1. Order Convexity of Vector-Valued Functions
Often in functional analysis one needs local algebraic linearity. Thus, one of the interactions of the algebraic and topological structure of a topological vector is manifested in the important properties of the class of convex functions. So far, we have allowed the convex functions defined on the convex subsets of a vector space to be real valued. We will extend the definition of convexity to the valued functions in a Banach lattice.
Definition 1.1 Let
be a Banach lattice. A function
on a convex set C in a vector space
is:
1) order convex (denoted by o-convex) if for all
and all
,
.
2) strictly o-convex if for all
with
and all
,
.
3) o-concave (respectively, strictly o-concave) if
is an o-convex (respectively, strictly o-concave) function.
It is easy to realize that,
is o-convex if and only if,
for every convex combination
.
Example 1.1 Here are some familiar examples of o-convex mappings.
• Obviously, any convex real function is o-convex.
• Let
be a Banach lattice. The absolute value
is an o-convex mappings from
to
.
• Let
be a commutative unital real Banach algebra. The set of all multiplicative linear functionals on
is denoted by
. It is well known that
, endowed with the Gelfand topology, is compact and the Gelfand representation
of
into
is an homomorphism ([1], Theorem 13). Thus
is o-convex.
Proposition 1.1 A function
on a convex subset of a vector space into a Banach lattice
is o-convex if and only if its epigraph,
, is convex. Similarly,
is o-concave if and only if its hypograph is concave.
Proof. We prove the first part of this proposition. The remaining assertion is identical. Suppose that
is o-convex, then for
and
we have
So,
. The “only if” part stems from the fact that
and
. ◼
Proposition 1.2 The collection of o-convex functions on a fixed convex set
into a Banach lattice
has the following properties:
1) Sums and nonnegative scalar multiples of o-convex functions are o-convex.
2) The (finite) pointwise order limit of a net of o-convex functions is o-convex.
3) The (finite) pointwise supremum of a family of o-convex functions is o-convex.
Proof. The first statement is trivial. For the second assertion, consider a net
of o-convex functions (finite) pointwise order convergent to
, that is, for any finite part
of
, there is a net
(with the same directed set) satisfying
and
for each
and every
. Let
and
. For
we have:
So,
is o-convex.
Now, let
be o-convex functions on a convex set
into a Banach lattice
. For all
, we define
. It is easy to see that:
So,
is o-convex, what completes the proof. ◼
Proposition 1.3 Let
be an o-convex function,
and
. Then for all
,
Proof.
because the hypothesis and the equality:
. Rearranging terms yields
(1.1)
(1.2)
Replacing
by
in (1.1) gives
(1.3)
Since
, we have
. Multiplying by two and rearranging terms we obtain
(1.4)
(1.3) implies
(1.5)
With definition of the absolute value in mind, (1.2) in conjunction with (1.5) yields the conclusion of the proposition. ◼
Recall that a subset
of a Riesz space
is order bounded, from above if there is a vector
(called an upper bound of
) that dominates each element of
, that is, satisfying
for each
. Sets order bounded from below are defined similarly. A box or an order interval, is any set of the form
Definition 1.2 A mapping
between Riesz spaces is o-bounded above (respectively, o-bounded) on a subset
of
, if
is order bounded from above (respectively, if
for some box
of
).
We would have liked an o-convex function
to be order continuous, but this is not true even in the trivial case when
. Indeed, let
, we emphasize: There is no nonzero
-order continuous linear functional on the Riesz space
(see for example ([2], p. 329)). However, for the topological continuity we have the following, which generalizes a similar result well known for the convex (real) functions.
Theorem 1.1 If an o-convex function
is o-bounded above in a neighborhood of an interior point
, then
is continuous at
.
Proof. We may assume that for some
there exist an open ball
of radius
at 0 and some
satisfying
and
for each
. Fix
and choose some
so that
. From Proposition1.3, it follows that for each
we have
. Now, the norm of
is lattice, then
. ◼
Remark 1.1
Provided that the interior
of the cone
is non-empty, semicontinuity can be generalized to vector functions as follows (For more details on the impact of a cone’s properties on the Riesz space it generates, the reader is referred to [3]).
Definition 1.3 A mapping
from a topological space
into a Banach lattice
is:
Lower o-semicontinuous if for each
the set
is open.
Upper o-semicontinuous if for each
the set
is open.
Obviously, a mapping
is lower o-semicontinuous if and only
is upper o-semicontinuous, and vice versa.
The classic example of a lower (resp. upper) o-semicontinuous mapping is given by the lower (resp. upper) semicontinuous real functions. Now assume that
is a Banach lattice with an order unit
. It is well known that the principal ideal
generated by
coincides with
which when provided with the norm
becomes an AM-space with unit. Let
be a continuous real function. Then the mapping
defined by
is a lower and upper o-semicontinuous mapping.
The
-valued mapping on a Banach lattice is a useful device, but it needs to be handled with care. For example, the complement of the set
in
is not at all the set
. However, the following lemma reduces this difficulty by reducing us to functions with real values.
Proposition 1.4 If
(respectively,
) is the positive cone of a Banach lattice
(respectively, of
), then
if and only if it exists
such that
.
Proof. The definition of the positive cone
gives a sense of lemma. Conversely, if
, since
is closed and convex, it follows from the Hahn-Banach theorem that there is a
with
for all
. Thus
and
for all non-negative integer number
. So
for all
. ◼
As a first application of the above definitions, we have the following result.
Theorem 1.2 For an o-convex mapping
on an open convex subset
, the following are equivalent.
(a)
is continuous on
.
(b)
is upper o-semicontinuous.
(c)
is o-bounded above on a neighborhood of each point in
.
(d)
is o-bounded above on a neighborhood of some point in
.
(e)
is continuous at some point in
.
Proof. (a)
(b) is obvious.
(b)
(c); Assume that
is upper o-semicontinuous. Let
and
. Then the set
is an open neighborhood of
on which
is o-bounded above.
(c)
(d) Obvious.
(d)
(e) This is Theorem 1.1.
(e)
(a) Suppose
is continuous at the point
, and let
be any other point in
. Since
is open and convex, therefore
does not contain extreme points. This implies that there exist
and
such that
. Fix
and choose some circled neighborhood
of zero
so that
for all
. We claim that,
for all
. Indeed, let
, Then
and the o-convexity of
implies
and
Thus
(1.6)
and
(1.7)
This shows that
(1.8)
Then
So,
is continuous at
. ◼
2. Order Lipschitzian Vector-Valued Functions
Lipchitzian and contractive real functions have important properties that we want to extend to infinite dimensional analysis. For this purpose, we adopt the following definition.
Definition 2.1 A mapping f from a subset
of a normed space
to a Banach lattice
is order Lipschitzian on
if there exists
such that for every
If moreover
then
is called an order contraction.
The following gives examples of order Lipschitzian functions.
Theorem 2.1 Let
be a positive o-convex function from a convex subset
into a Banach lattice
. If
is continuous at some interior point
of
, then
is order Lipschitzian on a neighborhood of
.
Proof. Since
is continuous at
, it follows from Theorem 1.2 that there exists
and
satisfying
and
. So,
implies
and
. By addition, we achieve
, for all
. Let
and
. Then
belongs to
and we have
. Therefore
Subtracting
from each side gives
Switching the roles of
and
allows us to conclude
◼
A net
in a Riesz space
is order convergent to some
, written
, if there is a net
(with the same directed set) satisfying
and
for each
. A function
between two Riesz spaces is order uniformly continuous if
in
implies
in
.
Proposition 2.1 If
is order Lipschitz continuous and
is an order continuous lattice norm on
, then
is order uniformly continuous.
Proof. Obvious. ◼
Now we will generalize, to convex order applications, one of the important themes of the analysis, namely the extreme points of a convex functions on a compact convex set. Let
be a convex subset of a vector space
. Recall that an extreme subset of
, is a nonempty subset
of
with the property that if
belongs to
, it cannot be written as a convex combination of points of
outside
. A point
is an extreme point of
if the singleton
is an extreme set.
Proposition 2.2 If
is o-convex and attains a maximum at some point, then the set of maximizers is an extreme set.
Proof. Suppose
achieves a maximum on
; that is,
satisfies the identity
for some
. Put
. Suppose that
,
and
. If
then
, so
a contradiction. Hence
, so
is an extreme subset of
. ◼
Recall that the order
of a Banach lattice
is continuous if
is a closed subset of
. Let us say that
is upper semicontinuous if
is closed for each
.
Theorem 2.2 Let
be compact and
continuous, with
a Banach lattice having continuous order. If
is closed under suprema (condition C), then
attains a minimum on
, and the minimizer set is compact.
Proof. Let
be a compact of a normed vector space
and let
be a continuous mapping from
to a Banach lattice
. For each
, put
. It follows from the continuity of
and of the order that the nonempty set
is closed (
implies
). Moreover, the family
has the finite intersection property. In deed, let
be a finite family in
. Since
satisfies condition
so,
. For all
and
we have
so,
and
. Since
is compact, ([2], Theorem.2.31)
implies that the set of minimizers
is compact and nonempty. ◼
We realize that, in its real context, the assumptions
and
in Theorem 2.2 are ensured from the fact that the order in
is total.
A complete lattice is a lattice in which every nonempty subset that is order bounded from above has a supremum. (Equivalently, if every nonempty subset that is bounded from below has an infimum).
Now consider a vector form of the Contraction Mapping Theorem.
Theorem 2.3 Let
be an order contraction on a closed subset
of a Banach lattice
, with contraction modulus
. Then f has a unique fixed point
, and for any
, the iterates
converges to
with
.
Proof. Let
such that
and
, for all
. If
and
then
. Since the norm of
is lattice, we have
and hence
. Thus
can have at most one fixed point.
Now, if
is chosen in
then the formula
,
defines inductively the sequence
which satisfies:
, for every
. The lattice property verified by the norm of
implies that
and by induction, we see that for all
,
. Hence, for
the triangle inequality yields
This implies that
is a Cauchy sequence. Since
is closed in the complete space
then,
. Obviously,
is continuous, and:
, so
is the fixed point of
.