Vector-Valued Convex Functions

Abstract

Convex analysis plays a fundamental role in mathematics. In this paper, we extend the concept of convexity to vector-valued functions in Banach lattices. We introduce the notion of “order convexity” (o-convexity) and explore its properties, generalizing several results from real-valued convex analysis. These include a continuity theorem for o-convex functions (Theorem 1.2), an analogue of Bauer’s maximal principle for o-convex functions on compact sets (Theorem 2.2), and a fixed-point theorem for order contraction maps (Theorem 2.3).

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Laayouni, M. (2025) Vector-Valued Convex Functions. Advances in Pure Mathematics, 15, 743-750. doi: 10.4236/apm.2025.1511040.

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 E be a Banach lattice. A function f:C→E on a convex set C in a vector space X is:

1) order convex (denoted by o-convex) if for all x,y∈C and all 0≤α≤1 , f( αx+( 1−α )y )≤αf( x )+( 1−α )f( y ) .

2) strictly o-convex if for all x,y∈C with x≠y and all 0<α<1 , f( αx+( 1−α )y )<αf( x )+( 1−α )f( y ) .

3) o-concave (respectively, strictly o-concave) if −f is an o-convex (respectively, strictly o-concave) function.

It is easy to realize that, f is o-convex if and only if,

f( ∑ k=1 n   α k x k )≤ ∑ k=1 n   α k f( x k )

for every convex combination ∑ k=1 n   α k x k .

Example 1.1 Here are some familiar examples of o-convex mappings.

• Obviously, any convex real function is o-convex.

• Let E be a Banach lattice. The absolute value x↦| x | is an o-convex mappings from E to E .

• Let A be a commutative unital real Banach algebra. The set of all multiplicative linear functionals on A is denoted by Δ A . It is well known that Δ A , endowed with the Gelfand topology, is compact and the Gelfand representation ϕ of A into C( Δ A ) is an homomorphism ([1], Theorem 13). Thus ϕ is o-convex.

Proposition 1.1 A function f:C→E on a convex subset of a vector space into a Banach lattice E is o-convex if and only if its epigraph, epi( f )={ ( x,χ )∈C×E:χ≥f( x ) } , is convex. Similarly, f 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 f is o-convex, then for ( x 1 , χ 1 ),( x 2 , χ 2 )∈epi( f ) and α∈[ 0,1 ] we have

α 1 χ 1 +( 1−α ) χ 2 ≥ α 1 f( x 1 )+( 1−α )f( x 2 ) ≥f( α x 1 +( 1−α ) x 2 )

So, ( α 1 χ 1 +( 1−α ) χ 2 , α 1 x 1 +( 1−α ) x 2 )∈epi( f ) . The “only if” part stems from the fact that ( x 1 ,f( x 1 ) )∈epi( f ) and ( x 2 ,f( x 2 ) )∈epi( f ) . ◼

Proposition 1.2 The collection of o-convex functions on a fixed convex set C into a Banach lattice E 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 { f i } of o-convex functions (finite) pointwise order convergent to f , that is, for any finite part F of C , there is a net { χ i } (with the same directed set) satisfying χ i ↓0 and | f i ( z )−f( z ) |≤ χ i for each i and every z∈F . Let x,y∈C and α∈[ 0,1 ] . For F={ x,y,αx+( 1−α )y } we have:

f( αx+( 1−α )y )≤ χ i + f i ( αx+( 1−α )y ) ≤ χ i +α f i ( x )+( 1−α ) f i ( y ) ≤ χ i +α[ f( x )+ f i ( x )−f( x ) ]   +( 1−α )[ f( y )+ f i ( y )−f( y ) ] ≤ χ i +αf( x )+( 1−α )f( y )+ χ i ≤[ 2 χ i +αf( x )+( 1−α )f( y ) ]↓αf( x )+( 1−α )f( y )

So, f is o-convex.

Now, let f 1 , f 2 ,⋯, f n be o-convex functions on a convex set C into a Banach lattice E . For all x∈C , we define f( x )= V 1≤k≤n   f k ( x ) . It is easy to see that:

f( αx+( 1−α )y )= V 1≤k≤n   f k ( αx+( 1−α )y ) ≤ V 1≤k≤n [ α f k ( x )+( 1−α ) f k ( y ) ] ≤ V 1≤k≤n [ α f k ( x ) ]+ V 1≤k≤n [ ( 1−α ) f k ( y ) ] ≤α V 1≤k≤n   f k ( x )+( 1−α ) V 1≤k≤n   f k ( y ) ≤αf( x )+( 1−α )f( y ).

So, f is o-convex, what completes the proof. ◼

Proposition 1.3 Let f:C→E be an o-convex function, x∈C and x∓z∈C . Then for all α∈[ 0,1 ] ,

| f( x+αz )−f( x ) |≤α( [ f( x+z )−f( x ) ]∨[ f( x−z )−f( x ) ] ).

Proof. f( x+αz )≤( 1−α )f( x )+αf( x+z ) because the hypothesis and the equality: x+αz=( 1−α )x+α( x+z ) . Rearranging terms yields

f( x+αz )−f( x )≤α[ f( x+z )−f( x ) ] (1.1)

≤α( [ f( x+z )−f( x ) ]∨[ f( x−z )−f( x ) ] ) (1.2)

Replacing z by −z in (1.1) gives

f( x−αz )−f( x )≤α[ f( x−z )−f( x ) ] (1.3)

Since x= 1 2 ( x+αz )+ 1 2 ( x−αz ) , we have f( x )≤ 1 2 f( x+αz )+ 1 2 f( x−αz ) . Multiplying by two and rearranging terms we obtain

f( x )−f( x+αz )≤f( x−αz )−f( x ) (1.4)

(1.3) implies

f( x )−f( x+αz )≤f( x−αz )−f( x ) ≤α[ f( x−z )−f( x ) ] ≤α( [ f( x+z )−f( x ) ]∨[ f( x−z )−f( x ) ] ) (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 A of a Riesz space X is order bounded, from above if there is a vector u (called an upper bound of A ) that dominates each element of A , that is, satisfying a≤u for each a∈A . Sets order bounded from below are defined similarly. A box or an order interval, is any set of the form

[ a,b ]={ x∈X:a≤x≤b }

Definition 1.2 A mapping f:X→E between Riesz spaces is o-bounded above (respectively, o-bounded) on a subset V of X , if f( V ) is order bounded from above (respectively, if f( V )⊂[ a,b ] for some box [ a,b ] of E ).

We would have liked an o-convex function f:X→E to be order continuous, but this is not true even in the trivial case when E=ℝ . Indeed, let X=C[ 0,1 ] , we emphasize: There is no nonzero σ -order continuous linear functional on the Riesz space X (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 f:C→E is o-bounded above in a neighborhood of an interior point x∈C , then f is continuous at x .

Proof. We may assume that for some x∈C there exist an open ball V of radius η at 0 and some χ∈E satisfying x+V⊂C and f( y )≤f( x )+χ for each y∈x+V . Fix ε>0 and choose some 0<α<1 so that α‖ χ ‖<ε . From Proposition1.3, it follows that for each y∈x+αV we have | f( y )−f( x ) |≤αχ . Now, the norm of E is lattice, then ‖ f( y )−f( x ) ‖≤α‖ χ ‖<ε . ◼

Remark 1.1

Provided that the interior Int( E + ) of the cone E + 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 f:X→E from a topological space X into a Banach lattice E is:

♦ Lower o-semicontinuous if for each c∈E the set

{ x∈X:f( x )−c∈Int( E + ) }

is open.

♦ Upper o-semicontinuous if for each c∈E the set

{ x∈X:c−f( x )∈Int( E + ) }

is open.

Obviously, a mapping f is lower o-semicontinuous if and only −f 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 E is a Banach lattice with an order unit e . It is well known that the principal ideal E e generated by e coincides with E which when provided with the norm ‖ x ‖ ∞ =inf{ λ>0:| x |≤λe } becomes an AM-space with unit. Let f:ℝ→ℝ be a continuous real function. Then the mapping f ˜ :E→E defined by f ˜ ( x )=f( ‖ x ‖ ∞ )e is a lower and upper o-semicontinuous mapping.

The E -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 { x∈X:f( x )≥c } in X is not at all the set { x∈X:f( x )<c } . However, the following lemma reduces this difficulty by reducing us to functions with real values.

Proposition 1.4 If E + (respectively, E ′ + ) is the positive cone of a Banach lattice E (respectively, of E' ), then x∉ E + if and only if it exists φ∈ E ′ + such that φ( x )<0 .

Proof. The definition of the positive cone E ′ + gives a sense of lemma. Conversely, if x∉ E + , since E + is closed and convex, it follows from the Hahn-Banach theorem that there is a φ∈ E ′ with φ( x )<φ( y ) for all y∈ E + . Thus φ( x )<0=f( 0 ) and φ( x )<φ( ny ) for all non-negative integer number n . So φ( x )<0≤φ( y ) for all y∈ E + . ◼

As a first application of the above definitions, we have the following result.

Theorem 1.2 For an o-convex mapping f:C→E on an open convex subset C⊂X , the following are equivalent.

(a) f is continuous on C .

(b) f is upper o-semicontinuous.

(c) f is o-bounded above on a neighborhood of each point in C .

(d) f is o-bounded above on a neighborhood of some point in C .

(e) f is continuous at some point in C .

Proof. (a) ⇒ (b) is obvious.

(b) ⇒ (c); Assume that f is upper o-semicontinuous. Let x∈C and a∈Int( E + )−{ 0 } . Then the set { y∈E:f( x )+a−f( y )∈Int( E + ) } is an open neighborhood of x on which f is o-bounded above.

(c) ⇒ (d) Obvious.

(d) ⇒ (e) This is Theorem 1.1.

(e) ⇒ (a) Suppose f is continuous at the point x , and let y be any other point in C . Since C is open and convex, therefore C does not contain extreme points. This implies that there exist z∈C and 0<λ<1 such that y=λx+( 1−λ )z . Fix ε>0 and choose some circled neighborhood V of zero

so that ‖ f( x )−f( x+v ) ‖< ε λ for all v∈V . We claim that, ‖ f( y )−f( y+v ) ‖<ε for all v∈λV . Indeed, let v∈V , Then y+λv=λ( x+v )+( 1−λ )z∈C and the o-convexity of f implies

f( y+λv )=f( λ( x+v )+( 1−λ )z ) ≤λf( x+v )+( 1−λ )f( z )

and

f( y )=f( λx+( 1−λ )z ) ≤λf( x )+( 1−λ )f( z )

Thus

f( y+λv )−f( y )≤λ( f( ( x+v )−f( x ) ) ) ≤λ| f( ( x+v )−f( x ) ) | (1.6)

and

f( y )−f( y+λv )≤λ( f( x )−f( ( x+v ) ) ) ≤λ| f( ( x+v )−f( x ) ) | (1.7)

This shows that

| f( y )−f( y+λv ) |≤λ| f( ( x+v )−f( x ) ) | (1.8)

Then

‖ f( y )−f( y+λv ) ‖≤λ‖ f( ( x+v )−f( x ) ) ‖<ε

So, f is continuous at y . ◼

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 B of a normed space ( X,‖   ‖ ) to a Banach lattice E is order Lipschitzian on B if there exists e∈ E + such that for every y,z∈B

| f( y )−f( z ) |≤‖ y−z ‖e

If moreover ‖ e ‖<1 then f is called an order contraction.

The following gives examples of order Lipschitzian functions.

Theorem 2.1 Let f:C→E be a positive o-convex function from a convex subset C⊂X into a Banach lattice E . If f is continuous at some interior point x of C , then f is order Lipschitzian on a neighborhood of x .

Proof. Since f is continuous at x , it follows from Theorem 1.2 that there exists e∈ E + and δ>0 satisfying B 2δ ( x )⊂C and f( y )≤e . So, w,z∈ B 2δ ( x ) implies 0≤f( w )≤e and −e≤−f( z )≤0 . By addition, we achieve | f( w )−f( z ) |≤e , for all w,z∈ B 2δ . Let y,z∈ B 2δ ( x ) and α=‖ y−z ‖ . Then

w=y+ δ α ( y+z ) belongs to B 2δ and we have y= α α+δ w+ δ α+δ z . Therefore

f( y )≤ α α+δ f( w )+ δ α+δ f( z )

Subtracting f( z ) from each side gives

f( y )−f( z )≤ α α+δ [ f( w )−f( z ) ] ≤ α α+δ e ≤αe

Switching the roles of y and z allows us to conclude

| f( y )−f( z ) |≤‖ y−z ‖e

◼

A net { x α } in a Riesz space E is order convergent to some x∈E , written { x α } → o x , if there is a net { q α } (with the same directed set) satisfying { q α }↓0 and | x α −x |≤ q α for each α . A function f:E→F between two Riesz spaces is order uniformly continuous if { y α − z α } → o 0 in E implies { f( y α )−f( z α ) } → o 0 in F .

Proposition 2.1 If f;X→E is order Lipschitz continuous and ‖  .  ‖ is an order continuous lattice norm on X , then f 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 C be a convex subset of a vector space X . Recall that an extreme subset of C , is a nonempty subset F of C with the property that if x belongs to F , it cannot be written as a convex combination of points of C outside F . A point x is an extreme point of C if the singleton { x } is an extreme set.

Proposition 2.2 If f:C→E is o-convex and attains a maximum at some point, then the set of maximizers is an extreme set.

Proof. Suppose f achieves a maximum on C ; that is, f satisfies the identity sup{ f( x ):x∈C }=f( e ) for some e∈C . Put ℳ={ x∈C:f( x )=f( e ) } . Suppose that x=αy+( 1−α )z∈ℳ , 0<α<1 and y,z∈C . If y∉ℳ then f( y )<f( e ) , so

f( e )=f( x )=f( αy+( 1−α )z ) ≤αf( y )+( 1−α )f( z ) <αf( e )+( 1−α )f( e )=f( e )

a contradiction. Hence y,z∈ℳ , so ℳ is an extreme subset of C . ◼

Recall that the order ≤ of a Banach lattice E is continuous if ≤ is a closed subset of E×E . Let us say that ≤ is upper semicontinuous if { x∈E:y≤x } is closed for each y .

Theorem 2.2 Let K be compact and f:K→E continuous, with E a Banach lattice having continuous order. If f( K ) is closed under suprema (condition C), then f attains a minimum on K , and the minimizer set is compact.

Proof. Let K be a compact of a normed vector space X and let f:K→E be a continuous mapping from K to a Banach lattice E . For each c∈f( K ) , put F c ={ x∈K:f( x )≥c } . It follows from the continuity of f and of the order that the nonempty set F c is closed ( c=f( x ) implies x∈ F c ). Moreover, the family ℱ={ F c :c∈f( K ) } has the finite intersection property. In deed, let F c 1 , F c 2 ,⋯, F c n be a finite family in ℱ . Since F( K ) satisfies condition ( C ) so, c 0 = V i=1 i=n   c i ∈f( K ) . For all x∈ F c 0 and 1≤i≤n we have

0≤f( x )− c 0 ≤f( x )− c i

so, x∈ ∩ 1≤i≤n F c i and F c 0 ⊂ ∩ 1≤i≤n F c i . Since K is compact, ([2], Theorem.2.31)

implies that the set of minimizers ∩ c∈f( K ) F c is compact and nonempty. ◼

We realize that, in its real context, the assumptions ( C ) and ( C ′ ) in Theorem 2.2 are ensured from the fact that the order in ℝ 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 f:B→B be an order contraction on a closed subset B of a Banach lattice E , with contraction modulus ‖ e ‖<1 . Then f has a unique fixed point x , and for any x 0 ∈B , the iterates x n+1 =f( x n ) converges to x with ‖ x n −x ‖≤ ‖ e ‖ n ‖ x 0 −x ‖ .

Proof. Let e∈ E + such that ‖ e ‖<1 and | f( y )−f( z ) |≤‖ y−z ‖e , for all y,z∈B . If f( x )=x and f( y )=y then | x−y |=| f( x )−f( x ) |≤‖ x−y ‖e . Since the norm of E is lattice, we have ‖ x−y ‖≤‖ x−y ‖‖ e ‖ and hence ‖ x−y ‖=0 . Thus f can have at most one fixed point.

Now, if x 0 is chosen in B then the formula x n+1 =f( x n ) , n=0,1,2,⋯ defines inductively the sequence ( x n ) which satisfies: | x n+1 − x n |≤‖ x n − x n−1 ‖e , for every n≥1 . The lattice property verified by the norm of E implies that ‖ x n+1 − x n ‖≤‖ x n − x n−1 ‖‖ e ‖ and by induction, we see that for all n⩾1 , ‖ x n+1 − x n ‖≤‖ x 1 − x 0 ‖ ‖ e ‖ n . Hence, for n>m the triangle inequality yields

‖ x m − x n ‖≤ ∑ k=m+1 n ‖ x k − x k−1 ‖ ≤‖ x 1 − x 0 ‖ ∑ k=m+1 n ‖ e ‖ k ≤‖ x 1 − x 0 ‖ ‖ e ‖ m 1−‖ e ‖

This implies that ( x n ) is a Cauchy sequence. Since B is closed in the complete space E then, ( x n )→x∈B . Obviously, f is continuous, and: x= lim n→∞ x n+1 = lim n→∞ f( x n )=f( x ) , so x is the fixed point of f .

Conflicts of Interest

The author declares no conflicts of interest regarding the publication of this paper.

References

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[3] Aliprantis, C.D. and Tourky, R. (2007) Cones and Duality. American Mathematical society, Providence, RI, 84.

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