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Capturing Polytopal Symmetries by Coloring the Edge-Graph

Martin Winter Faculty of Mathematics, University of Technology, 09107 Chemnitz, Germany martin.winter@mathematik.tu-chemnitz.de
(Date: September 30, 2025)
Abstract.

A general (convex) polytope PdP\subset\mathbb{R}^{d} and its edge-graph GPG_{P} can have very distinct symmetry properties. We construct a coloring (of the vertices and edges) of the edge-graph so that the combinatorial symmetry group of the colored edge-graph is isomorphic (in a natural way) to AutGL(P)\operatorname{Aut}_{\operatorname{GL}}(P), the group of linear symmetries of the polytope. We also construct an analogous coloring for AutO(P)\operatorname{Aut}_{\operatorname{O}}(P), the group of orthogonal symmetries of PP.

Key words and phrases:
convex polytopes, linear symmetries, orthogonal symmetries, edge-graph, graph coloring, graph symmetries
2010 Mathematics Subject Classification:
51M20, 52B05, 52B11, 52B15, 05C50

1. Introduction

In the context of this article, a polytope PdP\subset\mathbb{R}^{d} will always be a convex polytope, that is, PP is the convex hull of finitely many points. A symmetry of PP is a certain transformation of the ambient space that fixes the polytope set-wise. Our focus is specifically on the groups

AutGL(P)\displaystyle\operatorname{Aut}_{\operatorname{GL}}(P) :={TGL(d)TP=P},and\displaystyle:=\{T\in\operatorname{GL}(\mathbb{R}^{d})\mid TP=P\},\;\text{and}
AutO(P)\displaystyle\operatorname{Aut}_{\operatorname{O}}(P) :={TO(d)TP=P},\displaystyle:=\{T\in\operatorname{O}(\mathbb{R}^{d})\mid TP=P\},

called the linear resp. orthogonal symmetry group of PP.

Initially defined geometrically, one can ask whether it is possible to understand these symmetry groups combinatorially. This could mean to identify a purely combinatorial object 𝒞\mathcal{C} whose combinatorial symmetry group Aut(𝒞)\operatorname{Aut}(\mathcal{C}) is isomorphic to AutGL(P)\operatorname{Aut}_{\operatorname{GL}}(P) resp. AutO(P)\operatorname{Aut}_{\operatorname{O}}(P) in a natural way.

For example, consider the edge-graph GPG_{P} of the polytope. Every, say, linear symmetry TAutGL(P)T\in\operatorname{Aut}_{\operatorname{GL}}(P) induces a distinct combinatorial symmetry σTAut(GP)\sigma_{T}\in\operatorname{Aut}(G_{P}) of the edge-graph (see Figure˜1). We could state this as follows: the edge-graph is at least as symmetric as the polytope. Usually however, it is strictly more symmetric and is therefore unsuited for “capturing the polytope’s symmetries” in our sense.

Refer to caption
Figure 1. The clockwise 120120^{\circ}-rotational symmetry of the hexagon permutes its vertices. This permutation correspond to a combinatorial symmetry σ=(135)(246)\sigma=(135)(246) of the edge-graph. Not every combinatorial symmetry of GPG_{P} comes from such a geometric symmetry, e.g. (123456)Aut(GP)(123456)\in\operatorname{Aut}(G_{P}). The polygon is therefore strictly less symmetric than its edge-graph.

In this article we ask whether this can be fixed by coloring the vertices and edges of the edge-graph, thereby encoding further geometric information, and hopefully creating a combinatorial objects that is exactly as symmetric as PP (see Figure˜2). As we shall see, this is indeed possible.

Refer to caption
Figure 2. Various hexagons, and to each a coloring of its edge-graph that gives it “the same symmetries” as the polygon.

This should be surprising for at least two reasons. First, it is established wisdom that the edge-graph of a general polytope in dimension d4d\geq 4 carries only very little information about the polytope (a graph can be the edge-graph of several combinatorially distinct polytopes, potentially of different dimensions). Thus, whether the geometric symmetries of PP can be captured by coloring only the edges and vertices of PP (instead of, say, also higher dimensional faces) should be at least controversial. Second, the same statement is actually wrong for more general geometric objects (such as graph embeddings, see Example˜6.5). In fact, our proof for the existence of these colorings is based on a construction by Ivan Izmestiev [4], which relies heavily on the convexity of PP. Because of this, it is unclear whether our result generalizes to even some form of non-convex polytopes or polytopal complexes.

Our investigation is in part motivated from a result by Bremner et al. [3]: given a polytope PdP\subset\mathbb{R}^{d} with nn vertices, the authors construct a coloring of the complete graph KnK_{n}, so that the symmetry group of the colored graph is isomorphic to AutGL(P)\operatorname{Aut}_{\operatorname{GL}}(P) (resp. AutO(P)\operatorname{Aut}_{\operatorname{O}}(P); a more precise statement is given in Section˜2.1). We can interpret this as follows: if we are allowed to color not only the vertices and edges of PP, but also other pairs of vertices without a direct counterpart in the polytope’s combinatorics, then “capturing the polytope’s symmetries” is indeed possible. The major result of our article is then that coloring these “non-geometric edges” is not actually necessary.

We reiterate this introduction in a more formal manner.

1.1. Notation and setting

Throughout the text we let PdP\subset\mathbb{R}^{d} denote a convex polytope that is full-dimensional (i.e., not contained in any proper affine subspace of d\mathbb{R}^{d}) and contains the origin in its interior (i.e., 0int(P)0\in\operatorname{int}(P)).

By δ(P)\mathcal{F}_{\delta}(P) we denote the set of δ\delta-dimensional faces of PP. We assume a fixed enumeration v1,,vn0(P)v_{1},...,v_{n}\in\mathcal{F}_{0}(P) of the polytope’s vertices. In particular, nn will always denote the number of the vertices.

The edge-graph of PP is the finite simple graph GP=(V,E)G_{P}=(V,E) with vertex set V={1,,n}V=\{1,...,n\} and edge set E(V2)\smash{E\subseteq{V\choose 2}}. We implicitly assume that iVi\in V corresponds to the vertex vi0(P)v_{i}\in\mathcal{F}_{0}(P), and that ijEij\in E (short for {i,j}E\{i,j\}\in E) if and only if conv{vi,vj}1(P)\operatorname{conv}\{v_{i},v_{j}\}\in\mathcal{F}_{1}(P).

The (combinatorial) symmetry group of GPG_{P}111For convenience, notions like the symmetry group, colorings, the adjacency matrix, etc.  are only introduced for the edge-graph, but it is understood that they apply to more general graphs as well. is defined as

Aut(GP):={σSym(V)ijEσ(i)σ(j)E}Sym(V)222Sym(V) denotes the symmetric group, i.e., the group of permutations of the set V.,\operatorname{Aut}(G_{P}):=\{\sigma\in\operatorname{Sym}(V)\mid ij\in E\Leftrightarrow\sigma(i)\sigma(j)\in E\}\subseteq\operatorname{Sym}(V)\text{},

that is, the group of permutations of VV that fix the edge set of GPG_{P}.

A coloring of GPG_{P} is a map 𝔠:VΓE\mathfrak{c}\colon V\mathbin{\mathaccent 0{\cdot}\cup}E\to\mathfrak{C} (it assign colors to both, vertices and edges), where \mathfrak{C} denotes an abstract set of colors. The pair (GP,𝔠)(G_{P},\mathfrak{c}) is then a colored edge-graph and will be abbreviated by GP𝔠G_{P}^{\mathfrak{c}}. Its combinatorial symmetry group is

Aut(GP𝔠):={σAut(GP)|𝔠(i)=𝔠(σ(i))for all iV 𝔠(ij)=𝔠(σ(i)σ(j))for all ijE}.\operatorname{Aut}(G_{P}^{\mathfrak{c}}):=\Big{\{}\sigma\in\operatorname{Aut}(G_{P})\;\Big{|}{\small\begin{array}[]{rcll}\mathfrak{c}(i)&\!\!\!\!\!=\!\!\!\!\!&\mathfrak{c}(\sigma(i))&\text{for all $i\in V$ }\\ \mathfrak{c}(ij)&\!\!\!\!\!=\!\!\!\!\!&\mathfrak{c}(\sigma(i)\sigma(j))&\text{for all $ij\in E$}\end{array}}\Big{\}}.

If σAut(GP𝔠)\sigma\in\operatorname{Aut}(G_{P}^{\mathfrak{c}}), we also say that σ\sigma preserves the coloring 𝔠\mathfrak{c}.

The colored adjacency matrix of GP𝔠G_{P}^{\mathfrak{c}} is the matrix A𝔠(Γ{0})n×nA^{\mathfrak{c}}\in(\mathfrak{C}\mathbin{\mathaccent 0{\cdot}\cup}\{0\})^{n\times n} with entries

Aij𝔠:={𝔠(i)if i=j𝔠(ij)if ijE0otherwise.A^{\mathfrak{c}}_{ij}:=\begin{cases}\mathfrak{c}(i)&\text{if $i=j$}\\ \mathfrak{c}(ij)&\text{if $ij\in E$}\\ 0&\text{otherwise}\end{cases}.

Clearly, a coloring is completely determined by the colored adjacency matrix, and we might occasionally use A𝔠A^{\mathfrak{c}} to define a coloring.

A geometric symmetry TAutGL(P)T\in\operatorname{Aut}_{\operatorname{GL}}(P) of PP maps vertices of PP onto vertices of PP and thus describes a permutation of the vertex set. Let σTSym(V)\sigma_{T}\in\operatorname{Sym}(V) be the permutation of the vertex set of the edge-graph that permutes its vertices in the same way as TT permutes the vertices of PP. Formally, that is

(1.1) Tvi=vσT(i),for all iV.Tv_{i}=v_{\sigma_{T}(i)},\quad\text{for all $i\in V$}.

Since TT also maps edges of PP onto edges of PP, also σT\sigma_{T} maps edges to edges, and so we see that σT\sigma_{T} is a symmetry of the edge-graph, i.e., σTAut(GP\sigma_{T}\in\operatorname{Aut}(G_{P}). The assignment TσTT\mapsto\sigma_{T} then defines a group homomorphism ϕ:AutGL(P)Aut(GP)\phi\colon\operatorname{Aut}_{\operatorname{GL}}(P)\to\operatorname{Aut}(G_{P}) which we shall call the natural group homomorphism of the polytope PP.

Since PP is full-dimensional, its vertices contain a basis of d\mathbb{R}^{d}, and it follows that ϕ\phi must be injective. In general however, ϕ\phi is not an isomorphism and AutGL(P)≇Aut(GP)\operatorname{Aut}_{\operatorname{GL}}(P)\not\cong\operatorname{Aut}(G_{P}), which is a formal way to say that the edge-graph GPG_{P} can have many more symmetries than the polytope.

Our approach for rectifying this is to assign a coloring 𝔠:VΓE\mathfrak{c}\colon V\mathbin{\mathaccent 0{\cdot}\cup}E\to\mathfrak{C} to the edge-graph GPG_{P} with the hope that AutGL(P)Aut(GP𝔠)\operatorname{Aut}_{\operatorname{GL}}(P)\cong\operatorname{Aut}(G_{P}^{\mathfrak{c}}). The natural candidate for the isomorphism between the groups is a colored version of the natural homomorphism:

(1.2) ϕ𝔠:AutGL(P)Aut(GP𝔠),TσT\phi^{\mathfrak{c}}\colon\operatorname{Aut}_{\operatorname{GL}}(P)\to\operatorname{Aut}(G_{P}^{\mathfrak{c}}),\;\,T\mapsto\sigma_{T}

For this to work as desired, we need to check two things:

  • First, ϕ𝔠\phi^{\mathfrak{c}} needs to be well-defined. This is not the case for each coloring: one needs to check that for each TT \in AutGL(P)\operatorname{Aut}_{\operatorname{GL}}(P) the corresponding permutation σT\sigma_{T} is indeed a symmetry of GP𝔠G_{P}^{\mathfrak{c}} (that is, is in Aut(GP𝔠)\operatorname{Aut}(G_{P}^{\mathfrak{c}})). Intuitively, this amounts to checking that the edge-graph, even after coloring, is still at least as symmetric as PP.

  • Second, ϕ𝔠\phi^{\mathfrak{c}} must have an inverse. If so, then GP𝔠G_{P}^{\mathfrak{c}} is exactly as symmetric as PP. Providing such an inverse will go as follows: for each σAut(GP𝔠)\sigma\in\operatorname{Aut}(G_{P}^{\mathfrak{c}}) we need to construct a geometric symmetry TσAutGL(P)T_{\sigma}\in\operatorname{Aut}_{\operatorname{GL}}(P) with

    Tσvi=vσ(i),for all iV.T_{\sigma}v_{i}=v_{\sigma(i)},\quad\text{for all $i\in V$}.

    Since PP is full-dimensional, if TσT_{\sigma} exists then it is unique. The map σTσ\sigma\mapsto T_{\sigma} is then the desired inverse.

The discussion also applies verbatim to the orthogonal symmetry group AutO(P)\operatorname{Aut}_{\operatorname{O}}(P), and we shall use the same notation ϕ𝔠:AutO(P)Aut(GP𝔠)\phi^{\mathfrak{c}}\colon\operatorname{Aut}_{\operatorname{O}}(P)\to\operatorname{Aut}(G_{P}^{\mathfrak{c}}) to denote the natural homomorphism in this case.

With this in place, we can formalize “capturing symmetries”:

Definition 1.1.

A coloring 𝔠:VΓE\mathfrak{c}\colon V\mathbin{\mathaccent 0{\cdot}\cup}E\to\mathfrak{C} of GPG_{P} is said to capture the linear (resp. orthogonal) symmetries of PP if Aut(GP𝔠)AutGL(P)\operatorname{Aut}(G_{P}^{\mathfrak{c}})\cong\operatorname{Aut}_{\operatorname{GL}}(P) (resp. Aut(GP𝔠)AutO(P)\operatorname{Aut}(G_{P}^{\mathfrak{c}})\cong\operatorname{Aut}_{\operatorname{O}}(P)), where the isomorphism is realized by the natural homomorphism ϕ𝔠\phi^{\mathfrak{c}}.

The main results of this article are explicit constructions for colorings that

1.2. Overview

In Section˜2 we introduce the metric coloring and the orbit coloring, two very natural candidates for capturing certain polytopal symmetries. In this section we do not yet show that either coloring capture linear or orthogonal symmetries, but we establish relevant properties used in the upcoming sections.

In Section˜3 we derive a sufficient condition for a coloring of the form 𝔠:VΓE\mathfrak{c}\colon V\mathbin{\mathaccent 0{\cdot}\cup}E\to\mathbb{R} (the colors are real numbers) to capture linear symmetries. The criterion will be in terms of the eigenspaces of the (colored) adjacency matrix of the edge-graph. We shall call this the “linear algebra criterion”.

In Section˜4 we introduce the Izmestiev coloring (based on a construction by Ivan Izmestiev [4]) and we show that it satisfies the “linear algebra criterion” from Section˜3. We thereby establish the existence of a first coloring that captures linear symmetries (Theorem˜4.7). As a corollary we find that the orbit coloring captures linear symmetries as well (Corollary˜4.8).

In Section˜5 we show that a combination of the Izmestiev coloring and the metric coloring captures orthogonal symmetries (Theorem˜5.2).

2. Two useful colorings

This section is preliminary, in that it introduce two natural colorings of the edge-graph, the metric coloring and the orbit coloring, without establishing either coloring as capturing polytopal symmetries. In fact, this is an open question for the metric coloring (see ˜6.6). The orbit coloring captures polytopal symmetries, but we are not able to show this right away. Both colorings will play a role in the upcoming sections.

Figure˜3 shows a polygon and its edge-graph with either coloring applied.

Refer to caption
Figure 3. A hexagon and its edge-graph colored with the metric coloring (middle, Section˜2.1) resp. the orbit coloring (right, Section˜2.2).

2.1. The metric coloring

Our first coloring is motivated from the previously mentioned construction of Bremner et al. [3] – a coloring of the complete graph KnK_{n} that “captures orthogonal symmetries”. In our notation their result reads as follows:

Theorem 2.1 (​[3, Theorem 2]333This result is primarily based on [2, Proposition 3.1], but we found that its first explicit formulation is in [3] ).

Given a polytope PdP\subset\mathbb{R}^{d} with vertex set 0(P)={v1,,vn}\mathcal{F}_{0}(P)=\{v_{1},...,v_{n}\}. Consider the coloring 𝔠\mathfrak{c} on the complete graph KnK_{n} with

𝔠(i)\displaystyle\qquad\qquad\qquad\qquad\mathfrak{c}(i) :=vi2,\displaystyle:=\|v_{i}\|^{2},\quad for all i{1,,n},\displaystyle\text{for all $i\in\{1,...,n\}$},\qquad\qquad\qquad\qquad\qquad\qquad
𝔠(ij)\displaystyle\qquad\qquad\qquad\qquad\mathfrak{c}(ij) :=vi,vj,\displaystyle:=\langle v_{i},v_{j}\rangle,\quad for all distinct i,j{1,,n}.\displaystyle\text{for all distinct $i,j\in\{1,...,n\}$}.\qquad\qquad\qquad\qquad\qquad\qquad

Then Aut(Kn𝔠)AutO(P)\operatorname{Aut}(K_{n}^{\mathfrak{c}})\cong\operatorname{Aut}_{\operatorname{O}}(P).

The strength of this result lies in its immediate applicability: constructing this “complete metric coloring” requires no knowledge of the edge-graph (which is usually hard to come by), but only the vertex coordinates of PP444If PP is given in \mathcal{H}-representation, one can apply Theorem 2.1 to compute the orthogonal symmetry group of the dual polytope PP^{\circ}, which is identical to AutO(P)\operatorname{Aut}_{\operatorname{O}}(P) as a matrix group.. In practice, this is probably the best tool for an explicit computation of AutO(P)\operatorname{Aut}_{\operatorname{O}}(P).

From a theoretical and aesthetic perspective however, this construction has the flaw of containing massively redundant data and stepping outside the combinatorial structure of the polytope (we assign color to vertex-pairs that are not edges of the polytope). Naturally, we can ask whether one can get away with coloring fewer of these “non-edges”, ideally only the actual edges of the edge-graph.

Based on this hope, we define the following:

Definition 2.2.

The metric coloring of GPG_{P} is the coloring 𝔪:VΓE\mathfrak{m}\colon V\mathbin{\mathaccent 0{\cdot}\cup}E\to\mathbb{R}555 A coloring whose colors are real numbers is still a purely combinatorial objects. These numbers are just used for a concise definition and could be replaced by any other finite set of distinguishable values. The only information used from the coloring (in the form of the combinatorial symmetry group of the colored graph) is whether two vertices/edges receive the same or a different color. with

𝔪(i)\displaystyle\qquad\qquad\qquad\qquad\mathfrak{m}(i) :=vi2,\displaystyle:=\|v_{i}\|^{2},\quad for all iV,\displaystyle\text{for all $i\in V$},\qquad\qquad\qquad\qquad\qquad\qquad
𝔪(ij)\displaystyle\qquad\qquad\qquad\qquad\mathfrak{m}(ij) :=vi,vj,\displaystyle:=\langle v_{i},v_{j}\rangle,\quad for all ijE.\displaystyle\text{for all $ij\in E$}.\qquad\qquad\qquad\qquad\qquad\qquad

Whether the metric coloring captures orthogonal symmetries is an open question (see also ˜6.6). Our reason for introducing it anyway is that in Section˜5 the metric coloring will be one ingredient to a coloring that indeed captures orthogonal symmetries.

We close this section with another formulation of Theorem˜2.1 that also allows for capturing linear symmetries (in fact, this is closer to the original formulation in [3]). Note that the complete metric coloring of KnK_{n} in Theorem˜2.1 can also be described by its colored adjacency matrix A𝔠=ΦΦA^{\mathfrak{c}}=\Phi^{\top}\Phi, where Φ:=(v1,,vn)d×n\Phi:=(v_{1},...,v_{n})\in\mathbb{R}^{d\times n} is the matrix in which the vertex coordinates of PP appear as columns.

Theorem 2.3 (Another formulation of ​[3, Theorem 2]).

Let 𝔠\mathfrak{c} be a coloring of the complete graph KnK_{n} with colored adjacency matrix A𝔠A^{\mathfrak{c}}:

  1. ()

    if A𝔠=ΦΦA^{\mathfrak{c}}=\Phi^{\top}\Phi, then Aut(Kn𝔠)AutO(P)\operatorname{Aut}(K_{n}^{\mathfrak{c}})\cong\operatorname{Aut}_{\operatorname{O}}(P) (this is exactly Theorem˜2.1).

  2. ()

    if A𝔠=ΦΦA^{\mathfrak{c}}=\Phi^{\dagger}\Phi666Φn×d\Phi^{\dagger}\in\mathbb{R}^{n\times d} denotes the Moore-Penrose pseudo inverse of Φ\Phi, that is, ΦΦ=Idd\Phi\Phi^{\dagger}=\operatorname{Id}_{d}., then Aut(Kn𝔠)AutGL(P)\operatorname{Aut}(K_{n}^{\mathfrak{c}})\cong\operatorname{Aut}_{\operatorname{GL}}(P).

A proof for part (ii) will also follow from the theory developed in Section˜3 (see Remark˜3.2)

2.2. The orbit coloring

The next coloring is motivated from the following consideration: suppose that we are given two vertices vi,vj0(P)v_{i},v_{j}\in\mathcal{F}_{0}(P) in the same orbit w.r.t. AutGL(P)\operatorname{Aut}_{\operatorname{GL}}(P), which just means that there is a TAutGL(P)T\in\operatorname{Aut}_{\operatorname{GL}}(P) with Tvi=vjTv_{i}=v_{j}. The corresponding combinatorial symmetry σTAut(GP)\sigma_{T}\in\operatorname{Aut}(G_{P}) satisfies σT(i)=j\sigma_{T}(i)=j. If now 𝔠:VΓE\mathfrak{c}\colon V\mathbin{\mathaccent 0{\cdot}\cup}E\to\mathfrak{C} is a coloring that captures linear symmetries, then σT\sigma_{T} preserves the coloring 𝔠\mathfrak{c} and we have 𝔠(j)=𝔠(σT(i))=𝔠(i)\mathfrak{c}(j)=\mathfrak{c}(\sigma_{T}(i))=\mathfrak{c}(i). We can summarize this as follows: if 𝔠\mathfrak{c} is supposed to capture linear symmetries, then vertices in the same AutGL(P)\operatorname{Aut}_{\operatorname{GL}}(P)-orbit of PP must have the same color in GP𝔠G_{P}^{\mathfrak{c}}. With an analogous argument we see that the same holds for edges.

Having identified this first necessary condition for capturing symmetries, we can consider the “simplest” coloring that follows this idea:

Definition 2.4.

The (linear) orbit coloring 𝔬\mathfrak{o} of GPG_{P} assigns the same color to vertices (resp. edges) of GPG_{P} if and only if the corresponding vertices (resp. edges) of PP are in the same AutGL(P)\operatorname{Aut}_{\operatorname{GL}}(P)-orbit.

An analogous coloring can be defined for orthogonal symmetries, which we shall call the orthogonal orbit coloring of GPG_{P}, still denoted by 𝔬\mathfrak{o}. For the sake of conciseness, this section only discusses the (linear) orbit coloring, but all statements carry over to the orthogonal version in the obvious way.

As we shall learn in Section˜4 (see Corollary˜4.8), the orbit coloring indeed captures linear symmetries. However, this is surprisingly hard to show directly. In fact, our eventual proof of this will “just” use the following:

Lemma 2.5.

If there is any coloring that captures linear symmetries, then so does the orbit coloring 𝔬\mathfrak{o}.

Proof.

Suppose that 𝔠\mathfrak{c} is a coloring that captures linear symmetries, in particular, ϕ𝔠\phi^{\mathfrak{c}} is an isomorphism. Our proof that 𝔬\mathfrak{o} captures linear symmetries as well is based on two simple observations:

  1. ()

    the natural homomorphism ϕ𝔬\phi^{\mathfrak{o}} is well-defined (that is, GP𝔬G_{P}^{\mathfrak{o}} is at least as symmetric as PP), and

  2. ()

    Aut(GP𝔬)Aut(GP𝔠)\operatorname{Aut}(G_{P}^{\mathfrak{o}})\subseteq\operatorname{Aut}(G_{P}^{\mathfrak{c}}).

Showing either is straight-forwarded, but for the sake of completeness, both proofs are included below. Now, presupposing both, we can write down the following chain of groups in which the first and the last group are the same:

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Since all maps are injective, and the groups are finite, all maps must actually be isomorphisms. Thus, ϕ𝔬\phi^{\mathfrak{o}} is an isomorphism and 𝔬\mathfrak{o} captures linear symmetries. This concludes the proof, and it remains to verify (i) and (ii).

Proof of (i): let TAutGL(P)T\in\operatorname{Aut}_{\operatorname{GL}}(P) be a linear symmetry of PP with corresponding combinatorial symmetry σTAut(GP)\sigma_{T}\in\operatorname{Aut}(G_{P}). We need to show that σTAut(GP𝔬)\sigma_{T}\in\operatorname{Aut}(G_{P}^{\mathfrak{o}}). For this, we observe that for each iVi\in V the vertices viv_{i} and vσT(i)=Tviv_{\sigma_{T}(i)}=Tv_{i} belong to the same AutGL(P)\operatorname{Aut}_{\operatorname{GL}}(P)-orbit of PP. By the definition of the orbit coloring, ii and σT(i)\sigma_{T}(i) have then the same color in GP𝔬G_{P}^{\mathfrak{o}}. Thus, σT\sigma_{T} preserves the vertex colors of 𝔬\mathfrak{o}. Analogously, one shows that σT\sigma_{T} preserves edge colors. Thus, σTAut(GP𝔬)\sigma_{T}\in\operatorname{Aut}(G_{P}^{\mathfrak{o}}).

Proof of (ii): let σAut(GP𝔬)\sigma\in\operatorname{Aut}(G_{P}^{\mathfrak{o}}) be a permutation that preserves the orbit coloring. We need to show σAut(GP𝔠)\sigma\in\operatorname{Aut}(G_{P}^{\mathfrak{c}}). For this, we observe that for all iVi\in V the vertices ii and σ(i)\sigma(i) have the same color in GP𝔬G_{P}^{\mathfrak{o}}, which just means (by Definition˜2.4) that vi,vσ(i)0(P)v_{i},v_{\sigma(i)}\in\mathcal{F}_{0}(P) are in the same AutGL(P)\operatorname{Aut}_{\operatorname{GL}}(P)-orbit of PP. Repeating the argument of the introductory paragraph to this section we see that 𝔠(i)=𝔠(σ(i))\mathfrak{c}(i)=\mathfrak{c}(\sigma(i)). An analogous argument holds for edges. In other words, σ\sigma preserves the coloring 𝔠\mathfrak{c}, and hence σAut(GP𝔠)\sigma\in\operatorname{Aut}(G_{P}^{\mathfrak{c}}). ∎

3. A linear algebra condition for capturing symmetries

For this section, fix a coloring 𝔠:VΓE\mathfrak{c}\colon V\mathbin{\mathaccent 0{\cdot}\cup}E\to\mathfrak{C} for which GP𝔠G_{P}^{\mathfrak{c}} is at least as symmetric as PP. Then ϕ𝔠:AutGL(P)Aut(GP𝔠)\phi^{\mathfrak{c}}\colon\operatorname{Aut}_{\operatorname{GL}}(P)\to\operatorname{Aut}(G_{P}^{\mathfrak{c}}) is well-defined. The goal of this section is to derive a sufficient criterion for 𝔠\mathfrak{c} to capture linear symmetries.

Recall that this amounts to showing that ϕ𝔠\phi^{\mathfrak{c}} is an isomorphism. In other words, the desired criterion must ensure that for each σAut(GP𝔠)\sigma\in\operatorname{Aut}(G_{P}^{\mathfrak{c}}) we can find a linear symmetry TσAutGL(P)T_{\sigma}\in\operatorname{Aut}_{\operatorname{GL}}(P) with

(3.1) Tσvi=vσ(i),for all iV.T_{\sigma}v_{i}=v_{\sigma(i)},\quad\text{for all $i\in V$}.

Let us investigate the difficulties in constructing these transformations.

First, note that we can express \tagform@3.1 for all iVi\in V simultaneously by rewriting it into a single matrix equation as follows:

Tσ(v1,,vn)=(vσ(1),,vσ(n))=(v1,,vn)Πσ,T_{\sigma}(v_{1},...,v_{n})=(v_{\sigma(1)},...,v_{\sigma(n)})=(v_{1},...,v_{n})\Pi_{\sigma},

where ΠσPerm(n)\Pi_{\sigma}\in\operatorname{Perm}(n) denotes the corresponding permutation matrix777We chose to define Πσ\Pi_{\sigma} so that on multiplication from left it permutes the rows as prescribed by σ\sigma. We emphasize that this, counter-intuitively, means (Πσv)i=vσ1(i)(\Pi_{\sigma}v)_{i}=v_{\sigma^{-1}(i)} for a vector vnv\in\mathbb{R}^{n}.. If we define Φ\Phi :=:= (v1,,vn)d×n(v_{1},...,v_{n})\in\mathbb{R}^{d\times n} as the matrix in which the polytope’s vertices viv_{i} appear as columns, this further compactifies to

(3.2) TσΦ=ΦΠσ.T_{\sigma}\Phi=\Phi\Pi_{\sigma}.

This equation will be our benchmark: every ansatz for how to define the transformations TσT_{\sigma} must satisfy \tagform@3.2, which is then also sufficient.

Now, if Φ\Phi were invertible, we could just solve \tagform@3.2 for TσT_{\sigma}, satisfying \tagform@3.2 “by force”. However, Φd×n\Phi\in\mathbb{R}^{d\times n} is not a square matrix (since PP is full-dimensional, we have nd+1n\geq d+1). Instead, one naive hope to still “solve for TσT_{\sigma}” is to use the Moore-Penrose pseudo inverse of Φ\Phi: the unique matrix Φ\Phi^{\dagger} \in n×d\mathbb{R}^{n\times d} with ΦΦ=Idd\Phi\Phi^{\dagger}=\operatorname{Id}_{d} (the rows of Φ\Phi^{\dagger} form a dual basis to the columns of Φ\Phi). And so we make the following ansatz:

(3.3) Tσ:=ΦΠσΦ.T_{\sigma}:=\Phi\Pi_{\sigma}\Phi^{\dagger}.

It remains to investigate under which conditions this ansatz satisfies \tagform@3.2. We compute

(3.4) TσΦ=\tagform@3.3ΦΠσΦΦ=ΦΠσπU,T_{\sigma}\Phi\overset{\hyperref@@ii[eq:def_T]{\textup{\tagform@{\ref*{eq:def_T}}}}}{=}\Phi\Pi_{\sigma}\Phi^{\dagger}\Phi=\Phi\Pi_{\sigma}\pi_{U},

where πU:=ΦΦ\pi_{U}:=\Phi^{\dagger}\Phi is the orthogonal projector onto the subspace U:=spanΦnU:=\operatorname{span}\Phi^{\dagger}\subseteq\mathbb{R}^{n}. Apparently, to arrive at \tagform@3.2, we would need to get rid of the projector πU\pi_{U} on the right side of \tagform@3.4. And so we see that one possible sufficient criterion for our construction of the TσT_{\sigma} to work (and thus, for 𝔠\mathfrak{c} to capture linear symmetries) would be ΦΠσπU=ΦΠσ\Phi\Pi_{\sigma}\pi_{U}=\Phi\Pi_{\sigma} for all σAut(GP𝔠)\sigma\in\operatorname{Aut}(G_{P}^{\mathfrak{c}}).

This is still a rather cumbersome criterion to apply. The main result of this section is then to reformulate this in terms of the adjacency matrix of GP𝔠G_{P}^{\mathfrak{c}}.

Theorem 3.1.

Let 𝔠:VΓE\mathfrak{c}\colon V\mathbin{\mathaccent 0{\cdot}\cup}E\to\mathbb{R} be a coloring of the edge-graph GPG_{P} so that GP𝔠G_{P}^{\mathfrak{c}} is at least as symmetric as PP. If U:=spanΦU:=\operatorname{span}\Phi^{\dagger} is an eigenspace of the colored adjacency matrix A𝔠A^{\mathfrak{c}}, then 𝔠\mathfrak{c} captures the linear symmetries of PP.

Proof.

Fix a combinatorial symmetry σAut(GP𝔠)\sigma\in\operatorname{Aut}(G_{P}^{\mathfrak{c}}).

We use the following well-known (and easy to verify) property of the colored adjacency matrix: if σAut(GP𝔠)\sigma\in\operatorname{Aut}(G_{P}^{\mathfrak{c}}), then

ΠσA𝔠=A𝔠Πσ.\Pi_{\sigma}A^{\mathfrak{c}}=A^{\mathfrak{c}}\Pi_{\sigma}.

Now, if A𝔠A^{\mathfrak{c}} and Πσ\Pi_{\sigma} commute, then the eigenspaces of A𝔠A^{\mathfrak{c}} (including UU) are invariant subspaces of Πσ\Pi_{\sigma}, i.e., ΠσU=U\Pi_{\sigma}U=U. Equivalently, Πσ\Pi_{\sigma} commutes with the projector πU\pi_{U}. This suffices to show that the map Tσ:=ΦΠσΦT_{\sigma}:=\Phi\Pi_{\sigma}\Phi^{\dagger} satisfies \tagform@3.2:

TσΦ=ΦΠσΦΦ=ΦΠσπU=ΦπUΠσ=Φ(ΦΦ)Πσ=(ΦΦ)IddΦΠσ=ΦΠσ.T_{\sigma}\Phi=\Phi\Pi_{\sigma}\Phi^{\dagger}\Phi=\Phi\Pi_{\sigma}\pi_{U}=\Phi\pi_{U}\Pi_{\sigma}=\Phi(\Phi^{\dagger}\Phi)\Pi_{\sigma}=\overbrace{(\Phi\Phi^{\dagger})}^{\operatorname{Id}_{d}}\Phi\Pi_{\sigma}=\Phi\Pi_{\sigma}.

Therefore, the map σTσ\sigma\mapsto T_{\sigma} defines the desired inverse of ϕ𝔠\phi^{\mathfrak{c}}, and 𝔠\mathfrak{c} captures the linear symmetries of PP. ∎

It might not be immediately obvious how Theorem˜3.1 is a helpful reformulation of the problem. To apply it we need to construct a matrix A𝔠A^{\mathfrak{c}} with two very special properties: first, A𝔠A^{\mathfrak{c}} must be a (colored) adjacency matrix of the edge-graph GPG_{P}, that is, it must have non-zero entries only where GPG_{P} has edges. Second, we need to ensure that A𝔠A^{\mathfrak{c}} has UU as an eigenspace. It is not even clear that these two conditions are compatible.

Remark 3.2.

Consider the “obvious” matrix A𝔠A^{\mathfrak{c}} with eigenspace U:=spanΦU:=\operatorname{span}\Phi^{\dagger}:

A𝔠:=ΦΦ.A^{\mathfrak{c}}:=\Phi^{\dagger}\Phi.

Of course, this matrix has most likely no zero-entries and is therefore not a colored adjacency matrix of GPG_{P} (except if GPG_{P} is the complete graphs). However, it is exactly the colored adjacency matrix of the complete metric coloring as discussed in Theorem˜2.3 (ii).

As it turns out, the proof of Theorem˜3.1 makes no use of the fact that the coloring 𝔠\mathfrak{c} is defined on the edge-graph. In fact, we can apply it to the complete graph Kn𝔠K_{n}^{\mathfrak{c}} with colored adjacency matrix A𝔠A^{\mathfrak{c}}. In this way, the “linear algebra criterion” provides an alternative proof of Theorem˜2.3 (ii).

4. The Izmestiev coloring

In this section we introduce a coloring of GPG_{P} which satisfies the “linear algebra condition” Theorem˜3.1. This coloring is based on a construction by Ivan Izmestiev [4] and we shall call it the Izmestiev coloring.

The coloring is built in a quite unintuitive way. First, we need to recall that for a polytope PP with 0int(P)0\in\operatorname{int}(P) the polar dual PP^{\circ} is defined as

P:={xdx,vi1 for all iV}.P^{\circ}:=\{x\in\mathbb{R}^{d}\mid\langle x,v_{i}\rangle\leq 1\text{ for all $i\in V$}\}.

We generalize this notion: for a vector c=(c1,,cn)nc=(c_{1},...,c_{n})\in\mathbb{R}^{n} let

(4.1) P(c):={xdx,vici for all iV}.P^{\circ}(c):=\{x\in\mathbb{R}^{d}\mid\langle x,v_{i}\rangle\leq c_{i}\text{ for all $i\in V$}\}.

Then P(1,,1)=PP^{\circ}(1,...,1)=P^{\circ} and P(c)P^{\circ}(c) is obtained from PP^{\circ} by shifting facets along their normal vectors (see Figure˜4).

Refer to caption
Figure 4. Several instances of the generalized dual P(c)P^{\circ}(c) of the cube (the usual polar dual of the cube is the regular octahedron; the second from the left). The polytopes differ by a single facet-defining plane being shifted along its normal vector.

In the following, vol(C)\operatorname{vol}(C) denotes the relative volume (relative to the affine hull of CC) of a compact convex set CdC\subset\mathbb{R}^{d}.

Theorem 4.1 (Izmestiev [4], Theorem 2.4).

For a polytope PdP\subset\mathbb{R}^{d} with 0int(P)0\in\operatorname{int}(P) consider the matrix MM\in n×n\mathbb{R}^{n\times n} (which we shall call the Izmestiev matrix of PP) with components

Mij:=2vol(P(c))cicj|c=(1,,1).M_{ij}:=\frac{\partial^{2}\operatorname{vol}(P^{\circ}(c))}{\partial c_{i}\partial c_{j}}\Big{|}_{\,c=(1,...,1)}.

(in particular, vol(P(c))\operatorname{vol}(P^{\circ}(c)) is two times continuously differentiable in cc). MM then has the following properties:

  1. ()

    Mij<0M_{ij}<0 whenever ijEij\in E.

  2. ()

    Mij=0M_{ij}=0 whenever ijEij\not\in E and iji\not=j.

  3. ()

    MM has a unique negative eigenvalue of multiplicity one.

  4. ()

    MΦ=0M\Phi^{\top}=0, where Φ=(v1,,vn)d×n\Phi=(v_{1},...,v_{n})\in\mathbb{R}^{d\times n} is the matrix introduced in \tagform@3.2.

  5. ()

    dimkerM=d\dim\ker M=d.

Remark 4.2.

In the words of [4], the matrix MM constructed in Theorem˜4.1 is a Colin de Verdière matrix of the edge-graph, that is, a matrix satisfying a certain list of properties, including (i), (ii) and (iii) and the so-called strong Arnold property (for details, see e.g. [6]).

Among the Colin de Verdière matrices, one usually cares about the ones with the largest possible kernel. The dimension of this largest kernel is known as the Colin de Verdière graph invariant μ(GP)\mu(G_{P}) [6], and Theorem˜4.1 (v) then shows that μ(GP)d\mu(G_{P})\geq d. This is not too surprising and was known before. However, the result of Izmestiev is remarkable for a different reason: it shows that there is a Colin de Verdière matrix whose kernel has dimension exactly dd (property (v)) and that is compatible with the geometry of PP (property (iv)).

Remark 4.3.

Izmestiev also shows that the matrix MM can be expressed in terms of simple geometric properties of the polytope: for ijEij\in E let fijd2(P)f_{ij}\in\mathcal{F}_{d-2}(P^{\circ}) be the dual face to the edge conv{vi,vj}1(P)\operatorname{conv}\{v_{i},v_{j}\}\in\mathcal{F}_{1}(P). Then

(4.2) Mij=vol(fij)vivjsin(vi,vj).M_{ij}=-\frac{\operatorname{vol}(f_{ij})}{\|v_{i}\|\|v_{j}\|\sin\measuredangle(v_{i},v_{j})}.
Definition 4.4.

The Izmestiev coloring :VΓE\mathfrak{I}\colon V\mathbin{\mathaccent 0{\cdot}\cup}E\to\mathbb{R} of GPG_{P} is defined by

(i)\displaystyle\mathfrak{I}(i) :=Mii,for all iV,\displaystyle:=M_{ii},\quad\text{for all $i\in V$},
(ij)\displaystyle\mathfrak{I}(ij) :=Mij,for all ijE,\displaystyle:=M_{ij},\quad\text{for all $ij\in E$},

where Mn×nM\in\mathbb{R}^{n\times n} is the Izmestiev matrix of PP.

Observation 4.5.

Since Mij=0M_{ij}=0 whenever ijEij\notin E and iji\not=j (by Theorem˜4.1 (ii)), the colored adjacency matrix AA^{\mathfrak{I}} of GPG_{P}^{\mathfrak{I}} is exactly the Izmestiev matrix MM.

In order to apply the “linear algebra criterion” from Section˜3, showing that ϕ\phi^{\mathfrak{I}} is an isomorphism, we first need to show that ϕ\phi^{\mathfrak{I}} is well-defined, that is, that GPG_{P}^{\mathfrak{I}} is at least as symmetric as PP. This part is relatively straightforward if we use that the Izmestiev matrix is a linear invariant of PP. We include a proof for completeness:

Proposition 4.6.

GPG_{P}^{\mathfrak{I}} is at least as symmetric as PP, that is, ϕ\phi^{\mathfrak{I}} is well-defined.

Proof.

Fix a linear symmetry TAutGL(P)T\in\operatorname{Aut}_{\operatorname{GL}}(P) and let σTAut(GP)\sigma_{T}\in\operatorname{Aut}(G_{P}) be the induced combinatorial symmetry of the edge-graph. We need to show that σT\sigma_{T} preserves the Izmestiev coloring, that is, σTAut(GP)\sigma_{T}\in\operatorname{Aut}(G_{P}^{\mathfrak{I}}).

This requires two ingredients. For the first, one checks that the generalized polar dual P(c)P^{\circ}(c) (like the usual polar dual) satisfies

(TP)(c)=TP(c),(TP)^{\circ}(c)=T^{-\top}\!P^{\circ}(c),

which then gives us

(4.3) vol((TP)(c))=det(T)vol(P(c))=vol(P(c)),\operatorname{vol}\big{(}(TP)^{\circ}(c)\big{)}=\det(T^{-\top})\operatorname{vol}(P^{\circ}(c))=\operatorname{vol}(P^{\circ}(c)),

where we used that det(T)=det(T)=1\det(T^{-\top})=\det(T)=1 holds for all linear transformations in a finite matrix group such as AutGL(P)\operatorname{Aut}_{\operatorname{GL}}(P).

The second ingredient is the following:

(4.4) (TP)(c)\displaystyle(TP)^{\circ}(c) ={xdx,Tvici for all iV}\displaystyle=\{x\in\mathbb{R}^{d}\mid\langle x,Tv_{i}\rangle\leq c_{i}\text{ for all $i\in V$}\}
={xdx,vσT(i)ci for all iV}\displaystyle=\{x\in\mathbb{R}^{d}\mid\langle x,v_{\sigma_{T}(i)}\rangle\leq c_{i}\text{ for all $i\in V$}\}
={xdx,vicσT1(i) for all iV}\displaystyle=\{x\in\mathbb{R}^{d}\mid\langle x,v_{i}\rangle\leq c_{\sigma_{T}^{-1}(i)}\text{ for all $i\in V$}\}
=P(ΠσTc).\displaystyle=P^{\circ}(\Pi_{\sigma_{T}}c).

Putting everything together, we can show (i)=(σT(i))\mathfrak{I}(i)=\mathfrak{I}(\sigma_{T}(i)) for all iVi\in V, and equivalently for edges. We show both at the same time by proving Mij=MσT(i)σT(j)M_{ij}=M_{\sigma_{T}(i)\sigma_{T}(j)} for all i,j{1,,n}i,j\in\{1,...,n\}:

Mij=2vol(P(c))cicj|c=c0\displaystyle M_{ij}=\frac{\partial^{2}\operatorname{vol}(P^{\circ}(c))}{\partial c_{i}\partial c_{j}}\Big{|}_{c=c_{0}}\!\!\!\!\! =2vol(P(Πσc))cσT(i)cσT(j)|c=c0=\tagform@4.42vol((TP)(c))cσT(i)cσT(j)|c=c0\displaystyle=\frac{\partial^{2}\operatorname{vol}(P^{\circ}(\Pi_{\sigma}c))}{\partial c_{\sigma_{T}(i)}\partial c_{\sigma_{T}(j)}}\Big{|}_{c=c_{0}}\!\!\!\!\!\!\!\overset{\hyperref@@ii[eq:123]{\textup{\tagform@{\ref*{eq:123}}}}}{=}\frac{\partial^{2}\operatorname{vol}((TP)^{\circ}(c))}{\partial c_{\sigma_{T}(i)}\partial c_{\sigma_{T}(j)}}\Big{|}_{c=c_{0}}
=\tagform@4.32vol(P(c))cσT(i)cσT(j)|c=c0=MσT(i)σT(j),\displaystyle\overset{\hyperref@@ii[eq:1345]{\textup{\tagform@{\ref*{eq:1345}}}}}{=}\frac{\partial^{2}\operatorname{vol}(P^{\circ}(c))}{\partial c_{\sigma_{T}(i)}\partial c_{\sigma_{T}(j)}}\Big{|}_{c=c_{0}}\!\!\!\!\!=M_{\sigma_{T}(i)\sigma_{T}(j)},

where we set c0:=(1,,1)nc_{0}:=(1,...,1)\in\mathbb{R}^{n}. ∎

Theorem 4.7.

The Izmestiev coloring captures the linear symmetries of PP.

Proof.

By Proposition˜4.6, the Izmestiev coloring \mathfrak{I} is at least as symmetric as PP, and so we can try to apply the “linear algebra criterion” (Theorem˜3.1) to show that \mathfrak{I} captures linear symmetries. That is, we need to show that U:=spanΦU:=\operatorname{span}\Phi^{\dagger} is an eigenspace of the colored adjacency matrix AA^{\mathfrak{I}} of GPG_{P}^{\mathfrak{I}}. Recall that AA^{\mathfrak{I}} is exactly the Izmestiev matrix (Observation˜4.5), and so we can try to use the various properties of this matrix established in Theorem˜4.1.

First, U=spanΦ=spanΦU=\operatorname{span}\Phi^{\dagger}=\operatorname{span}\Phi^{\top} (since the columns of Φ\Phi^{\top} and Φ\Phi^{\dagger} are dual bases of UU), and so Theorem˜4.1 (iv) can be read as UkerAU\subseteq\ker A^{\mathfrak{I}}. Second, we have both dimU=rankΦ=d\dim U=\operatorname{rank}\Phi=d (since PP is full-dimensional) and dimkerA=d\dim\ker A^{\mathfrak{I}}=d (by Theorem˜4.1 (v)). Comparing dimensions, we thus have U=kerAU=\ker A^{\mathfrak{I}}.

We conclude that UU is an eigenspace of AA^{\mathfrak{I}} (namely, the eigenspace to eigenvalue 0). The “linear algebra criterion” Theorem˜3.1 then asserts that \mathfrak{I} captures the linear symmetries of PP. ∎

By Lemma˜2.5, if there is any coloring that captures linear symmetries, then the orbit coloring does so as well:

Corollary 4.8.

The orbit coloring captures the linear symmetries of PP.

Remark 4.9.

A coloring 𝔠\mathfrak{c} is said to be finer than a coloring 𝔠¯\bar{\mathfrak{c}} if

𝔠(i)=𝔠(ı^)\displaystyle\qquad\qquad\qquad\qquad\mathfrak{c}(i)=\mathfrak{c}(\hat{\imath})\phantom{\hat{\jmath}}\;\; 𝔠¯(i)=𝔠¯(ı^),\displaystyle\implies\;\;\phantom{j}\bar{\mathfrak{c}}(i)=\bar{\mathfrak{c}}(\hat{\imath}),\quad for all i,ı^V,\displaystyle\text{for all $i,\hat{\imath}\in V$},\qquad\qquad\qquad\qquad
𝔠(ij)=𝔠(ı^ȷ^)\displaystyle\qquad\qquad\qquad\qquad\mathfrak{c}(ij)=\mathfrak{c}(\hat{\imath}\hat{\jmath})\;\; 𝔠¯(ij)=𝔠¯(ı^ȷ^),\displaystyle\implies\;\;\bar{\mathfrak{c}}(ij)=\bar{\mathfrak{c}}(\hat{\imath}\hat{\jmath}),\quad for all ij,ı^ȷ^E.\displaystyle\text{for all $ij,\hat{\imath}\hat{\jmath}\in E$}.\qquad\qquad\qquad\qquad

Conversely, 𝔠¯\bar{\mathfrak{c}} is said to be coarser than 𝔠\mathfrak{c}.

It is easy to see that the orbit coloring is the finest coloring that captures linear symmetries, that is, it uses the most colors (consider the argument in the first paragraph of Section˜2.2). In contrast, the Izmestiev coloring is in general neither the finest nor the coarsest coloring with this property. Actually determining the coarsest such coloring (i.e., using the fewest colors) seems like a challenging task.

5. Capturing orthogonal symmetries

For this section we consider the orthogonal symmetry group AutO(P)\operatorname{Aut}_{\operatorname{O}}(P) and all notations without an explicit hint to the kind of symmetry (such as ϕ𝔠\phi^{\mathfrak{c}} or 𝔬\mathfrak{o}) implicitly refer to their orthogonal versions.

Recall the metric coloring 𝔪:VΓE\mathfrak{m}\colon V\mathbin{\mathaccent 0{\cdot}\cup}E\to\mathbb{R} (Definition˜2.2) with

𝔪(i)\displaystyle\qquad\qquad\qquad\mathfrak{m}(i) =vi2,\displaystyle=\|v_{i}\|^{2},\quad for all iV,\displaystyle\text{for all $i\in V$},\qquad\qquad\qquad
𝔪(ij)\displaystyle\qquad\qquad\qquad\mathfrak{m}(ij) =vi,vj,\displaystyle=\langle v_{i},v_{j}\rangle,\quad for all ijE.\displaystyle\text{for all $ij\in E$}.\qquad\qquad\qquad

As previously mentioned, we consider 𝔪\mathfrak{m} a candidate for capturing orthogonal symmetries, but we are yet unable to prove this (see ˜6.6).

Nevertheless, combining the metric coloring and the Izmestiev coloring allows us to construct a coloring for which we can actually prove this.

Definition 5.1.

Given two colorings 𝔠:VΓE\mathfrak{c}\colon V\mathbin{\mathaccent 0{\cdot}\cup}E\to\mathfrak{C} and 𝔠¯:VΓE¯\bar{\mathfrak{c}}\colon V\mathbin{\mathaccent 0{\cdot}\cup}E\to\bar{\mathfrak{C}}, the product coloring 𝔠×𝔠¯:VΓEׯ\mathfrak{c}\times\bar{\mathfrak{c}}\colon V\mathbin{\mathaccent 0{\cdot}\cup}E\to\mathfrak{C}\times\bar{\mathfrak{C}} is defined by

(𝔠×𝔠¯)(i)\displaystyle\qquad\qquad\qquad\mathfrak{(}\mathfrak{c}\times\bar{\mathfrak{c}})(i)\phantom{j} :=(𝔠(i),𝔠¯(i)),\displaystyle:=(\mathfrak{c}(i),\phantom{j}\bar{\mathfrak{c}}(i)),\quad for all iV,\displaystyle\text{for all $i\in V$},\qquad\qquad\qquad
(𝔠×𝔠¯)(ij)\displaystyle\qquad\qquad\qquad\mathfrak{(}\mathfrak{c}\times\bar{\mathfrak{c}})(ij) :=(𝔠(ij),𝔠¯(ij)),\displaystyle:=(\mathfrak{c}(ij),\bar{\mathfrak{c}}(ij)),\quad for all ijE.\displaystyle\text{for all $ij\in E$}.\qquad\qquad\qquad

The relevant (and easy to verify) property of the product coloring is

(5.1) Aut(GP𝔠×𝔠¯)=Aut(GP𝔠)Aut(GP𝔠¯).\operatorname{Aut}(G_{P}^{\mathfrak{c}\times\bar{\mathfrak{c}}})=\operatorname{Aut}(G_{P}^{\mathfrak{c}})\cap\operatorname{Aut}(G_{P}^{\bar{\mathfrak{c}}}).

In particular, if both ϕ𝔠\phi^{\mathfrak{c}} and ϕ𝔠¯\phi^{\bar{\mathfrak{c}}} are well-defined, then so is ϕ𝔠×𝔠¯\phi^{\mathfrak{c}\times\bar{\mathfrak{c}}}.

Theorem 5.2.

The coloring ×𝔪\mathfrak{I}\times\mathfrak{m} captures the orthogonal symmetries of PP.

Proof.

The Izmestiev coloring \mathfrak{I} is at least as symmetric as PP (we know this for linear symmetries by Proposition˜4.6, which include the orthogonal symmetries as a special case). Like-wise, the metric coloring 𝔪\mathfrak{m} is at least as symmetric as PP (every orthogonal symmetry preserves norms and inner products, and therefore also the metric coloring). So, since ϕ\phi^{\mathfrak{I}} and ϕ𝔪\phi^{\mathfrak{m}} are well-defined, so is ϕ×𝔪\phi^{\mathfrak{I}\times\mathfrak{m}}.

It remains to show that ϕ×𝔪\phi^{\mathfrak{I}\times\mathfrak{m}} has an inverse. For that, fix a σAut(GP×𝔪)\sigma\in\operatorname{Aut}(G_{P}^{\mathfrak{I}\times\mathfrak{m}}). By \tagform@5.1 we have σAut(GP)\sigma\in\operatorname{Aut}(G_{P}^{\mathfrak{I}}). By Theorem˜4.7 there is a corresponding TσAutGL(P)T_{\sigma}\in\operatorname{Aut}_{\operatorname{GL}}(P) with Tσvi=T_{\sigma}v_{i}= vσ(i)v_{\sigma(i)} for all iVi\in V. It remains to show that TσO(d)T_{\sigma}\in\operatorname{O}(\mathbb{R}^{d}).

Since PP is full-dimensional, a set SS that contains any vertex viv_{i} together with its neighbors {vjijE}\{v_{j}\mid ij\in E\} spans d\mathbb{R}^{d}, and so it suffices to verify Tσvk,Tσv=vk,v\langle T_{\sigma}v_{k},T_{\sigma}v_{\ell}\rangle=\langle v_{k},v_{\ell}\rangle for every two vk,vSv_{k},v_{\ell}\in S to prove the orthogonality of TσT_{\sigma}.

Also by \tagform@5.1, σ\sigma preserves the metric coloring 𝔪\mathfrak{m}. The claim then follows via

vk,v=vσ(k),vσ()=Tσvk,Tσv,for all vk,vS,\langle v_{k},v_{\ell}\rangle=\langle v_{\sigma(k)},v_{\sigma(\ell)}\rangle=\langle T_{\sigma}v_{k},T_{\sigma}v_{\ell}\rangle,\qquad\text{for all $v_{k},v_{\ell}\in S$},

where we used that vk,vSv_{k},v_{\ell}\in S implies k=k=\ell or kEk\ell\in E. ∎

By (the orthogonal version of) Lemma˜2.5, if there is any coloring that captures orthogonal symmetries, then so does the orthogonal orbit coloring:

Corollary 5.3.

The orthogonal orbit coloring captures orthogonal symmetries.

6. Outlook, open questions and further notes

In this article we have shown that the edge-graph of a convex polytope, while generally a very weak representative of the polytope’s geometric nature, still has sufficient structure to let us encode two important types of geometric symmetries: linear and orthogonal symmetries. We achieved this by coloring the vertices and edges of the edge-graph.

The first coloring for which we established that it “captures the polytope’s linear symmetries” was the Izmestiev coloring (Theorem˜4.7), based on an ingenious construction by Ivan Izmestiev. But we also found that the orbit coloring, a conceptually very easy coloring, does the job as well (Corollary˜4.8). Analogous colorings exist for the orthogonal symmetries as well (Theorem˜5.2 and Corollary˜5.3).

In the following we briefly discuss various potential generalizations and follow up questions concerning these results. This further highlights the very special structure of convex polytopes that went into our theorems, emphasizing again that these results are non-trivial to achieve and to generalize.

We also want to mention the following neat consequence for “very symmetric” polytopes:

Corollary 6.1.

If PdP\subset\mathbb{R}^{d} is vertex- and edge-transitive (i.e., its linear resp. orthogonal symmetry group has a single orbit on vertices and edges), then PP is exactly as symmetric as its edge-graph.

This observation has previously been made in [7, Theorem 5.2]. No classification of simultaneously vertex- and edge-transitive polytopes is known so far, and so this fact might help in the study of this class.

6.1. Capturing other types of symmetries

Besides linear and orthogonal symmetries, there are at least two further common groups of symmetries associated with a polytope: the projective symmetries and the combinatorial symmetries (that is, the symmetries of the face lattice).

We can ask whether those too can be captured by a colored edge-graph:

Question 6.2.

Is there a coloring 𝔠:VΓE\mathfrak{c}:V\mathbin{\mathaccent 0{\cdot}\cup}E\to\mathfrak{C} that captures projective resp. combinatorial symmetries:

Aut(GP𝔠)AutPGL(P)resp.Aut(GP𝔠)AutComb(P)?\operatorname{Aut}(G_{P}^{\mathfrak{c}})\cong\operatorname{Aut}_{\mathrm{PGL}}(P)\quad\text{resp.}\quad\operatorname{Aut}(G_{P}^{\mathfrak{c}})\cong\operatorname{Aut}_{\mathrm{Comb}}(P)\;?

There might be a general strategy derived from the following (informal) inclusion chain of the symmetry groups:

AutO(P)AutGL(P)AutPGL(P)AutComb(P).\operatorname{Aut}_{\operatorname{O}}(P)\;\subseteq\;\operatorname{Aut}_{\operatorname{GL}}(P)\;\subseteq\;\operatorname{Aut}_{\mathrm{PGL}}(P)\;\;\text{``$\subseteq$''}\;\operatorname{Aut}_{\mathrm{Comb}(P)}.

As it turns out, having solved the coloring problem further to the left in the chain can help to solve the problem further to the right – at least to some degree.

For example, note that every polytope PP can be linearly transformed via a transformation TGL(d)T\in\operatorname{GL}(\mathbb{R}^{d}) so that AutGL(P)=AutO(TP)\operatorname{Aut}_{\operatorname{GL}}(P)=\operatorname{Aut}_{\operatorname{O}}(TP). That is, a coloring of GPG_{P} that captures the orthogonal symmetries of TPTP (which has the same edge-graph) also captures the linear symmetries of PP. In still other words, we solved the problem of capturing linear symmetries by making use of our ability to capture orthogonal symmetries.

In our approach, we have not made use of this because we needed to solved the linear case before the orthogonal one. However, this can be of use for capturing projective symmetries. More explicitly, the question is as follows: for every polytope PP, is there a projective transformation TPGL(d)T\in\operatorname{PGL}(\mathbb{R}^{d}) so that AutPGL(P)=AutGL(TP)\operatorname{Aut}_{\operatorname{PGL}}(P)=\operatorname{Aut}_{\operatorname{GL}}(TP)?

The same approach seems doomed for capturing combinatorial symmetries: there are polytopes with combinatorial symmetries that cannot be realized geometrically (​[1] discusses the case of a combinatorial symmetry that cannot be made linear; to our knowledge, realizing them as projective symmetries remains to be discussed).

6.2. Edge-only coloring

For capturing the symmetries of certain 2-dimensional polytopes it is necessary to color both vertices and edges (cf. Figure˜2). But it is unclear whether this is still necessary in higher dimensions.

Question 6.3.

Is it sufficient to color only the edges if d3d\geq 3? That is, is there an edge-only coloring 𝔠:E\mathfrak{c}\colon E\to\mathfrak{C} that captures (for example) linear symmetries?

A vertex-only coloring is not always sufficient. For example, in even dimensions exist vertex-transitive neighborly polytopes other than the simplex: e.g. for n6n\geq 6 we have the following cyclic 4-polytope with nn vertices that is not a simplex:

P:=conv{(cos(2πi/n)sin(2πi/n)cos(πi/n)sin(πi/n))4|i{1,,n}}.P:=\operatorname{conv}\left\{\begin{pmatrix}\cos(2\pi i/n)\\ \sin(2\pi i/n)\\ \cos(\phantom{4}\pi i/n)\\ \sin(\phantom{4}\pi i/n)\end{pmatrix}\in\mathbb{R}^{4}\;\Bigg{|}\;i\in\{1,...,n\}\right\}.

The edge-graph of PP is the complete graph KnK_{n}, and PP has a single orbit of vertices. Thus, if 𝔠:V\mathfrak{c}\colon V\to\mathfrak{C} is a vertex-only coloring that captures the symmetries of PP, then all vertices of KnK_{n} must receive the same color. But if the edges receive no color, then Aut(Kn𝔠)\operatorname{Aut}(K_{n}^{\mathfrak{c}}) =Sym(V)=\operatorname{Sym}(V). However, it is known that the linear symmetry group of the cyclic polytope PP other than a simplex is strictly smaller than Sym(V)\operatorname{Sym}(V) [5].

6.3. Non-convex polytopes and general graph embeddings

Our approach suggests no immediate generalization to non-convex polytopes or various forms of polytopal complexes.

Question 6.4.

What is the most general geometric setting in which the symmetries can be “captured” by coloring the edge-graph? Does it work for non-convex and/or self-intersecting polytopes? What about more general polytopal complexes?

A vast generalization of polytope skeleta are graph embeddings. For a graph G=G= (V,E)(V,E), a graph embedding is simply a map v:Vdv\colon V\to\mathbb{R}^{d}. There are natural notions of symmetry for such embeddings, and so one might ask whether it is possible to “capture” them by coloring the graph. The following example shows that this is not possible in general:

Example 6.5.

Consider the complete bipartite graph K4,4K_{4,4} with vertex set V1ΓV2={1,2,3,4}Γ{5,6,7,8}V_{1}\mathbin{\mathaccent 0{\cdot}\cup}V_{2}=\{1,2,3,4\}\mathbin{\mathaccent 0{\cdot}\cup}\{5,6,7,8\} and an embedding into 4\mathbb{R}^{4} defined as follows:

v1=(+1,0,0,0),v5=(0,0,+1,0),\displaystyle v_{1}=(+1,0,0,0),\qquad v_{5}=(0,0,+1,0),
v2=(0,+1,0,0),v6=(0,0,0,+1),\displaystyle v_{2}=(0,+1,0,0),\qquad v_{6}=(0,0,0,+1),
v3=(1,0,0,0),v7=(0,0,1,0),\displaystyle v_{3}=(-1,0,0,0),\qquad v_{7}=(0,0,-1,0),
v4=(0,1,0,0),v8=(0,0,0,1).\displaystyle v_{4}=(0,-1,0,0),\qquad v_{8}=(0,0,0,-1).

One can check that the linear symmetry group of this embedding acts transitively on the vertices as well as the edges. Thus, a coloring 𝔠\mathfrak{c} that is at least as symmetric as the graph embedding must assign the same color to all vertices, and like-wise, the same color to all edges. That is, Aut(K4,4𝔠)=Aut(K4,4)\operatorname{Aut}(K_{4,4}^{\mathfrak{c}})=\operatorname{Aut}(K_{4,4}).

However, one can also see that the given embedding has a strictly smaller symmetry group than Aut(K4,4)\operatorname{Aut}(K_{4,4}). For example, σ:=(12)Aut(K4,4)\sigma:=(12)\in\operatorname{Aut}(K_{4,4}) cannot be realized as a geometric symmetry.

It might be interesting to determine conditions under which “capturing symmetries” is possible even in this very general case.

6.4. The metric coloring

It is yet unknown whether the metric coloring alone can capture orthogonal symmetries (cf. Section˜2.1 and Section˜5).

Question 6.6.

Can the metric coloring 𝔪\mathfrak{m} capture orthogonal symmetries?

Any potential affirmative answer to ˜6.6 will need to make use of similar assumptions as the construction of the Izmestiev coloring, namely, convexity and 0int(P)0\in\mathrm{int}(P), as there are known counterexamples for the other cases (see Figure˜5 and Figure˜6).

Refer to caption
Figure 5. A non-convex shape and two drawings of its edge-graph with metric coloring. The colored edge-graph has more symmetries than the polygon.
Refer to caption
Figure 6. A convex polygon PP with 0int(P)0\not\in\operatorname{int}(P) (the gray dot indicates the origin) and two drawings of its edge-graph with metric coloring. The colored edge-graph has more symmetries than the polygon.

An interesting special case is the following:

Question 6.7.

If PP is inscribed (i.e., it has all its vertices on a common sphere around the origin) and has all edges of the same length, then is it true that PP is as symmetric as its edge-graph, that is, AutO(P)Aut(GP)\operatorname{Aut}_{\operatorname{O}}(P)\cong\operatorname{Aut}(G_{P})?


Acknowledgments. The author thanks Frank Göring (TU Chemnitz) for insightful discussions.

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