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The intrinsic core and minimal faces of convex sets in general vector spaces

R. Díaz Millán and Vera Roshchina Deakin University, Melbourne, AustraliaUNSW Sydney, Australia
Abstract

Intrinsic core generalises the finite-dimensional notion of the relative interior to arbitrary (real) vector spaces. Our main goal is to provide a self-contained overview of the key results pertaining to the intrinsic core and to elucidate the relations between intrinsic core and facial structure of convex sets in this general context.

We gather several equivalent definitions of the intrinsic core, cover much of the folklore, review relevant recent results and present examples illustrating some of the phenomena specific to the facial structure of infinite-dimensional sets.

Keywords: intrinsic core, pseudo-relative interior, inner points, convex sets, general vector spaces, minimal faces

MSC2020 Classification: 46N10, 52-02, 52A05.

1 Introduction

The relative interior of a convex set in a finite-dimensional real vector space is the interior of this convex set relative to its affine hull; a direct generalisation of this notion to real vector spaces is the intrinsic core, introduced in [kleepart1] and developed in [Holmes] (also called pseudo-relative interior [BorweinGoebel]and , the set of inner points [RelintInnerPoints] and the set of weak internal points [DYE1992983]).

In contrast to the relative interior in finite dimensions, intrinsic core may be empty for fairly regular sets, which was a key motivation for introducing the notion of quasi-relative interior [BorweinLewisPartiallyFinite] in the context of topological vector spaces. Even though the quasi-relative interior is a very interesting mathematical object (for instance, [ZalinescuThreePb, ZalinescuOnTheUse] study duality and separation in the context of quasi-relative interior and resolve a number of open questions), and has much practical importance(e.g. see [WeakEfficiency, SetValuedSystems, InfDim, RemarksInf, Lagrange, Regularity, Subconvex, EfficientCoderivatives, MR3297972, MR3735852]), we find that limiting our study to the interplay between the intrinsic core and facial structure already offers rich material and provides a valuable perspective on the structure of convex sets in general vector spaces. Such a narrowly focused overview is long overdue: the well-known results and examples discussed here are scattered in the literature, and it may be difficult to find neat references for widely known statements. Besides, we were able to prove some new results provide some new insights and discuss several examples of interesting infinite-dimensional convex sets from the perspective of facial structure (for instance, see Example 2.7, where we construct uncountable chains of faces of the Hilbert cube).

Our take on this topic is consistent with the approach of several recent papers that focus on the intrinsic core. For instance, [UnitBall, InnerStructure, MinExp] study the facial structure of convex sets in linear vector spaces with relation to the intrinsic core, [cones] is specifically focused on the intrinsic core of convex cones, and [DangEtAl] reviews the basic properties of a (less general) notion of the algebraic core, with an emphasis on separation. Notably [cones] and [DangEtAl] do not mention the facial structure that is central to our exposition.

We begin with a discussion on faces of convex sets in Section 2, obtaining a new characterisation of minimal faces in terms of the lineality subspace of the cone of feasible directions (Proposition 2.4), leading to a (known) characterisation of a face as an intersection of line segments (Corollary 2.6). We then talk about chains of faces and present an example of a convex set with uncountable chains of faces (Example 2.7).

In Section 3 we discuss several equivalent definitions of intrinsic core, and focus on characterising the intrinsic core in terms of minimal faces: specifically, in Proposition 3.11 we show that the intrinsic core of a convex set coincides with the set of points for which the minimal face of these points is the entire set CC, using Proposition 2.4. We also provide an extensive list of properties of the intrinsic core, with self-contained proofs and references. We end the section with the decomposition result (a convex set is a disjoint union of the intrinsic cores of its faces, Theorem 3.14)and a conjecture that every face of a convex set must be the union of a chain of minimal faces (Conjecture LABEL:conj:russian-doll).Intuitively this means that faces of convex sets are always ‘nested’, and never ‘stacked’. .

In Section 4 we discuss the notion of algebraic closure and algebraic boundary , notions of linear closure and boundary and relate these to the intrinsic core and faces of convex sets. We also talk about separation and supporting hyperplanes in general real vector spaces, outlining classic separation results pertaining to the setting of general vector spaces, and highlighting the role of intrinsic core in convex separation.We then provide a brief summary in Section 5.

2 Convex sets and their faces

Recall that a subset CC of a real vector space XX is convex if for any two points x,yCx,y\in C we have [x,y]C[x,y]\subseteq C, where [x,y][x,y] is the line segment connecting the points xx and yy,

[x,y]={αx+(1α)y|α[0,1]}.[x,y]=\{\alpha x+(1-\alpha)y\,|\,\alpha\in[0,1]\}.

We will also use the notation (x,y)(x,y) to denote the open line segments connecting xx and yy, also [x,y)[x,y) and (x,y](x,y] for the relevant half-open segments. Note that algebraically it makes perfect sense to consider degenerate line segments of the form (x,x)=(x,x]=[x,x)=[x,x](x,x)=(x,x]=[x,x)=[x,x], since in this case

(x,x)={tx+(1t)x|t(0,1)}={x}.(x,x)=\{tx+(1-t)x\,|\,t\in(0,1)\}=\{x\}.

Even though this notation may appear confusing geometrically, it allows to treat the endpoints of all line segments including the degenerate singleton ones in a unified fashion, and hence streamline much of our discussion.

Throughout the paper we assume that XX is a real vector space, and that all the sets we consider live in this space (unless stated otherwise). We reiterate this assumption in some of the statements for the ease of reference.

A convex subset FF of a convex set CC is called a face of CC if for every xFx\in F and every y,zCy,z\in C such that x(y,z)x\in(y,z), we have y,zFy,z\in F. The set CC itself is its own face, and the empty set is a face of any convex set CC. A face FF of CC is proper if FF is nonempty and does not coincide with CC. We write FCF\lhd C for the faces FF that do not coincide with CC and FCF\unlhd C for all faces FF of CC. Singleton faces Faces that consist of one point only are called extreme points. Some faces of two-dimensional convex sets are shown schematically in Figure 1.

Refer to caption
Figure 1: Faces of two-dimensional convex sets: proper faces are shown in black.

The following two statements are well-known and follow from the definition of a face.

Lemma 2.1.

Let CC be a convex subset of a real vector space XX. If FCF\unlhd C and EFE\subseteq F, then EFE\unlhd F if and only if ECE\unlhd C.

Proof.

Suppose that CC is a convex set, FCF\unlhd C and EFE\subseteq F. By the definition of a face, ECE\unlhd C is equivalent to having for any x,yCx,y\in C with (x,y)E(x,y)\cap E\neq\emptyset that x,yCx,y\in C. However since FF is a face of CC, and EFE\subseteq F, any such pair x,yx,y must also be in FF (and vice versa, as x,yFCx,y\in F\subset C). Hence, ECE\unlhd C is equivalent to EFE\unlhd F. ∎

Lemma 2.2.

Let \mathcal{F} be a collection of faces of a convex set CXC\subseteq X, that is, every member of the set \mathcal{F} is a face of CC. Then

E:=FFE:=\bigcap_{F\in\mathcal{F}}F

is also a face of CC.

Proof.

First notice that EE is a convex subset of CC. Now, if for some x,yCx,y\in C the open line segment (x,y)(x,y) intersects EE, then it intersects each FF\in\mathcal{F}, and hence [x,y]E[x,y]\subseteq E, so EE is a face of CC by definition. ∎

2.1 Minimal faces

For any subset SS of a convex set CC there exists a unique minimal (in terms of the set inclusion) face FCF\unlhd C that contains SS. We can define minimal faces of a set in a constructive way, with the help of Lemma 2.2.

Let SCS\subseteq C, where CC is a convex subset of a real vector space XX. The minimal face of CC containing SS or just the minimal face of SS in CC, is

Fmin(S,C):={F|FC,SF}.F_{\min}(S,C):=\bigcap\{F\,|\,F\unlhd C,S\subseteq F\}.

The set Fmin(S,C)F_{\min}(S,C) is a face due to Lemma 2.2, and it is the minimal smaller face (with respect to set inclusion) that contains SS. When S={x}S=\{x\} is a singleton we use the notation Fmin(x,C)=Fmin({x},C)F_{\min}(x,C)=F_{\min}(\{x\},C).

By coneC\operatorname{cone}C we denote the conic hull of CXC\subseteq X, that is, the set of all finite nonnegative combinations of points in CXC\subseteq X.

coneC={iIαixi|xiC,αi0iI,|I|<}.\operatorname{cone}C=\left\{\sum_{i\in I}\alpha_{i}x_{i}\,|\,x_{i}\in C,\,\alpha_{i}\geq 0\quad\forall i\in I,\;|I|<\infty\right\}.

The conic hull coneC\operatorname{cone}C of any set CXC\subseteq X is a convex cone (it is convex and positively homogeneous, λxK\lambda x\in K for all xKx\in K and λ>0\lambda>0). When CC is convex, we have coneC=+C={αx|xC,α0}\operatorname{cone}C=\mathbb{R}_{+}C=\{\alpha x\,|\,x\in C,\alpha\geq 0\}. In particular, when CC is convex and xCx\in C, then cone(Cx)\operatorname{cone}(C-x) is the cone of feasible directions of CC at xx, that is, it consists of the rays along which one can move away from xx while staying within CC.

The lineality space linK\operatorname{lin}K linspaceK\operatorname{linspace}K of a nonempty convex cone KXK\subseteq X is the largest linear subspace contained in KK (the lineality space can be defined for general convex sets, but we will only need this notion for cones). The following facts are well-known.

Proposition 2.3.

Let KK be a convex cone, KXK\subseteq X. Then

  1. (i)

    for any xKx\in K, we have xlinKx\in\operatorname{lin}K xlinspaceKx\in\operatorname{linspace}K if and only if xK-x\in K;

  2. (ii)

    the lineality space linK\operatorname{lin}K linspaceK\operatorname{linspace}K is nonempty if and only if 0K0\in K.

Proof.

The part (ii) is straightforward: since 0 must belong to every linear subspace, if 0K0\notin K, then no linear subspace is contained in KK. Likewise, if 0K0\in K, then {0}\{0\} is a trivial linear subspace contained in KK, and the lineality space can only be larger than that.

The necessary part of ((i)) is evident: assuming xlinKKx\in\operatorname{lin}K\subseteq KxlinspaceKKx\in\operatorname{linspace}K\subseteq K, and recalling that linK\operatorname{lin}K linspaceK\operatorname{linspace}K is a linear subspace, we must have xlinK-x\in\operatorname{lin}KxlinspaceK-x\in\operatorname{linspace}K.

To show the sufficiency, that is, if x,xKx,-x\in K, then xlinKx\in\operatorname{lin}KxlinspaceKx\in\operatorname{linspace}K, first observe that it follows from convexity that 0[x,x]K0\in[-x,x]\subset K, and hence by (i) linK\operatorname{lin}K\neq\emptysetlinspaceK\operatorname{linspace}K\neq\emptyset. Assume that nevertheless xlinKx\notin\operatorname{lin}K. Let xlinspaceKx\notin\operatorname{linspace}K. Let

L=span(linlinspaceK{x}).L=\operatorname{span}({\color[rgb]{1,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{1,0,0}\sout{\operatorname{lin}}}{\color[rgb]{0,0,1}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,1}\uwave{\operatorname{linspace}}}K\cup\{x\}).

For any wLw\in L we have w=v+uw=v+u, with vlinKKv\in\operatorname{lin}K\subseteq K vlinspaceKKv\in\operatorname{linspace}K\subseteq K and u=λxu=\lambda x for some λ\lambda\in\mathbb{R}. Now λxK\lambda x\in K, irrespective of the sign of λ\lambda, since KK is a cone and both xx and x-x are in KK. Now u,vKu,v\in K, hence w=u+vKw=u+v\in K (as u+v=2(12u+12v)Ku+v=2(\frac{1}{2}u+\frac{1}{2}v)\in K by convexity and homogeneity), and we have demonstrated that LL is a linear subspace contained in KK that is strictly larger than linK\operatorname{lin}KlinspaceK\operatorname{linspace}K, which is impossible. ∎

It follows from (i) that the lineality space of a convex cone is uniquely defined, since it is fully characterised by the set of points that belong to the cone along with their negatives.

Also recall that the Minkowski sum of two (convex) sets C,DXC,D\subseteq X is

C+D={x+y|xC,yD}.C+D=\{x+y\,|\,x\in C,y\in D\}.

When one of the sets is a singleton, we abuse the notation and write C+xC+x for C+{x}C+\{x\}.

We show next that minimal faces the minimal face of any point, can be characterised in terms of the lineality linearity space of the cone of feasible directions of the point.

Proposition 2.4.

Let CXC\subseteq X be a nonempty convex set and let xCx\in C. Then

Fmin(x,C)=C(linlinspacecone(Cx)+x).F_{\min}(x,C)=C\cap({\color[rgb]{1,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{1,0,0}\sout{\operatorname{lin}}}{\color[rgb]{0,0,1}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,1}\uwave{\operatorname{linspace}}}\operatorname{cone}(C-x)+x).

Before moving on to the proof of Proposition 2.4, we state and prove the following elementary technical proposition that will be used as a building block in several proofs.

Proposition 2.5.

Let a,b,u,vXa,b,u,v\in X. If a(u,v)a\in(u,v), then for any c(a,b)c\in(a,b) there exists w(v,b)w\in(v,b) such that c(u,w)c\in(u,w).

Proof.

Let a,b,u,vXa,b,u,v\in X, and assume that a(u,v)a\in(u,v) and c(a,b)c\in(a,b) (see Fig. 2).

Refer to caption
Figure 2: Illustration of the proof for Proposition 2.5

There are α,β(0,1)\alpha,\beta\in(0,1) such that

a=αu+(1α)v,c=βa+(1β)b.a=\alpha u+(1-\alpha)v,\quad c=\beta a+(1-\beta)b. (1)

We have from (1)

c\displaystyle c =β(αu+(1α)v)+(1β)b\displaystyle=\beta(\alpha u+(1-\alpha)v)+(1-\beta)b
=αβu+(1αβ)(β(1α)(1αβ)v+1β(1αβ)b).\displaystyle=\alpha\beta u+(1-\alpha\beta)\left(\frac{\beta(1-\alpha)}{(1-\alpha\beta)}v+\frac{1-\beta}{(1-\alpha\beta)}b\right). (2)

Let

w=β(1α)1αβv+1β1αβb.w=\frac{\beta(1-\alpha)}{1-\alpha\beta}v+\frac{1-\beta}{1-\alpha\beta}b.

It is evident that w(b,v)w\in(b,v), while from (2.1) we have c(u,w)c\in(u,w). ∎

Proof of Proposition 2.4.

We first show that

C(linlinspacecone(Cx)+x)Fmin(x,C).C\cap({\color[rgb]{1,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{1,0,0}\sout{\operatorname{lin}}}{\color[rgb]{0,0,1}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,1}\uwave{\operatorname{linspace}}}\operatorname{cone}(C-x)+x)\subseteq F_{\min}(x,C).

Let yC(lincone(Cx)+x)y\in C\cap(\operatorname{lin}\operatorname{cone}(C-x)+x)yC(linspacecone(Cx)+x)y\in C\cap(\operatorname{linspace}\operatorname{cone}(C-x)+x). Then y=x+uy=x+u for some ulincone(Cx)u\in\operatorname{lin}\operatorname{cone}(C-x)ulinspacecone(Cx)u\in\operatorname{linspace}\operatorname{cone}(C-x), and by Proposition 2.3 (i) we have ulincone(Cx)cone(Cx)-u\in\operatorname{lin}\operatorname{cone}(C-x)\subseteq\operatorname{cone}(C-x)ulinspacecone(Cx)cone(Cx)-u\in\operatorname{linspace}\operatorname{cone}(C-x)\subseteq\operatorname{cone}(C-x). This means that there exists some t>0t>0 such that xtuCx-tu\in C. We have

t1+t(x+u)+11+t(xtu)=x,\frac{t}{1+t}(x+u)+\frac{1}{1+t}(x-tu)=x,

hence x(x+u,xtu)x\in(x+u,x-tu) and for every face FF of CC containing xx we must have y=x+uFy=x+u\in F. We conclude that

yFCxFF=Fmin(x,C).y\in\bigcap_{\begin{subarray}{c}F\unlhd C\\ x\in F\end{subarray}}F=F_{\min}(x,C).

It remains to show the converse, that

Fmin(x,C)C(linlinspacecone(Cx)+x).F_{\min}(x,C)\subseteq C\cap({\color[rgb]{1,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{1,0,0}\sout{\operatorname{lin}}}{\color[rgb]{0,0,1}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,1}\uwave{\operatorname{linspace}}}\operatorname{cone}(C-x)+x).

Since Fmin(x,C)CF_{\min}(x,C)\subseteq C, it is then sufficient to show that

Fmin(x,C)=Fmin(x,C)(linlinspacecone(Cx)+x).F_{\min}(x,C)=F_{\min}(x,C)\cap({\color[rgb]{1,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{1,0,0}\sout{\operatorname{lin}}}{\color[rgb]{0,0,1}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,1}\uwave{\operatorname{linspace}}}\operatorname{cone}(C-x)+x). (3)

If we prove that

F=Fmin(x,C)(linlinspacecone(Cx)+x)F=F_{\min}(x,C)\cap({\color[rgb]{1,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{1,0,0}\sout{\operatorname{lin}}}{\color[rgb]{0,0,1}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,1}\uwave{\operatorname{linspace}}}\operatorname{cone}(C-x)+x) (4)

is a face of CC, then from xFx\in F, we must have FFmin(x,C)F\subseteq F_{\min}(x,C), hence F=Fmin(x,C)F=F_{\min}(x,C), and so (3) holds.

Let yFy\in F and u,vCu,v\in C and α(0,1)\alpha\in(0,1) be such that

y=(1α)u+αv.y=(1-\alpha)u+\alpha v. (5)

Our goal is to show that uFu\in F.

Let p=yxp=y-x. Since ylincone(Cx)+xy\in\operatorname{lin}\operatorname{cone}(C-x)+xylinspacecone(Cx)+xy\in\operatorname{linspace}\operatorname{cone}(C-x)+x, we have plincone(Cx)p\in\operatorname{lin}\operatorname{cone}(C-x)plinspacecone(Cx)p\in\operatorname{linspace}\operatorname{cone}(C-x), and by Proposition 2.3 (i) we have plincone(Cx)cone(Cx)-p\in\operatorname{lin}\operatorname{cone}(C-x)\subseteq\operatorname{cone}(C-x)plinspacecone(Cx)cone(Cx)-p\in\operatorname{linspace}\operatorname{cone}(C-x)\subseteq\operatorname{cone}(C-x). There exists t>0t>0 such that z:=xtpCz:=x-tp\in C. Applying Proposition 2.5 to the points y,z,u,vy,z,u,v and xx (cf. Figs. 2 and 3),

Refer to caption
Figure 3: The two-dimensional gadget used in the proof of Proposition 2.4

we conclude that there exists some w(v,z)w\in(v,z) such that x(u,w)x\in(u,w), which yields [u,w]Fmin(x,C)[u,w]\subseteq F_{\min}(x,C), hence, ux,(ux)cone(Cx)u-x,-(u-x)\in\operatorname{cone}(C-x), so uxlincone(Cx)u-x\in\operatorname{lin}\operatorname{cone}(C-x)uxlinspacecone(Cx)u-x\in\operatorname{linspace}\operatorname{cone}(C-x). We conclude that ulincone(Cx)+xu\in\operatorname{lin}\operatorname{cone}(C-x)+xulinspacecone(Cx)+xu\in\operatorname{linspace}\operatorname{cone}(C-x)+x. Hence, from (4) we conclude that uFu\in F.

It follows from Proposition 2.4 that the minimal face Fmin(x,C)F_{\min}(x,C) of xCx\in C consists precisely of the line segments in CC that contain xx in their interiors (cf. Proposition 2.3 item 7 of [FacelessProblem]).

Corollary 2.6.

The minimal face Fmin(x,C)F_{\min}(x,C) for xCx\in C (where CC is a convex subset of a real vector space XX) can be represented as

Fmin(x,C)={[y,z]C,x(y,z)}.F_{\min}(x,C)=\bigcup\{[y,z]\subseteq C,\,x\in(y,z)\}. (6)
Proof.

It is evident that the set on the right-hand side is a subset of any face that contains xx, hence, it must be contained in the minimal face of xx in CC as well. It remains to show that the minimal face of xx in CC is a subset of the right-hand side. Let yFmin(x,C)y\in F_{\min}(x,C). Then by Proposition 2.4 we have

ylinlinspacecone(Cx)+x,y\in{\color[rgb]{1,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{1,0,0}\sout{\operatorname{lin}}}{\color[rgb]{0,0,1}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,1}\uwave{\operatorname{linspace}}}\operatorname{cone}(C-x)+x,

equivalently yxlincone(Cx)y-x\in\operatorname{lin}\operatorname{cone}(C-x)yxlinspacecone(Cx)y-x\in\operatorname{linspace}\operatorname{cone}(C-x). This means that xylincone(Cx)cone(Cx)x-y\in\operatorname{lin}\operatorname{cone}(C-x)\subseteq\operatorname{cone}(C-x)xylinspacecone(Cx)cone(Cx)x-y\in\operatorname{linspace}\operatorname{cone}(C-x)\subseteq\operatorname{cone}(C-x), and so there exists some t>0t>0 such that t(xy)Cxt(x-y)\in C-x, equivalently x+t(xy)Cx+t(x-y)\in C. It remains to note that

x=t1+ty+11+t(x+t(xy)),x=\frac{t}{1+t}y+\frac{1}{1+t}(x+t(x-y)),

hence x(y,x+t(xy))x\in(y,x+t(x-y)), and so yy belongs to the right-hand side of (6). ∎

2.2 Chains of faces

Recall that a chain is a totally ordered set. A chain \mathcal{F} of faces of a convex set CC is such a set of faces of CC that for every F,EF,E\in\mathcal{F} we have either EFE\subsetneq F or FEF\subsetneq E (Lemma 2.1 implies that in this case either EFE\lhd F or FEF\lhd E).

In the finite-dimensional setting a chain of faces of a convex set is always finite, since the affine hulls generated by strictly nested faces must have strictly increasing dimensions, and there is a natural bound coming from the dimension of the ambient space (the nesting of affine hulls also works in the infinite dimensional case, see Proposition 2.9 below). The length of any chain of faces in n\mathbb{R}^{n} is at most n+2n+2, since a chain of faces may include the empty set as well as an extreme point, which is zero-dimensional (see Fig. 4).

Refer to caption
Figure 4: A maximal chain of faces of a tetrahedron.

Polytopes have chains of faces of maximal possible length d+2d+2, where dd is the intrinsic dimension of the polytope. In the infinite dimensional setting however there are convex sets that may have not only infinite, but uncountable chains of faces. An example of a set with uncountable chains of faces is the Hilbert cube discussed next.

Example 2.7 (Hilbert cube).

Consider the subset CC of l2l_{2} defined as the infinite product of diminishing line segments,

C:=[0,1n]nnC:=\prod{\color[rgb]{1,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{1,0,0}\sout{{}_{n\in\mathbb{N}}}}{\color[rgb]{0,0,1}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,1}\uwave{{}_{n\in\mathbb{N}^{*}}}}\left[0,\frac{1}{n}\right]

(here we assume that ={1,2,3,}\mathbb{N}^{*}=\{1,2,3,\dots\} is the set of natural numbers does not that doesn’t contain 0, so ={1,2,3,}\mathbb{N}=\{1,2,3,\dots\}). It is not difficult to observe that the subsets of CC defined by

snnn,sn{{0},{1n},[0,1n]}\prod{\color[rgb]{1,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{1,0,0}\sout{{}_{n\in\mathbb{N}}}}{\color[rgb]{0,0,1}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,1}\uwave{{}_{n\in\mathbb{N}^{*}}}}s_{n},\quad s_{n}\in\left\{\{0\},\left\{\frac{1}{n}\right\},\left[0,\frac{1}{n}\right]\right\}

are faces of CC. We can now construct an uncountable chain of faces using a well-known trick. There exists an uncountable chain 𝒞\mathcal{C} in the powerset 𝒫()\mathcal{P}(\mathbb{N}) power set 𝒫()\mathcal{P}(\mathbb{N}^{*}) of natural numbers (to see this consider any bijection between \mathbb{N} \mathbb{N}^{*} and \mathbb{Q}, and then construct the ϕ:𝒫()\phi:\mathbb{R}\to\mathcal{P}(\mathbb{Q}) that maps a real number tt to the set of all rational numbers that are strictly smaller than tt: this mapping generates the required chain).

For any c𝒫()c\in\mathcal{P}(\mathbb{N}) we let c𝒫()c\in\mathcal{P}(\mathbb{N}^{*}) we let

Fc:=snnn,sn={[0,1n],nc,[0],nc.F_{c}:=\prod{\color[rgb]{1,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{1,0,0}\sout{{}_{n\in\mathbb{N}}}}{\color[rgb]{0,0,1}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,1}\uwave{{}_{n\in\mathbb{N}^{*}}}}s_{n},\quad s_{n}=\begin{cases}\left[0,\frac{1}{n}\right],&n\in c,\\ [0],&n\notin c.\end{cases}

It is evident that if c1c2c_{1}\subset c_{2} for some c1,c2𝒞c_{1},c_{2}\in\mathcal{C}, then Fc1Fc2F_{c_{1}}\subset F_{c_{2}}, hence, we have constructed an uncountable chain of faces {Fc}c𝒞\{F_{c}\}_{c\in\mathcal{C}}.

Notice that in Example 2.7 we didn’t need to make the ‘edge length’ of the hypercube diminishing in nn, and could have considered a hypercube in the general sequence space, say with a unit edge length. However having an example in a well-behaved Hilbert space like l2l_{2} demonstrates that the phenomenon occurs naturally in a fairly standard setting.

Another instance of a set with uncountable chains of faces is discussed in Section 4, its construction relies on an infinite Hamel basis (see Example 4.3).

Proposition 2.8.

Let CC be a convex set in a real vector space XX and suppose that \mathcal{F} is a chain of its faces. Then

E=FFE=\bigcup_{F\in\mathcal{F}}F

is a face of CC.

Proof.

First observe that EE is a convex subset of CC: every point xEx\in E belongs to some face of CC, and hence belongs to CC; furthermore, for any u,vEu,v\in E there exist Fu,FvF_{u},F_{v}\in\mathcal{F} such that uFuu\in F_{u} and vFvv\in F_{v}. Since \mathcal{F} is a chain, without loss of generality we can assume that FuFvF_{u}\subset F_{v}, and hence u,vFvu,v\in F_{v}. Therefore, [u,v]FvC[u,v]\subseteq F_{v}\subseteq C, and so CC is convex.

Let xEx\in E, and let y,zCy,z\in C be such that x(y,z)x\in(y,z). Since EE is a union of faces of FF, there must be a face FxF_{x}\in\mathcal{F} such that xFxx\in F_{x}. Since FxF_{x} is a face of CC, we have

[y,z]FxE,[y,z]\subseteq F_{x}\subseteq E,

and so EE is a face of CC. ∎

Every chain of faces generates an associated set of affine subspaces, which are the affine hulls of these faces. Recall that for any (convex) subset CC of a real vector space XX we define the affine hull of CC as

affC={iIαixi,|iIαi=1,xiCiI,|I|<}.\operatorname{aff}C=\left\{\sum_{i\in I}\alpha_{i}x_{i},\,|\,\sum_{i\in I}\alpha_{i}=1,x_{i}\in C\,\forall i\in I,|I|<\infty\right\}.

Equivalently affC\operatorname{aff}C is the smallest affine subspace containing CC (a set of the form x+Lx+L, where LL is a linear subspace, and xCx\in C), or affC=x+span(Cx)\operatorname{aff}C=x+\operatorname{span}(C-x).

The next proposition shows that chains of faces generate chains of affine subspaces, which results in a finite bound on chains of faces in finite dimensions, as was discussed in the beginning of this section.

Proposition 2.9.

If E,FCE,F\unlhd C and EFE\subsetneq F, then affEaffF\operatorname{aff}E\subsetneq\operatorname{aff}F.

Proof.

It is evident from the definition of the affine hull that if EFE\subseteq F, then affEaffF\operatorname{aff}E\subseteq\operatorname{aff}F. It is hence sufficient to show that if EFE\subsetneq F then there exists some uaffFaffEu\in\operatorname{aff}F\setminus\operatorname{aff}E. Assume the contrary, then in particular FaffEF\subseteq\operatorname{aff}E. Since EFE\subsetneq F, there exists xFEx\in F\setminus E, which can be represented as

x=iIαiui,iIαi=1,uiCEiI,|I|<.x=\sum_{i\in I}\alpha_{i}u_{i},\qquad\sum_{i\in I}\alpha_{i}=1,\quad u_{i}\in{\color[rgb]{1,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{1,0,0}\sout{C}}{\color[rgb]{0,0,1}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,1}\uwave{E}}\quad\forall i\in I,\;|I|<\infty.

By convexity

y=iI1|I|uiE.y=\sum_{i\in I}\frac{1}{|I|}u_{i}\in E.

Now there must exist a t>0t>0 such that

1|I|t(αi1|I|)iI.\frac{1}{|I|}\geq t\left(\alpha_{i}-\frac{1}{|I|}\right)\quad\forall i\in I.

Let

λi=1|I|t(αi1|I|),iI.\lambda_{i}=\frac{1}{|I|}-t\left(\alpha_{i}-\frac{1}{|I|}\right),\quad i\in I.

Since λi>0\lambda_{i}>0 for all iIi\in I and

iIλi=1,\sum_{i\in I}\lambda_{i}=1,

we have

z=y+t(yx)=iIλiuiEC,z=y+t(y-x)=\sum_{i\in I}\lambda_{i}u_{i}\in E\subset C,

and since yECy\in E\subseteq C and y(x,z)Cy\in(x,z)\subset C, by the definition of a face we must have xEx\in E, which contradicts our assumption. ∎

3 Intrinsic core

In finite-dimensional spaces the relative interior of a convex set CC is usually defined as its interior with respect to the affine hull of CC. Intrinsic core is a generalisation of this notion to the real vector spaces (although note that for topological vector spaces the relative interior is alternatively generalised as the interior relative to the topological closure of the affine hull [BorweinGoebel]).

3.1 Definition and basic properties of the intrinsic core

There are several equivalent definitions of the intrinsic core: we discuss all of them throughout this section, however we start with the one that is perhaps the most elegant (also see the original work of Klee [kleepart1] where this approach first appears).

Definition 3.1 (Intrinsic core via line segments).

Let CC be a convex subset of a real vector space XX. Then the intrinsic core of CC is

icrC={xC|yCzC such that x(y,z)}.\operatorname{icr}C=\{x\in C\,|\,\forall y\in C\,\exists z\in C\,\text{ such that }x\in(y,z)\}.

In other words, xicrCx\in\operatorname{icr}C if and only if one can extend the line segment connecting xx to any point of the set CC beyond the point xx, while staying within CC. This is also the definition used in [DYE1992983] (called the set of weak internal points), in [FacelessProblem] (the set of inner points) , and also in [BorweinGoebel] (called the set of relatively absorbing points or pseudo-relative interior).

Note that is a difference in the interpretation of the definition of the intrinsic core between our exposition and other literature: for instance, the discussion in [FacelessProblem, Section 1]) implies that singletons must have empty intrinsic cores, for the lack of any line segments, which we find inconsistent. It follows from Proposition 3.11 that whenever CC is a singleton C={x}C=\{x\}, we have Fmin(x,C)={x}=CF_{\min}(x,C)=\{x\}=C, and hence icrC=C\operatorname{icr}C=C.

The definition of the intrinsic core can also be extended to general subsets of XX, as it is done in [InnerStructure]: in this case the instinsic core of some SXS\subseteq X is defined as all points xSx\in S such that for any line LL through xx either there are some uvu\neq v such that x(u,v)LSx\in(u,v)\subseteq L\cap S or LS={x}L\cap S=\{x\}.

Proposition 3.2.

The intrinsic core of a convex subset CC of a vector space XX is a convex set.

Proof.

Let x,yicrCx,y\in\operatorname{icr}C and suppose that z(x,y)z\in(x,y). Then for any uCu\in C there exist v,wCv,w\in C such that x(u,v)x\in(u,v) and y(u,w)y\in(u,w). Then we have for some s,t,r(0,1)s,t,r\in(0,1)

x=u+s(vu),y=u+t(wu),z=x+r(yx).x=u+s(v-u),\quad y=u+t(w-u),\quad z=x+r(y-x).

Substituting the expressions for xx and yy into the one for zz, we obtain

z=u+(s(1r)+tr)(qu),z=u+(s(1-r)+tr)(q-u),

where

q=s(1r)s(1r)+trv+ts(1r)+trwCq=\frac{s(1-r)}{s(1-r)+tr}v+\frac{t}{s(1-r)+tr}w\in C

by convexity (see Fig. 5).

Refer to caption
Figure 5: An illustration to the proof of Proposition 3.2.

Hence, z(u,q)z\in(u,q) with qCq\in C, and by the arbitrariness of the choice of uu we have zicrCz\in\operatorname{icr}C. ∎

In contrast to the finite-dimensional setting, where the relative interior of any nonempty convex set is also nonempty (e.g. see [RockConvAn, Theorem 6.2]), the intrinsic core of a convex set may be empty. The next example illustrates this phenomenon (cf. [InnerStructure, Theorem 5.2]).

Example 3.3.

Let CC be a subset of c00c_{00} (eventually zero sequences),

C={xc00|xi[0,1]}.C=\{x\in c_{00}\,|\,x_{i}\in[0,1]\}. (7)

First observe that CC is a convex set. We will show that for any xCx\in C there exists yCy\in C such that there isn’t any zCz\in C with x(y,z)x\in(y,z), and hence icrC=\operatorname{icr}C=\emptyset.

Indeed, let x=(x1,,xk,)Cx=(x_{1},\dots,x_{k},\dots)\in C. There is an index ii\in\mathbb{N} such that xk=0x_{k}=0 for every kik\geq i. Let y=(y1,,yk,)y=(y_{1},\dots,y_{k},\dots) be such that

yk={xk,k{i},1,k=i.y_{k}=\begin{cases}x_{k},&k\in\mathbb{N}\setminus\{i\},\\ 1,&k=i.\end{cases}

In other words, yy coincides with xx up to and including i1i-1-st entry, has 1 in the ii-th position and zeros beyond ii. If there was a zCz\in C such that x(y,z)x\in(y,z), then for some t(0,1)t\in(0,1) x=ty+(1t)zx=ty+(1-t)z, and in particular 0=xi=tyi+(1t)z=(1t)+tzi0=x_{i}=ty_{i}+(1-t)z=(1-t)+tz_{i}, hence zi<0z_{i}<0, which is impossible by the definition of CC.

Convex sets that have nonempty intrinsic cores are called relatively solid in [cones]. Perhaps a better term would be to call such sets intrinsically solid. It is tempting to think about sets with empty intrinsic cores as ‘small’, however this intuition is misleading, as such sets can be very large, with their algebraic closure covering the entire space: we discuss this well-known phenomenon in more detail in Section 4.

Since an intrinsic core of some convex set may be empty, having ABA\subset B for two convex sets AA and BB it doesn’t yield icrAicrB\operatorname{icr}A\subseteq\operatorname{icr}B. Even though this behaviour already happens in finite-dimensional spaces, the general case is in a sense more extreme, since it may happen that the intrinsic core of AA is nonempty, and the intrinsic core of BB is empty. A trivial example is to consider some set BB such that BB\neq\emptyset and icrB=\operatorname{icr}B=\emptyset, and let A={x}A=\{x\} for some xBx\in B.

We next prove a well-known technical result that effectively means that the intersection of a line passing through the intrinsic core of a convex set with this convex set lies in the intrinsic core, except possibly the endpoints of this intersection. We later prove a similar result involving the algebraic closure (Proposition 4.1 (iii)).

Proposition 3.4.

Let CC be a convex subset of a real vector space XX. If xicrCx\in\operatorname{icr}C and yCy\in C, then there exists zicrCz\in\operatorname{icr}C such that x(z,y)icrCx\in(z,y)\subseteq\operatorname{icr}C. In particular, [x,y)icrC[x,y)\subset\operatorname{icr}C.

Proof.

Consider some xicrCx\in\operatorname{icr}C and yCy\in C. Fix an arbitrary q(x,y)q\in(x,y). For any uCu\in C there exists vCv\in C such that x(u,v)x\in(u,v) by the definition of the intrinsic core. Applying Proposition 2.5 to x,y,u,vx,y,u,v and qq, we deduce that there exists w(v,y)w\in(v,y) such that q(u,w)q\in(u,w). Since v,yCv,y\in C, by convexity wCw\in C. Then qicrCq\in\operatorname{icr}C by the definition of the intrinsic core. We have hence shown that [x,y)icrC[x,y)\subseteq\operatorname{icr}C. Now since xicrCx\in\operatorname{icr}C and yCy\in C, there must be some zCz\in C such that x(z,y)x\in(z,y). Then by the earlier proven statement we must have [x,z)icrC[x,z)\subset\operatorname{icr}C. Hence x(z,y)icrCx\in(z,y)\subseteq\operatorname{icr}C. ∎

Proposition 3.5.

Let CC be a convex subset of a real vector space XX. Then icr(icrC)=icrC\operatorname{icr}(\operatorname{icr}C)=\operatorname{icr}C.

Proof.

Observe that icricrCicrC\operatorname{icr}\operatorname{icr}C\subseteq\operatorname{icr}C, so we only need to prove the converse. Now take any xicrCx\in\operatorname{icr}C. Then for any yicrCy\in\operatorname{icr}C we also have yCy\in C. Since xicrCx\in\operatorname{icr}C and yCy\in C, by Proposition 3.4 there must be zicrCz\in\operatorname{icr}C such that x(z,y)icrCx\in(z,y)\subseteq\operatorname{icr}C. This shows that xicricrCx\in\operatorname{icr}\operatorname{icr}C. ∎

Note that the statement of Proposition 3.5 is discussed in a more general setting of not necessarily convex sets in Theorem 2.3 in [InnerStructure]; even though the notion of intrinsic core can be generalised to arbitrary (non-convex) sets, the statement of Proposition 3.5 is not true for nonconvex sets (see [RelintInnerPoints, Example 3.1]).

In the next proposition we show that when a convex subset of a convex set has empty intrinsic core, the minimal face that contains of this subset coincides with the minimal face of any point in the intrinsic core of the subset. This property is well-known in the finite dimensional case for the relative interior.

Proposition 3.6.

Let SS and CC be convex sets in a real vector space XX. If SCS\subseteq C and icrS\operatorname{icr}S\neq\emptyset, then for any xicrSx\in\operatorname{icr}S we have Fmin(x,C)=Fmin(S,C)F_{\min}(x,C)=F_{\min}(S,C). Moreover, icrSicrFmin(S,C)\operatorname{icr}S\subseteq\operatorname{icr}F_{\min}(S,C).

Proof.

Under the assumptions of the proposition, suppose that xicrSx\in\operatorname{icr}S. Then by Proposition 3.4 for any ySy\in S there exists zSz\in S such that x(y,z)x\in(y,z). Since SCS\subset C, Corollary 2.6 then yields SFmin(x,C)S\subseteq F_{\min}(x,C). Hence the minimal face of SS must be a subset of Fmin(x,C)F_{\min}(x,C), however there can be no smaller face containing xx (and hence SS), therefore, Fmin(x,C)=Fmin(S,C)F_{\min}(x,C)=F_{\min}(S,C).

Now for any xicrSx\in\operatorname{icr}S we have xicrFmin(x,C)=Fmin(S,C)x\in\operatorname{icr}F_{\min}(x,C)=F_{\min}(S,C), hence, icrSicrFmin(S,C)\operatorname{icr}S\subseteq\operatorname{icr}F_{\min}(S,C). ∎

It doesn’t look like anything similar to Proposition 3.6 can be said about the minimal face Fmin(S,C)F_{\min}(S,C) when the subset SS of CC has empty intrinsic core. The minimal face of SS may or may not have empty intrinsic core in this case.

3.2 Alternative definitions of the intrinsic core

In [BorweinGoebel] the intrinsic core of a convex set CXC\subseteq X (called the pseudo-relative interior, priC\operatorname{pri}C) is defined as follows.

Definition 3.7 (Intrinsic core via the cone of feasible directions).
icrC={xC:cone(Cx) is a linear subspace}.\operatorname{icr}C=\{x\in C:\operatorname{cone}(C-x)\text{ is a linear subspace}\}.

We next show that this definition describes the same object as the one considered previously.

Proposition 3.8 (Lemma 2.3 in [BorweinGoebel]).

Let CC be a convex subset of a real vector space XX. Then cone(Cx)\operatorname{cone}(C-x) is a linear subspace if and only if for every uCu\in C there exists vCv\in C such that x(u,v)x\in(u,v).

Proof.

Since CC is a convex set, the set cone(Cx)\operatorname{cone}(C-x) is a linear subspace if and only if cone(Cx)=cone(Cx)\operatorname{cone}(C-x)=-\operatorname{cone}(C-x) (additivity holds naturally), equivalently for every y=t(ux)y=t(u-x), where t>0t>0 and uCu\in C there exists s>0s>0 and vCv\in C such that y=s(vx)-y=s(v-x). This is in turn equivalent to having, for each uCu\in C, the existence of vCv\in C and r=s/t>0r=s/t>0 such that ux=r(vx)u-x=-r(v-x), which is equivalent to

x=11+ru+r1+rv(u,v).x=\frac{1}{1+r}u+\frac{r}{1+r}v\in(u,v).

In a similar way to the intrinsic core we can also define the core (or algebraic interior) of a convex set (introduced by Klee [kleepart1], also see [Holmes]). For some xCx\in C we say that xcoreCx\in\operatorname{core}C if for every yX{x}y\in X\setminus\{x\} there exists z(x,y)z\in(x,y) such that [x,z]C[x,z]\subseteq C. If 0coreC0\in\operatorname{core}C, then CC is called absorbing. Note that icrC=coreC\operatorname{icr}C=\operatorname{core}C if and only if either coreC\operatorname{core}C\neq\emptyset or icrC=\operatorname{icr}C=\emptyset. We can equivalently define the intrinsic core of a convex set CC as its core with respect to its affine hull. Notice that generally speaking the affine hull of a convex set is not necessarily a linear subspace of the ambient vector space XX, since it may not contain zero. However as this space closed with respect to lines and segments, there is no impediment to defining the core of a convex set living in this affine subspace.

Definition 3.9 (Intrinsic core via the affine hull).

Let CC be a convex set of a real vector space XX. The intrinsic core of CC is the algebraic core of CC with respect to the affine hull of CC, that is,

icrXC=coreaffCC.\operatorname{icr}_{X}C=\operatorname{core}_{\operatorname{aff}C}C.

The next proposition ensures that this new definition aligns with the original Definition 3.1.

Proposition 3.10.

Let CC be a convex subset of a real vector space XX, and let xCx\in C. The following statements are equivalent:

  1. (i)

    for every yCy\in C there exists zCz\in C such that x(y,z)x\in(y,z);

  2. (ii)

    for every yaffCy\in\operatorname{aff}C there exists z(x,y)z\in(x,y) such that zCz\in C.

Proof.

Let xCx\in C and suppose (i) holds. Take any yaffCy\in\operatorname{aff}C. Then

y=i=1mλiui+λ0x,iIλi+λ0=1,uiC{x}i{1,,m}.y=\sum_{i=1}^{m}\lambda_{i}u_{i}+\lambda_{0}x,\quad\sum_{i\in I}\lambda_{i}+\lambda_{0}=1,\quad u_{i}\in C\setminus\{x\}\;\;\forall\,i\in\{1,\dots,m\}. (8)

If m=0m=0, then y=xy=x and (ii) holds with z=x=yz=x=y. Otherwise for any i{1,,m}i\in\{1,\dots,m\} there exists viCv_{i}\in C such that x(ui,vi)x\in(u_{i},v_{i}); explicitly, there is some ti(0,1)t_{i}\in(0,1) such that

x=tiui+(1ti)vi.x=t_{i}u_{i}+(1-t_{i})v_{i}. (9)

Now let

αi:={λi<0,i1λiti+λ0,i=0λi,λi0,i1,(ti1)λiti,λi<0,i1,wi={ui,λi0,vi,λi<0.\displaystyle\alpha_{i}:=\begin{cases}\displaystyle\sum_{\begin{subarray}{c}\lambda_{i}<0,\\ i\geq 1\end{subarray}}\frac{\lambda_{i}}{t_{i}}+\lambda_{0},&i=0\\ \lambda_{i},&\lambda_{i}\geq 0,i\geq 1,\\ \frac{(t_{i}-1)\lambda_{i}}{t_{i}},&\lambda_{i}<0,i\geq 1,\\ \end{cases}\qquad w_{i}=\begin{cases}u_{i},&\lambda_{i}\geq 0,\\ v_{i},&\lambda_{i}<0.\end{cases}

It is easy to see using (8) and (9) that the affine representation of yy can be rewritten in this new notation as

y=i=1mαiwi+α0x.y=\sum_{i=1}^{m}\alpha_{i}w_{i}+\alpha_{0}x. (10)

Here i=1mαi+α0=1\sum_{i=1}^{m}\alpha_{i}+\alpha_{0}=1, αi0\alpha_{i}\geq 0 and wiCw_{i}\in C for i{1,,m}i\in\{1,\dots,m\} Now if α00\alpha_{0}\geq 0, then yCy\in C, hence [x,y]C[x,y]\subseteq C and for any z(x,y)z\in(x,y) we have [x,z]C[x,z]\subseteq C. Otherwise (if α0<0\alpha_{0}<0) let

z:=11α0y+α01α0x.z:=\frac{1}{1-\alpha_{0}}y+\frac{-\alpha_{0}}{1-\alpha_{0}}x.

Observe that z(x,y)z\in(x,y) and also substituting the expression for yy from (10) we have

z\displaystyle z =i=1mαi1α0wi.\displaystyle=\sum_{i=1}^{m}\frac{\alpha_{i}}{1-\alpha_{0}}w_{i}.

It is evident that zCz\in C, and therefore [x,z]C[x,z]\subseteq C. We have shown that (i) yields (ii). It remains to show the converse.

Assume now that (ii) holds, and let yCy\in C. Since x,yCx,y\in C, we have

x+(xy)affC.x+(x-y)\in\operatorname{aff}C.

By (ii) there exists z(x,x+(xy))z\in(x,x+(x-y)) such that zCz\in C. Explicitly, there is some t(0,1)t\in(0,1) such that

z=(1t)x+t(x+(xy))=(1+t)xty.z=(1-t)x+t(x+(x-y))=(1+t)x-ty.

Hence,

x=11+tz+t1+ty(z,y).x=\frac{1}{1+t}z+\frac{t}{1+t}y\in(z,y).

This shows (i). ∎

3.3 Intrinsic core and minimal faces

Even though the intrinsic core of a convex set may be empty (as in Example 3.3), it provides a disjoint decomposition of a convex set into the intrinsic cores of its faces. We prove this in Theorem 3.15 (cf. [FacelessProblem, Corollary 2.4]), but first we obtain a characterisation of the intrinsic core via minimal faces. Notice also that the next proposition was effectively proved in [FacelessProblem, Proposition 2.3].

Proposition 3.11.

Let CXC\subseteq X be a convex set, then

icrC={xC,Fmin(x,C)=C}.\operatorname{icr}C=\{x\in C,F_{\min}(x,C)=C\}.
Proof.

From the definition of intrinsic core we then have xicrCx\in\operatorname{icr}C if and only if cone(Cx)\operatorname{cone}(C-x) is a linear subspace, this is equivalent to lincone(Cx)=cone(Cx)\operatorname{lin}\operatorname{cone}(C-x)=\operatorname{cone}(C-x)linspacecone(Cx)=cone(Cx)\operatorname{linspace}\operatorname{cone}(C-x)=\operatorname{cone}(C-x).

If xicrCx\in\operatorname{icr}C we then have by Proposition 2.4

Fmin(x,C)=C(linlinspacecone(Cx)+x)=C(cone(Cx)+x)=C.F_{\min}(x,C)=C\cap({\color[rgb]{1,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{1,0,0}\sout{\operatorname{lin}}}{\color[rgb]{0,0,1}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,1}\uwave{\operatorname{linspace}}}\operatorname{cone}(C-x)+x)=C\cap(\operatorname{cone}(C-x)+x)=C.

Conversely, if Fmin(x,C)=CF_{\min}(x,C)=C, we have

C=C(linlinspacecone(Cx)+x).C=C\cap({\color[rgb]{1,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{1,0,0}\sout{\operatorname{lin}}}{\color[rgb]{0,0,1}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,1}\uwave{\operatorname{linspace}}}\operatorname{cone}(C-x)+x). (11)

In this case, if lincone(Cx)cone(Cx)\operatorname{lin}\operatorname{cone}(C-x)\neq\operatorname{cone}(C-x)linspacecone(Cx)cone(Cx)\operatorname{linspace}\operatorname{cone}(C-x)\neq\operatorname{cone}(C-x), there must be a point ucone(Cx)lincone(Cx)u\in\operatorname{cone}(C-x)\setminus\operatorname{lin}\operatorname{cone}(C-x)ucone(Cx)linspacecone(Cx)u\in\operatorname{cone}(C-x)\setminus\operatorname{linspace}\operatorname{cone}(C-x), and hence there’s some t>0t>0 such that x+tuCx+tu\in C, but x+tulincone(Cx)+xx+tu\notin\operatorname{lin}\operatorname{cone}(C-x)+xx+tulinspacecone(Cx)+xx+tu\notin\operatorname{linspace}\operatorname{cone}(C-x)+x, which contradicts (11). ∎

Proposition 3.11 that we have just proved allows us to state yet another equivalent definition of the intrinsic core.

Definition 3.12 (Intrinsic core via minimal faces).

Let CC be a convex subset of a real vector space XX. The intrinsic core of CC can be defined as follows,

icrC={xC,Fmin(x,C)=C}.\operatorname{icr}C=\{x\in C,F_{\min}(x,C)=C\}.
Example 3.13 (Revisiting Example 3.3).

Using the last definition of the intrinsic core, we can provide an alternative explanation on why the intrinsic core of the set CC in Example 3.3 defined by (7) is empty.

For xCx\in C let

Fx={uC|ui=0i:xi=0}.F_{x}=\{u\in C\,|\,u_{i}=0\,\forall i:\,x_{i}=0\}.

It is not difficult to observe that FxF_{x} is a face of CC, does not coincide with CC, and contains xx. Hence the minimal face Fmin(x,C)FxF_{\min}(x,C)\subseteq F_{x} is strictly smaller than CC for every xCx\in C. We conclude that icrC=\operatorname{icr}C=\emptyset.

Corollary 3.14.

Let CC be a convex subset of a real vector space XX, and let FF be a face of CC. Then F=Fmin(x,C)F=F_{\min}(x,C) if and only if xicrFx\in\operatorname{icr}F.

Proof.

Observe that for xFCx\in F\unlhd C we have

Fmin(x,F)=Fmin(x,C),F_{\min}(x,F)=F_{\min}(x,C),

hence, under the assumptions of this corollary, and using Proposition 3.11, we obtain

icrF={xF|Fmin(x,F)=F}={xF|Fmin(x,C)=F}\operatorname{icr}F=\{x\in F\,|\,F_{\min}(x,F)=F\}=\{x\in F\,|\,F_{\min}(x,C)=F\}

which shows the equivalence. ∎

Theorem 3.15 (cf. Corollary 2.4 in [FacelessProblem]).

For a convex set CXC\subset X and any xCx\in C there is a unique face FCF\unlhd C such that xicrFx\in\operatorname{icr}F. Consequently,

C=˙FCicrF,C=\operatorname*{\dot{\bigcup}}_{F\unlhd C}\operatorname{icr}F, (12)

where by ˙\operatorname*{\dot{\bigcup}} we denote a disjoint union.

Proof.

We obtain this decomposition by observing that for every xCx\in C there exists a unique minimal face Fmin(x,C)F_{\min}(x,C). Evidently Fmin(x,Fmin(x,C))=Fmin(x,C)F_{\min}(x,F_{\min}(x,C))=F_{\min}(x,C), hence by Proposition 3.11 we have xicrFx\in\operatorname{icr}F. This shows that CC is the union of intrinsic cores of its faces. It remains to show that the union is disjoint.

Suppose that xicrEicrFx\in\operatorname{icr}E\cap\operatorname{icr}F, where EE and FF are different faces of CC. Then we must have Fmin(x,E)=EF=Fmin(x,F)F_{\min}(x,E)=E\neq F=F_{\min}(x,F). However xEFx\in E\cap F and hence

Fmin(x,E)=Fmin(x,F)=Fmin(x,C),F_{\min}(x,E)=F_{\min}(x,F)=F_{\min}(x,C),

a contradiction. ∎

The next statement is Theorem 2.7 from [FacelessProblem], rephrased in our notation. The author calls the problem of characterising convex sets with no proper faces the ‘faceless problem’, hence the name of the theorem. It appears that this result follows from the decomposition of Theorem 3.15.

Theorem 3.16 (Faceless theorem).

A nonempty convex subset CC of a real vector space XX is free of proper faces if and only if C=icrCC=\operatorname{icr}C.

Proof.

Suppose that a nonempty convex set CC has no proper faces. Then the only nonempty face of CC is the set CC itself, and by Theorem 3.15 we have C=icrCC=\operatorname{icr}C.

Conversely, assume that CC is a nonempty convex set such that C=icrCC=\operatorname{icr}C. Assume that on the contrary CC has a proper face FF. Since FF is nonempty, there is some point xFx\in F, and by Theorem 3.15 we can find a face EFE\lhd F such that xicrEx\in\operatorname{icr}E. By Lemma 2.1 the convex set EE is also a face of CC, while by our construction ECE\subsetneq C. We conclude that xicrCicrEx\in\operatorname{icr}C\cap\operatorname{icr}E, which contradicts the uniqueness claim of Theorem 3.15. ∎

Since only the faces that are minimal with respect to some point in the convex set feature in the decomposition (12), only minimal faces ‘contribute’ to the set. It is natural to expect that the faces that aren’t minimal can be represented in some natural way via the minimal ones. Note that in Example 3.3 the set CC is the union of the chainof its faces

C=iFi,{\color[rgb]{1,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{1,0,0}\sout{C=\bigcup_{i\in\mathbb{N}}F_{i},}}

where

Fi={xC|xk=0k>i},{\color[rgb]{1,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{1,0,0}\sout{F_{i}=\{x\in C\,|\,x_{k}=0\;\forall k>i\},}}

however the set CC itself is not a member of the chain ={Fi|i}\mathcal{F}=\{F_{i}\,|\,i\in\mathbb{N}\}. Based on this discussion and observation, we propose the following conjecture. , for instance as the union of a chain, as is the case in Example 3.3. However this is not true, as is clear from a construction suggested by one of our referees. We come back to this construction in Section 4.1.

Every face of a convex set CC is the union of a chain of minimal faces.

3.4 Calculus of intrinsic cores

We next gather several key calculus rules pertaining to intrinsic cores that haven’t been covered earlier. Even though this section contains mostly well-known results, we reprove them for completeness of our exposition.

Proposition 3.17 (Lemma 3.6 (a) in [BorweinGoebel]).

If AA and BB are convex sets in a real vector space XX, then

icrA+icrBicr(A+B).\operatorname{icr}A+\operatorname{icr}B\subseteq\operatorname{icr}(A+B). (13)

Moreover, if icrA\operatorname{icr}A\neq\emptyset and icrB\operatorname{icr}B\neq\emptyset, then

icrA+icrB=icr(A+B).\operatorname{icr}A+\operatorname{icr}B=\operatorname{icr}(A+B). (14)
Proof.

Note that if either one of icrA\operatorname{icr}A or icrB\operatorname{icr}B is nonempty, then (13) holds trivially. To show that (13) holds in general, take aicrAa\in\operatorname{icr}A, bicrBb\in\operatorname{icr}B, and let c=a+bc=a+b. Now pick any zA+Bz\in A+B. We can represent zz as z=x+yz=x+y, where xAx\in A and yBy\in B. By the definition of intrinsic core there exist uAu\in A and vBv\in B such that a(x,u)a\in(x,u) and b(y,v)b\in(y,v). Algebraically this means that there exist t,s>1t,s>1 such that

u=x+t(ax),v=y+s(by).u=x+t(a-x),\quad v=y+s(b-y).

Let α:=min{s,t}\alpha:=\min\{s,t\}. By convexity

x+α(ax)(x,u)A,y+α(by)(y,v)B.x+\alpha(a-x)\in(x,u)\subseteq A,\quad y+\alpha(b-y)\in(y,v)\subseteq B.

Hence

w=(x+α(ax))+(y+α(by))A+B.w=(x+\alpha(a-x))+(y+\alpha(b-y))\in A+B.

It is evident that c(z,w)c\in(z,w), where wA+Bw\in A+B. Since our choice of zz was arbitrary, we conclude that cicr(A+B)c\in\operatorname{icr}(A+B).

Now assume that icrA,icrB\operatorname{icr}A,\operatorname{icr}B\neq\emptyset. To show (14), in view of (13) it remains to demonstrate that

icr(A+B)icrA+icrB.\operatorname{icr}(A+B)\subseteq\operatorname{icr}A+\operatorname{icr}B.

Pick any point cicr(A+B)c\in\operatorname{icr}(A+B). Since icr(A+B)A+B\operatorname{icr}(A+B)\subseteq A+B, there must be aAa\in A and bBb\in B such that c=a+bc=a+b. Now choose any xicrAx\in\operatorname{icr}A and yicrBy\in\operatorname{icr}B. Then z=x+yA+Bz=x+y\in A+B. Now zA+Bz\in A+B, cicr(A+B)c\in\operatorname{icr}(A+B), hence there must be some wA+Bw\in A+B such that c(z,w)icr(A+B)c\in(z,w)\subseteq\operatorname{icr}(A+B).

Since wA+Bw\in A+B, there must be uAu\in A, vBv\in B such that w=u+vw=u+v. Now by Proposition 3.5 we have [x,u)icrA[x,u)\subseteq\operatorname{icr}A and [y,v)icrB[y,v)\subseteq\operatorname{icr}B. Now c=(1α)z+αwc=(1-\alpha)z+\alpha w for some α(0,1)\alpha\in(0,1). Since z=x+yz=x+y and w=u+vw=u+v, we conclude that

c=[(1α)x+αu]+[(1α)y+αv]icrA+icrB.c=[(1-\alpha)x+\alpha u]+[(1-\alpha)y+\alpha v]\in\operatorname{icr}A+\operatorname{icr}B.

Corollary 3.18.

If CC is a convex subset of a real vector space XX, then for any xXx\in X

icr(C+{x})=icrC+{x}.\operatorname{icr}(C+\{x\})=\operatorname{icr}C+\{x\}.
Proof.

From Proposition 3.17 we have

icr(C+{x})icrC+icr{x}=icrC+{x},\operatorname{icr}(C+\{x\})\subseteq\operatorname{icr}C+\operatorname{icr}\{x\}=\operatorname{icr}C+\{x\},

also since C=(C+{x}){x}C=(C+\{x\})-\{x\},

icrCicr(C+{x}){x},\operatorname{icr}C\subseteq\operatorname{icr}(C+\{x\})-\{x\},

hence

icrC+{x}icr(C+{x}),\operatorname{icr}C+\{x\}\subseteq\operatorname{icr}(C+\{x\}),

and we have the required equality. ∎

Note that in [DangEtAl, Proposition 2.5] it is shown that if coreC=C\operatorname{core}C=C, then for any convex DXD\subseteq X we have

core(C+D)=C+D.\operatorname{core}(C+D)=C+D.

This property doesn’t generalise to intrinsic cores, even for finite dimensions. For instance, take C=(0,1)×{0}C=(0,1)\times\{0\}, D={0}×[0,1]D=\{0\}\times[0,1] in the plane. Then icrC=C\operatorname{icr}C=C, but

icr(C+D)=(0,1)×(0,1)(0,1)×[0,1]=C+D.\operatorname{icr}(C+D)=(0,1)\times(0,1)\neq(0,1)\times[0,1]=C+D.
Proposition 3.19 (cf. Lemma 3.3 in [BorweinGoebel]).

Let CC be a convex subset of a real vector space XX, and let A:XYA:X\to Y be a linear mapping from XX to another real vector space YY. Then

AicrCicr(AC).A\operatorname{icr}C\subseteq\operatorname{icr}(AC). (15)

Moreover, if AA maps CC to ACAC injectively or icrC\operatorname{icr}C\neq\emptyset, then

AicrC=icr(AC).A\operatorname{icr}C=\operatorname{icr}(AC). (16)
Proof.

Let CC be a subset of XX, and suppose A:XYA:X\to Y is a linear mapping. If xicrCx\in\operatorname{icr}C, let u=Axu=Ax and take any vACv\in AC. There must be a yCy\in C such that v=Ayv=Ay. Since xicrCx\in\operatorname{icr}C, by the definition of the intrinsic core there exists zCz\in C such that x(y,z)x\in(y,z). Hence u(v,Az)u\in(v,Az) and we conclude that uicrACu\in\operatorname{icr}AC. This proves (15).

We first prove that (16) holds for an injective linear mapping AA: we only need to show the converse of (15). So assume that AA maps CXC\subseteq X to ACYAC\subseteq Y injectively. Consider any xicrACx\in\operatorname{icr}AC. There is a unique uCu\in C such that x=Aux=Au. Take any vCv\in C, and let y=Avy=Av. Since xicrACx\in\operatorname{icr}AC, there must be zACz\in AC such that x(y,z)x\in(y,z), and hence there is some wCw\in C such that z=Awz=Aw. Now observe that u(v,w)u\in(v,w). Indeed, we have x=(1t)y+tzx=(1-t)y+tz for some t(0,1)t\in(0,1), and hence Au=(1t)Av+Atw=A((1t)v+tw)Au=(1-t)Av+Atw=A((1-t)v+tw). We must have u=(1t)v+twu=(1-t)v+tw, otherwise AA is not injective. We have therefore shown that u(v,w)Cu\in(v,w)\subseteq C, and by the arbitrariness of vv we conclude that uicrCu\in\operatorname{icr}C, hence x=AuAicrCx=Au\in A\operatorname{icr}C.

Now let’s deal with the case when AA is not injective, but icrC\operatorname{icr}C\neq\emptyset. Let LL be the kernel of AA, i.e.

L={xX|Ax=0}.L=\{x\in X\,|\,Ax=0\}.

Since LL is a linear subspace of XX, it has a Hamel basis that can be completed to the entire space. Denote by MM the span of this complement, then for any xXx\in X we have x=xL+xMx=x_{L}+x_{M}, where xLLx_{L}\in L and xMMx_{M}\in M.

Let DD be the projection of CC onto the complement MM of LL, in other words,

D={xM|yL:x+yC}.D=\{x\in M\,|\,\exists y\in L:x+y\in C\}.

It is evident that D+L=C+LD+L=C+L, and therefore

AC=A(C+L)=AD.AC=A(C+L)=AD. (17)

Furthermore, observe that DD is the linear image of CC under the projection PP that takes an element of XX to the subset MM by trimming off all of the LL-space coordinates. From (15) we have

PicrCicr(PC)=icrD,P\operatorname{icr}C\subseteq\operatorname{icr}(PC)=\operatorname{icr}D,

and so icrD\operatorname{icr}D is nonempty. It is evident that icrL=L\operatorname{icr}L=L\neq\emptyset, hence we can apply Proposition 3.17 to icrC\operatorname{icr}C and LL (and to icrD\operatorname{icr}D and LL) to obtain

icrC+L=icr(C+L)=icr(D+L)=icrD+L,\operatorname{icr}C+L=\operatorname{icr}(C+L)=\operatorname{icr}(D+L)=\operatorname{icr}D+L,

we therefore have

A(icrC)=A(icrC+L)=A(icrD+L)=A(icrD),A(\operatorname{icr}C)=A(\operatorname{icr}C+L)=A(\operatorname{icr}D+L)=A(\operatorname{icr}D), (18)

In view of (17) and (18) it remains to show that

icr(AD)=A(icrD).\operatorname{icr}(AD)=A(\operatorname{icr}D).

Since AA is an injective mapping on DD, the result is true by the previously proved claim. ∎

The following corollary of Proposition 3.19 was proved in [DangEtAl] for the algebraic core under the assumption of surjectivity of AA (see Lemma 4.1 in [DangEtAl]).

Corollary 3.20 (cf. [DangEtAl, Lemma 4.1]).

If A:XYA:X\to Y is a surjective linear operator between two vector spaces XX and YY, and CXC\subseteq X, then

A(coreC)core(AC),A(\operatorname{core}C)\subseteq\operatorname{core}(AC), (19)

moreover if coreC\operatorname{core}C\neq\emptyset, then (19) holds as equality.

Proof.

If coreC=\operatorname{core}C=\emptyset, then (19) is a triviality. Assume that coreC\operatorname{core}C\neq\emptyset. Then icrC=coreC\operatorname{icr}C=\operatorname{core}C, and by Proposition 3.19 we have

A(coreC)=A(icrC)=icr(AC).A(\operatorname{core}C)=A(\operatorname{icr}C)=\operatorname{icr}(AC).

It remains to show that icr(AC)=core(AC)\operatorname{icr}(AC)=\operatorname{core}(AC), and for that it is sufficiently to show that core(AC)\operatorname{core}(AC)\neq\emptyset. Take any xicr(AC)x\in\operatorname{icr}(AC) and yYy\in Y. Since AA is surjective, there must be some uCu\in C and vXv\in X such that x=Aux=Au and y=Avy=Av. Since coreA\operatorname{core}A\neq\emptyset, there must be some w(u,v)w\in(u,v) suth that [u,w]C[u,w]\subseteq C. Hence [z,x]AC[z,x]\subseteq AC, with z=Aw(Au,Av)=(x,y)z=Aw\in(Au,Av)=(x,y), so xcore(AC)x\in\operatorname{core}(AC)\neq\emptyset.

Corollary 3.21 (Lemma 3.6 (c) in [BorweinGoebel]).

If CC is a convex subset of a real vector space XX, then

icrλC=λicrCλ{0}.\operatorname{icr}\lambda C=\lambda\operatorname{icr}C\quad\forall\lambda\in\mathbb{R}\setminus\{0\}.
Proof.

Follows from λ\lambda defining an injective linear mapping and Proposition 3.19. ∎

Corollary 3.22.

Let CC be a convex subset of a real vector space XX and let DD be a convex subset of a real vector space YY, then

icr(C×D)=icrC×icrD.\operatorname{icr}(C\times D)=\operatorname{icr}C\times\operatorname{icr}D.
Proof.

First assume that icrC\operatorname{icr}C\neq\emptyset and icrD\operatorname{icr}D\neq\emptyset, and define two linear mappings, M:XX×YM:X\to X\times Y and N:YX×YN:Y\to X\times Y as follows:

M(x)=(x,0Y),N(y)=(0X,y).M(x)=(x,0_{Y}),\quad N(y)=(0_{X},y).

These two mappings are injective, and therefore by Proposition 3.19 we have

M(icrC)=icr(MC),N(icrD)=icr(ND).M(\operatorname{icr}C)=\operatorname{icr}(MC),\quad N(\operatorname{icr}D)=\operatorname{icr}(ND).

Now C×D=MC+NDC\times D=MC+ND, therefore by Proposition 3.17 we have

icr(C×D)\displaystyle\operatorname{icr}(C\times D) =icr(MC+ND)\displaystyle=\operatorname{icr}(MC+ND)
=icr(MC)+icr(ND)\displaystyle=\operatorname{icr}(MC)+\operatorname{icr}(ND)
=M(icrC)+N(icrD)\displaystyle=M(\operatorname{icr}C)+N(\operatorname{icr}D)
={(x,y)|xicrC,yicrD}\displaystyle=\{(x,y)\,|\,x\in\operatorname{icr}C,y\in\operatorname{icr}D\}
=icrC×icrD.\displaystyle=\operatorname{icr}C\times\operatorname{icr}D.

It remains to consider the case when one of the intrinsic cores icrC\operatorname{icr}C or icrD\operatorname{icr}D is empty and to show that the left-hand side icr(C×D)\operatorname{icr}(C\times D) must also be empty in this case. Without loss of generality assume that icrC=\operatorname{icr}C=\emptyset, but there is some (x,y)icr(C×D)(x,y)\in\operatorname{icr}(C\times D). Pick any aCa\in C. The point (a,y)(a,y) must be in C×DC\times D, hence, there exists (b,c)C×D(b,c)\in C\times D and t(0,1)t\in(0,1) such that

(x,y)=t(a,y)+(1t)(b,c).(x,y)=t(a,y)+(1-t)(b,c).

We conclude that x=ta+(1t)bx=ta+(1-t)b, where bCb\in C and t(0,1)t\in(0,1), and hence xicrCx\in\operatorname{icr}C, which contradicts the assumption. ∎

4 Algebraic Linear Closure

In this section we discuss the notion of algebraic linear closure that can be defined using the natural topology of the real line. Our main goal is to relate this notion to the notion of the intrinsic core, and discuss some interesting phenomena pertaining to algebraic closures linear closure of convex sets.

4.1 Algebraic Linear closure and the intrinsic core

We say that a convex subset CC of a real vector space XX is algebraically linearly closed if for every (x,y)C(x,y)\subseteq C we have [x,y]C[x,y]\subseteq C. The algebraic linear closure \aclC\acl C lclC\operatorname{lcl}C is the smallest convex algebraically linearly closed subset of XX that contains CC. Equivalently the algebraic In a finite dimensional space the linear closure of a convex set CC consists of all line segments contained in CC together with their endpoints. It is evident that any algebraically closed set that contains CC must contain the closures of all line segments from CC, moreover, this union of closures of line segments must be a convex set. Indeed, observe that if xx and yy are such endpoints, then for some u,vu,v we have

(1t)u+txC,(1t)v+tyCt(0,1),{\color[rgb]{1,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{1,0,0}\sout{(1-t)u+tx\in C,\;(1-t)v+ty\in C\quad\forall t\in(0,1),}}

where it can possibly happen that u=xu=x or v=yv=y. Then for any z(x,y)z\in(x,y) we have z=αx+(1α)yz=\alpha x+(1-\alpha)y and from the convexity of CC

(1t)[αu+(1α)v]+t[αx+(1α)y]Ct(0,1),{\color[rgb]{1,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{1,0,0}\sout{(1-t)}}[{\color[rgb]{1,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{1,0,0}\sout{\alpha u+(1-\alpha)v}}]{\color[rgb]{1,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{1,0,0}\sout{+t}}[{\color[rgb]{1,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{1,0,0}\sout{\alpha x+(1-\alpha)y}}]{\color[rgb]{1,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{1,0,0}\sout{\in C\quad\forall t\in(0,1),}}

hence zz is an endpoint of a (possibly degenerate) line segment from CC, but this is not the case in the infinite-dimensional setting (see [KleeLin, nikodym] for a detailed discussion on the fascinating differences between these two notions; note also that it may happen that linClinlinC\operatorname{lin}C\neq\operatorname{lin}\operatorname{lin}C). We hence distinguish between the (strong) linear closure lclC\operatorname{lcl}C and the (weak) linear closure linC\operatorname{lin}C.

There is a related notion of linearly accessible points discussed [Holmes, cones]: a point xXx\in X is linearly accessible from CC if and only if there is some yXy\in X such that (x,y)C(x,y)\subseteq C. Since our notation allows x=yx=y in this case, this notion coincides with the algebraic closure weak linear closure linC\operatorname{lin}C (cf. [DangEtAl], where the notation linC\operatorname{lin}C is used to denote the algebraic closure). It appears that some references do not allow x=yx=y in the definition of linearly accessible points, and in this interpretation the set of linearly accessible points coincides with \aclC\acl C our definition linC\operatorname{lin}C whenever CC contains at least 2 points (see [Holmes]).

We can then define the intrinsic (weak) algebraic boundary of a convex subset CC of a real vector space XX as the set of all endpoints of the line segments that are contained in CC, that is,

abdlbdC={xX|uX:(x,x+u)C,(x,xu)C=}.{\color[rgb]{1,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{1,0,0}\sout{\operatorname{abd}}}{\color[rgb]{0,0,1}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,1}\uwave{\operatorname{lbd}}}C=\{x\in X\,|\,\exists u\in X:(x,x+u)\subseteq C,(x,x-u)\cap C=\emptyset\}.

Note that abdC\operatorname{abd}C lbdC\operatorname{lbd}C is called the set of outer points in [InnerStructure], also this reference defines such points for general (not necessarily convex) subsets of XX.

Proposition 4.1.

Let C1C_{1}, C2C_{2} and CC be convex subsets of a real vector space XX. Then we have the following relations between the algebraic weak linear closure and the intrinsic core.

  1. (i)

    icrC=CabdC\operatorname{icr}C=C\setminus\operatorname{abd}CicrC=ClbdC\operatorname{icr}C=C\setminus\operatorname{lbd}C.

  2. (ii)

    \aclC=CabdC=icrCabdC.\acl C=C\cup\operatorname{abd}C=\operatorname{icr}C\cup\operatorname{abd}C. linC=ClbdC=icrClbdC.\operatorname{lin}C=C\cup\operatorname{lbd}C=\operatorname{icr}C\cup\operatorname{lbd}C.

  3. (iii)

    If xicrCx\in\operatorname{icr}C and y\aclCy\in\acl CylinCy\in\operatorname{lin}C, then there exists zicrCz\in\operatorname{icr}C such that x(z,y)icrCx\in(z,y)\subseteq\operatorname{icr}C. In particular, [x,y)icrC[x,y)\subset\operatorname{icr}C.

  4. (iv)

    If icrC\operatorname{icr}C\neq\emptyset, then C\aclicrCC\subseteq\acl\operatorname{icr}C, icr(\aclC)=icrC\operatorname{icr}(\acl C)=\operatorname{icr}C and \aclC=\aclicrC\acl C=\acl\operatorname{icr}CClinicrCC\subseteq\operatorname{lin}\operatorname{icr}C, icr(linC)=icrC\operatorname{icr}(\operatorname{lin}C)=\operatorname{icr}C and linC=linicrC\operatorname{lin}C=\operatorname{lin}\operatorname{icr}C.

  5. (v)

    We have affC=aff\aclC\operatorname{aff}C=\operatorname{aff}\acl CaffC=afflinC\operatorname{aff}C=\operatorname{aff}\operatorname{lin}C, and if icrC\operatorname{icr}C\neq\emptyset, then afficrC=affC\operatorname{aff}\operatorname{icr}C=\operatorname{aff}C.

Proof.

To show (i), let xCx\in C and observe that xabdCx\in\operatorname{abd}C xlbdCx\in\operatorname{lbd}C if and only if there is a line segment with the endpoint xx that can not be continued beyond xx, which is equivalent to xicrCx\notin\operatorname{icr}C.

To show (ii), first observe that the second equality follows from (i): CabdC=(CabdC)abdC=icrCabdCC\cup\operatorname{abd}C=(C\setminus\operatorname{abd}C)\cup\operatorname{abd}C=\operatorname{icr}C\cup\operatorname{abd}CClbdC=(ClbdC)lbdC=icrClbdCC\cup\operatorname{lbd}C=(C\setminus\operatorname{lbd}C)\cup\operatorname{lbd}C=\operatorname{icr}C\cup\operatorname{lbd}C. For the first relation, observe that abdC\aclC\operatorname{abd}C\subseteq\acl C and C\aclCC\subseteq\acl ClbdClinC\operatorname{lbd}C\subseteq\operatorname{lin}C and ClinCC\subseteq\operatorname{lin}C, hence we only need to show that \aclCCabdC\acl C\subseteq C\cup\operatorname{abd}C. If x\aclCx\in\acl ClinCClbdC\operatorname{lin}C\subseteq C\cup\operatorname{lbd}C. If xlinCx\in\operatorname{lin}C, and xCx\notin C, then xx must be the endpoint of some segment (x,x+u)(x,x+u) contained in CC. Since xCx\notin C, the extension of this segment beyond xx, that is, (x,xu)(x,x-u) must have an empty intersection with CC. We conclude that xabdCx\in\operatorname{abd}CxlbdCx\in\operatorname{lbd}C.

For (iii) let xicrCx\in\operatorname{icr}C, y\aclCy\in\acl CylinCy\in\operatorname{lin}C. If yCy\in C, then we are done by Proposition 3.4. Otherwise there must exist some uCu\in C such that uyu\neq y and (y,u]C(y,u]\subseteq C.

Since xicrCx\in\operatorname{icr}C, there exists vCv\in C such that x(u,v)x\in(u,v). Then by Proposition 2.5 for any point z(x,y)z^{\prime}\in(x,y) there exits w(v,y)w\in(v,y) such that z(u,w)z^{\prime}\in(u,w). Now fix any p(z,w)(u,w)p\in(z^{\prime},w)\subseteq(u,w). Applying Proposition 2.5 again, this time to y,v,w,uy,v,w,u and pp (see Fig. 6),

Refer to caption
Figure 6: Applying Proposition 2.5 twice in the proof of Proposition 4.1 (iii).

we conclude that there exists q(u,y)q\in(u,y) such that p(v,q)p\in(v,q). Now q(u,y)Cq\in(u,y)\subseteq C, vCv\in C and p(v,q)Cp\in(v,q)\subseteq C. Since z(u,p)z^{\prime}\in(u,p), with uCu\in C, and hence zCz^{\prime}\in C, by the arbitrariness of zz^{\prime} this proves that [x,y)C[x,y)\subseteq C. Now for any point r(x,y)r\in(x,y) we have rCr\in C, hence, by Proposition 3.4 we obtain [x,r)icrC[x,r)\subseteq\operatorname{icr}C, and hence [x,y)icrC[x,y)\subseteq\operatorname{icr}C, and also by the same proposition there exists some zicrCz\in\operatorname{icr}C such that x(z,r)(z,y)x\in(z,r)\subset(z,y).

For (iv), we begin with \aclC=\aclicrC\acl C=\acl\operatorname{icr}ClinC=linicrC\operatorname{lin}C=\operatorname{lin}\operatorname{icr}C. Since CicrCC\subset\operatorname{icr}C, we only need to prove that \aclC\aclicrC\acl C\subseteq\acl\operatorname{icr}ClinClinicrC\operatorname{lin}C\subseteq\operatorname{lin}\operatorname{icr}C. Take any y\aclicrCy\in\acl\operatorname{icr}CylinicrCy\in\operatorname{lin}\operatorname{icr}C. If yicrCy\in\operatorname{icr}C, then yC\aclCy\in C\subset\acl C yClinCy\in C\subset\operatorname{lin}C and we are done. If y\aclicrCy\in\acl\operatorname{icr}CylinicrCy\in\operatorname{lin}\operatorname{icr}C, take any xicrCx\in\operatorname{icr}C. By (iii) we have [x,y)icrCC[x,y)\subseteq\operatorname{icr}C\subset C. Hence yy must be in \aclC\acl ClinC\operatorname{lin}C.

Observing that C\aclCC\subset\acl CClinCC\subset\operatorname{lin}C, the relation \aclC=\aclicrC\acl C=\acl\operatorname{icr}C linC=linicrC\operatorname{lin}C=\operatorname{lin}\operatorname{icr}C that we just proved yields C\aclicrCC\subseteq\acl\operatorname{icr}CClinicrCC\subseteq\operatorname{lin}\operatorname{icr}C. It remains to prove icr(\aclC)=icrC\operatorname{icr}(\acl C)=\operatorname{icr}C. Since icrCicr(\aclC)\operatorname{icr}C\subseteq\operatorname{icr}(\acl C) icr(linC)=icrC\operatorname{icr}(\operatorname{lin}C)=\operatorname{icr}C. Since icrCicr(linC)\operatorname{icr}C\subseteq\operatorname{icr}(\operatorname{lin}C) we only need to prove that icr(\aclC)icrC\operatorname{icr}(\acl C)\subseteq\operatorname{icr}Cicr(linC)icrC\operatorname{icr}(\operatorname{lin}C)\subseteq\operatorname{icr}C. Take any yicr(\aclC)y\in\operatorname{icr}(\acl C)yicr(linC)y\in\operatorname{icr}(\operatorname{lin}C). Since icrC\operatorname{icr}C\neq\emptyset, there exists some xicrCx\in\operatorname{icr}C. Since y\aclCy\in\acl CylinCy\in\operatorname{lin}C, by (iii) there exists zicrCz\in\operatorname{icr}C such that x(y,z)icrCx\in(y,z)\subseteq\operatorname{icr}C, concluding the proof of this item.

For (v) first note that affC\operatorname{aff}C contains all lines with two different points on CC, then \aclCaffC\acl C\subseteq\operatorname{aff}ClinCaffC\operatorname{lin}C\subseteq\operatorname{aff}C, implying that affC=aff(\aclC)\operatorname{aff}C=\operatorname{aff}(\acl C)affC=aff(linC)\operatorname{aff}C=\operatorname{aff}(\operatorname{lin}C). Using (iv), C\acl(icrC)C\subseteq\acl(\operatorname{icr}C), then affCaff(\acl(icrC))=aff(icrC)\operatorname{aff}C\subseteq\operatorname{aff}(\acl(\operatorname{icr}C))=\operatorname{aff}(\operatorname{icr}C)Clin(icrC)C\subseteq\operatorname{lin}(\operatorname{icr}C), then affCaff(lin(icrC))=aff(icrC)\operatorname{aff}C\subseteq\operatorname{aff}(\operatorname{lin}(\operatorname{icr}C))=\operatorname{aff}(\operatorname{icr}C). The conclusion follows because by other hand, aff(icrC)affC\operatorname{aff}(\operatorname{icr}C)\subseteq\operatorname{aff}C. ∎

Note that in [DangEtAl] the relation (iii) is shown using separation theorems (see Theorem 3.7), however our proof is much more elementary.

In [InnerStructure, Theorem 2.1] it was shown that CXC\subseteq X MXM\subseteq X is an affine subspace if and only if CC MM is convex, icrM=M\operatorname{icr}M=M and abdM=\operatorname{abd}M=\emptysetlbdM=\operatorname{lbd}M=\emptyset. Indeed we observed earlier that for a linear subspace MM we have icrM=M\operatorname{icr}M=M, abdMaffM=M\operatorname{abd}M\subset\operatorname{aff}M=M and M=icrM=MabdMM=\operatorname{icr}M=M\setminus\operatorname{abd}MlbdMaffM=M\operatorname{lbd}M\subset\operatorname{aff}M=M and M=icrM=MlbdMM=\operatorname{icr}M=M\setminus\operatorname{lbd}M.

The relation icr(\aclC)=icrC\operatorname{icr}(\acl C)=\operatorname{icr}C Since in general linClinlinClclC\operatorname{lin}C\neq\operatorname{lin}\operatorname{lin}C\neq\operatorname{lcl}C, it makes sense to define nn-th (weak) linear closure, and we can do this for any ordinal in the following way (see [KleeLin]):

linβC={linlinβ1Cif β1 exists,α<βlinαCif β is a limit ordinal.{\color[rgb]{0,0,1}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,1}\uwave{\operatorname{lin}^{\beta}C=\begin{cases}\operatorname{lin}\operatorname{lin}^{\beta-1}C&\text{if }\beta-1\text{ exists,}\\ \cup_{\alpha<\beta}\operatorname{lin}^{\alpha}C&\text{if }\beta\text{ is a limit ordinal.}\end{cases}}}

It was shown in [nikodym] that there exists a convex set for which linαClinα1C\operatorname{lin}^{\alpha}C\neq\operatorname{lin}^{\alpha-1}C for all ordinals α<ω1\alpha<\omega_{1} (where ω1\omega_{1} is the first uncountable ordinal). At the same time, lclC=linω1C\operatorname{lcl}C=\operatorname{lin}^{\omega_{1}}C (see [nikodym]).

We can define the nn-th (weak) linear boundary as lbdnC=linnCicrC{\operatorname{lbd}}^{n}C=\operatorname{lin}^{n}C\setminus\operatorname{icr}C, and likewise the strong linear boundary as lclCicrC\operatorname{lcl}C\setminus\operatorname{icr}C.

The relation icr(linC)=icrC\operatorname{icr}(\operatorname{lin}C)=\operatorname{icr}C fails to hold generically in infinite-dimensional spaces, this is due to the existence of proper convex sets that are ubiquitous, that is, such that their algebraic linear closure coincides with the entire space.

Theorem 4.2 (see [kleepart1]).

The linear space XX is inifinte dimensional if and only if XX contains a proper convex subset CC such that \aclC=X\acl C=XlinC=X\operatorname{lin}C=X.

The proof of this theorem is based on an explicit construction of such set, which we study in the next example. This example was publishedin [kleepart1] and according to our Referee, can be attributed to M. M. Day.

Example 4.3.

Since any Any linear vector space has a Hamel basis that can be totally ordered, we can moreover, if the space is infinite-dimensional, we can choose the order in a way that there isn’t a greatest element. We can then represent any element as a finite linear combination of the basic points. There will be the one coordinate that is maximal with respect to the total order, and we can define the set CC as the set of all points with the positive last coordinate. This set is convex, and its algebraic linear closure constitutes the entire space XX: indeed, any point xXx\in X is either in CC, or is the endpoint of some line segment in CC obtained by adding a small positive coordinate with a higher index. Somewhat counterintuitively, the complement of this set is also convex and ubiquitous (following the same logic).

It is easy to see why the intrinsic core of such a set is empty: take any element xx in this set, and then pick yy with a higher positive coordinate. Evidently the line through xx and yy can not be extended beyond xx, so xx is not in the intrinsic core. Hence icrC=\operatorname{icr}C=\emptyset.

When it comes to the facial structure of this set, observe that for any x,yCx,y\in C there exists zCz\in C such that x(y,z)x\in(y,z) if and only if the ‘index’ of the last coordinate of yy is no larger than the index of the last coordinate of xx. Therefore, the minimal face of xx is the subset of CC that contains all elements with the last coordinate not larger than xx. This in particular means that the set CC is the union of the chain of all minimal faces, the kind of structure that we expect in all convex sets given that Conjecture LABEL:conj:russian-doll is true. .

This property of chain of minimal faces of points is not always satisfied. Take XX a vector space with an uncountable Hamel basis, and define the convex set CC as the set of all the elements from XX with non-negative coordinates. The faces of CC are the cones spanned by subsets of the Hamel basis, while minimal faces of any point in CC is the cone spanned by finite subsets of the Hamel basis. The only faces which are reunions of chains of minimal faces of points in CC are the cones spanned by finite or countable subsets of the Hamel basis. Accordingly, all the faces spanned by infinite uncountable subsets of the Hamel basis (including CC) cannot be represented as the union of a chain of minimal faces of points of CC. We thank our referee for suggesting this example.

Recall how in finite-dimensional case faces of closed convex sets are always closed. It appears that this phenomenon is specific to the algebraic linear and not topological closure.

Proposition 4.4.

If CC is an algebraically a linearly closed convex subset of a vector space XX, then every face of CC is algebraically linearly closed.

Proof.

Let CC be an algebraically linearly closed subset of XX, and let FF be an arbitrary face of CC. Our goal is to show that for any (x,y)F(x,y)\subseteq F we have [x,y]F[x,y]\subseteq F.

Let (x,y)FC(x,y)\subseteq F\subseteq C. Since CC is algebraically linearly closed, x,yCx,y\in C. Now pick any z(x,y)z\in(x,y). Since zFz\in F, by the definition of a face we must have x,yFx,y\in F. ∎

The next example shows that faces of (topologically) closed convex sets do not need to be closed. This example is used in [hitchhiker] to demonstrate that the convex hull of a compact set may not be compact (and even closed).

Example 4.5 (Example 5.34 from [hitchhiker]).

Let A2A\subset\ell_{2} be defined as A={0,e1,e22,,enn,}A=\{0,e_{1},\frac{e_{2}}{2},\dots,\frac{e_{n}}{n},\dots\}, where eie_{i} is the sequence of all ones except for 11 in the ii-th position. Let F=coAF=\operatorname{co}A and C=clcoAC=\operatorname{cl}\operatorname{co}A be the l2l_{2}-closure of the convex hull of AA. We will show that FCF\unlhd C. Note that FF is not closed (but AA is compact, see [hitchhiker]), while CC is closed by the definition. Also both FF and CC are convex and FCF\subset C. It remains to show that FF is a face of CC.

Take xFx\in F, and suppose that y,zCy,z\in C are such that x=ty+(1t)zx=ty+(1-t)z for some t(0,1)t\in(0,1). Since e1,e2,e_{1},e_{2},\dots is an orthonormal basis of l2l_{2}, we can write

y=i=0αiei,z=i=0βiei,y=\sum_{i=0}^{\infty}\alpha_{i}e_{i},\quad z=\sum_{i=0}^{\infty}\beta_{i}e_{i},

where αi0\alpha_{i}\geq 0, βi0\beta_{i}\geq 0 for all i{0}i\in\mathbb{N}\cup\{0\}, and αi=βi=1\sum\alpha_{i}=\sum\beta_{i}=1.

Since xFx\in F, there exists a finite index set I{0}I\subset\mathbb{N}\cup\{0\} such that

x=iIγiei,γi0,iIγi=1.x=\sum_{i\in I}\gamma_{i}e_{i},\quad\gamma_{i}\geq 0,\sum_{i\in I}\gamma_{i}=1.

From ty+(1t)zx=0ty+(1-t)z-x=0 we have

tαi+(1t)βi=0iI,t\alpha_{i}+(1-t)\beta_{i}=0\quad\forall i\notin I,

hence, yy and zz are finite convex combinations of points in AA, and so y,zFy,z\in F. Hence by the definition of a face FCF\lhd C.

In [zbMATH03075149], there is an interesting example of two convex and linearly closed sets whose convex hull is not linearly closed.

Thinking in terms of algebraic linear closure helps further refine the relations between faces and the intrinsic core.

Proposition 4.6.

Let DD be a convex subset of a real vector space XX, and let FF be a proper face of DD (that is, FF is nonempty and does not coincide with DD). Then FlbdDF\subseteq\operatorname{lbd}D. Then FabdDF\subseteq\operatorname{abd}D.

Proof.

Under the assumptions of the proposition, suppose that the conclusion is not true. Then there exists a point xFabdCx\in F\setminus\operatorname{abd}CxFlbdCx\in F\setminus\operatorname{lbd}C. By Proposition 4.1 (i) we then have xicrCx\in\operatorname{icr}C. By Corollary 3.14 we must have D=Fmin(x,D)FD=F_{\min}(x,D)\subseteq F, which contradicts the assumption that FF is a proper face of DD. ∎

Notice that ABA\subseteq B does not yield icrAB\operatorname{icr}A\subseteq B, even in the finite-dimensional case. For example, letting A={x}A=\{x\} and B=[x,y]B=[x,y], where xx and yy are distinct elements of some vector space XX, results in ABA\subset B, but icrA={x}(x,y)=icrB\operatorname{icr}A=\{x\}\notin(x,y)=\operatorname{icr}B. We can however obtain meaningful relations between the intrinsic cores of certain structured subsets of convex sets, as shown next.

Proposition 4.7.

Let FF be a proper face of DD, where DD is a convex subset of a real vector space XX. The set DFD\setminus F is convex; moreover, icr(DF)=icrD\operatorname{icr}(D\setminus F)=\operatorname{icr}D.

Proof.

To show that DFD\setminus F is a convex set, let x,yDFx,y\in D\setminus F. If there is a z(x,y)z\in(x,y) such that zFz\in F, then by the definition of a face we must have [x,y]F[x,y]\subseteq F, a contradiction with the original choice of xx and yy.

To show that icrD=icr(DF)\operatorname{icr}D=\operatorname{icr}(D\setminus F), we first demonstrate that icrDicr(DF)\operatorname{icr}D\subseteq\operatorname{icr}(D\setminus F). Take any xicrDx\in\operatorname{icr}D. From Proposition 4.6 we know that xDFx\in D\setminus F. Now pick any yDFDy\in D\setminus F\subset D. Since xicrDx\in\operatorname{icr}D, by Proposition 4.1 (iii) there exists zicrDz\in\operatorname{icr}D such that x(y,z)icrDDFx\in(y,z)\subseteq\operatorname{icr}D\subseteq D\setminus F (applying Proposition 4.6 again). Since yDFy\in D\setminus F was arbitrary, we deduce that xicr(DF)x\in\operatorname{icr}(D\setminus F).

It remains to show that icr(DF)icrD\operatorname{icr}(D\setminus F)\subseteq\operatorname{icr}D. Take any xicr(DF)x\in\operatorname{icr}(D\setminus F), and let yDy\in D. If yDFy\in D\setminus F, then there exists a zDFDz\in D\setminus F\subset D such that x(y,z)x\in(y,z), so we only need to deal with the situation when yFy\in F. In this case xyx\neq y. Let u(x,y)u\in(x,y). If uFu\in F, then by the definition of a face xFx\in F, which is impossible. We conclude that uDFu\in D\setminus F. Then there exists vDFv\in D\setminus F such that x(u,v)(y,v)Dx\in(u,v)\subseteq(y,v)\subset D. We conclude that xicrDx\in\operatorname{icr}D. ∎

4.2 Separation and support points

Recall (see [Holmes]) that a hyperplane in a real vector space XX is a maximal proper affine subspace of XX, or equivalently a level set of some nontrivial linear functional φ:X\varphi:X\to\mathbb{R},

H={xX|φ(x)=α}.H=\{x\in X\,|\,\varphi(x)=\alpha\}. (20)

A hyperplane HH defined as in (20) is said to support a convex set CC if φ(x)α\varphi(x)\leq\alpha for all xCx\in C (alternatively φ(x)α\varphi(x)\geq\alpha for all xCx\in C), where the equality is satisfied for some xCx\in C.

Two convex sets AA and BB are separated by a hyperplane HH if they lie in the two different (algebraically linearly closed) subspaces defined by the hyperplane HH, so that, for instance,

φ(x)αxA,φ(x)αyB.\varphi(x)\leq\alpha\quad\forall x\in A,\qquad\varphi(x)\geq\alpha\quad\forall y\in B.

The sets AA and BB are properly separated by HH if they are separated by HH and do not lie in their entirety on HH, so (AB)H(A\cup B)\setminus H\neq\emptyset. This is equivalent to the existence of a linear functional φ:X\varphi:X\to\mathbb{R} such that

supxAφ(x)infyBφ(y),infxAφ(x)<supyBφ(y).\sup_{x\in A}\varphi(x)\leq\inf_{y\in B}\varphi(y),\quad\inf_{x\in A}\varphi(x)<\sup_{y\in B}\varphi(y).

Hyperplane separation in infinite-dimensional vector spaces is hinged on the following statement (see [Holmes, I.2.B]).

Lemma 4.8 (Stone).

Let AA and BB be disjoint convex subsets of XX. Then there exist complementary convex sets CC and DD such that ACA\subseteq C and BDB\subseteq D.

The proof is based on using Zorn’s lemma to build a maximal convex set that contains AA but not BB and showing that the complement must be convex.

It can be further shown that the intersection of algebraic weak linear closures of two nonempty complementary convex sets is either the entire space or a hyperplane (see [Holmes, I.4.A.]). In the former case there is no hyperplane separating these complementary convex sets. A sufficient condition for the separation of two disjoint convex sets is that at least one of them has a nonempty core (see [Holmes, I.4.B.] and [hitchhiker, 5.61]).

Theorem 4.9 (Theorem 5.61 from [hitchhiker]).

Two disjoint nonempty convex sets can be properly separated by a nonzero linear functional provided one of them has a nonempty core.

This theorem can be generalised as shown in the following corollary (both these results can be proved using the Hanh-Banach extension theorem).

Corollary 4.10 (Corollary 5.62 from [hitchhiker]).

Let AA and BB be two nonempty disjoint convex subsets of a vector space XX. If there exists a vector subspace YY including AA and BB such that either AA or BB has nonempty core in YY, then AA and BB can be properly separated by a nonzero linear functional on XX.

It is tempting to assume that it may be enough for the intrinsic core of one of the disjoint convex sets to be nonempty to achieve proper separation. However this is not true: for instance, let AA be the set considered in Example 4.3, and suppose BB is any single point in the complement of AA, meaning that BB has a nonempty intrinsic core. Notice that any hyperplane supporting some convex set should also support its algebraic linear closure. In out case the algebraic our case the linear closure of AA is the entire space, and hence the only linear functional supporting AA is the trivial one (whose kernel coincides with XX) that a) doesn’t define a hyperplane and b) doesn’t properly separate AA and BB. However if both sets have nonempty intrinsic cores, this is enough to ensure proper separation (see Proposition 4.12 below).

A point xCx\in C is called a support point if it belongs to some supporting hyperplane. A support point is proper if it lies in a supporting hyperplane that does not contain all of CC (see [Holmes]). It is well-known that whenever icrC\operatorname{icr}C\neq\emptyset, the proper support points of a convex set CC are exactly CicrCC\setminus\operatorname{icr}C. We provide a proof for completeness.

Proposition 4.11.

If icrC\operatorname{icr}C\neq\emptyset for some convex subset CC of a real vector space XX, then every xCicrCx\in C\setminus\operatorname{icr}C is a support point of CC.

Proof.

Suppose that a convex set CC has nonempty intrinsic core, and that xCicrCx\in C\setminus\operatorname{icr}C. Our goal is to show that there exists a linear functional φ:X\varphi:X\to\mathbb{R} such that

supuCφ(u)φ(x),\sup_{u\in C}\varphi(u)\leq\varphi(x),

and moreover there is some yCy\in C such that φ(y)<φ(x)\varphi(y)<\varphi(x), so the separation is proper.

We first prove this claim for under the assumption that x=0x=0. Let F=Fmin(0,C)F=F_{\min}(0,C). Since xicrCx\notin\operatorname{icr}C, FF is a proper face of CC. By Proposition 4.6 the set CFC\setminus F is convex, moreover, icrC=icr(CF)\operatorname{icr}C=\operatorname{icr}(C\setminus F). By Proposition 4.1 (v) we have afficrC=affC\operatorname{aff}\operatorname{icr}C=\operatorname{aff}C, and together with icrC=icr(CF)CFC\operatorname{icr}C=\operatorname{icr}(C\setminus F)\subseteq C\setminus F\subset C this yields affC=aff(CF)\operatorname{aff}C=\operatorname{aff}(C\setminus F). By Definition 3.9 of the intrinsic core, icr(CF)\operatorname{icr}(C\setminus F) coincides with the core of CFC\setminus F with respect to affC\operatorname{aff}C. Notice that since 0CaffC0\in C\subset\operatorname{aff}C, the affine hull affC\operatorname{aff}C is actually a linear subspace of XX. We can now apply Corollary 4.10 to A=FA=F, B=CFB=C\setminus F and Y=affDY=\operatorname{aff}DY=affCY=\operatorname{aff}C. There exist a linear functional φ:X\varphi:X\to\mathbb{R} such that

supuCFφ(u)infvFφ(v);infuCFφ(u)<supvFφ(v).\sup_{u\in C\setminus F}\varphi(u)\leq\inf_{v\in F}\varphi(v);\qquad\inf_{u\in C\setminus F}\varphi(u)<\sup_{v\in F}\varphi(v). (21)

Take any xicrCx\in\operatorname{icr}C. By Proposition 4.1 (iii) we have (0,x)icrCCF(0,x)\subseteq\operatorname{icr}C\subseteq C\setminus F. Hence for any t>0t>0

tφ(x)=φ(tx)φ(0)=0,t\varphi(x)=\varphi(tx)\leq\varphi(0)=0,

and we conclude that

supuCFφ(u)=0.\sup_{u\in C\setminus F}\varphi(u)=0.

Now take any pFp\in F. Since 0icrF0\in\operatorname{icr}F, there exists qFq\in F such that 0(p,q)0\in(p,q). It follows from φ(q),φ(q)0\varphi(q),\varphi(q)\geq 0 and φ(0)=0\varphi(0)=0 that φ(p)=φ(q)=0\varphi(p)=\varphi(q)=0.

Using all this knowledge, we rewrite the earlier relations (21) as

supuCφ(u)φ(0)=0;infuCφ(u)<0.\displaystyle\sup_{u\in C}\varphi(u)\leq\varphi(0)=0;\qquad\inf_{u\in C}\varphi(u)<0.

We have found a linear functional φ:X\varphi:X\to\mathbb{R} such that this linear functional properly separates 0 from CC.

We can now return to the case when x0x\neq 0. Let D=C{x}D=C-\{x\}. Then by Corollary 3.18 we have icrD=icrC+{x}\operatorname{icr}D=\operatorname{icr}C+\{x\}\neq\emptyset; moreover, since 0D0\in D, Y=affDY=\operatorname{aff}D is a linear subspace. Observe also that 0icrD0\notin\operatorname{icr}D. By our earlier result, there exists a linear functional φ:X\varphi:X\to\mathbb{R} such that

supuDφ(u)φ(0)=0;infuDφ(u)<0.\displaystyle\sup_{u\in D}\varphi(u)\leq\varphi(0)=0;\qquad\inf_{u\in D}\varphi(u)<0.

Observe that uDu\in D iff u=vxu=v-x for some vCv\in C, therefore this is equivalent to

φ(v)\displaystyle\varphi(v) φ(x)vC;\displaystyle\leq\varphi(x)\quad\forall v\in C;
φ(v)\displaystyle\varphi(v^{\prime}) <φ(x) for some vC{x},\displaystyle<\varphi(x)\quad\text{ for some }v^{\prime}\in C\setminus\{x\},

hence we found a linear functional φ:X\varphi:X\to\mathbb{R} and a constant α=φ(x)\alpha=\varphi(x) such that φ\varphi properly separates {x}\{x\} from CC. ∎

Proposition 4.12.

If AA and BB are convex subsets of a real vector space XX such that icrA,icrB\operatorname{icr}A,\operatorname{icr}B\neq\emptyset and moreover icrAicrB=\operatorname{icr}A\cap\operatorname{icr}B=\emptyset, then AA and BB can be properly separated.

We first need the following technical claim.

Proposition 4.13.

Let CC be a convex subset of a real vector space XX. If icrC\operatorname{icr}C\neq\emptyset then the positive hull of CC,

D={tx|xC,t>0}D=\{tx\,|\,x\in C,t>0\}

has nonempty intrinsic core, moreover, icrCicrD\operatorname{icr}C\subseteq\operatorname{icr}D.

Proof.

Under the assumptions of the proposition, take any xicrCx\in\operatorname{icr}C and yDy\in D. There exists some t>0t>0 such that y=tyy=ty^{\prime} for some yCy^{\prime}\in C. Since xicrCx\in\operatorname{icr}C, there must exist zCz^{\prime}\in C such that x(y,z)x\in(y^{\prime},z^{\prime}), and explicitly there is some s(0,1)s\in(0,1) such that

x=sy+(1s)z.x=sy^{\prime}+(1-s)z^{\prime}.

Now if t1t\geq 1, observe that

x=stty+(1st)(1s)ttsz=sty+(1st)z,x=\frac{s}{t}ty^{\prime}+\left(1-\frac{s}{t}\right)\frac{(1-s)t}{t-s}z^{\prime}=\frac{s}{t}y+\left(1-\frac{s}{t}\right)z,

where

z=(1s)ttszD,z=\frac{(1-s)t}{t-s}z^{\prime}\in D,

since the coefficient at zz^{\prime} is positive. Moreover, since s/t(0,1)s/t\in(0,1), we conclude that x(y,z)Dx\in(y,z)\subseteq D.

It remains to consider the case when t<1t<1. In this case notice that v:=2yDv:=2y^{\prime}\in D, and also y(y,v)y^{\prime}\in(y,v). Applying Proposition 2.5 to the points y,z,y,vy^{\prime},z^{\prime},y,v and xx, we deduce that there exists z(z,v)Dz\in(z^{\prime},v)\subseteq D such that x(y,z)Dx\in(y,z)\subseteq D.

We conclude that icrCicrD\operatorname{icr}C\subseteq\operatorname{icr}D. ∎

Proof of Proposition 4.12.

Under the assumptions of the proposition, let C:=ABC:=A-B. By Proposition 3.17 we have icrC=icrAicrB\operatorname{icr}C=\operatorname{icr}A-\operatorname{icr}B. Moreover, since icrAicrB=\operatorname{icr}A\cap\operatorname{icr}B=\emptyset, we have 0icrC0\notin\operatorname{icr}C. If 0afficrC0\in\operatorname{aff}\operatorname{icr}C, then by Corollary 4.10 icrC\operatorname{icr}C can be properly separated from {0}\{0\}. Otherwise let DD be the positive hull of CC. From Proposition 4.13 we know that icrCicrD\operatorname{icr}C\subseteq\operatorname{icr}D, moreover it is evident that 0D0\notin D, but 0affD0\in\operatorname{aff}D. Therefore {0}\{0\} can be separated properly from icrD\operatorname{icr}D. Since DD is the positive hull of CC, this means that the same linear functional properly separates CC from {0}\{0\}.

Since we now found that there exists a linear functional φ:X\varphi:X\to\mathbb{R} that properly separates icrC\operatorname{icr}C from 0, this yields

φ(x)0xicrC,x0icrC,φ(x0)<0,\varphi(x)\leq 0\quad\forall x\in\operatorname{icr}C,\quad\exists x_{0}\in\operatorname{icr}C,\varphi(x_{0})<0,

Since CicrCabdicrCC\setminus\operatorname{icr}C\subseteq\operatorname{abd}\operatorname{icr}C CicrClbdicrCC\setminus\operatorname{icr}C\subseteq\operatorname{lbd}\operatorname{icr}C (by Proposition 4.1 (ii) and (iv)), we also have

φ(x)0xC,x0C,φ(x0)<0.\varphi(x)\leq 0\quad\forall x\in C,\quad\exists x_{0}\in C,\varphi(x_{0})<0.

Recall that C=ABC=A-B. We hence have

supuAφ(u)infvBφ(v),infuAφ(u)<supvBφ(v),\sup_{u\in A}\varphi(u)\leq\inf_{v\in B}\varphi(v),\quad\inf_{u\in A}\varphi(u)<\sup_{v\in B}\varphi(v),

and so AA and BB can be properly separated. ∎

Recall that a face FCF\unlhd C is exposed if there exists a supporting hyperplane HH to CC such that F=CHF=C\cap H.

In the finite-dimensional setting whenever a face is not exposed, it is always eventually exposed: we can choose a hyperplane exposing some face of CC, then expose a face of that face and so on, until we reach the required face. It is unclear whether this result generalises in a sensible way to the infinite-dimensional setting (under the assumption that icrC\operatorname{icr}C\neq\emptyset).

We also note that some properties of exposed faces that naturally hold in finite dimensions are not true in infinite-dimensional spaces, for instance, the intersection of two exposed faces is not necessarily exposed [ShortNote] in a Banach space, and in [MinExp] sufficient conditions are obtained for the intersection of exposed faces to be exposed.

5 Summary

We have provided an overview of the definitions and properties of intrinsic core, intentionally focusing on the restricted setting of real vector spaces without topological structure. We note once more that the intrinsic core appears in the literature under different names, including the set of relatively absorbing points, the pseudo-relative interior and the set of inner points (see the discussion at the beginning of Section 3).

We have discussed four equivalent definitions of the intrinsic core, via line segments (Definition 3.1), the cone of feasible directions (Definition 3.7), as a core with respect to the affine hull (Defintion 3.9) and in terms of minimal faces (Definition 3.12). We have also made explicit the differences in notation and discrepancies in implicit assumptions used by different authors that should aid future work involving intrinsic cores.

We have provided an extensive collection of properties and calculus rules of intrinsic cores, and discussed in much detail the relation between the intrinsic core and algebraic boundary. We have also provided an overview and elementary proofs of separation results pertaining to this purely algebraic setting.

Acknowledgements

The authors are indebted to the two referees, who not only carefully read an earlier draft of our paper, but also generously provided many insights, including examples and references, that significantly improved both the quality of this work and our understanding of the subject matter.

We are also grateful to the Australian Research Council for financial support provided by means of the Discovery Project “An optimisation-based framework for non-classical Chebyshev approximation”, DP180100602.