P be such that

f (w) f (x) = (w; x)h(x; w x):
Assume that for all w; x 2 C; t 2 [0; 1] one has (x t ; x) (w; x) where

x t := x + t(w x). Then f is convex on C. Proof. Let w; x 2 C; x t := x + t(w x) for t 2 [0; 1]: Since f is h-colin…ne, one has f (x t ) f (x) = (x t ; x)h(x; x t x) = t (x t ; x)h(x; w x); t[f (w) f (x)] = t (w; x)h(x; w x):
By assumption, we have

(x t ; x) (w; x); hence f (x t ) f (x) t(f (w) f (x)): Thus, f is convex.
Even for a smooth bifunction h; a h-colin…ne function maybe nonsmooth, and not even continuous. Example 3. Let f : R ! R be de…ned by f (x) = x + 1 for x > 0; f (x) = x 2 for x 0; and let h : R R ! R be given by h(x; u) = u for all x; u 2 R. Then f is h-colin…ne. Note that h is neither the radial derivative nor one of the Dini derivatives of f: The function f is not continuous at 0 and f is neither convex nor concave. Example 4. If h is as in the preceding example, the function f : R ! R given by f (x) := x 3 is not pseudoa¢ ne but f is h-colin…ne.

The preceding example shows that a function may be h 1 -colin…ne for some bifunction h 1 : (x; u) ! u but not h 2 -colin…ne (and not even h 2 -pseudoconvex) for some other bifunction h 2 := f 0 ; even when f; h 1 and h 2 are smooth.

The next statement shows that h-colin…ne functions have a very special behavior. Here, we do not need any continuity of f:

Proposition 10 Let f : C ! R: There exists h : V C ! R such that f is h-colin…ne on C if and
only if for all w; x 2 C; the function t 7 ! f (x + t(w x)) is either increasing or decreasing or constant on the interval on which it is de…ned.

Proof. Let f be h-colin…ne on C: Let w; x 2 C. If f (w) = f (x) then f (x + t(w x)) = f (x) for all t 2 R such that x t := x + t(w x) 2 C since we have h(x; x t x) = th(x; w x) = 0: If f (w) > f (x) then h(x; w x) > 0: For all t > 0 such that x t := x + t(w x) 2 C; one has h(x; x t x) > 0 by homogeneity. Thus f (x + t(w x)) > f (x): Also, given u := x + r(w x) 2 C and v := x + s(w x) 2 C with s > r; one has f (v) > f (u): Otherwise, one would have either f (v) = f (u) and then f (w) = f (x) by what precedes or f (v) < f (u) and then h(v; u v) > 0; h(v; x v) < 0 (as f (x) < f (v)
), a contradiction since x v = q(u v) for some q > 0: A similar proof shows that for every t > 0 we have f (w + t(x w)) < f (w) and for s > r; f (w + s(x w)) < f (w + r(x w)) when the involved points are in C:

If f (x) > f (w); then we also have the conclusion by interchanging the role of w and x in what precedes.

Conversely, a possible choice for h is as follow. Given (x; u) 2 V C with kuk = 1; we can …nd some t 2 P such that w := x + tu 2 C: If f (w) = f (x), for r 2 P we take h(x; ru) = 0 and (x + ru; x) := 1:

If f (w) 6 = f (x); then for r 2 P, we set h(x; ru) := rt 1 (f (w) f (x)); (x + ru; x) := t(f (x + ru) f (x)) r(f (x + tu) f (x)) :
Then h is clearly positively homogeneous in its second variable, (x + ru; x) 2 P by our assumption, and if (x + ru; x) 2 C C we have f (x + ru) f (x) = (x + ru; x)h(x; ru); as required. From Proposition 10, the set of all minimizers and the set of all maximizers of a non constant h-colin…ne function are contained in the boundary its domain.

Corollary 11 Let f be h-colin…ne and let w; x 2 C: Then for any x t := x + t(w x); t 2 Rnf0; 1g such that x t 2 C; the followings statements are equivalent:

(a) f (x) = f (w): (b) h(x; w x) = 0: (c) h(w; x w) = 0: (d) f (x t ) = f (x): (e) h(x t ; w x) = 0: (f ) f (x t ) = f (w):
Proof. One has (a),(b),(c) by the de…nition of a h-colin…ne function and (a),(d),(f) by the preceding proposition. If t > 0 then x t x = t(w x); hence (d),(e); Otherwise t < 0 then w x t = (1 t)(w x); hence (f),(e).

Although the preceding de…nitions are quite restrictive, they are satis…ed in some cases of significant interest. Example 5. Let f : R ! R be increasing and let h : R R ! R be positively homogeneous in its second variable and such that h(x; 1) > 0; h(x; 1) < 0: Then f is h-colin…ne. Note that we do not require h is the directional derivative nor some Dini derivative of f: Example 6. Let X be a n.v.s., let a; b 2 X ; ; 2 R. For C := fx 2 X : bx

+ > 0g; let f : C ! R be given by 8x 2 C f (x) = (ax + )=(bx + ): Since f 0 (x; w x) = (awbx axbw + ( a b)(w x))=(bx + ) 2 ;
one has

f (w) f (x) = awbx axbw + ( a b)(w x) (bw + )(bx + ) = bx + bw + f 0 (x; w x)
and f is colin…ne on C with (w;

x) := (bx + )(bw + ) 1 : But f is not convex when b 6 = 0. Example 7. Let g : X ! R be colin…ne. For C := fx 2 X : g(x) > 0g; let f (x) = p g(x)
: Since g is colin…ne, we have g(w) g(x) = g (w; x)g 0 (x; w x) with g (w; x) > 0: Since

f 0 (x; w x) = 1 2 p g(x) g 0 (x; w x); one has f (w) f (x) = (g(w) g(x))=( p g(w) + p g(x)) = g (w; x)g 0 (x; w x)=( p g(w) + p g(x)): Thus f is colin…ne with f (w; x) := 2 g (w; x) p g(x)=( p g(w) + p g(x)
): Example 8. More generally, let X be a n.v.s., g : X ! R be colin…ne on C := fx 2 X : g(x) > 0g: Then, for any p 2 N, f (:) := g p (:) := (g(:)) p is colin…ne on C; In fact, since g is colin…ne on C, for any w; x 2 C we have g(w) g(x) = g (w; x)g 0 (x; w x) with g (w; x) > 0: Since f 0 (x; w x) = p(g(x)) p 1 g 0 (x; w x); we get

f (w) f (x) = g p (w) g p (x) = (g(w) g(x))( p 1 X k=0 g k (w)g p k 1 (x)) = g (w; x)g 0 (x; w x)( p 1 X k=0 g k (w)g p k 1 (x)): Thus f is colin…ne with f (w; x) := g (w; x)p 1 g 1 p (x)( p 1 P k=0 g k (w)g p k 1 (x)):
Composition properties which will be established in the next section will enlarge the …eld of colin…ne functions. For the moment, we delineate some properties and characterizations.

In the sequel, we will consider the following hypothesis: (H + ) If h(x; w x) > 0 for some w; x 2 C; then there exists z 2]w;

x[ such that f (z) > f (x): (H ) If h(x; w x) < 0 for some w; x 2 C; then there exists z 2]w; x[ such that f (z) < f (x): (H + 0 ) If h(x; w x) > 0; f (w) = f (x) for some w; x 2 C, then there exists z 2]w; x[ such that f (z) > f (x): (H 0 ) If h(x; w x) < 0; f (w) = f (x) for some w; x 2 C, then there exists z 2]w; x[ such that f (z) < f (x):
We say that f satis…es (H + ) (resp. (H ), (H + 0 ), (H 0 )) if it satis…es (H + ) (resp. (H ), (H + 0 ), (H 0 )). For the rest of this section, we assume that C is convex. Remark 2. (a) Clearly (H + ) implies (H + 0 ) and (H ) implies (H 0 ). (b) These assumptions are rather mild. Hypothesis (H + ) (resp. (H )) is weaker than hprotoconvexity (resp. h-quasiconcavity) of f: They are automatically satis…ed if h is the radial derivative of f: More generally, we can make a precise comparison with the Dini derivatives of f: Lemma 12 (a) If, for all x 2 C; h(x; )

D + f (x; ) (resp. h(x; ) D + f (x;
)) then assumption (H + ) (resp. (H )) is satis…ed. In fact, if for all w; x 2 C; h(x; w x) > 0 =) D + f (x; w x) > 0

(2)

(resp. h(x; w x) < 0 =) D + f (x; w x) < 0), (3)
then f satis…es (H + ) (resp. (H )). (b) Conversely, if condition (H + ) (resp. (H )) is satis…ed, then for all w; x 2 C one has h(x; w x) > 0 =) D + f (x; w x) 0 (resp. h(x; w x) < 0 =) D + f (x; w x) 0).
Proof. (a) Suppose (2) holds. Given w; x 2 C such that h(x; w x) > 0, one has D + f (x; w x) > 0; so that there exists a sequence (t n ) ! 0 + satisfying f (x + t n (w x)) > f (x): Taking n large enough, we get some z := x + t n (w x) 2 (w; x) such that f (z) > f (x): Similarly, if (3) holds, then f satis…es (H ).

(b) Conversely, when f satis…es (H + ) and w; x 2 C are such that h(x; w x) > 0; for every (" n ) ! 0 + one also has h(x; " n (w x)) > 0; so that there exists t n 2]0; " n [ such that f (x + t n (w x)) > f (x): Thus D + f (x; w x) 0: The case of (H ) is similar.

Let us deal with a characterization which has been obtained in [START_REF] Aggarwal | Pseudolinearity and e¢ ciency via Dini derivatives[END_REF], [START_REF] Cambini | On generalized linearity of quadratic fractional functions[END_REF], [START_REF] Chew | Pseudolinearity and e¢ ciency[END_REF], [START_REF] Komlósi | First and second characterizations of pseudolinear functions[END_REF], [START_REF] Lalitha | Vector variational inequalities with cone-pseudomonotone bifunction[END_REF], [START_REF] Lalitha | A note of pseudolinearity in terms of bifunctions[END_REF], [START_REF] Śmietański | Characterization of solution sets to pseudolinear programming problems[END_REF], [START_REF] Yang | Vector variational inequality and vector pseudolinear optimization[END_REF] under additional assumptions (lower semicontinuity of f or the assumption that h is equal to the radial derivative of f or to one of the Dini derivatives of f ). In the following statement, given functions f; h; we say that f satis…es (R) if for every w; x; y 2 C, x 2]w; y[ the function s := h(x; ) satis…es (R).

Proposition 13 (a) A function

f : C ! R is h-pseudoa¢ ne and satis…es (R), (H + 0 ), (H 0 ) if and only if f is h-colin…ne. (b) If f is h-pseudoa¢ ne, if D + f h D + f and if either f satis…es (R) or is radially continuous, then f is h-colin…ne.
Proof. (a) Let f be h-pseudoa¢ ne and let (R), (H + 0 ), (H 0 ) be satis…ed. Given w; x 2 C; let us …nd some (w; x) 2 P such that f (w) f (x) = (w; x)h(x; w x). If f (w) < f (x) then, by h-pseudoconvexity we have h(x; w x) < 0 and we can take

(w; x) = [f (w) f (x)]=h(x; w x):
If f (w) > f (x) then, by h-pseudoconcavity, we have h(x; w x) > 0 and we can take

(w; x) = [f (w) f (x)]=h(x; w x):
In the case f (w) = f (x); we will prove h(x; w x) = 0; so that we take (w; x) = 1: We may suppose x 6 = w: If h(x; w x) > 0; by hypothesis (H + 0 ), there exists z 2]w; x[ such that f (z) > f (x) = f (w): Thus, h(z; x z) < 0 and h(z; w z) < 0, a contradiction with (R). Similarly, if h(x; w x) < 0; hypothesis (H 0 ) yields a contradiction with (R).

Conversely, suppose f is h-colin…ne. Then f is h-semia¢ ne by Proposition 8. Let us …rst check that for all x 2 C condition (R) is satis…ed by f , or, equivalently, that, for all x 2 C; h(x; ) and h(x; ) satisfy condition (T). Let w; y 2 C be such that x 2]w; y[ and h(x; w x) < 0; we have to show that h(x; y x) 0: Suppose h(x; y x) < 0: Since f is h-colin…ne, we have f (y) < f (x). By Proposition 10 we obtain f (x) < f (w), hence h(x; w x) > 0; a contradiction. Thus, (R) is satis…ed. Now let us prove that (H + ) is satis…ed. Let w; x 2 C be such that h(x; w x) > 0: Then for all z 2]x; w[; h(x; z x) > 0 by homogeneity, hence f (z) > f (x) for all z 2]x; w[: Thus (H + ) is satis…ed.

Similarly, (H ) is satis…ed. Now (H + 0 ), (H 0 ) are satis…ed too, by Remark 2(a). (b) When f is h-pseudoa¢ ne and satis…es (R) and when D + f h D + f; conditions (H + 0 ), (H 0 ) are ful…lled by Remark 2 and Lemma 12; then the conclusion follows from (a). In the case f is h-pseudoa¢ ne, radially continuous and D + f h D + f; one has that f and f are quasiconvex by Proposition 5(a) and then f and f are h-protoconvex and h-protoconvex by Proposition 4(a). Thus f is h-protoa¢ ne and then f is h-semia¢ ne. By Proposition 8, f is h-colin…ne.

Let us consider the property:

(E 0 ) 8x; w 2 C; h(x; w x) = 0 ) f (x) = f (w): Remark 3. (a) This property is satis…ed if f is h-pseudoa¢ ne or if h(x; u) 6 = 0 for all x 2 C; u 2 Xnf0g: (b) If f is h-protoa¢ ne the reverse implication f (x) = f (w) ) h(x; w x) = 0 holds. (c)
Condition (E 0 ) implies that if there exist w; x 2 C such that h(x; w x) = 0; then for all t > 0; f (x + t(w x)) = f (x):

A related property is presented in the following corollary.

Corollary 14 Let f satisfy properties (R), (H + ), (H ). If f is h-pseudoa¢ ne on C then the following condition is satis…ed:

(E) 8x; w 2 C; h(x; w x) = 0 , f (x) = f (w):
The converse is true if f is radially continuous on C. In fact, if f is radially continuous on C and satis…es conditions (E) and (H + ) (resp. (H )) then f is h-pseudoconvex (resp. h-pseudoconcave).

Proof. The …rst assertion follows from Proposition 13(a) by de…nition of a h-colin…ne function.

To prove the converse assertion, changing f and h into f and h respectively, it su¢ ces to show that if f (w) < f (x); then one has h(x; w x) < 0: Since f satis…es condition (E 0 ), it is enough to prove that assuming h(x; w x) > 0 leads to a contradiction. By hypothesis (H + ), there exists

z 2]w; x[ such that f (z) > f (x): By continuity of f on [w; z], since f (x) 2]f (w); f (z)[; there exists u 2 [w; z] such that f (u) = f (x): Then u 6 =
x and there exists r 2 P such that w x = r(u x): By condition (E), 0 = h(x; u x) = h(x; w x); a contradiction with our assumption h(x; w x) > 0.

Corollary 15 If h D + f and if f is radially continuous on C and satis…es condition (E) then f is h-pseudoconvex.

If h D + f and if f is radially continuous on C and satis…es condition (E) then f is hpseudoconcave.

The second assertion of the next corollary slightly extends assertion ii) of Theorem 4.13 in [START_REF] Cambini | Generalized convexity and optimality conditions in scalar and vector optimization[END_REF]; see also [START_REF] Chew | Pseudolinearity and e¢ ciency[END_REF], [START_REF] Komlósi | First and second characterizations of pseudolinear functions[END_REF], [START_REF] Kortanek | Pseudoconcave programming and Lagrange regularity[END_REF].

Corollary 16 If D + f and D + f are …nite, if D + f h D + f and if f satis…es condition (E) then f is h-pseudoa¢ ne.
In particular, if f is radially di¤ erentiable on C and such that for every x 2 C the derivative f 0 (x; ) is sublinear, then f is pseudoa¢ ne on C if, and only if f satis…es condition (E).

The following illuminating Theorem has been given in [8, Theorems 4.13, 4.14], [START_REF] Komlósi | First and second characterizations of pseudolinear functions[END_REF], [START_REF] Kortanek | Pseudoconcave programming and Lagrange regularity[END_REF] in the case the function f is di¤erentiable and h := f 0 . Here f is nonsmooth. We start with a preparatory lemma dealing with the one-dimensional case.

Lemma 17 Let C be an open interval of R and let ' : C ! R be continuous, satisfy (H + ) and (H ) with respect to h ' ; where h ' : C R ! R is given for some function `' : R ! R by h ' (t; r) := `'(t)r for all (t; r) 2 C R. Then ' is h ' -colin…ne if, and only if, either (E 0 ) is satis…ed and there exists some t 2 C such that `'(t) = 0 or the sign of `' is constant on level sets of '.

Proof. Suppose that ' is h ' -colin…ne. Then there exists : C C ! P such that for any s 6 = t in C '(s) '(t) s t = (s; t)`'(t):

In addition, by Proposition 10, ' is constant or increasing or decreasing. By relation ( 4), if ' is constant then `' = 0 and (E 0 ) is satis…ed; otherwise the sign of `' is constant.

For the converse, let us …rst suppose (E 0 ) is satis…ed and `'(t) = 0 for some t 2 C. Then, for any s 2 C; `'(t)(s t) = 0; so that, by (E 0 ), '(s) = '(t): Hence, ' is constant and, in view of conditions (H + ) and (H ), we must have `'(r) = 0 for all r 2 C: then ' is h ' -colin…ne. Now suppose that the sign of `' is constant on level sets of '. Given r 2 C such that `'(r) > 0; we will prove that '(t) > '(r) for all t > r: Let us …rst prove that we cannot have '(t) < '(r): If '(t) < '(r), let s := supfp 2 [r; t] : '(p) = '(r)g: Since ' is continuous, we have s < t and '(s) = '(r): Then our assumption yields `'(s) > 0; so that, by condition (H + ) there exists some p 2]s; t[ with '(p) > '(s) and the intermediate value theorem yields some q 2 [p; t] with '(q) = '(s) = '(r); a contradiction with the de…nition of s:

Now let us suppose that '(t) = '(r): Then `'(t) > 0 by our assumption, so that h ' (t; r t) = `'(t)(r t) < 0: Then condition (H ) yields some t 0 2]r; t[ such that '(t 0 ) < '(t) = '(r): Replacing t by t 0 ; the …rst part of the proof yields the expected contradiction.

A similar proof shows that '(q) < '(r) for all q < r: Changing ' and `' into their opposites, we see that when `'(r) < 0, the function ' is decreasing on C: Thus, ' is h ' -colin…ne.

Theorem 18 Let C be an open, convex subset in X; f : C ! R be radially continuous and h : V C ! R be linear and continuous in its second variable, with `(x) := h(x; ) for all x 2 C.

(a) Let f be h-colin…ne. Then, for all w 2 C; the set K w := fu 2 X : h(w; u) = 0g is a linear subspace of X: Moreover, for every

x 2 C such that f (x) = f (w) one has K x = K w : (b) If f is h-colin…ne on C then for any w; x 2 C such that f (w) = f (x)
, the linear forms `(w) and `(x) are positively colinear:

w; x 2 C : f (w) = f (x) ) 9r 2 P; `(w) = r`(x): (5)
(c) Conversely, suppose that f and h satis…es (E 0 ), (H + ) and (H ), in particular, D + f h D + f and `(x) 6 = 0 for all x 2 C:

If x 7 ! `(x) jj`(x)jj
is constant on the level sets of f; then f is h-colin…ne.

(d) Assume that C = X, f satis…es (E 0 ), (H + ) and (H ) and `(x) 6 = 0 for all x 2 C. Then f is h-colin…ne if, and only if, x 7 ! `(x) jj`(x)jj is constant on X.

Proof. (a) Let u; v 2 K w . Since C is open, T r (C; w) = fwg X and for t 2 R + with t small enough one has w +tu 2 C and f (w +tu) = f (w)+ (w +tu; w)th(w; u) = f (w): Thus, by Proposition 10, f (w + ru) = f (w) for all r 2 R such that w + ru 2 C: It follows that h(w; tu) = 0 for t > 0 small enough and u 2 K w : Now for t > 0 small enough, we also have w + tv 2 C and w + t(u + v) 2 C: Since f (w + tv) = f (w) = f (w + tu), by Proposition 10 again, we obtain

f (w + t u + v 2 ) = f ( w + tu 2 + w + tv 2 ) = f (w):
Thus u + v 2 K w : It follows that K w is a linear subspace. Now let w; x 2 C be such that f (w) = f (x) and let u 2 K w . Since f is h-colin…ne, we have x w 2 K w , hence x w + tu 2 K w for all t 2 R since K w is a linear subspace. Thus, for jtj small enough, we have x + tu 2 C and f (x + tu) = f (w) = f (x): Therefore h(x; u) = 0 and u 2 K x . So, K w K x . The symmetry of the roles of w and x yields K w = K x .

(b) Let w; x 2 C be such that f (w) = f (x): Since K w = ker l(w), K x = ker l(x) and K w = K x either `(w) and `(x) are both 0 and ( 5) is satis…ed with r = 1 or both of them are non zero and there exits r 2 Rnf0g such that `(w) = r`(x): Let us prove that r is positive. Suppose that r < 0. Pick u 2 X such that h`(x); ui = 1 and set z 1 = w + tu and z 2 = x + tu, with t > 0 small enough to ensure that

z 1 ; z 2 2 C: Then, since f is h-colin…ne, one has f (z 1 ) < f (w) = f (x) < f (z 2 )
and h`(x); w xi = h(x; w x) = 0: Since f is radially continuous, there exists z 2]z 1 ; z 2 [ such that f (z) = f (x): Thus, there exists some s 2]0; 1[ such that z := sz 1 +(1 s)z 2 ; hence z x = sw sx+tu and 0 = h(x; z x) = `(x)(z x) = `(x)(sw sx + tu) = t > 0;

a contradiction. Hence, the case r < 0 is excluded and (b) is established. (c) Let w; x 2 C and for t 2 C w;x := ft 2 R : x t 2 Cg with x t := (1 t)x + tw; set '(t) := f (x t ). For t 2 C w;x ; let `'(t) := `(x t )(w x) and h ' (t; r) := `'(t)r for r 2 R. Let us show that ' satis…es (H + ), (H ) and (E 0 ) with respect to h ' . Suppose that h ' (t; s t) > 0 for t; s 2 C w;x , with s 6 = t: Then h(x t ; x s x t ) = (s t)`(x t )(w x) = h ' (t; s t) > 0: Since f satis…es (H + ), there exists q 2 (t; s) such that '(q) := f (x q ) > f (x t ) := '(t): Thus, ' satis…es (H + ). With a similar proof, we see that ' satis…es (H ). Suppose that h ' (t; s t) = 0 for t; s 2 C w;x , with s 6 = t: Then

h(x t ; x s x t ) = h ' (t; s t) = 0: Since f satis…es (E 0 ), '(s) := f (x s ) = f (x t ) := '(t), hence ' satis…es (E 0 ).
Suppose that `'(t) 6 = 0 for all t 2 C w;x . We shall prove that the sign of `' is constant on the level sets of '. Suppose that r; s 2 C w;x are such that '(r) = '(s) and `'(r)`'(s) < 0: We may assume that r < s, `'(r) > 0 and `'(s) < 0. Set x r := (1 r)x + rw and

x s := (1 s)x + sw: Since f (x r ) = '(r) = '(s) = f (x s ), we have h ' (r; 1) = `(x r )(w x) > 0 and 0 > h ' (s; 1) = `(x s )(w x) = jj`(x s )jj jj`(x r )jj
`(x r )(w x) > 0: a contradiction. Thus, there exists some t 2 C such that `'(t) = 0 or the sign of `' is constant on level sets of ' and then ' is h ' -colin…ne, by the preceding lemma. Since for any w; x 2 C, ' is h ' -colin…ne and

f (w) f (x) = '(1) '(0) = ' (1; 0)h ' (0; 1 0) = (w; x)h(x; w x)
where (w; x) := ' (1; 0) > 0; thus f is a h-colin…ne function.

(d) If C = X and the mapping x 7 ! `(x) jj`(x)jj is constant on X; then f is h-colin…ne by (c).

Conversely, suppose that f is h-colin…ne and there exist w; x 2 C such that `(w) jj`(w)jj 6 = `(x) jj`(x)jj .

From (b), we have f (w) 6 = f (x): Let K w and K x be as in (a). Since `(w) jj`(w)jj 6 = `(x) jj`(x)jj , there exists

z 2 (w +K w )\(x+K x ). One has z w 2 K w , z x 2 K x , hence `(w)(z w) = 0 and `(x)(z x) = 0. Since f is h-colin…ne, we have f (x) = f (z) = f (w) : a contradiction.
The next proposition is related to the preceding results; however, the assumptions are di¤erent.

Proposition 19

Let C be open, let f be radially continuous and `(x

) := h(x; ) 2 X nf0g for all x 2 C. If f is h-quasiconvex then f is h-pseudoconvex. If f is h-protoa¢ ne then f is h-colin…ne and condition (E) is satis…ed.
Proof. Let w; x 2 C be such that f (w) < f (x): Then `(x)(w x) 0 by h-quasioconvexity of f: Suppose that `(x)(w x) = 0: Since `(x) 6 = 0; we can …nd u 2 X such that `(x)(u) = 1: By the radial continuity of f; there exists " > 0 such that y := w + "u 2 C and f (y

) < f (x): By h-quasiconvexity of f; 0 `(x)(y x) = "`(x)(u) = " > 0; a contradiction. Hence, `(x)(w x) < 0 and then f is h-pseudoconvex. Since a h-protoconvexity function is h-quasiconvex, if f is h-protoconvex and `(x) 6 = 0, then f is h-pseudoconvex. Similarly, if f is h-protoa¢ ne then f is h-pseudoa¢ ne,
hence h-semia¢ ne and h-colin…ne by Proposition 8. The last assertion follows from Remark 3.

The following example illustrates the preceding theorem; note that since f is not di¤erentiable, [8, Theorems 4.13, 4.14], [START_REF] Komlósi | First and second characterizations of pseudolinear functions[END_REF], [START_REF] Kortanek | Pseudoconcave programming and Lagrange regularity[END_REF] cannot be applied. Example 9. (a) Let f : R 2 ! R be de…ned by f (x 1 ; x 2 ) = x 2 for x 2 0, f (x 1 ; x 2 ) = x 2 2 for x 2 > 0; and let h : R 2 R 2 ! R be given by for all u 2 R 2 , h((x 1 ; x 2 ); (u 1 ; u 2 )) = u 2 for x 2 0 and h((x 1 ; x 2 ); (u 1 ; u 2 )) = x 2 u 2 for x 2 > 0. Then, as easily seen, f satis…es (E 0 ), (H + ) and (H ), and

x 7 ! `(x) jj`(x)jj
is constant with l(x) := h(x; ): Thus, f is h-colin…ne.

We deduce from [START_REF] Martínez-Legaz | Quasiconvex duality theory by generalized conjugation methods[END_REF], [START_REF] Penot | Glimpses upon quasiconvex analysis[END_REF] a characterization of continuous h-colin…ne functions. We suppose that X is …nite dimensional.

Proposition 20 A continuous (resp. lower semicontinuous), nonconstant function f : X ! R is h-colin…ne for some h : X X ! R if, and only if, there exist a continuous linear form `on X; `6 = 0 and a continuous (resp. lower semicontinuous) increasing function g : R ! R such that f = g `:

Proof. The condition is clearly su¢ cient: for w; x 2 X one has f (w) f (x) = (w; x)h(x; w x); with h(x; u) := `(u) and

(w; x) := g(`(w)) g(`(x)) `(w) `(x) for `(w) 6 = `(x); (w; x) := 1 for `(w) = `(x)
Conversely, let f : X ! R be l.s.c. (resp. continuous) and h-colin…ne, hence protoa¢ ne. According to [START_REF] Martínez-Legaz | Quasiconvex duality theory by generalized conjugation methods[END_REF], [START_REF] Penot | Glimpses upon quasiconvex analysis[END_REF], there exist a continuous linear form `on X and a l.s.c. (resp. continuous) nondecreasing function g : R ! R such that f = g `: Since f is nonconstant, `6 = 0: Let us prove that g is increasing. Suppose that there exist r 1 < r 2 in R such that for all r 2 [r 1 ; r 2 ]; g(r) = g(r 1 ) = g(r 2 ). Since f is nonconstant, g is nonconstant and then for t > 1 large enough, g(r 1 + t(r 2 r 1 )) > g(r 2 ) or g(r 2 + t(r 1 r 2 )) < g(r 1 ): Without loss of generality, we can assume that g(r 1 + t(r 2 r 1 )) > g(r 2 ) for t large enough. Since `is linear and continuous with `6 = 0, there exist w; x 2 X such that `(w) = r 1 < `(x) = r 2 . Taking t large enough `(w + t(x w)) > `(x) = r 2 and then f (w + t(x w)) = g(`(w + t(x w))) = g(r 1 + t(r 2 r 1 )) > g(r 1 ) = f (w) = f (x): By Proposition 10, f is not h-colin…ne. Therefore g is increasing.

Note that if f is constant, then one can take g constant and `= 0: Conversely, if g is constant or if `= 0 then g l is constant.

Vector-valued maps and preservation properties

Extensions of the preceding concepts to vector-valued maps can be given. The cases of quasiconvex and pseudoconvex maps have been studied in detail by several authors ( [START_REF] Cambini | Generalized convexity and optimality conditions in scalar and vector optimization[END_REF], [START_REF] Luc | Handbook of generalized convexity and generalized monotonicity[END_REF], [START_REF] Flores | Semistrictly quasiconvex mappings and non-convex vector optimization[END_REF]...). The cases of colin…ne and semia¢ ne maps seem to be new. Our purpose here is limited. We just intend to use vector-valued concepts in order to get composition properties, and thus some more means for constructing examples. In the sequel, Y is another n.v.s., with dual space Y .

De…nition 21 Given a map H : V C ! Y which is positively homogeneous in its second variable, a map F : C ! Y is said to be H-colin…ne at x 2 C if there exists some function :

C C ! P such that for all w 2 C F (w) F (x) = H(x; (w; x)(w x)): It is H-colin…ne on C if it is H-colin…ne at every x 2 C:
An immediate composition result can be stated. Proof. Let us prove that if F is scalarly H-pseudoconvex then it is H-pseudoconvex; the converse is obvious. Let w; x 2 C be such that H(x; w x) 0: Then, for all y 2 Y + we have (y H) (x; w x) 0; hence (y F ) (w) (y F ) (x): Then, the bipolar theorem ensures that F (w) F (x): The proofs of the other cases are similar.

It is H-pseudoconvex on C if it is H-pseudoconvex at every x 2 C: (b) F is said to be H-protoconvex at x 2 C if w 2 C; F (w) F (x) =) H(x; w x) 0: It is H-protoconvex on C if it is H-protoconvex at
Given some n.v.s. X; Y; a subset C of X; we say that a map F : C ! Y is directionally di¤ erentiable (resp. radially di¤erentiable) at x 2 C if for any u 2 T (C; x) (resp. u 2 T r (C; x)) the quotient (1=t)(F (x + tv) F (x)) (resp. (1=t)(F (x + tu) F (x))) has a limit as (t; v) ! (0 + ; u) with x + tv 2 C (resp. t ! 0 + ). The preceding limit is then called the directional derivative (resp. the radial derivative) of F at x in the direction u and is denoted by F 0 (x; u): Such a concept is a natural extension of the notion described above for real-valued functions.

If F : C ! Y has a radial or a directional derivative, we say that F is colin…ne if H is the radial derivative and F is H-colin…ne. We use a similar convention for the other concepts introduced above. Then F is quasiconvex but it is not pseudoconvex as y F is not pseudoconvex when y is the second projection. Note that F is of class C 1 and it derivative is never 0: Remark 4. Here, H-colin…ne maps and H-scalarly semia¢ ne maps are di¤erent from vector pseudolinear in [START_REF] Yang | Vector variational inequality and vector pseudolinear optimization[END_REF], where its components are colin…ne. Also, our de…nition of a pseudoa¢ ne map di¤ers from the one in [8, De…nition 4.13], even when one takes for H the derivative of F . Let us give some preservation properties. The …rst one is immediate.

C ! Y , G : D ! Z, H : V C ! Y , K : V D ! Z: Assume that F (C) + Y + D;
F is H-colinvex on C and that for every (y; v) 2 V D; v 0 2 Y + one has K(y; v + v 0 ) K(y; v): Let L : V C ! Z be given by L(x; x 0 ) := K(F (x); H(x; x 0 )) for (x;

x 0 ) 2 V C: (a) If G is K-pseudoconvex on D; then G F is L-pseudoconvex on C. (b) If G is K-protoconvex on D; then G F is L-protoconvex on C. (c) If G is K-colinvex on D; then G F is L-colinvex on C.
Proof. (a) Since F is H-colinvex on C; given w; x 2 C one can …nd (w; x) 2 P such that

F (w) F (x) = (w; x)H(x; w x) + v 0 with v 0 2 Y + . Since G is K-pseudoconvex on D; for u := F (w); v := F (x) one has L(x; w x) 0 ) K(F (x); H(x; w x)) 0 ) K(F (x); (w; x)H(x; w x) + v 0 ) 0 ) K(F (x); F (w) F (x)) 0 ) G(u) G(v): so that G F is L-pseudoconvex on C. (b) Given w; x 2 C such that G(F (w)) G(F (x)) one has K(F (x); F (w) F (x)) 0 by K-quasiconvexity of G; hence K(F (x); H(x; w x)) 0 since F is H-colinvex and K(F (x); ) is nondecreasing. (c) let u := F (w); v := F (x). Then G(u) G(v) (u; v)K(v; u v) (u; v)K(v;
(w; x)H(x; w x)); for some (u; v) 2 P by K-colinvexity of G. Remark 5. Note that assertion (c) yields Proposition 22 by taking Y + := f0g; Z + := f0g.

Taking Z := R, the preceding proposition yields a means to generate more examples of generalized convex functions. Observing that a¢ ne mappings being colin…ne, the same can be said for precompositions with a¢ ne maps. Now let us give a chain rule in which the …rst map is not supposed to be colinvex.

Proposition 28 Let f : C ! R, g : R ! R and let h : V C ! R, k : R 2 ! R be positively homogeneous in their second variables and let `: T r C ! R be given by `(x; u)

:= k(f (x); h(x; u)). (a) If f is h-pseudoconvex, if g is nondecreasing and if k(r; 1) < 0 for all r 2 f (C), then g f is `-pseudoconvex. (b) If f is h-protoconvex, if g is increasing and if D + g k, then g f is `-protoconvex; (c) If f is h-semiconvex, if g is increasing and if k(y; 1) < 0 for all y 2 f (C), then g f is `-semiconvex.
Note that when f and g have radial derivatives and h = f 0 ; k = g 0 ; then `is the radial derivative of g f (observe that the radial derivative of g is also the directional derivative of g).

Proof. (a) Let w; x 2 C be such that `(x; w x) = k(f (x); h(x; w x)) 0: Since k(f (x); 1) < 0, we have h(x; w x) 0: Since f is h-pseudoconvex we have f (w) f (x) and since g is nondecreasing, we get (g f )(w) (g f )(x): Thus g f is `-pseudoconvex.

(b) Let w; x 2 C be such that `(x; w x) = k(f (x); h(x; w x)) > 0. Since g is nondecreasing and k D + g, we have k(f (x); 1) D + g(f (x); 1) 0; thus, we must have h(x; w x) > 0 (recall that k(f (x); 0) = 0). Since f is h-protoconvex, we have f (w) > f (x) and since g is increasing, we get (g f )(w) > (g f )(x):

(c) We already know that g f is `-protoconvex. Let us prove that g f is `-protoconvex by slightly modifying (b). Let w; x 2 C be such that `(x; w x) = k(f (x); h(x; w x)) > 0. Since k(f (x); 1) < 0; we have h(x; w x) > 0; thus, as above, we get (g f )(w) > (g f )(x).

Characterizations of solution sets

In the present section, we apply the previous concepts to the constrained minimization problem

(C) minimize f (x) subject to x 2 C;
where C is a subset of X and f : C ! R: We denote by S the solution of the constrained problem (C). Let h : V C ! R be a generalized directional derivative of f which is positively homogeneous in the second variable and h( ; 0) = 0:

We say that f satis…es (H ) at a 2 C (relatively to h) if h(a; x a) < 0 for some x 2 C; then there exists z 2]a; x[ such that f (z) < f (a). Thus, f satis…es (H ) if, and only if, f satis…es (H ) at every a 2 C:

Proposition 29 Let f be h-pseudoconvex. If a 2 C is such that h(a; x a) 0 for all x 2 C; then a 2 S.
Conversely, if a 2 S, if C is starshaped at a; and if f satis…es (H ) at a (or in particular, if h(a; ) D + f (a; )), then h(a; x a) 0 for all x 2 C: Proof. The …rst assertion follows from the de…nition of h-pseudoconvexity of f at a. Conversely, let us suppose f satis…es (H ) at a 2 S: We must prove h(a; x a) 0; for all x 2 C: If there exists some x 2 C such that h(a; x a) < 0; then by (H ) and starshapedness of C, there exists z 2]a; x[ C such that f (z) < f (a): this is a contradiction with the assumption that a is a solution to (C).

The following variant is a direct consequence of the de…nition of an h-semia¢ ne function (or of the fact that f is h-colin…ne). Let us consider now the case in which the constraint set C is de…ned by a …nite family of inequalities, so that problem (C) turns into the mathematical programming problem (M) minimize f (x) subject to x 2 C := fx 2 X : g 1 (x) 0; :::; g m (x) 0g;

where f : W ! R, g i : W ! R and W is an open convex subset of X.

We assume that h : W X ! R, h i : W X ! R (i 2 N m := f1; :::; mg) are generalized directional derivatives of f and g i ; respectively, which are positively homogeneous in their second variables and such that h( ; 0) = h i ( ; 0) = 0. Given a 2 C, let I(a) := fi 2 N m : g i (a) = 0g and let P (a) stand for a set of i 2 I(a) such that g i is h i -pseudoconcave at a; while N (a) := I(a)nP (a).

Each continuous linear functional x on X satisfying hx ; i h(x; ) is said to be a subderivative of f with respect to h at x: The set @ h f (x) of all subderivatives at x is called the h-subdi¤ erential of f at x; it is a weak closed convex subset of X : Obviously, for a 2 X; h(a; u) 0; 8u 2 X , 0 2 @ h f (a):

The subdi¤erential of a convex function k : X ! R 1 at x 2 dom k := k 1 (R) is the set @k(x) := fx : k(w) k(x) hx ; w xi; 8w 2 Xg:

The su¢ cient optimality criteria which follows is an easy consequence of the preceding proposition. If f and g i (i 2 N m ) are di¤erentiable, then the su¢ cient optimality condition is the same as the classical result in [START_REF] Mangasarian | Nonlinear programming[END_REF] and [START_REF] Cambini | Generalized convexity and optimality conditions in scalar and vector optimization[END_REF]Thm. 4.5].

Proposition 31 Let f be h-pseudoconvex at a 2 W and let g i be h i -protoconvex at a for i = 1; :::; m. If a is such that the following conditions are satis…ed for some y i 2 R + , i = 1; :::; m, then a is a solution to problem (M) :

Proof. Suppose on the contrary that there exists some x 2 C such that f (x) < f (a): By hpseudoconvexity of f at a; we have h(a; x a) < 0.

Then, for i 2 I(a); by h i -protoconvexity of g i at a, we have h i (a; x a) 0. Consequently, we have h(a; x a) < 0; Corollary 32 Let f be h-pseudoconvex at a 2 X and let g i be h i -protoconvex at a for i = 1; :::; m.

If a is such that the following conditions are satis…ed for some y i 2 R + , i = 1; :::; m, then a is a solution to problem (M) : 0 2 @ h f (a) + y 1 @ h 1 g 1 (a) + ::: + y m @ hm g m (a); g 1 (a) 0; :::; g m (a) 0; y 1 g 1 (a) = 0; :::; y m g m (a) = 0:

Proof. Let a 2 @ h f (a), a i 2 @ h i g i (a) for i = 1; :::; m be such that a + y 1 a 1 + ::: + y m a m = 0:

Examples 12. Let f be de…ned by f (x) = x + 1 for x > 0; f (x) = x 2 for x 0 and let g 1 : R ! R be given by g 1 (x) = x: Let h : R R ! R be given by h(0; u) = juj for u 2 R and let h 1 := g 0 1 : Then f; g 1 satisfy condition (H ) at a = 0 and g 1 is quasiconvex. We can take y 1 = 1 as a multiplier since @ h f (a) = [ 1; 1]; @g 1 (a) = f 1g: Examples 13. One may take for f a continuous function, for instance f given by f (x) = p x for x > 0; f (x) = x 2 for x 0, with g 1 and h : R R ! R as in the preceding example.

In convex mathematical programming, the multipliers are the same for all solutions. A related result has been given in [START_REF] Dinh | Lagrange multiplier characterizations of solution sets of contrained pseudolinear optimizations problems, Optimization[END_REF] in the case the function f is pseudoa¢ ne, di¤erentiable, h := f 0 and the functions g i (i 2 f1; :::; mg) are linear. Here f and g i are nonlinear and even nonsmooth.

Proposition 37 Let a 2 S be such that there exists y = (y i ) 2 R m + satisfying the optimality conditions ( 6) and ( 7).

(a) If f is h-colinvex at a and g i is h i -protoconvex at a, then P i2I(a) y i h i (a; b a) = 0 for every b 2 S.

(b) Let f be h-colinvex at a and g = (g i ) be H-colinvex at a where H = (h i ) i2Nm . Let L be the Lagrangian given by L(x; z) := f (x) + zg(x) for (x;

  R 1 := R [ f+1g, R := R [ f 1; +1g and P :=]0; +1[. For w; x 2 X; we set [w; x] := f(1 t)w + tx : t 2 [0; 1]g; [w; x[:= [w; x]nfxg and ]w; x[:= [w; x[nfwg. We de…ne the visibility cone V (C; x) of C at x 2 C as the cone generated by C x: V (C; x) := P(C x) := fr(c x) : r 2 P; c 2 Cg:The visibility bundle of C is the setV C := f(x; u) 2 C X : 9r 2 P; w 2 C; u = r(w x)g = [ x2C fxg V (C; x):

Proposition 22

 22 Let X; Y; Z be n.v.s., let C (resp. D) be a convex subset of X (resp. Y ) and let H : V C ! Y , K : V D ! Z: If F : C ! Y and G : D ! Z are H-colin…ne on C and K-colin…ne on D respectively and if F (C) D; then G F is L-colin…ne on C, for L : V C ! Z given by L(x; x 0 ) := K(F (x); H(x; x 0 )) for (x; x 0 ) 2 V C: To get an extension of the concepts of h-pseudoconvexity and h-colinvexity, one needs an order structure on Y . Thus we assume that Y is ordered by a closed convex cone Y + : We denote by Y + the dual cone of Y + : Y + := fy 2 Y : 8y 2 Y + hy ; yi 0g. De…nition 23 Given a map F : C ! Y . (a) F is said to be H-pseudoconvex at x 2 C if w 2 C; H(x; w x) 0 =) F (w) F (x):

Example 10 .

 10 Given n.v.s. X; Y; A 2 L(X; Y ); y 2 Y; b 2 X ; 2 R, C := fx 2 X : b(x) + > 0g, the map F : C ! Y given by F (x) := (A(x) + y)=(b(x) + )is a colin…ne map on C; as a computation similar to the one of Example 6 shows. Example 11. Given X = R; Y = R 2 ; Y + := R 2 + , let F : X ! Y be given by F (x) := (x; x 3 ):

Proposition 30

 30 Suppose that f is h-semia¢ ne on C and C is starshaped at a: Then (a) a 2 S if and only if h(a; x a) 0 for all x 2 C and if and only if h(x; a x) 0 for all x 2 C; (b) If a; b are in S; then (a + R(b a)) \ C is included in S; (c) If a is a solution to (C) then the set S of solutions to (C) is given by S = S h ; where S h := fx 2 C : h(a; x a) = 0g; (d) If the feasible set C is convex, then a local minimizer to (C) is a global minimizer to (C):

  h(a; x a) + m X i=1 y i h i (a; x a) 0; 8x 2 C;(6)g 1 (a) 0; :::; g m (a) 0; y 1 g 1 (a) = 0; :::; y m g m (a) = 0:

  h i (a; x a) 0; 8i 2 I(a): Multiplying each side of the last inequalities by y i and adding the obtained sides to the ones of the preceding relation, since y i = 0 for i = 2 I(a), we get 0 h(a; x a) + m X i=1 y i h i (a; x a) < 0; a contradiction.

  z) 2 C R m and let b 2 C: Then, b 2 S if, and only if, yg(b) = 0 and L(a; y) = L(b; y). Proof. (a) Let b 2 S: Since f is h-colinvex at a, there exists (b; a) 2 P such that 0 = f (b) f (a) (b; a)h(a; b a): By condition (6), one has X i2I(a) y i h i (a; b a) h(a; b a) 0: On the other hand, since g i is h i -protoconvex at a and for all i 2 I(a); g i (b) g i (a) = 0, one has h i (a; b a) 0 for all i 2 I(a). Hence P i2I(a) y i h i (a; b a) = 0. (b) Let b 2 S: Since g is H-colinvex at a then there exists (b; a) 2 P such that for any i 2 I(a); g i (b) = g i (b) g i (a) (b; a)h i (a; b a): By (a), y 2 R m + and b 2 C, 0 yg(b) (b; a) P i2I(a) y i h i (a; b a) = 0 and thus L(a; y) := f (a) + yg(a) = f (a) = f (b) = f (b) + yg(b) =: L(b; y): Conversely, let b 2 C. If yg(b) = 0 and L(a; y) = L(b; y) then f (a) = L(a; y) = L(b; y) = f (b) + yg(b) = f (b); hence b 2 S:

  and if f is either radially continuous or radially lower semicontinuous on C and satis…es the NC-property, then f is quasiconvex.(c) If f is radially di¤ erentiable and if h := f 0 ; then f is quasiconvex, if, and only if, it is h-quasiconvex, if, and only if, it is h-protoconvex.

  every x 2 C: (c) F is said to be H-semiconvex at x 2 C if it is both H-pseudoconvex and H-protoconvex at x 2 C: It is said to be H-protoa¢ ne (resp. H-pseudoa¢ ne) at x if it is H-protoconvex (resp. H-pseudoconvex) at x and if F is H-protoa¢ ne (resp. H-pseudoa¢ ne) at x:(d) F is said to be H-colinvex on C if there exists some function : C C ! P such that for all w; x 2 CIf Y + := f0g; F is H-colin…ne on C if, and only if, F is H-colinvex on C: If F is H-colinvex on C then F is clearly H-pseudoconvex on C.Let us give another means to extend the previous notions to the vectorial case. De…nition 24 A map F : C ! Y is said to be scalarly H-pseudoconvex (resp. scalarly Hprotoconvex, scalarly H-semiconvex, scalarly H-pseudoa¢ ne, scalarly H-semia¢ ne) on C if, for all y 2 Y + ; the function y F is y H-pseudoconvex (resp. y H-protoconvex, y H-semiconvex, y H-pseudoa¢ ne, y H-semia¢ ne). Proposition 25 A map F : C ! Y is H-pseudoconvex (resp. H-protoconvex, H-semiconvex, H-pseudoa¢ ne, H-semia¢ ne) if, and only if, it is scalarly H-pseudoconvex (resp. scalarly Hprotoconvex, scalarly H-semiconvex, scalarly H-pseudoa¢ ne, scalarly H-semia¢ ne).

F (w) F (x) H(x; (w; x)(w x)):

  Proposition 27 Let X; Y; Z be n.v.s., Y and Z being ordered by closed convex cones Y + and Z + respectively. Let C (resp. D) be a convex subset of X (resp. Y ) and let F :

	Now let us consider composition properties.

Proposition 26 Given H : V C ! Y and H-colin…ne (resp. H-scalarly semia¢ ne) maps F : C ! Y , G : C ! Y and r 2 R, the map F + G is H-colin…ne (resp. H-scalarly semia¢ ne) and rF is rH-colin…ne (resp. rH-scalarly semia¢ ne).

and the preceding proposition applies. Now let us give a necessary optimality condition in a limiting form. We assume now that X is a Banach space.

Proposition 33 Let a 2 S. Assume that h, h i (i 2 I(a)) are …nite, sublinear and l.s.c. in their second variables. Assume that f satis…es condition (H ) at a (relatively to h), g i are u.s.c. at a for i = 2 I(a). Suppose that for i 2 N (a); g i satis…es condition (H ) at a (relatively to h i ) and g i is quasiconvex at a. Then there exists 0 6 = (y 0 ;

y n @ hn g n (a) + X p2P (a)

In particular, if @ h i g i (a) 6 = ? for i 2 N m nI(a) then

y n = 1g:

Proof. Let a 2 S: In the …rst step, we prove that the system 0 @ h(a; x a) < 0

Let us suppose, on the contrary, that x satisfy these inequalities. Then, since W is open, there exists > 0 such that x t := a+t(x a) 2 W for t 2 [0; ]: Thus, for i 2 P (a) one has h i (a; x t a) 0; so that g i (x t ) g i (a) for t 2]0; ] by h i -pseudoconcavity of g i at a: For i 2 N (a); and t 2]0; ]; since h i (a; x t a) < 0; by condition (H ) there exists z t := x s 2]x t ; a[\W such that g i (z t ) < g i (a): In addition, as g i is quasiconvex, one has g i (x r ) g i (a) for all r 2 [0; s]. Moreover, since for i = 2 I(a), g i (a) < 0 and g i is u.s.c, we can take s small so that g i (x r ) < 0 for r 2 [0; s]: Since h(a; x s a) < 0; by condition (H ) we get f (x r ) < f (a) for some r 2 [0; s], a contradiction with a 2 S. Our claim is proved.

In the second step, we prove that relation ( 8) is satis…ed. According to the …rst step, the system 0 @ h(a; x a) > 0

has no solution x in X: Since h(a; ) and h i (a; ) are sublinear and l.s.c., using the generalized Farkas lemma [21, Theorem 3], there exists 0 6 = (y 0 ;

y n h n (a; )

y p h p (a; )

+ g:

Since the Attouch-Brézis quali…cation condition for a sum of convex functions is ful…lled, this condition can be rewritten as in [START_REF] Cambini | Generalized convexity and optimality conditions in scalar and vector optimization[END_REF], taking into account the notation @ h f (a) = @h(a; )(0) and @ h i g i (a) = @h i (a; )(0) for i 2 I(a). The last assertion is obtained by taking y i = 0 for i 2 N m nI(a) and by using a normalization, replacing (y 0 ; (y i )) by (t 1 y 0 ; (t 1 y i )); where t := y 0 + P n2N (a) y n : In the case we can take for P (a) the empty set, we get the following consequence.

Corollary 34 Let a 2 S: Assume that h, h i (i 2 I(a)) be …nite, sublinear and l.s.c. in their second variables. Assume that f satis…es condition (H ) at a (relatively to h), g i are u.s.c. at a for i = 2 I(a). Suppose that for i 2 I(a); g i satisfy condition (H ) at a (relatively to h i ) and g i is quasiconvex at a. Then there exists (y 0 ;

y i @ h i g i (a)g:

Moreover, if the quali…cation condition

holds, then there exists

y i @ h i g i (a):

Proof. The …rst assertion is a direct consequence of the preceding proposition, with N (a) = I(a); P (a) = ?: The second assertion stems from the fact that if y 0 = 0; replacing (y i ) by (t 1 y i ) with t := P i2I(a) y i we get 0 2 cl(

@ h i g i (a)); a contradiction with (9).

The case P (a) = I(a); N (a) = ? is considered in the following statement; then we have y 0 6 = 0; or, equivalently, y 0 = 1 after normalization.

Corollary 35 Let a 2 S: Assume that h, h i (i 2 I(a)) are sublinear and l.s.c. in their second variables. Assume that f satis…es condition (H ) at a (relatively to h), g i are h i -pseudoconcave at a for i 2 I(a) and g i is u.s.c at a for i = 2 I(a). Then 0 2 cl(f@ h f (a) + y 1 @ h 1 g 1 (a) + ::: + y m @ hm g m (a) :

Let us gather a necessary condition deduced from the preceding statement with the su¢ cient condition of Corollary 32.

Corollary 36 Let a 2 W: Assume that f is h-pseudoconvex at a and g i is h i -colin…ne at a for some bifunctions h : W X ! R, h i : W X ! R (i 2 f1; :::; mg) which are such that h(a; ) and h i (a; ) are positively homogeneous and h D + f . Assume that g i is u.s.c for i = 2 I(a): Then a 2 C is a solution to (M) if there exists y := (y 1 ; :::; y m ) 2 R m + such that y i g i (a) = 0 for i = 1; :::; m and 0 2 @ h f (a) + y 1 @ h 1 g 1 (a) + ::: + y m @ hm g m (a):

If h(a; ) and h i (a; ) (i 2 I(a)) are sublinear and continuous and if the following quali…cation condition holds, condition ( 11) is necessary for a to be in S :

Proof. The su¢ cient condition is obvious from Corollary 32. For the necessary condition, since f is h-colin…ne at a, it satis…es condition (H ) by Remark 2 (b). Given a sequence (" n ) in P + with limit 0; relation [START_REF] Clarke | Optimization and Nonsmooth Analysis[END_REF] yields for each i 2 I(a) some sequences (y i;n ) in R + ; (y n ); (z i;n ) in X such that y n 2 @ h f (a), z i;n 2 @ h i g i (a) for all n 2 N and

Let us show that the sequences (y i;n ) are bounded. If one of them is unbounded, taking a subsequence if necessary, we may assume that (ky n k) ! +1; where y n = (y i;n ) i2I(a) 2 R

I(a)

+ ; the space R I(a) being endowed with the norm given by kyk := P i2I(a) jy i j : Since for i 2 I(a) the functions h i (a; ) are sublinear and continuous, the sets @ h i g i (a) are weak compact; @ h f (a) is also weak compact. It follows that we can …nd a limit point (u; z ) of the sequence (u n ; z n ); where u n := y n = ky n k 2 R

I(a)

+ : Dividing by ky n k both sides of relation [START_REF] Chew | Pseudolinearity and e¢ ciency[END_REF] and taking limits we get X i2I(a) u i z i = 0: Since z i 2 @ h i g i (a) and u := (u i ) i2I(a) is in the canonical simplex of R I(a) ; we get a contradiction with [START_REF] Crouzeix | Criteria for generalized convexity and generalized monotonicity in the di¤erentiable case[END_REF].

Since the sequence (y n ) is bounded, and since (y n ); (z i;n ) are also bounded in X ; we can take weak limit points y; y ; z i and pass to the limit in [START_REF] Chew | Pseudolinearity and e¢ ciency[END_REF]. Thus, we get y 2 R

I(a)

+ ; y 2 @ h f (a); z i 2 @ h i g i (a) such that y + X i2I(a)

y i z i = 0:

Hence condition ( 11) is satis…ed.

The next example illustrates the corollary; note that since f is not di¤erentiable at a = 0; [41, page 153] cannot be applied. Since f is not a Plastria function at a; [38, Theorem 10] cannot be applied either. Moreover, f is not locally Lipschitz at a; thus [START_REF] Martínez-Legaz | On lower subdi¤erentiable function[END_REF]Prop. 6.1] and [START_REF] Ho¤manm | Martínez-Legaz and Romano-Rogríguez, -lower subdi¤erentiable function[END_REF]Prop. 6.3] cannot be applied. Finally, let us note the usefulness of admitting that the bifunction h may di¤er from the Dini derivatives of f .