Alternative Theorems and Necessary Optimality Conditions for ...

unwieldycodpieceElectronics - Devices

Oct 8, 2013 (4 years and 1 month ago)

117 views

Journal of Convex Analysis
Volume 9 (2002),No.1,97Û116
Alternative Theorems and Necessary Optimality
Conditions for Directionally DiÞerentiable
Multiobjective Programs
B.Jimæenez
Departamento de Matemæatica Aplicada,Universidad Nacional de
Educaciæon a Distancia,Apartado 60149,28080 Madrid,Spain
bjimen1@encina.pntic.mec.es
V.Novo
Departamento de Matemæatica Aplicada,Universidad Nacional de
Educaciæon a Distancia,Apartado 60149,28080 Madrid,Spain
vnovo@ind.uned.es
Received May 4,2001
Revised manuscript received September 20,2001
In this paper we study,in a unißed way,some alternative theorems that involve linear and sublinear
functions between ßnite dimensional spaces and a convex set,and we propose several generalizations of
them.These theorems are applied to obtain,under diÞerent constraint qualißcations,several necessary
conditions for a point to be Pareto optimum,both Fritz John and Kuhn-Tucker type,in multiobjective
programming problems which are deßned by directionally diÞerentiable functions and which include three
types of constraints:inequality,equality and set constraints.In particular,these necessary conditions
are applicable to convex programs and to diÞerentiable programs.
Keywords:Multiobjective programming,alternative theorems,necessary conditions for Pareto mini-
mum,Lagrange multipliers
2000 Mathematics Subject Classißcation:90C29,90C46
1.Introduction
Alternative theorems are indispensable tools in mathematical programming since they
allow the transformation of inequality systems,of a diácult direct approach,into equality
systems.
The ßrst alternative theorems come from the beginning of the century and from then
numerous generalizations have been proposed until today.For example,see Mangasarian
[10] for a study of the classic theorems.These deal with a ßnite number of linear functions
fromR
n
to R,and fromthere,the generalizations have included a ßnite or inßnite number
of convex or with some type of generalized convexity functions and even multifunctions
and increasingly abstract spaces.See Jeyakumar [7] for a review.
These theorems are used in the optimization theory to obtain necessary conditions,in
terms of Lagrange multipliers (dual form),so that a point will be an optimum for a
mathematical programming problem.Usually a necessary condition expressed through
the incompatibility of a system of equations and inequations,formed with the directional
derivatives of the functions involved in the problem (primal form),is transformed by an
ISSN 0944-6532/$ 2.50
c
­ Heldermann Verlag
98 B.Jimæenez,V.Novo/Alternative Theorems and Optimality Conditions
alternative theorem in the checking of the existence of some multipliers,or what is the
same,in the checking of the compatibility of a system of equations,whose verißcation is
usually much more simple.Hence the great relevance of these theorems.
In this work the results obtained are not directly applied to the functions of the problem,
but to their directional derivatives,that is why,in the systems considered,the inequalities
are given by sublinear (positively homogeneous and convex) functions and the equalities,
by linear functions.
After introducing the notations in Section 2,we study,in Section 3,various generalizations
of some of the classic alternative theorems,working with linear or sublinear functions in
spaces of ßnite dimension and substituting the gradient by the subdiÞerential of the
Convex Analysis.The emphasis has been put in a unißed treatment of the diÞerent
situations,including a convex set constraint and giving a diÞerentiated treatment to the
equality constraints,which could be treated as two inequalities in some occasions,but in
others we obtain advantages from specißc treatment,otherwise we would obtain trivial
or inapplicable results.The equality constraints considered are given by linear functions,
suácient in many cases,since they are referred to derivatives in the applications.
Finally,in Section 4,the obtained alternative theorems are applied to the demonstration of
diÞerent necessary optimality conditions for multiobjective programs (in ßnite dimension)
with directionally diÞerentiable functions (not necessarily diÞerentiable neither convex).
This permits us to generalize,for example,the results of Singh [16] and of Giorgi and
Guerraggio [3],that deal with diÞerentiable functions,and also several results on convex
programs,in particular those of Kanniappan [8] and Islam [6].
2.Notations
Let x and y be two points of R
n
.Throughout this paper,we shall use the following
notations.
x ´ y if x
i
´ y
i
;i = 1;:::;n;x < y if x
i
< y
i
;i = 1;:::;n:
Let S be a subset of R
n
,as usual,cl S,int S,ri S,co S,aÞS,cone S,linS,will denote
the closure,interior,relative interior,convex hull,aáne hull,generated cone and linear
span by S,respectively.B(x
0
;®) is the open ball centered at x
0
and radius ® > 0.
Given a point x
0
2 S and a function f:R
n
!R
p
,the following multiobjective optimiza-
tion problem is considered
Minff(x):x 2 Sg.
It is said that x
0
is to be a weak Pareto minimumif there exists no x 2 S such that f(x) <
f(x
0
).The point x
0
is to be a local weak Pareto minimum,written x
0
2 LWMin(f;S),
if the previous condition is verißed on S\B(x
0
;®),for some ® > 0.The usual notion of
Pareto minimum is also used,will be denoted Min(f;S).
The following cones (Deßnition 2.1) and directional derivatives (Deßnition 2.2) are con-
sidered.
Deßnition 2.1.Let S º R
n
,x
0
2 cl S,
(a) The tangent cone (or contingent cone) to S at x
0
is
T(S;x
0
) = fv 2 R
n
:9t
k
> 0;9x
k
2 S;x
k
!x
0
such that t
k
(x
k
 x
0
)!vg.
B.Jimæenez,V.Novo/Alternative Theorems and Optimality Conditions 99
(b) The cone of linear directions (or radial tangent cone) is
Z(S;x
0
) = fv 2 R
n
:9® > 0 such that x
0
+tv 2 S 8t 2 (0;®]g.
(c) The cone of sequential linear directions (or sequential radial tangent cone,Penot
[12,Deßnition 2.3]) is
Z
s
(S;x
0
) = fv 2 R
n
:9t
k
!0
+
such that x
0
+t
k
v 2 S 8k 2 Ng.
We have that Z(S;x
0
) º Z
s
(S;x
0
) º T(S;x
0
).
Let D º R
n
,the polar cone to D is D
£
= fv 2 R
n
:hv;di ´ 0 8d 2 Dg and the strict
polar cone is D

= fv 2 R
n
:hv;di < 0 8d 2 D;d 6= 0g.If D is a subspace,then
D
£
= D
?
,orthogonal subspace to D.The normal cone to S at x
0
is the polar of tangent
cone:N(S;x
0
) = T(S;x
0
)
£
.If S is a convex set,one has that N(S;x
0
) = (S  x
0
)
£
.
Deßnition 2.2.Let f:R
n
!R
p
;x
0
;v 2 R
n
.
(a) The Dini derivative (or directional derivative) of f at x
0
in the direction v is
Df(x
0
;v) = lim
t!0
+
[f(x
0
+tv)  f(x
0
)]=t.
(b) The Hadamard derivative of f at x
0
in the direction v is
df(x
0
;v) = lim
(t;u)!(0
+
;v)
[f(x
0
+tu)  f(x
0
)]=t.
(c) f is Dini diÞerentiable or directionally diÞerentiable (resp.Hadamard diÞerentiable)
at x
0
if its Dini derivative (resp.Hadamard derivative) exists in all directions.
The following properties hold:
Û If f is Fræechet diÞerentiable at x
0
,with Fræechet derivative rf(x
0
),then df(x
0
;v) =
rf(x
0
)v.
Û If df(x
0
;v) exists,then also Df(x
0
;v) exists and they are equal.
Û Df(x
0
;v) (resp.df(x
0
;v)) is the vector of components Df
i
(x
0
;v) (resp.df
i
(x
0
;v)),
i = 1;:::;p.
Let f:R
n
!R be a Dini diÞerentiable function at x
0
.The concept of subdiÞerential is
well known (see Penot [12]).
Deßnition 2.3.The Dini subdiÞerential of f at x
0
is
@
D
f(x
0
) = f¸ 2 R
n
:h¸;vi ´ Df(x
0
;v) 8v 2 R
n
g:
If Df(x
0
;v) is a convex function in v,then its subdiÞerential (in the Convex Analysis
sense) at v = 0 exists,and it is denoted @Df(x
0
;¡)(0).This is a nonempty,compact and
convex set of R
n
and the following asserts are true:
@
D
f(x
0
) = @Df(x
0
;¡)(0);
Df(x
0
;v) = Maxfh¸;vi:¸ 2 @
D
f(x
0
)g:
If Df(x
0
;v) is not convex,then @
D
f(x
0
) can be empty.
A function f whose Dini derivative Df(x
0
;¡) is convex was called by Pshenichnyi qua-
sidiÞerentiable at x
0
.This concept was later extended by Demyanov (see Demyanov and
Rubinov [2]).
100 B.Jimæenez,V.Novo/Alternative Theorems and Optimality Conditions
3.Alternative Theorems
In this section,several generalizations of some alternative theorems are demonstrated.In
particular,we extend the classic theorems of Gordan and Motzkin [10] and the results of
Robinson [14,Theorem 3],and Ishizuka and Shimizu [5,Lemma 2].In the ßrst place,the
case of equalities is considered and,then,we work with both equalities and inequalities.
The proof of the following Lemma 3.1 is omitted because it is very simple.
Lemma 3.1.Let B be a nonempty set and C a cone of R
n
with 0 2 C.Then
0 =2 B +C,( B)\C =;,0 =2 B and ( cone B)\C = f0g.
Lemma 3.2.Let Q º R
n
be a convex set with 0 2 Q and h:R
n
!R
r
linear with
component functions h
k
;k 2 K = f1;:::;rg given by h
k
(u) = hc
k
;ui.Suppose the aáne
hull of Q is given by aÞQ = fx 2 R
n
:hd
j
;xi = 0;j = 1;:::;lg,being d
1
;:::;d
l
linearly
independent.Consider the following propositions:
(h1) 0 2
P
r
k=1
·
k
c
k
+N(Q;0);· 2 R
r
) · = 0.
(h2)
P
r
k=1
·
k
h
k
(u) µ 0 8u 2 Q;· 2 R
r
) · = 0.
(h3) (h3.1) c
1
;:::;c
r
;d
1
;:::;d
l
are linearly independent and (h3.2) Ker h\ri Q 6=;.
(h4) (h4.1) c
1
;:::;c
r
are linearly independent and (h4.2) linfc
k
:k 2 Kg\N(Q;0) =
f0g.
(h5) 0 2 int h(Q).
(h6) Ker h\ri Q 6=;.
(h7) 0 2 ri h(Q).
(h8) linfc
k
:k 2 Kg\(ri Q)

=;.
Then
(i) (h6) to (h8) are equivalent.
(ii) (h1) to (h5) are equivalent.
(iii) Each proposition (h1) to (h5) implies (h6),(h7) and (h8).
(iv) If int h(Q) 6=;then
1) (h4.1) holds.
2) (h1) to (h8) are equivalent.
Proof.(i) (h7) )(h8).Suppose that (h8) is false and take ¶ 2 linfc
k
:k 2 Kg\(ri Q)

.
Then ¶ =
P
r
k=1
·
k
c
k
and h
P
r
k=1
·
k
c
k
;qi < 0 8q 2 ri Qn f0g.
Let':R
r
!R be the linear application deßned by'(y) = h·;yi and À ='® h,which
is given by À(x) = h¶;xi.One has
À(q) < 0 8q 2 ri Qn f0g:(1)
By convexity of Q,Q º cl ri Q,and by the continuity of À,À(q) ´ 0 8q 2 Q.Thus
'(y) ´ 0 8y 2 h(Q).Therefore,y = 0 is a maximum of the convex function'on the
convex h(Q).By hypothesis,0 2 ri h(Q).But if a convex function reaches its maximum
at a relative interior point of its domain,then the function is constant (Rockafellar [15,
Theorem 32.1]).It follows that'(y) = 0 8y 2 h(Q),that is,'(h(q)) = 0 8q 2 Q,which
contradicts (1).
(h6),(h7).It is clear if we take into account that ri h(Q) = h(ri Q) [15,Theorem 6.6].
Not (h6) ) Not (h8).As Ker h\ri Q =;and Ker h is a convex cone,using Theorems
B.Jimæenez,V.Novo/Alternative Theorems and Optimality Conditions 101
11.3 and 11.7 in [15],Ker h and Q are separated properly by a hyperplane M through the
origin.That is,there exists ¶ 2 R
n
;¶ 6= 0 such that M = Kerh¶;¡i and
h¶;qi ´ 0 ´ h¶;xi 8q 2 Q;8x 2 Ker h:
Hence ¶ 2  (Ker h)
£
= (Ker h)
?
= linfc
k
:k 2 Kg.It follows that Ker h º M,and as
M separates properly to Q and Ker h,there exists q
0
2 Q such that h¶;q
0
i < 0.As a
matter of fact
h¶;qi < 0 8q 2 ri Q;(2)
because if for some q
1
2 ri Q,h¶;q
1
i = 0,then the convex function h¶;¡i has a maximum
on Q at q
1
and,therefore,it is constant on Q,this is h¶;qi = 0 8q 2 Q,which contradicts
that M separates properly.
Accordingly,from (2),¶ 2 linfc
k
:k 2 Kg\(ri Q)

.
(ii) (h1),(h2).This is obvious.
(h1) )(h4).Let us prove the linear independence,since the second part is obvious.Let
P
r
k=1
·
k
c
k
= 0.Taking d = 0 2 N(Q;0) one has
P
r
k=1
·
k
c
k
+d = 0.By (h1),· = 0.
(h4) ) (h3).Let us observe in the ßrst place that (h4.2) ) (h8) because (ri Q)

º
Q
£
n f0g.Now,by (i),(h8),(h6)=(h3.2).We have to prove (h3.1).
Let
P
r
k=1
·
k
c
k
+
P
l
j=1
«
j
d
j
= 0.As Q º aÞQ = (linfd
1
;:::;d
l
g)
?
results that
N(aÞQ;0) = (aÞQ)
?
= linfd
1
;:::;d
l
g º N(Q;0).
Hence,d =
P
l
j=1
«
j
d
j
=  
P
r
k=1
·
k
c
k
2 linfc
k
:k 2 Kg\N(Q;0);by (h4.2),d = 0.
Since the vectors d
j
are linearly independent,« = 0,and by (h4.1),· = 0.
(h3) )(h2).Suppose that the hypothesis of (h2) holds.Thus
h¶;ui = h·;h(u)i µ 0 8u 2 Q,being ¶ =
P
r
k=1
·
k
c
k
.Take q 2 Ker h\ri Q.It is verißed
that the concave function h¶;¡i has a minimum of value 0 on Q at q 2 ri Q.Hence it is
constant on Q,then h¶;ui = 0 8u 2 Q and,therefore,h¶;ui = 0 8u 2 aÞQ.Thereby
¶ 2 (aÞQ)
?
= linfd
1
;:::;d
l
g.Consequently,¶ =
P
l
j=1
«
j
d
j
=
P
r
k=1
·
k
c
k
,taking into
account (h3.1),« = 0 and · = 0.
The equivalence of (h2) with (h5) is proved in a similar way to the equivalence of (b) with
(e) in Theorem 3.9.
(iii) It is obvious since (h3.2)=(h6).
(iv) 1) int h(Q) 6=;)(h4.1).In fact,the hypothesis implies that h(R
n
) = R
r
,that is,h
has rank r.The conclusion follows from observing that the vectors c
1
;:::;c
r
are the rows
of the matrix of h in the canonical bases.
2) It is immediate since (h7) becomes (h5).
Lemma 3.3.If (h6),(h7) or (h8) is verißed then linfc
k
:k 2 Kg +N(Q;0) is closed.
Proof.Since ri Ker h = Ker h,one has by hypothesis (h6),ri Ker h\ri Q 6=;.By
Corollary 23.8.1 in [15],it follows
N[Ker h\Q;0] = N[Ker h;0] +N(Q;0) = linfc
k
:k 2 Kg +N(Q;0),
which is closed.
102 B.Jimæenez,V.Novo/Alternative Theorems and Optimality Conditions
Let us observe that (h4.2) is a form more interesting than (h8),since (ri Q)

,which is,
in general,neither open nor closed,is substituted by N(Q;0),which is closed.
Let us prove with an example that in (h8) of Lemma 3.2 (ri Q)

cannot be substituted,
in general,by Q
£
(Example 3.4(a)),either by Q

,or by ri(Q
£
) (Example 3.4(b)) and that
the converse of (iii) is false.Also it is easy to prove that (h4.1) is neither a necessary nor
suácient condition so that (h6) to (h8) are verißed.
Example 3.4.
(a) Let h:R
3
!R
2
given by h(x
1
;x
2
;x
3
) = (x
1
;x
2
) and Q = fx = (x
1
;x
2
;x
3
) 2 R
3
:
x
1
= 0;x
3
µ 0g.It is easy to prove the following results.
1.L = linfc
k
:k 2 Kg = linf(1;0;0);(0;1;0)g = fx:x
3
= 0g,Ker h =
fx:x
1
= 0;x
2
= 0g.
2.h(Q) = fy = (y
1
;y
2
) 2 R
2
:y
1
= 0g,ri Q = fx:x
1
= 0;x
3
> 0g,
Q
£
= N(Q;0) = fx:x
2
= 0;x
3
´ 0g,ri(Q
£
) = fx:x
2
= 0;x
3
< 0g,Q

=;.
3.(h7) in Lemma 3.2 holds,and logically its equivalents (h6) and (h8).But,
(1;0;0) 2 L\Q
£
6= f0g,this means that (h4.2) does not hold.Then,the
converse of (iii) is false.Let us observe that int h(Q) =;.
(b) We change the set Q.It will be now Q = fx:x
1
= 0;x
2
µ 0;x
3
µ 0g.It is
proved without diáculty:
4.h(Q) = fy:y
1
= 0;y
2
µ 0g,ri Q = fx:x
1
= 0;x
2
> 0;x
3
> 0g,
Q
£
= fx:x
2
´ 0;x
3
´ 0g,ri(Q
£
) = Q

= fx:x
2
< 0;x
3
< 0g,
(ri Q)

= Q
£
n fx:x
2
= 0;x
3
= 0g.
5.(h7) in Lemma 3.2 does not hold,neither does (h8),obviously.However,L\
Q

=;and L\ri(Q
£
) =;.Note that c
1
;c
2
are linearly independent,therefore,
this is not a suácient condition so that (h6) to (h8) are fulßlled.
(c) In part (a) we change h.Now h(x
1
;x
2
;x
3
) = (x
1
;x
2
;x
1
+x
2
).3 of part (a) holds
since the sets of 1 and 2 are all maintained except h(Q) which now is h(Q) = fy 2
R
3
:y
1
= 0;y
2
= y
3
g and one has that 0 2 ri h(Q),that is,(h6),(h7) and (h8)
are fulßlled.The vectors c
1
= (1;0;0);c
2
= (0;1;0);c
3
= (1;1;0) are linearly
dependent,hence,this is not a necessary condition so that (h6) to (h8) are fulßlled.
Notice that Theorem 21.2 of Rockafellar [15] is not applicable to the situation described
by Lemma 3.2 since there are no strict inequality constraints.Example 3.4(a) above shows
that the two incompatible alternatives of the aforesaid theoremwould be verißed if it were
applicable.In fact,the vector u = (0;0;1) 2 ri Q verißes the system h
1
(u) ´ 0;h
2
(u) ´ 0
and considering µ
1
= 1;µ
2
= 0 we have the inequality µ
1
h
1
(u) +µ
2
h
2
(u) µ 0 8u 2 Q.
Theorem 3.5.Let f
1
;:::;f
p
be sublinear functions from R
n
to R with p µ 1,f =
(f
1
;:::;f
p
),h:R
n
!R
r
linear given by h = (h
1
;:::;h
r
) being h
k
(u) = hc
k
;ui,k 2 K =
f1;:::;rg,and Q º R
n
a convex set with 0 2 Q.Consider the following propositions:
(a) 0 2
P
p
i=1
µ
i
@f
i
(0) +
P
r
k=1
·
k
c
k
+N(Q;0);µ µ 0 implies µ = 0.
(b)
p
X
i=1
µ
i
f
i
(u) +
r
X
k=1
·
k
h
k
(u) µ 0 8u 2 Q;µ µ 0 (3)
implies µ = 0.
(c) There exists v 2 R
n
such that f(v) < 0;h(v) = 0;v 2 Q.
B.Jimæenez,V.Novo/Alternative Theorems and Optimality Conditions 103
(d) 0 =2 co([
p
i=1
@f
i
(0)) +linfc
k
:k 2 Kg +N(Q;0).
Then
(i) (a),(b) and (d) are equivalent.
(ii) (c) ) (b).
(iii) If 0 2 ri h(Q),then (b) ) (c) and,consequently,the four are equivalent.
Proof.(b) )(a).Suppose that the hypothesis of (a) holds.Then there exist a
i
2 @f
i
(0)
and d 2 N(Q;0) such that
p
X
i=1
µ
i
a
i
+
r
X
k=1
·
k
c
k
+d = 0:(4)
As f
i
(u) µ ha
i
;ui 8u 2 R
n
and 0 µ hd;ui 8u 2 Q,it follows:
P
p
i=1
µ
i
f
i
(u) +
P
r
k=1
·
k
h
k
(u) µ
P
p
i=1
µ
i
ha
i
;ui +
P
r
k=1
·
k
hc
k
;ui +hd;ui = 0 8u 2 Q.
By (b),µ = 0.
(a) )(b).Suppose that (3) is verißed.Then,u = 0 is a minimum of the convex function
'=
P
p
i=1
µ
i
f
i
+
P
r
k=1
·
k
h
k
on Q.Hence,
0 2 @'(0) +N(Q;0) =
P
p
i=1
µ
i
@f
i
(0) +
P
r
k=1
·
k
c
k
+N(Q;0).
By (a),µ = 0.
Not (a),Not (d).Suppose that (4) holds for some µ µ 0,µ 6= 0,a
i
2 @f
i
(0),d 2
N(Q;0).We can assume that
P
p
i=1
µ
i
= 1,otherwise we just divide by
P
p
i=1
µ
i
> 0.
Hence,(4) means that 0 2 co([
p
i=1
@f
i
(0)) +linfc
k
:k 2 Kg +N(Q;0).The converse is
now obvious.
(c) ) (b).Suppose that (3) holds for some µ 6= 0 and let v be a vector satisfying (c).
Then
P
p
i=1
µ
i
f
i
(v)+
P
r
k=1
·
k
h
k
(v) < 0 in contradiction with what is obtained in (3) taking
u = v.Hence µ = 0.
(iii) Suppose that (c) is not fulßlled.Using Lemma 3.2(i) on has Ker h\ri Q 6=;
and then we can apply [15,Theorem 21.2 ] to f,h and  h,resulting that there exist
(µ;«;¬) 2 R
p
¢R
r
¢R
r
such that (µ;«;¬) µ 0;µ 6= 0 and
P
p
i=1
µ
i
f
i
(u) +
P
r
k=1
«
k
h
k
(u)  
P
r
k=1
¬
k
h
k
(u) µ 0 8u 2 Q,
therefore,(b) is not verißed taking ·
k
= «
k
 ¬
k
.
The following proposition shows the connection with subsequent theorems.Corollary 3.7
simplißes this theorem when there are no linear constraints.
Proposition 3.6.In the hypotheses of Theorem 3.5,if (d) holds and Ker h\ri Q 6=;,
then
D:= cone co([
p
i=1
@f
i
(0)) +linfc
k
:k 2 Kg +N(Q;0) is closed.
Proof.By Lemma 3.1,proposition (d) is equivalent to
(e)
(
(e1) 0 =2 co([
p
i=1
@f
i
(0))
(e2) [ cone co([
p
i=1
@f
i
(0))]\[linfc
k
:k 2 Kg +N(Q;0)] = f0g;
104 B.Jimæenez,V.Novo/Alternative Theorems and Optimality Conditions
but (e1),by [4,Proposition 1.4.7,Chap.3],implies that cone co([
p
i=1
@f
i
(0)) is closed and
by Lemma 3.3,linfc
k
:k 2 Kg + N(Q;0) is closed.Taking into account (e2),by [15,
Corollary 9.1.3],we deduce that D is closed.
Corollary 3.7.Let f
1
;:::;f
p
be sublinear functions from R
n
to R,f = (f
1
;:::;f
p
) and
Q º R
n
a convex set which contains to 0.The following propositions are equivalent:
(a) 0 2
P
p
i=1
µ
i
@f
i
(0) +N(Q;0);µ µ 0 ) µ = 0.
(b)
P
p
i=1
µ
i
f
i
(u) µ 0 8u 2 Q;µ µ 0 ) µ = 0.
(c) There exists v 2 R
n
such that f(v) < 0;v 2 Q.
(d) 0 =2 co([
p
i=1
@f
i
(0)) +N(Q;0).
If some of the conditions (a)-(d) with Q = R
n
is satisßed,we will say that the sets
@f
1
(0);:::;@f
p
(0) are positively linearly independent.
This corollary is a generalization of the Gordan alternative theorem [10] since we obtain
it if Q = R
n
and if @f
i
(0) = fa
i
g,this is,if f
i
(u) = ha
i
;ui is linear.
Remark 3.8.It can be seen in Theorem 3.5 (with Q = R
n
,and thus,propositions (a)
to (d) are equivalent) a characterization of the compatibility of a system with inßnite
equations through the compatibility of inßnite systems with a ßnite number of equations.
In fact,proposition (c) is equivalent to
(c
0
) There exists a solution u 2 R
n
of the system
º
Max
a
i
2A
i
ha
i
;ui < 0;i = 1;:::;p
hc
k
;ui = 0;k = 1;:::;r
(5)
being A
i
= @f
i
(0),since f
i
(u) = Maxfha
i
;ui:a
i
2 A
i
g.
Proposition (a) can be formulated as:
(a
0
) For every a
i
2 A
i
;i = 1;:::;p,there exist no (µ;·) 2 R
p
¢R
r
;µ µ 0;µ 6= 0 such
that
P
p
i=1
µ
i
a
i
+
P
r
k=1
·
k
c
k
= 0,
and,by the classic Motzkin alternative theorem,this is equivalent to
(a
00
) For every a
i
2 A
i
;i = 1;:::;p,there exists a solution u 2 R
n
of the system
º
ha
i
;ui < 0;i = 1;:::;p
hc
k
;ui = 0;k = 1;:::;r:
(6)
(c
0
) expresses that a system of inßnite equations has a solution and it is equivalent to
(a
00
),that expresses that the inßnite ßnite systems of type (6) have a solution.
If in the equivalence (c
0
),(a
00
) the equality constraints are removed,we obtain Theorem
2.1 in Wang,Dong and Liu [18].
Note that every solution u of (5) it is also of (6),but not inversely.The solutions of (6)
depend on the election on a
1
;:::;a
p
.
The results obtained in Lemma 3.2 and Theorem 3.5 are combined in the following theo-
rem.
B.Jimæenez,V.Novo/Alternative Theorems and Optimality Conditions 105
Theorem 3.9.In the hypotheses of Theorem 3.5,assume that the aáne hull to Q is
aÞQ = fx 2 R
n
:hd
j
;xi = 0;j = 1;:::;lg,being d
1
;:::;d
l
linearly independent.The
following propositions are equivalent:
(a) 0 2
P
p
i=1
µ
i
@f
i
(0) +
P
r
k=1
·
k
c
k
+N(Q;0);µ µ 0 ) µ = 0;· = 0.
(b)
P
p
i=1
µ
i
f
i
(u) +
P
r
k=1
·
k
h
k
(u) µ 0 8u 2 Q;µ µ 0 ) µ = 0;· = 0.
(c) (c1) c
1
;:::;c
r
;d
1
;:::;d
l
are linearly independent and (c2) there exists v 2 R
n
such
that
f(v) < 0;h(v) = 0;v 2 ri Q.
(d) (d1) c
1
;:::;c
r
are linearly independent,(d2) linfc
k
:k 2 Kg\N(Q;0) = f0g and
(d3) 0 =2 co([
p
i=1
@f
i
(0)) +linfc
k
:k 2 Kg +N(Q;0).
(e) 0 2 int[(f ¢h)(Q) +R
p
+
¢f0
r
g].
(f ) (f ¢h)(cone Q) +R
p
+
¢f0
r
g = R
p
¢R
r
.
For the demonstration of this theorem we need the following lemma.
Lemma 3.10.
(c2),
º
(c
0
) there exists w 2 R
n
such that h(w) = 0;w 2 ri Q
(c
00
) there exists v 2 R
n
such that f(v) < 0;h(v) = 0;v 2 Q:
Proof.It is clear that (c2) ) (c
0
) and (c
00
).For the other implication let w and v be
vectors satisfying (c
0
) and (c
00
),respectively,and v
µ
= µv +(1  µ)w.By linearity of h,
h(v
µ
) = 0,and by [4,Lemma 2.1.6,Chap.2],we deduce that v
µ
2 ri Q 8µ 2 [0;1).As
lim
µ!1
 
v
µ
= v,by the continuity of f,f(v
µ
) < 0 for µ near enough 1 and,consequently,all
these vectors v
µ
verify (c2).
Proof of Theorem 3.9.One has,obviously,the following equivalences:(a),(h1) and
(3.5a),(b),(h2) and (3.5b) and (d),(h4) and (3.5d),where (h1) denotes the same
proposition of Lemma 3.2,(3.5a) denotes proposition (a) of Theorem 3.5,etc.By Lemma
3.2(ii) and Theorem 3.5(i),it follows the equivalence of (a),(b) and (d).
By Lemma 3.10 one has that (c2),(h3.2) and (3.5c),and as (c1)=(h3.1) it follows
(c) = (c1) and (c2),(h3.1) and (h3.2) and (3.5c).
By Theorem 3.5(ii),(3.5c) ) (3.5a);by Lemma 3.2,(h3.1) and (h3.2),(h1) and by
Theorem 3.5(iii),(h3.2) and (3.5a) )(3.5c).Hence,(h3.1) and (h3.2) and (3.5c),(h1)
and (3.5a),(a).Consequently,(c),(a).
Let us prove that (b) )(e).Let
A = (f ¢h)(Q) +R
p
+
¢f0
r
g
= f(x;y) 2 R
p
¢R
r
:9u 2 Q such that f(u) ´ x;h(u) = yg:
A is a non-empty convex set and verißes that int A 6=;.In fact,if int A =;,A is
contained in a hyperplane,that is,there exist (µ;·) 2 R
p
¢R
r
n f(0;0)g such that A º
Kerh(µ;·);(¡;¡)i.Therefore,hµ;f(u)i +h·;h(u)i = 0 8u 2 Q and,by (b),(µ;·) = (0;0),
which is a contradiction.
Let us prove that 0 2 int A.Otherwise,as 0 2 A,there exists a supporting hyperplane to
A at 0 and by a similar reasoning to the previous one it also results a contradiction.
106 B.Jimæenez,V.Novo/Alternative Theorems and Optimality Conditions
(e) ) (b).Suppose that the hypothesis of (b) holds,this is that hµ;f(u)i +h·;h(u)i µ
0 8u 2 Q.Then hµ;xi +h·;yi µ 0 8(x;y) 2 A.Hence,the concave function h(µ;·);(¡;¡)i
has a minimum on the convex A at (0;0) 2 int A (from hypothesis).Therefore,this
function is constant on A,namely,hµ;xi + h·;yi = 0 8(x;y) 2 A.Since int A 6=;,it
follows that (µ;·) = (0;0).
Finally,(e),(f).By [15,Corollary 6.4.1],condition (e) is equivalent to say that for all
u 2 R
p
¢R
r
there exists t > 0 such that 0 +tu 2 A,this means,cone A = R
p
¢R
r
.Now,
it is immediate that
cone A = cone[(f ¢h)(Q) +R
p
+
¢f0
r
g] = (f ¢h)(cone Q) +R
p
+
¢f0
r
g,
with what one has the equivalence of (e) and (f).
If some of the six equivalent conditions of Theorem 3.9 with Q = R
n
is verißed,we
will say f@
D
f
i
(0):i = 1;:::;pg is posindependent of fc
1
;:::;c
r
g,or,that f
1
;:::;f
p
are
posindependent of h
1
;:::;h
r
.This notion generalizes that of positive-linearly independent
vectors in Qi and Wei [13,Deßnition 2.1].
Theorem 3.10 extends Theorem 3 in Robinson [14] who supposes f linear and only con-
siders the propositions (b),(c) and (e).
Remark 3.11.
(1) Taking into account Lemmas 3.2 and 3.10,condition (c) can be expressed:
(c) 0 2 int h(Q) and there exists v 2 R
n
such that f(v) < 0;h(v) = 0;v 2 Q.
(2) When f is linear,condition (e) becomes the so-called Robinson constraint qualiß-
cation [14],(f) becomes Zowe-Kurcyusz constraint qualißcation [19] and,if further-
more Q = R
n
,(c) becomes the classic Mangasarian-Fromovitz constraint qualißca-
tion [11].
Finally we approach the Motzkin alternative theorem in the most general situation that
includes inequality constraints,both strict and not strict,equality constraints and convex
set constraint.
Theorem 3.12.Let f
1
;:::;f
p
;g
1
;:::;g
m
be sublinear functions from R
n
to R with p µ 1
and m µ 0,f = (f
1
;:::;f
p
),g = (g
1
;:::;g
m
),h:R
n
!R
r
linear given by h =
(h
1
;:::;h
r
),h
k
(u) = hc
k
;ui,k 2 K = f1;:::;rg,and Q º R
n
convex subset with 0 2 Q.
Consider the following propositions:
(a) 0 2
P
p
i=1
µ
i
@f
i
(0) +
P
m
j=1

j
@g
j
(0) +
P
r
k=1
·
k
c
k
+N(Q;0);(µ;¶) µ 0 ) µ = 0.
(b)
P
p
i=1
µ
i
f
i
(u) +
P
m
j=1

j
g
j
(u) +
P
r
k=1
·
k
h
k
(u) µ 0 8u 2 Q;(µ;¶) µ 0 ) µ = 0.
(c) There exists v 2 R
n
such that f(v) < 0;g(v) ´ 0;h(v) = 0;v 2 Q.
(d) 0 =2 co([
p
i=1
@f
i
(0)) +cone co([
m
j=1
@g
j
(0)) +linfc
k
:k 2 Kg +N(Q;0).
Then
(i) (a),(b) and (d) are equivalent.
(ii) (c) ) (b).
(iii) If the condition
(c
0
) there exists w 2 R
n
such that g(w) < 0;h(w) = 0;w 2 ri Q
holds,then (b) ) (c) and,consequently,the four are equivalent.
B.Jimæenez,V.Novo/Alternative Theorems and Optimality Conditions 107
Proof.(i) and (ii) are proved in a similar way to (i) and (ii) of Theorem 3.5.
(iii) Suppose that (c) is not true.Then there exists no v 2 R
n
such that
f(v) < 0;g(v) < 0;h(v) = 0;v 2 Q.
By Theorem 3.5(iii),(which is applicable since by (c
0
),Ker h\ri Q 6=;,and by Lemma
3.2(i),0 2 ri h(Q)),there exist (µ;¶;·) 2 R
p
¢R
m
¢R
r
such that (µ;¶) µ 0;(µ;¶) 6= 0
and
p
X
i=1
µ
i
f
i
(u) +
m
X
j=1

j
g
j
(u) +
r
X
k=1
·
k
h
k
(u) µ 0 8u 2 Q:(7)
By (b),µ = 0.Let w be a vector verifying (c
0
),then
P
m
j=1

j
g
j
(w) +
P
r
k=1
·
k
h
k
(w) < 0,
in contradiction with which is obtained applying (7) to u = w (with µ = 0).
If in this theorem we take m= 0,we obtain Theorem 3.5.
In the next theorem it is proved that the implication (b) ) (c) of Theorem 3.12 is also
verißed with other diÞerent conditions to (iii) in the aforesaid theorem.
Theorem 3.13.In the hypotheses of Theorem 3.12,assume that some of the equivalent
conditions (a),(b),(d) of that theorem holds.
If D = cone co([
m
j=1
@g
j
(0)) +linfc
k
:k 2 Kg +N(Q;0) is closed and cone Q is closed,
then (c) holds,so the four are equivalent.
Proof.Let C = co([
p
i=1
@f
i
(0)).C is a non-empty compact convex set and D is a closed
convex cone.By assumption (d),0 =2 C +D,and by Lemma 3.1,C\( D) =;.By the
strong separation theorem,there exist v 2 R
n
n f0g and « 2 R such that
hv;xi < « < hv;yi 8x 2 C;8y 2  D:(8)
Taking y = 0 2  D it follows that « < 0.If for some y 2  D we have hv;yi < 0,then
lim
t!+1
hv;tyi =  1 with ty 2  D and (8) does not hold.Hence,hv;yi µ 0 8y 2  D,or
rather,hv;yi ´ 0 8y 2 D.In particular:
1) 8b
j
2 @g
j
(0) one has hv;b
j
i ´ 0.As g
j
(v) = Maxfhb
j
;vi:b
j
2 @g
j
(0)g,it follows
g
j
(v) ´ 0;for j = 1;:::;m:(9)
2) hv;c
k
i ´ 0 and hv; c
k
i ´ 0,hence
hv;c
k
i = 0 8k 2 K:(10)
3) hv;di ´ 0 8d 2 Q
£
,therefore v 2 Q
££
= cl cone Q and since cone Q is closed,
v 2 cone Q:(11)
From (7),hv;xi < « < 0 8x 2 C.In particular,8a
i
2 @f
i
(0) hv;a
i
i < 0.Thus,
f
i
(v) < 0;for i = 1;:::;p:(12)
From (11) there exist t > 0,u 2 Q such that v = tu (since v 6= 0) and substituting v by
tu in (9),(10) and (12),these equations are verißed for u in the place of v,this means
that u is solution of system (c).
108 B.Jimæenez,V.Novo/Alternative Theorems and Optimality Conditions
According to Theorem 3.5 and Proposition 3.6 if 0 2 ri h(Q) and there exists v 2 R
n
such that g(v) < 0;h(v) = 0;v 2 Q,then D is closed and,if furthermore cone Q is
closed,Theorem 3.13 can be applied,but in this case we get no advantage since Theorem
3.12(iii) is applicable (by Lemma 3.10) with slightly weaker hypotheses (it is not required
that cone Q be closed).Nevertheless,there are,obviously,cases in which Theorem 3.13
is applicable and Theorem 3.12 is not applicable as in Example 3.4(b) considering,for
example,f(x
1
;x
2
;x
3
) =  x
3
.
This theorem generalizes Lemma 2 in Ishizuka and Shimizu [5] which does not include
equality constraints or constraint set.This theorem also generalizes the classic Motzkin
alternative theorem,because if f
i
(u) = ha
i
;ui and g
j
(u) = hb
j
;ui are linear and Q = R
n
,
then cone cofb
j
:j = 1;:::;mg +linfc
k
:k 2 Kg is closed.
4.Optimality conditions
In this section,as an application of the results of the previous section,we obtain both Fritz
John and Kuhn-Tucker type necessary optimality conditions for multiobjective optimiza-
tion problems.These problems are deßned by Dini or Hadamard diÞerentiable functions
with convex derivative in the direction,and they include three types of constraints:in-
equality,equality and set constraints.For this purpose,we need a constraint qualißcation
of extended Abadie type,if the objective function is Hadamard diÞerentiable,or one of
extended Zangwill type,if only it is Dini diÞerentiable.To obtain the Kuhn-Tucker type
conditions we need the addition of other regularity conditions.If there are no equality
constraints (Theorem 4.8) the extended qualißcations (Abadie or Zangwill) are not nec-
essary.These results can be applied to study the diÞerentiable programs and the convex
programs,so that the results of Singh [16] and of Giorgi and Guerraggio [3],for diÞer-
entiable programs,and those of Kanniappan [8],Islam [6] and Kouada [9],for convex
programs,are just particular cases.
Consider the following multiobjective optimization problem
(P) Minff(x):x 2 S\Qg,
where f:R
n
!R
p
,S = fx 2 R
n
:g(x) ´ 0;h(x) = 0g,g:R
n
!R
m
,h:R
n
!R
r
and
Q is an arbitrary set of R
n
.
Let f
i
;i 2 I = f1;:::;pg,g
j
;j 2 J = f1;:::;mg,h
k
;k 2 K = f1;:::;rg be the
component functions of f,g and h,respectively.Given x
0
2 S,the set of active indexes
at x
0
is J
0
= fj 2 J:g
j
(x
0
) = 0g.The sets deßned by the constraints g and h are
denoted,respectively,G = fx 2 R
n
:g(x) ´ 0g,H = fx 2 R
n
:h(x) = 0g,accordingly,
S = G\H,and the set of points ÕbetterÔ than x
0
is F = fx 2 R
n
:f(x) ´ f(x
0
)g.
Hereafter we will suppose that the involved functions are Dini diÞerentiable at x
0
.We
will consider the following cones:
C
0
(G) = fv 2 R
n
:Dg
j
(x
0
;v) < 0 8j 2 J
0
g;
C(G) = fv 2 R
n
:Dg
j
(x
0
;v) ´ 0 8j 2 J
0
g;
C
0
(S) = C
0
(G)\Ker Dh(x
0
;¡),C(S) = C(G)\Ker Dh(x
0
;¡);
C
0
(F) = fv 2 R
n
:Df
i
(x
0
;v) < 0 8i 2 Ig;
C(F) = fv 2 R
n
:Df
i
(x
0
;v) ´ 0 8i 2 Ig.
(C(S) is called the linearized cone).Let us point out that the active inequality constraints
B.Jimæenez,V.Novo/Alternative Theorems and Optimality Conditions 109
take part with Õ<Ô or Õ´Ô,according to be C or C
0
and those of equality,with Õ=Ô,and
that the point x
0
is omitted to cut it short.
It is assumed that the functions g
j
;j 2 J n J
0
are continuous at x
0
.
Theorem 4.1.Let x
0
2 S\Qbe a feasible point of problem(P) and suppose the following:
(a) T(Q;x
0
) is convex.
(b) g
j
;j 2 J
0
are Dini diÞerentiable at x
0
with convex derivative at x
0
and h is Dini
diÞerentiable at x
0
with linear derivative given by Dh
k
(x
0
;¡) = hc
k
;¡i;k 2 K.
(c) The extended Abadie constraint qualißcation (EACQ) is verißed:
C(S)\T(Q;x
0
) º T(S\Q;x
0
).
(d) x
0
2 LWMin(f;S\Q).
(e) f is Hadamard diÞerentiable at x
0
with convex derivative at x
0
.
Then
(i) There exist (µ;¶;·) 2 R
p
¢R
m
¢R
r
such that (µ;¶) µ 0;(µ;¶;·) 6= 0 and
0 2
p
X
i=1
µ
i
@
D
f
i
(x
0
) +
m
X
j=1

j
@
D
g
j
(x
0
) +
r
X
k=1
·
k
c
k
+N(Q;x
0
);

j
g
j
(x
0
) = 0;j = 1;:::;m:
(ii) If linfc
k
:k 2 Kg +N(Q;x
0
) is closed,then (i) is satisßed with (µ;¶) 6= 0.
(iii) If
cone co([
j2J
0
@
D
g
j
(x
0
)) +linfc
k
:k 2 Kg +N(Q;x
0
) is closed,(13)
then (i) is satisßed with µ 6= 0.
Proof.It is known that if the objective function is Hadamard diÞerentiable and (d)
holds,then T(S\Q;x
0
)\C
0
(F) =;.From here,by the extended Abadie constraint
qualißcation,it follows that there exists no v 2 R
n
such that
8
>
>
<
>
>
:
Df
i
(x
0
;v) < 0 8i 2 I
Dg
j
(x
0
;v) ´ 0 8j 2 J
0
Dh
k
(x
0
;v) = 0 8k 2 K
v 2 T(Q;x
0
):
(14)
Therefore,we also ßnd the incompatibility in v 2 R
n
of the systems
(a)
8
>
>
<
>
>
:
Df
i
(x
0
;v) < 0 8i 2 I
Dg
j
(x
0
;v) < 0 8j 2 J
0
Dh
k
(x
0
;v) = 0 8k 2 K
v 2 T(Q;x
0
)
(b)
8
>
>
<
>
>
:
Df
i
(x
0
;v) < 0 8i 2 I
Dg
j
(x
0
;v) < 0 8j 2 J
0
Dh
k
(x
0
;v) = 0 8k 2 K
v 2 ri T(Q;x
0
):
(15)
By Theorem 3.9 applied to system (15)(b),part (i) is obtained (taking ¶
j
= 0,for j 2
J n J
0
,as usual).
Parts (ii) and (iii) are deduced,respectively,by Theorem 3.13 (with m = 0) applied to
system (15)(a) and by Theorem 3.13 applied to system (14).
110 B.Jimæenez,V.Novo/Alternative Theorems and Optimality Conditions
Part (iii) of this theorem generalizes Theorem 3.1 of Singh [16] (which is identical to
Theorem 2 of Wang [17]),valid for diÞerentiable functions and without constraint set
(Q = R
n
),and therefore @
D
f
i
(x
0
) = frf
i
(x
0
)g,@
D
g
j
(x
0
) = frg
j
(x
0
)g,c
k
= rh
k
(x
0
) and
condition (i) becomes the one used by Singh.Condition (13) is always satisßed,since
cone co([
j2J
0
@
D
g
j
(x
0
)) +linfc
k
:k 2 Kg
= cone cofrg
j
(x
0
):j 2 J
0
g +linfrh
k
(x
0
):k 2 Kg
is a polyhedral convex set and therefore,it is closed.Condition (c) in this case becomes
C(S) º T(S;x
0
),which is the constraint qualißcation used by Singh.
Remark 4.2.
(1) If
0 2 ri Dh(x
0
;¡)(T(Q;x
0
));(16)
then (i) is satisßed with (µ;¶) 6= 0.This follows from Lemma 3.3 (applied to the
convex T(Q;x
0
)) and part (ii).
(2) If the constraint qualißcation
C
0
(S)\ri T(Q;x
0
) 6=;;(17)
is satisßed,then (13) holds by Theorem 3.5 and Proposition 3.6.Therefore,(13) is
more general than (17),but this is simpler to check.
(3) Notice that hypotheses of part (ii) can be false and (13) can be true though.There-
fore these criteria are of independent application.On the other hand,(17) implies
(16) by Lemma 3.2.
The following example shows that,in fact,there are situations in which part (iii) is
applicable but (17) is not.
Example 4.3.In R
2
,let x
0
= (0;0);f(x;y) =  2y;g
1
(x;y) =  2x and g
2
the support
function of the set B = f(x;y):(x  2)
2
+y
2
´ 2;y µ 0g,that is to say
g
2
(x;y) =
º
2x +
p
2x
2
+2y
2
if y µ 0
2x +
p
2x
2
if y < 0:
Obviously Dg
2
(x
0
;v) = g
2
(v);@
D
g
2
(x
0
) = B.
The feasible set is G = f(0;y):y ´ 0g.The point x
0
is an (absolute) minimum of f on
G.We have:C
0
(G) =;(hence,(17) is false),C(G) = G,T(G;x
0
) = G = C(G) and,
therefore,the Abadie constraint qualißcation holds at x
0
.Furthermore,condition (13)
holds,because
cone co([
j2J
0
@
D
g
j
(x
0
)) = cone co(B [ f( 2;0)g) = f(x;y):y µ 0g is closed.
Hence,Theorem4.1(iii) is applicable.Concretely the conclusion is satisßed with (µ;¶
1

2
)
= (1;1;2) and the element b = (1;1) 2 B (there are inßnite solutions).
When the constraint set Q is not present (or if Q is a closed convex set) and the functions
are continuously Fræechet diÞerentiable,no regularity condition is required in obtaining the
usual Fritz John conditions.However,under the hypotheses of Theorem4.1,the following
example shows that (EACQ) cannot be eliminated to obtain part (i).
B.Jimæenez,V.Novo/Alternative Theorems and Optimality Conditions 111
Example 4.4.Let h:R
2
!R given by
h(x;y) =
º
 y
2
if x = 0;y < 0
x +x
2
otherwise,
x
0
= (0;0) and f(x;y) = y.It is clear that x
0
is a local minimum of f on H:= h
 1
(0) =
f(0;y):y µ 0g [ f(x;y):x =  1g.However,the Fritz John conditions
µrf(x
0
) +·rh(x
0
) = (0;0) with (µ;·) 6= (0;0);µ µ 0;
are not satisßed.Notice that (EACQ) is not verißed:Ker rh(x
0
) 6º T(H;x
0
).
Similar conditions to (EACQ) are used by other authors.For example,Bender,who
considers Hadamard diÞerentiable functions with linear derivative,uses the following [1,
Condition (3)]:
Ker Dh(x
0
;¡)\T(Q;x
0
) º T(H\Q;x
0
):(18)
If the inequality constraints are not considered,(EACQ) becomes (18),but if they are
present,there is not implications between (EACQ) and (18).Thus,if we incorporate the
constraint g(x;y) =  y in the example 4.4,then (EACQ) holds but (18) is not verißed.
On the other hand,if we consider h(x;y) = y,g(x;y) =  y  x
3
and Q = f(x;y):y µ 0g
then though (18) holds,(EACQ) is not true.
It is still possible to hold up the conclusions of Theorem4.1 if f is only Dini diÞerentiable.
In return,the constraint qualißcation must be more restrictive.
Theorem 4.5.Suppose that conditions (a),(b),(d) of Theorem 4.1 are verißed and the
following ones:
(c) The extended Zangwill constraint qualißcation is satisßed:
cl Z
s
(S\Q;x
0
) = C(S)\T(Q;x
0
).
(e) f is Dini diÞerentiable at x
0
with convex derivative.
Then the conclusions are the same (i)-(iii) that in Theorem 4.1.
Proof.It is known ([12,Proposition 4.1]) that if f is Dini diÞerentiable and (d) is verißed,
then
Z
s
(S\Q;x
0
)\C
0
(F) =;.
Therefore,cl Z
s
(S\Q;x
0
)\int C
0
(F) =;.As Df(x
0
;¡) is continuous,C
0
(F) is open,
hence,cl Z
s
(S\Q;x
0
)\C
0
(F) =;,and taking into account (c) we deduce that C(S)\
T(Q;x
0
)\C
0
(F) =;,this means that system (14) is incompatible.From here,it is
continued just as in the proof of Theorem 4.1.
Remark 4.6.
(1) Notice that if the extended Zangwill constraint qualißcation holds,also the extended
Abadie constraint qualißcation holds,since Z
s
(S\Q;x
0
) º T(S\Q;x
0
).
(2) In Theorems 4.1 and 4.4 can be used any convex subcone T
1
(Q) of T(Q;x
0
) instead of
the T(Q;x
0
) itself,in whose case the normal cone N(Q;x
0
) would be substituted by
the polar cone T
1
(Q)
£
,condition (c) of Theorem4.1 would be C(S)\T
1
(Q) º T(S\
Q;x
0
) and condition (c) of Theorem 4.5 would be C(S)\T
1
(Q) º cl Z
s
(S\Q;x
0
).
Of course,the best results are obtained choosing the largest convex subcone.
112 B.Jimæenez,V.Novo/Alternative Theorems and Optimality Conditions
The following proposition provides us with a suácient condition for the extended Zangwill
constraint qualißcation to hold.
Proposition 4.7.If Q is a convex set,g
j
;j 2 J
0
are Dini diÞerentiable at x
0
with
convex derivative,Z
s
(H;x
0
) = Ker Dh(x
0
;¡) and C
0
(S)\ri cone(Q x
0
) 6=;,then the
extended Zangwill constraint qualißcation holds.
Proof.For the set G,we have C
0
(G) º Z(G;x
0
) º C(G).Therefore,
C
0
(G)\Ker Dh(x
0
;¡) º Z(G;x
0
)\Z
s
(H;x
0
) º Z
s
(G\H;x
0
) º C(G)\Ker Dh(x
0
;¡),
hence C
0
(S) º Z
s
(S;x
0
) º C(S),so
C
0
(S)\cone(Q x
0
) º Z
s
(S;x
0
)\Z(Q;x
0
) º Z
s
(S\Q;x
0
) º C(S)\T(Q;x
0
).
The conclusion follows by taking closure,since cl[C
0
(S)\cone(Q x
0
)] = C(S)\T(Q;x
0
)
by [4,Proposition 2.1.10,Chap.3].
In the following theorem,equality constraints are removed.
Theorem 4.8.Let x
0
2 G\Q and suppose the following:
(a) T
1
(Q) is a convex subcone of T(Q;x
0
).
(b) x
0
2 LWMin(f;G\Q).
Then
(i) If f and g
j
;j 2 J
0
are Hadamard diÞerentiable at x
0
with convex derivative,then
there exist (µ;¶) 2 R
p
¢R
m
,(µ;¶) 6= 0 such that
(µ;¶) µ 0;
0 2
P
p
i=1
µ
i
@
D
f
i
(x
0
) +
P
m
j=1

j
@
D
g
j
(x
0
) +T
1
(Q)
£
;

j
g
j
(x
0
) = 0;j = 1;:::;m:
9
=
;
(19)
(ii) If f and g
j
;j 2 J
0
are Dini diÞerentiable at x
0
with convex derivative and T
1
(Q) º
Z
s
(Q;x
0
),then (19) holds with (µ;¶) 6= 0.
(iii) If,moreover,
C
0
(G)\T
1
(Q) 6=;;(20)
then (19) holds with µ 6= 0.
Proof.(i) Since f is Hadamard diÞerentiable and (b) holds,as it has been pointed out
in the proof of Theorem 4.1,
T(G\Q;x
0
)\C
0
(F) =;:(21)
Let us show that
C
0
(G)\T(Q;x
0
) º T(G\Q;x
0
):(22)
In fact,if v 2 C
0
(G)\T(Q;x
0
),then there exist sequences x
n
2 Q;t
n
!0
+
such that
x
n
 x
0
t
n
= v
n
!v.As g
j
;j 2 J
0
are Hadamard diÞerentiable,one has
B.Jimæenez,V.Novo/Alternative Theorems and Optimality Conditions 113
dg
j
(x
0
;v) = lim
n!1
g
j
(x
0
+t
n
v
n
)  g
j
(x
0
)
t
n
= lim
n!1
g
j
(x
n
)
t
n
< 0;8j 2 J
0
,
because v 2 C
0
(G).Hence,for n large enough,g
j
(x
n
) < 0.For j 2 J n J
0
,by the
continuity of g
j
at x
0
,also it is g
j
(x
n
) < 0,for n large.Therefore,x
n
2 G\Q and
consequently v 2 T(G\Q;x
0
).
From (21),taking into account (22) and hypothesis (a),it follows that C
0
(G)\T
1
(Q)\
C
0
(F) =;,that is,there exists no v 2 R
n
such that
Df(x
0
;v) < 0;Dg
j
(x
0
;v) < 0 8j 2 J
0
;v 2 T
1
(Q):
By Corollary 3.7 one has the conclusion.
(ii) Since f is Dini diÞerentiable and (c) holds,as has been pointed out in the proof of
Theorem 4.5,
Z
s
(G\Q;x
0
)\C
0
(F) =;:(23)
On the other hand,since C
0
(G) º Z(G;x
0
),we deduce that
C
0
(G)\Z
s
(Q;x
0
) º Z(G;x
0
)\Z
s
(Q;x
0
) º Z
s
(G\Q;x
0
):(24)
From (23),taking into account (24) and hypothesis of (ii),it follows that C
0
(G)\T
1
(Q)\
C
0
(F) =;.From here we would continue as in part (i) above.
(iii) By reduction to the absurd,suppose that µ = 0.Then we have
0 2
P
j2J
0

j
@
D
g
j
(x
0
) +T
1
(Q)
£
;¶ µ 0;¶ 6= 0.
By Corollary 3.7,C
0
(G)\T
1
(Q) =;,which contradicts (20).
Remark 4.9.
(1) If Q is a convex set,the sharpest results in Theorem 4.8 are obtained for T
1
(Q) =
cone(Q x
0
),with what T
1
(Q)
£
= N(Q;x
0
) and (20) is equivalent to
C
0
(G)\(Q x
0
) 6=;.
(2) The condition obtained in (i) is Fritz John type.If we wish one of Kuhn-Tucker
type,a constraint qualißcation can be used,as for example C
0
(G)\T
1
(Q) 6=;,but
in this case,if T
1
(Q) º Z
s
(Q;x
0
),it is preferable to use (iii) which is less restrictive.
Part (i) generalizes Theorem 5 in Giorgi and Guerraggio [3] in which the functions f
and g are diÞerentiable.Parts (i) and (ii) generalize Theorem 3.2 in Kanniappan [8] and
Theorem 3.1 in Islam [6] in which it is supposed that the functions f and g are convex
on R
n
and Q is convex.In fact,if a function is convex,it is Dini diÞerentiable with
convex derivative,and in addition,it is locally Lipschitz,with what it is also Hadamard
diÞerentiable,and (i) and (ii) can be applied by taking T
1
(Q) = cone(Q x
0
) (the results
of these authors are obtained since,for a convex real function',@
D
'(x
0
) = @'(x
0
)).
Theorems 3.4 and 3.2 of the same authors can be deduced frompart (iii).These theorems
state:ÕIf f and g are convex,Q is convex,x
0
2 Min(f;G\Q) and the Slater constraint
qualißcation (SCQ) holds,that is,for each i = 1;:::;p there exists x
i
such that f
k
(x
i
) <
f
k
(x
0
) 8k 6= i,g
j
(x
i
) < 0 8j 2 J;x
i
2 Q,then (19) holds with µ > 0.Ô In fact,let
114 B.Jimæenez,V.Novo/Alternative Theorems and Optimality Conditions
us observe in the ßrst place that if':R
n
!R is convex and'(x) <'(x
0
) for some
x;x
0
2 R
n
,then
D'(x
0
;x  x
0
) < 0:(25)
By convexity,
'(x
0
+t(x  x
0
)) ´'(x
0
) +t('(x)  '(x
0
)) 8t 2 (0;1).
Hence,
'(x
0
+t(x  x
0
))  '(x
0
)
t
´'(x)  '(x
0
) < 0.Taking the limit when t!0
+
,
(25) is obtained.
Therefore,applying (25),by (SCQ),for each i = 1;:::;p there exists v = x
i
 x
0
such
that
Df
k
(x
0
;v) < 0 8k 6= i;Dg
j
(x
0
;v) < 0 8j 2 J
0
;v 2 Q x
0
:(26)
Theorem 4.6(iii) (with T
1
(Q) = cone(Q x
0
)) can be applied,since any Pareto minimum
is a weak local Pareto minimum,resulting (19) with µ 6= 0.If p = 1,one has µ > 0.
If p µ 2,suppose that some µ
i
= 0 (with some µ
j
6= 0,since µ 6= 0).By Corollary 3.7
(applied to the p 1 functions Df
k
(x
0
;¡);k 6= i and to Dg
j
(x
0
;¡);j 2 J
0
) there exists no
vector v satisfying system(26),in contradiction to the existence of the solution v = x x
i
.
Therefore,µ > 0.
Notice that in (SCQ) it is suácient to require the condition g
j
(x
i
) < 0 for every j 2 J
0
(instead of for all j 2 J).Observe also that if the weak Slater constraint qualißcation
holds:there exists x 2 Q such that g
j
(x) < 0 8j 2 J
0
,then (19) holds with µ 6= 0.
Finally,notice that at the same time it has been proved that if (SCQ) holds,every Pareto
minimum is a solution for a scalarized problem,Minfhµ;f(x)i:x 2 G\Qg,with µ > 0
([9,Theorem 5]).
The next lemma provides us with a simple expression for the tangent cone to a set which
is intersection of two sets:the ßrst convex and the second is deßned by quasiconvex or
quasilinear functions.This expression will allow us to obtain an optimality criterion with
this type of functions.Let us recall these concepts previously.
Let È º R
n
be a convex set,':È!R,x
0
2 È.'is quasiconvex at x
0
on È if
8x 2 È;'(x) ´'(x
0
) )'(µx +(1  µ)x
0
) ´'(x
0
) 8µ 2 (0;1).
'is quasilinear at x
0
if'and  'are quasiconvex at x
0
.
Lemma 4.10.Suppose that Q º R
n
is a convex set and g
j
,j 2 J
0
are quasiconvex at x
0
on a neighborhood of x
0
and h is quasilinear at x
0
on a neighborhood of x
0
.Then
cl Z(S\Q;x
0
) = T(S\Q;x
0
).
Proof.Let B(x
0
;®) be a neighborhood of x
0
on which g
j
,j 2 J
0
are quasiconvex at x
0
and h is quasilinear.Hence,8x 2 B(x
0
;®) if g
j
(x) ´ g
j
(x
0
),then
g
j
(µx +(1  µ)x
0
) ´ g
j
(x
0
) 8µ 2 [0;1].
Therefore,if x 2 G\B(x
0
;®) we derive g
j
(µx + (1   µ)x
0
) ´ 0 8µ 2 [0;1];8j 2 J
0
.
If j 2 J n J
0
,by the continuity of g
j
,there exists a neighborhood B(x
0

1
) such that
g
j
(x) < 0 8x 2 B(x
0

1
);8j 2 J n J
0
.Taking ®
0
= Minf®;®
1
g,both conditions are
verißed and,therefore [x
0
;x] º G 8x 2 G\B
0
,being B
0
= B(x
0

0
).Similarly,since
B.Jimæenez,V.Novo/Alternative Theorems and Optimality Conditions 115
h and  h are quasiconvex at x
0
,[x
0
;x] º H.Hence [x
0
;x] º S 8x 2 S\B
0
.As Q is
convex,one has [x
0
;x] º S\Q 8x 2 S\Q\B
0
.Hence,v = x  x
0
2 Z(S\Q;x
0
).It
follows
cone(S\Q\B
0
 x
0
) º Z(S\Q;x
0
) º T(S\Q;x
0
).
Now,T(S\Q;x
0
) = T(S\Q\B
0
;x
0
) º cl cone(S\Q\B
0
 x
0
) º cl Z(S\Q;x
0
).
In conclusion,by closedness of the tangent cone,
T(S\Q;x
0
) = cl Z(S\Q;x
0
) = cl cone(S\Q\B
0
 x
0
).
Theorem 4.11.In the hypotheses of Lemma 4.10.Suppose that conditions (b),(c) and
(d) of Theorem 4.1 are verißed and that f is Dini diÞerentiable at x
0
with convex deriva-
tive.Then the conclusions are the same (i)-(iii) that in Theorem 4.1.
Proof.By Lemma 4.10,condition (c) becomes the extended Zangwill constraint qualiß-
cation,i.e.,condition (c) of Theorem 4.5,and it is enough to apply this theorem.
Acknowledgements.The authors are grateful to the anonymous referee for his helpful
comments and suggestions which have been included in the ßnal version of the paper.
References
[1] P.J.Bender:Nonlinear programming in normed linear spaces,J.Optim.Theory Appl.
24(2) (1978) 263Û285.
[2] V.F.Demyanov,A.M.Rubinov:Constructive Nonsmooth Analysis,Verlag Peter Lang,
Frankfurt am Main (1995).
[3] G.Giorgi,A.Guerraggio:First order generalized optimality conditions for programming
problems with a set constraint,Lecture Notes in Econom.and Math.Systems 405 (1994)
171Û185.
[4] J.B.Hiriart-Urruty,C.Lemaræechal:Convex Analysis and Minimization Algorithms I,
Springer-Verlag,Berlin (1996).
[5] Y.Ishizuka,K.Shimizu:Necessary and suácient conditions for the eácient solutions of
nondiÞerentiable multiobjective problems,IEEE Transactions on Systems,Man and Cyber-
netics SMC-14(4) (1984) 625Û629.
[6] M.Islam:Suáciency and duality in nondiÞerentiable multiobjective programming,Pure
Appl.Math.Sci.39(1Û2) (1994) 31Û39.
[7] V.Jeyakumar:Farkas'lemma and extensions,in:Encyclopedia of optimization,C.A.
Floudas,P.M.Pardalos (eds.),Kluwer Academic Publishers,Boston (2001).
[8] P.Kanniappan:Necessary conditions for optimality of nondiÞerentiable convex multiobjec-
tive programming,J.Optim.Theory Appl.40 (1983) 167Û174.
[9] I.Kouada:Fritz John's type conditions and associated duality forms in convex nondiÞer-
entiable vector-optimization,RAIRO Rech.Oper.28(4) (1994) 399Û412.
[10] O.L.Mangasarian:Nonlinear Programming,McGraw-Hill,New York (1969).
116 B.Jimæenez,V.Novo/Alternative Theorems and Optimality Conditions
[11] O.L.Mangasarian,S.Fromovitz:The Fritz John necessary optimality conditions in the
presence of equality and inequality constraints,J.Math.Anal.Appl.17 (1967) 37Û47.
[12] J.P.Penot:Calcul sous-diÞerential et optimisation,J.Funct.Anal.27(2) (1978) 248Û276.
[13] L.Qi,Z.Wei:On the constant positive linear dependence condition and its application to
SQP methods,SIAM J.Optim.10(4) (2000) 963Û981.
[14] S.M.Robinson:Stability theory for systems of inequalities,part II:diÞerentiable nonlinear
systems,SIAM J.on Numer.Anal.13(4) (1976) 497Û513.
[15] R.T.Rockafellar:Convex Analysis,Princeton University Press,Princeton (1970).
[16] C.Singh:Optimality conditions in multiobjective diÞerentiable programming,J.Optim.
Theory Appl.53(1) (1987) 115Û123.
[17] S.Wang:A note on optimality conditions in multiobjective programming,Systems Sci.
Math.Sci.1(2) (1988) 184Û190.
[18] L.Wang,J.Dong,Q.Liu:Optimality conditions in nonsmooth multiobjective program-
ming,Systems Sci.Math.Sci.7(3) (1994) 250Û255.
[19] J.Zowe,S.Kurcyusz:Regularity and stability for the mathematical programming problem
in Banach spaces,Appl.Math.Optim.5 (1979) 49Û62.