Abstract 1 Introduction 2 Gale Duality and Dependency Patterns 3 Continuous Motion and the 𝒈-Matrix 4 The Space 𝕲𝒏,𝒓 Spanned by 𝒈-Matrices 5 Bounding the Spherical Arc Crossing Number References

Levels in Arrangements: Linear Relations, the g-Matrix, and Applications to Crossing Numbers

Elizaveta Streltsova ORCID Institute of Science and Technology Austria (ISTA), Klosterneuburg, Austria Uli Wagner ORCID Institute of Science and Technology Austria (ISTA), Klosterneuburg, Austria
Abstract

A long-standing conjecture of Eckhoff, Linhart, and Welzl, which would generalize McMullen’s Upper Bound Theorem for polytopes and refine asymptotic bounds due to Clarkson, asserts that for knd22, the complexity of the (k)-level in a simple arrangement of n hemispheres in Sd is maximized for arrangements that are polar duals of neighborly d-polytopes. We prove this conjecture in the case n=d+4. By Gale duality, this implies the following result about crossing numbers: In every spherical arc drawing of Kn in S2 (given by a set VS2 of n unit vectors connected by spherical arcs), the number of crossings is at least 14n2n12n22n32. This lower bound is attained if every open linear halfspace contains at least (n2)/2 of the vectors in V.

Moreover, we determine the space of all linear and affine relations that hold between the face numbers of levels in simple arrangements of n hemispheres in Sd. This completes a long line of research on such relations, answers a question posed by Andrzejak and Welzl in 2003, and generalizes the classical fact that the Dehn–Sommerville relations generate all linear relations between the face numbers of simple polytopes (which correspond to the 0-level).

To prove these results, we introduce the notion of the g-matrix, which encodes the face numbers of levels in an arrangement and generalizes the classical g-vector of a polytope.

Keywords and phrases:
Levels in arrangements, k-sets, k-facets, convex polytopes, f-vector, h-vector, g-vector, Dehn–Sommerville relations, Radon partitions, Gale duality, g-matrix
Copyright and License:
[Uncaptioned image] © Elizaveta Streltsova and Uli Wagner; licensed under Creative Commons License CC-BY 4.0
2012 ACM Subject Classification:
Theory of computation Computational geometry
; Mathematics of computing Discrete mathematics
Related Version:
Full Version: http://arxiv.org/abs/2504.07752 [40]
Editors:
Oswin Aichholzer and Haitao Wang

1 Introduction

Levels in arrangements (and the dual notions of k-sets and k-facets) play a fundamental role in discrete and computational geometry and are a natural generalization of convex polytopes (which correspond to the 0-level); we refer to [21, 30, 45] for more background.

It is a classical result in polytope theory that the Euler–Poincaré relation is the only linear relation between the face numbers of arbitrary d-dimensional convex polytopes, and that the Dehn–Sommerville relations (which we will review below) generate all linear relations between the face numbers of simple (or, dually, simplicial) polytopes [23, Chs. 8–9]. Another central result in polytope theory is McMullen’s Upper Bound Theorem [31], which asserts that cyclic polytopes have the largest possible number of faces among all d-dimensional convex polytopes with a given number of vertices.

Here, we are interested in generalizations of these results to levels and sublevels in arrangements. To state our results formally, it will be convenient to work with spherical arrangements in Sd (which can be seen as a “compactification” of arrangements of affine hyperplanes or halfspaces in 𝐑d that avoids technical issues related to unbounded faces).

1.1 Levels in Arrangements, Dissection Patterns, and Polytopes

Throughout this paper, let r=d+11, and let Sd be the unit sphere in 𝐑r. We denote the standard inner product in 𝐑r by ,, and write sgn(x){1,0,+1} for the sign of a real number x𝐑. For a sign vector F{1,0,+1}n, let F+, F0, and F denote the subsets of coordinates i[n] such that Fi=+1, Fi=0, and Fi=1, respectively.

Let V={v1,,vn}𝐑r be a set of nr vectors; fixing the labeling of the vectors, we will also view V as a matrix V=[v1||vn]𝐑r×n with columns vi. Unless stated otherwise, we assume that V is in general position, i.e., that any r of the vectors are linearly independent. We refer to V as a vector configuration of rank r.

Every vector viV defines a great (d1)-sphere Hi={xSdvi,x=0} in Sd and two open hemispheres

Hi+={xSdvi,x>0},Hi={xSdvi,x<0}.

The resulting arrangement 𝒜(V)={H1+,,Hn+} of hemispheres in Sd determines a decomposition of Sd into faces of dimensions 0 through d, where two points u,uSd lie in the relative interior of the same face of 𝒜(V) iff sgn(vi,u)=sgn(vi,u) for 1in. Let (V) be the set of all sign vectors (sgn(v1,u),,sgn(vn,u)){1,0,+1}n, where u ranges over all non-zero vectors (equivalently, unit vectors) in 𝐑r; we can identify each face of 𝒜(V) with its signature F(V). By general position, the face with signature F has dimension d|F0| (the arrangement is simple, i.e., there are no faces with |F0|>d). Moreover, we call |F| the level of the face. Equivalently, the elements of (V) correspond bijectively to the partitions of V by oriented linear hyperplanes, and we will also call them the dissection patterns of V. In what follows, we will pass freely back and forth between a vector configuration V and the corresponding arrangement 𝒜(V) and refer to this correspondence as polar duality (to distinguish it from Gale duality, see below).

Definition 1 (f-matrix and f-polynomial).

For integers s and t, let111By general position, fs,t(V)=0 unless 0sd and 0tns.

fs,t:=fs,t(V):=|{F(V)|F0|=s,|F|=t}|.

Thus, fs,t(V) counts the (ds)-dimensional faces of level t in 𝒜(V).

Together, these numbers form the f-matrix f(V)=[fs,t(V)]. Equivalently, we can encode this data into the bivariate f-polynomial fV(x,y)𝐙[x,y] defined by

fV(x,y):=s,tfs,t(V)xsyt=F(V)x|F0|y|F|.

We call a vector configuration V pointed if it is contained in an open linear halfspace {x𝐑r:u,x>0}, for some uSd, or equivalently, if i=1nHi+. The closure of this intersection is then a simple (spherical) polytope P, the 0-level of 𝒜(V). By radial projection onto the tangent hyperplane {x𝐑r:u,x=1}, every pointed configuration V𝐑r corresponds to a point set S𝐑d, see [30, Sec. 5.6]. The convex hull P=conv(S) is a simplicial polytope (the polar dual of P), and the elements of (V) correspond to the partitions of S by affine hyperplanes; in particular, fs,0(V) counts the (s1)-dimensional faces of P, and f0,k(V) counts the k-sets of S.

1.2 Exact Upper Bounds for Sublevels

The following two special vector configurations will play an important role in this paper.

Example 2 (Cyclic and Cocyclic Configurations).

Let t1<t2<<tn be real numbers and define vi:=(1,ti,ti2,,tir1)𝐑r. We call Vcyclic(n,r):={v1,vn} and Vcocyclic(n,r):={(1)ivi:1in} the cyclic and cocyclic configurations of n vectors in 𝐑r, respectively.222The combinatorial types of these configurations are independent of the choice of the parameters ti.

Cyclic and cocyclic configurations are examples of neighborly and coneighborly configurations, which we define next. Let V={v1,,vn}𝐑r be a vector configuration in general position. A subset WV is extremal if there exists a linear hyperplane H bounding an open halfspace H+ such that WH and VWH+. In particular, V is pointed iff V is extremal.

Definition 3 (Neighborly and Coneighborly Configurations).

A vector configuration V={v1,,vn}𝐑r is coneighborly if every open linear halfspace contains at least nr+12 vectors of V. It is neighborly if every subset WV of size |W|r12 is extremal.

Cyclic configurations are neighborly, cocyclic configurations are coneighborly [48, Cor. 0.8], and these notions are Gale dual to each other (see Section 2). Every neighborly vector configuration V𝐑r is pointed, hence corresponds to a point set S𝐑d, d=r1, and V being neighborly means the simplicial d-polytope conv(S) is a neighborly polytope, i.e., every subset of S of size at most d2 forms a face. We note that for r=1,2 (d=0,1) neighborliness is the same as being pointed, and for r=3,4 (d=2,3) V is neighborly iff the point set S is in convex position. By a celebrated result of McMullen [31] neighborly polytopes maximize the number of faces of any dimension:

Theorem 4 (Upper Bound Theorem for Convex Polytopes).

Let V𝐑r be a configuration of n vectors in general position. Then

fs,0(V)fs,0(Vcyclic(n,r))(0sd=r1)

with equality if V is neighborly.

Eckhoff [20, Conj. 9.8], Linhart [26], and Welzl [46], independently of one another (and in slightly different forms) conjectured a far-reaching generalization of Theorem 4 for sublevels of arrangements. To state this conjecture, let fs,k(V):=tkfs,t(V).

Conjecture 5 (Generalized Upper Bound Conjecture for Sublevels).

Let V𝐑r be a configuration of n vectors in general position, and 0knr12. Then

fs,k(V)fs,k(Vcyclic(n,r))(0sd=r1)

Equality holds if V is neighborly.

A random sampling argument due to Clarkson (see [18]) shows that Conjecture 5 is true asymptotically, for fixed r and n,k, up to a constant factor depending on r. For the case s=d of vertices at sublevel (k), Conjecture 5 was proved by Peck [36] and by Alon and Győri [10] for r3 and by Welzl [46] for pointed vector configurations in rank r=4. The second author [44] proved that it is true up to a factor of 4 for arbitrary rank r. Here, we prove the conjecture for corank nr=3.

Theorem 6.

Let V𝐑n3 be a configuration of n vectors in general position. Then

fs,k(V)fs,k(Vcyclic(n,r))(0k1,0sd=r1)

Equality holds if V is neighborly.

 Remark 7.

Bounding the maximum number of faces at level exactly k is of a rather different flavor. For coneighborly configurations, all 2(nr1) vertices of the dual arrangement are concentrated at one or two consecutive levels k=nr+12 and k=nr+12. By contrast, determining the maximum number fd,k of vertices at level k for pointed vector configurations in 𝐑r is a difficult open problem, first studied by Lovász [28] and Erdős, Lovász, Simmons, and Straus [22] in the 1970s (see [45] or [30, Ch. 11] for more details and background); even for r=3 (i.e., d=2) there remains a big gap between the best upper and lower bounds to date, which are O(nk1/3) and neΩ(logk), respectively (due to Dey [19] and Tóth [43]).

1.3 The Spherical Arc Crossing Number of 𝑲𝒏

By Gale duality (see Section 2), Theorem 6 yields the following result about crossing numbers.

Determining the crossing number cr(Kn) of the complete graph Kn (the minimum number of crossings in any drawing of Kn in the plane, or equivalently in the sphere S2, with edges represented by arbitrary Jordan arcs) is one of the foundational unsolved problems in geometric graph theory, first studied by Hill in the 1950’s. Hill conjectured the following:

Conjecture 8 (Hill).
cr(Kn)=X(n):=14n2n12n22n32=38(n4)+O(n3) (1)

This is known to hold for n12, but remains open in general (see [37, Sec. 1.3] or [42] for further background and references). There are several families of drawings showing that cr(Kn)X(n) for all n, but the best lower bound to date [14] is cr(Kn)0.985X(n).

We prove Hill’s conjecture for the following class of drawings. Let V={v1,,vn}S2 be a configuration of n unit vectors in general position. If we connect every pair of vectors in V by the shortest geodesic arc between them in S2 (which is unique, since no two vectors are antipodal, by general position) we obtain a drawing of the complete graph Kn in S2, which we call a spherical arc drawing. Let cr(V) denote the number of crossings in this drawing.

Theorem 9.

For every configuration VS2 of n unit vectors in general position,

cr(V)14n2n12n22n32

Moreover, the lower bound is attained with equality if V is coneighborly.

The fact that coneighborly configurations yield spherical arc drawings of Kn achieving the number X(n) of crossings in Hill’s conjecture, and the connection to the Eckhoff–Linhart–Welzl conjecture were first observed by Wagner [44, 45]. It is known [35] that there are at least n32(1o(1))n combinatorially different coneighborly configurations of n vectors in S2.

Spherical arc drawings generalize the well-studied class of rectilinear drawings of Kn (given by n points in general position in 𝐑2 connected by straight-line segments), which correspond to spherical arc drawings given by pointed vector configurations VS2.

Theorem 9 complements earlier results of Lovász, Vesztergombi, Wagner, and Welzl [29] and Ábrego and Fernández-Merchant [6], who showed that the rectilinear crossing number cr¯(Kn) (the minimum number of crossings in any rectilinear drawing of Kn) is at least X(n); in fact, cr¯(Kn)(38+ε+o(1))(n4) for some constant ε>0 [29]. Thus, unlike the spherical arc crossing number, the rectilinear crossing number cr¯(Kn) is strictly larger than X(n)cr(Kn) in the asymptotically leading term. We refer to [8] for a detailed survey, including a series of subsequent improvements [15, 9, 7, 4] leading to the currently best bound [5] cr¯(Kn)>277/729(n4)+O(n3)>0.37997(n4)+O(n3). We remark that the arguments in [29, 6] have been generalized to verify Hill’s conjecture for other classes of drawings, including 2-page drawings [2], monotone drawings [13], cylindrical, x-bounded, and shellable drawings [3], bishellable drawings [1], and seq-shellable drawings [34]. Currently we do not know how spherical arc drawings relate to these other classes of drawings.

1.4 The 𝒈-Matrix

The central notion of this paper is the g-matrix g(VW) of a pair V,W𝐑r×n of vector configurations, which encodes the difference f(W)f(V) between the f-matrices. The geometric definition of the g-matrix is given in Sec. 3, based on how the f-matrix changes by mutations in the course of generic continuous motion (an idea with a long history, see, e.g., [11]). The g-matrix is characterized by the following properties:

Theorem 10.

Let V,W𝐑r×n be a pair of vector configurations in general position.

The g-matrix g(VW) of the pair is an (r+1)×(nr+1)-matrix with integer entries gj,k:=gj,k(VW), 0jr, 0knr, which has the following properties:

  1. 1.

    For 0jr and 0knr, the g-matrix satisfies the skew-symmetries

    gj,k=grj,k=gj,nrk=grj,nrk (2)

    Thus, the g-matrix is determined by the submatrix [gj,k:0jr12,0knr12], which we call the small g-matrix. Equivalently, the g-polynomial g(x,y):=gVW(x,y):=j,kgj,kxjyk𝐙[x,y] satisfies

    g(x,y)=xrg(1x,y)=ynrg(x,1y)=xrynrg(1x,1y) (3)
  2. 2.

    The g-polynomial determines the difference fW(x,y)fV(x,y) of f-polynomials by

    fW(x,y)fV(x,y)=(1+x)rg(x+y1+x,y)=j=0rk=0nrgj,k(x+y)j(1+x)rjyk (4)

    Equivalently (by comparing coefficients), for 0sd and 0tn,

    fs,t(W)fs,t(V)=j,k(jtk)(rjsj+tk)gj,k(VW), (5)
  3. 3.

    g(WV)=g(VW), and g(UW)=g(UV)+g(VW)

 Remark 11.

The system of equations (5) yields a linear transformation T=Tn,r through which the g-matrix g=g(VW) of the pair determines the difference Δf=f(W)f(V) of f-matrices by Δf=T(g). In the presence of the skew-symmetries (2), the transformation T is injective (Lemma 25), i.e., g(VW) is uniquely determined by Δf=f(W)f(V). Thus, Theorem 10 could be taken as a formal definition of the g-matrix.

1.5 Linear Relations

Linear relations between face numbers of levels in simple arrangements have been studied extensively [33, 24, 27, 12, 16].

The first set of linear relations is given by the antipodal symmetry FF of (V):

Observation 12.

Let V={v1,,vn}𝐑r. Then fV(x,y)=ynfV(xy,1y); equivalently,

fs,t(V)=fs,nst(V)for all s and t (6)

Moreover, it is well-known [23, Sec. 18.1] that the total number of faces of a given dimension ds (of any level) in a simple arrangement in Sd depends only on n, d, and s:

Lemma 13.

Let 𝒜(V) be a simple arrangement of n hemispheres in Sd. Then, for 0sd, the total number of (ds)-dimensional faces (of any level) in 𝒜(V) equals

tfs,t(V)=2(ns)i=0ds(ns1i)=i=0d(1+(1)i)(ndi)(dis) (7)

In terms of the f-polynomial, this can be expressed very compactly as

fV(x,1)=i=0d(ni)(1+(1)di)(1+x)i=2((nd)(x+1)d+(nd2)(x+1)d2+) (8)

Linhart, Yang, and Philipp [27] proved the following result, which generalizes the classical Dehn–Sommerville relations for simple polytopes:

Theorem 14 (Dehn–Sommerville Relations for Levels in Simple Arrangements).

Let V𝐑r×n be a vector configuration in general position. Then

fV(x,y)=(1)dfV((x+y+1),y) (9)

Equivalently (by comparing coefficients), for 0sd and 0tn,

fs,t(V)=j(1)dj(js)(jst)fj,(V) (10)
 Remark 15.

The Dehn–Sommerville relations for polytopes correspond to the identity fV(x,0)=(1)dfV((x+1),0). The coefficients on the right-hand side of (10) are zero unless t (and js). This yields, for every k, a linear system of equations among the numbers fs,t, 0sd and tk, of face numbers of the (k)-sublevel of the arrangement 𝒜(V). An equivalent system of equations (expressed in terms of an h-matrix that generalizes the h-vector of a simple polytope) was proved earlier by Mulmuley [33], under the additional assumption that the (k)-sublevel is contained in an open hemisphere. Related relations have been rediscovered several times (e.g., in the recent work of Biswas et al. [16]).

 Remark 16.

The skew-symmetry g(x,y)=xrg(1x,y) of the g-matrix reflects the Dehn–Sommerville relation (9), and the symmetry g(x,y)=xrynrg(1x,1y) reflects the antipodal symmetry (6).

Let 𝒱n,r denote the set of vector configurations V𝐑r×n in general position. Let

𝔉n,r:=aff{f(V):V𝒱n,r},𝔊n,r:=lin{g(VW):V,W𝒱n,r}

be the affine space spanned by all f-matrices, and the linear space spanned by all g-matrices of pairs, respectively. Let 𝒱n,r0𝒱n,r be the subset of pointed configurations, and let

𝔉n,r0:=aff{f(V):V𝒱n,r0},𝔊n,r0:=lin{g(VW):V,W𝒱n,r0}

be the corresponding subspaces of 𝔉n,r and 𝔊r,n. Answering a question posed by Andrzejak and Welzl [12], we determine these spaces:

Theorem 17.

dim𝔉n,r=dim𝔊n,r=r+12nr+12. More precisely,

𝔊n,r={g𝐑(r+1)×(nr+1):gj,k=grj,k=gj,nrk=grj,nrkfor 0jr,0knr} (11)

is the space of all real (r+1)×(nr+1)-matrices satisfying the skew-symmetries (2), and

𝔉n,r=f(V0)+T(𝔊n,r)

for any fixed V0𝒱n,r, where T=Tn,r is the injective linear transformation given by (5).

Theorem 18.

dim𝔉n,r0=dim𝔊n,r0=r12nr+12. More precisely,

𝔊n,r0={g𝔊n,r:g0,k=0,0knr},and𝔉n,r0=f(V0)+T(𝔊n,r0)

for any V0𝒱n,r0.

As a specific base configuration V0 in both theorems, one can take the cyclic vector configuration Vcyclic(n,r) (see Example 2), whose f-matrix is known explicitly [12].

2 Gale Duality and Dependency Patterns

Let V={v1,,vn}𝐑r be a vector configuration in general position, and let (V) be the set of all sign vectors (sgn(λ1),,sgn(λn)){1,0,+1}n given by non-trivial linear dependencies i=1nλivi=0 (with coefficients λi𝐑, not all of them are zero). We call (V) the dependency patterns of V. Both (V) and (V) are invariant under invertible linear transformations of 𝐑r and under positive rescaling (multiplying each vector vi by some positive scalar αi>0).

If V is a pointed configuration corresponding to a point set S𝐑d, d=r1, then the elements of (V) encode the sign patterns of affine dependencies of S, hence they correspond bijectively to (ordered) Radon partitions S=SS0S+, conv(S+)conv(S).

Definition 19 (f-matrix and f-polynomial).

For integers s and t, define333Note that fs,t(V)=0 unless r+1sn and 0ts.

fs,t(V):=|{F(V)|F|=t,|F+|=st}|

Together, these numbers form the f-matrix f(V)=[fs,t(V)]. Equivalently, we can encode this data into the bivariate f-polynomial fV(x,y)𝐙[x,y] defined by

fV(x,y):=F(V)x|F0|y|F|=s,tfs,t(V)xnsyt

Dependency patterns and dissection patterns are, in a precise sense, dual to each other:

Definition 20 (Gale Duality).

Two vector configurations V𝐑r×n and W𝐑(nr)×n are called Gale duals of one another if the rows of V and the rows of W span subspaces of 𝐑n that are orthogonal complements of one another.

Since we always assume that V and W are in general position and of full rank, V and W are Gale dual to each other iff VW=0.

It is well-known that Gale dual configurations determine each other up to linear isomorphisms of their ambient spaces 𝐑r and 𝐑nr, respectively [30, Sec. 5.6]. Thus, we will speak of the Gale dual of V, which we denote by V. It follows from the definition that (V)=V.

As another consequence of the definition, (V)=(V), hence fs,t(V)=fns,t(V) for all s,t; equivalently, fV(x,y)=fV(x,y).

By the well-known separation theorem for convex sets [30, Sec. 1.2], a vector configuration V in general position is either pointed (equivalently, f0,0(V)=1), or there is a linear dependence of the vectors all of whose coefficients are positive (i.e., fn,0(V)=1); the same holds for all subsets WV. It follows from this that (V) and (V) determine each other (see, e.g., [40, Sec. 2] for more details). In particular, a vector configuration V𝐑r is neighborly iff fs,t(V)=0 for tr12, i.e., V is neighborly iff V is coneighborly.

Moreover, one can show that the f-matrix and the f-matrix of a vector configuration, or equivalently the polynomials fV(x,y) and fV(x,y) determine each other as well [40, Sec. 2]:

Theorem 21.

Let V𝐑r×n be a vector configuration in general position. Then

fV(x,y)=(x+y+1)n(1)rxn(x+1)nfV(xx+1,x+yx+1) (12)

and

fV(x,y)=(x+y+1)n(1)nrxn(x+1)nfV(xx+1,x+yx+1) (13)

By Gale duality, Theorem 17 immediately gives a complete description of the affine space 𝔉n,r spanned by the f-matrices of vector configurations V𝐑r×n, and there is an analogous characterization for the subspace (𝔉n,r)0 spanned by the f-matrices of pointed vector configurations in 𝐑r (which count the number of Radon partitions of given types for the corresponding point sets in 𝐑d), see [40, Sec. 1.2]

We say that two vector configurations V,W𝐑r×n have the same combinatorial type if (up to a permutation of the vectors) (V)=(W) (equivalently, (V)=(W)). Furthermore, we call V and W weakly equivalent if they have identical f-matrices (equivalently, identical f-matrices).

 Remark 22.

For readers familiar with oriented matroids (see [48, Ch. 6] or [17]), (V) and (V) are precisely the sets of vectors and covectors, respectively, of the oriented matroid realized by V. However, speaking of “(co)vectors of a vector configuration” seems potentially confusing, and we hope that the terminology of dissection and dependency patterns is more descriptive. The Dehn–Sommerville relations hold for (uniform, not necessarily realizable) oriented matroids. The definition of the g-matrix via continuous motion does not carry over to the oriented matroid setting, but one can still define the g-matrix formally, by the properties described in Theorem 10. We plan to treat this in detail in a future paper.

3 Continuous Motion and the 𝒈-Matrix

3.1 The 𝒈-Matrix of a Pair

Any two configurations V={v1,,vn} and W={w1,,wn} of n vectors in general position in 𝐑r can be deformed into one another through a continuous family V(t)={v1(t),,vn(t)} of vector configurations, where vi(t) describes a continuous path from vi(0)=vi to vi(1)=wi in 𝐑r. If we choose this continuous motion sufficiently generically, then there is only a finite set of events 0<t1<<tN<1, called mutations, during which the combinatorial type of V(t) (which is encoded by (V(t))), changes, in a controlled way (see Figures 1 and 2 for an illustration in the case d=2). Specifically, during a mutation, a unique r-tuple of vectors in V(t), indexed by some R={i1,,ir}[n], momentarily becomes linearly dependent and the orientation of this r-tuple (i.e., the sign of det[vi1||vir]) changes, while the orientations of all other r-tuples of vectors remain unchanged. Thus, any two vector configurations are connected by a finite sequence V=V0,V1,,VN=W such that Vs1 and Vs differ by a mutation, 1sN.

We describe the change from (V) to (W) when V and W differ by a mutation. Let R([n]r) index the unique r-tuple of vectors that become linearly dependent. In terms of the polar dual arrangements, this means that the r-tuple of great (d1)-spheres Hi, iR, momentarily intersect in an antipodal pair u,u of points in Sd. Immediately before and immediately after the mutation, these r great (d1)-spheres bound an antipodal pair of simplicial d-faces σ,σ in 𝒜(V) and a corresponding pair of simplicial d-faces τ,τ in 𝒜(W), respectively. We have F(V)(W) iff the face of 𝒜(V) with signature F is contained in σ or σ, and F(W)(V) iff the face of 𝒜(W) with signature F is contained in τ or τ. All other faces are preserved, i.e., they belong to (V)(W).

Let Y(W) be the signature of τ. We define a partition [n]=IJAB by

I:=RY+,J:=RY,A:=([n]R)Y+,B:=([n]R)Y

Define j:=|J| and k:=|B|. We call the pair (j,k) the type of the simplicial face τ. The signature X(V) of the corresponding simplicial face σ of 𝒜(V) satisfies Xi=Yi for iR and Xi=Yi for i[n]R. Thus, σ is of type (rj,k). Analogously, τ and σ are of type (rj,nrk) and (j,nrk), respectively, see Figures 1 and 2.

Let us define fσ(x,y):=Fσx|F0|y|F|, where we use the notation Fσ to indicate that the sum ranges over all F(V) corresponding to faces of 𝒜(V) contained in σ. The polynomials fσ(x,y), fτ(x,y), and fτ(x,y) are defined analogously. These four polynomials have a simple form:

fσ(x,y)=yk[(x+1)j(x+y)rjxr],fσ(x,y)=ynrk[(x+1)rj(x+y)jxr]
fτ(x,y)=yk[(x+1)rj(x+y)jxr],fτ(x,y)=ynrk[(x+1)j(x+y)rjxr]

We say that the mutation VW is of Type (j,k)(rj,nrk). The reverse mutation WV is of Type (rj,k)(j,nrk). We can summarize the discussion as follows:

Lemma 23.

Let VW be a mutation of Type (j,k)(rj,nrk) between configurations of n vectors in 𝐑r. Then

fW(x,y)fV(x,y)=(ykynrk)[(x+1)rj(x+y)j(x+1)j(x+y)rj] (14)

Note that the right-hand side of (14) is zero if 2j=r or 2k=nr.

Figure 1: A mutation of Type (0,k)(3,n3k) (from left to right), respectively (3,k)(0,n3k) (from right to left) in S2. The upper row shows the triangular faces σ and τ before and after the mutation, and the lower row shows the corresponding antipodal faces σ and τ. The little arrows indicate positive halfspaces, and the labels in full-dimensional faces indicate levels.
Figure 2: A mutation of Type (1,k)(2,n3k) (from left to right), respectively (2,k)(1,n3k) (from right to left) in S2.

We are now ready to define the g-matrix g(VW) of a pair of vector configurations.

Definition 24 (g-Matrix of a pair).

Let V,W be configurations of n vectors in 𝐑r.

If VW is a single mutation of Type (i,)(ri,nr) then we define the g-matrix g(VW)=[gj,k(VW)], 0jr and 0knr, as follows:

If 2i=r or 2=nr, then gj,k(VW)=0 for all j,k. If 2ir and 2nr, then

gj,k(VW):={+1if (j,k)=(i,) or (j,k)=(ri,nr)1if (j,k)=(ri,) or (j,k)=(i,nr)0else.

More generally, if V and W are connected by a sequence V=V0,V1,,VN=W, where Vs1 and Vs differ by a single mutation, then we define

gj,k(VW):=s=1Ngj,k(Vs1Vs)

Proof of Thm 10.

All three properties follow directly from Definition 24 and Lemma 23.

A priori, it may seem that the definition of the g-matrix depends on the choice of a particular sequence of mutations transforming V to W. However, this is not the case:

Lemma 25.

Let f(x,y)=s,tfs,txsyt and g(x,y)=j,kgj,kxjyk be polynomials (with real coefficients fs,t and gj,k that are zero unless 0sd and 0tn, respectively 0jr and 0knr). Suppose that f(x,y) and g(x,y) satisfy the identity

f(x,y)=(1+x)rg(x+y1+x,y)=j=0rk=0nrgj,k(x+y)j(1+x)rjyk (15)

Then, for every fixed t, the numbers gj,t, 0jr, are linear combinations of the numbers fs,, 0sd and 0t, with coefficients given inductively by the polynomial equations

jgj,txrj=sfs,t(x1)sjk<tgj,k(jtk)xrj

Proof.

The coefficient of yt in (x+y)j(x+1)rjyk equals (jtk)(x+1)rj (which is zero unless 0kt). Thus, fixing t and collecting terms in (15) according to yt, we get

sfs,txs=jktgj,k(jtk)(1+x)rj

Moving the terms with k<t to the other side yields

jgj,t(1+x)rj=sfs,txsjk<tgj,k(jtk)(1+x)rj

The result follows by a change of variable from x to x1 (inductively, the numbers gj,k, k<t, are determined by the numbers fs,, <t.)

By Theorem 21, the f-polynomial and the f-polynomial of a vector configuration determine each other. This yields the following analogue of Theorem 10 (which can also proved directly, by studying how changes during mutations):

Theorem 26.

Let V,W be configurations of n vectors in 𝐑r. Then

fW(x,y)fV(x,y)=j,kgj,k(WV)=gj,k(VW)(x+y)k(x+1)nrkyj (16)

Theorems 10 and 26 imply the following:

Corollary 27.

Let V,W𝐑r×n be vector configurations, and let V,W𝐑(nr)×n be their Gale duals. Then gj,k(VW)=gk,j(VW).

Using the above results, one can show [40, Sec. 3.1] that any pair of neighborly configurations of n vectors in 𝐑r have the same f-matrix and the same f-matrix (equivalently, the g-matrix of the pair is identically zero); analogously for coneighborly configurations.

Moreover, an inductive argument based on deletions and contractions [40, Sec. 3.2] yields the following result. We say that a vector configuration V𝐑r is j-neighborly if every subset of V of size j is extremal, and we call V is k-coneighborly if fs,t=0 for tk, i.e., if every open linear halfspace contains at least k+1 vectors from V.

Lemma 28.

Let 0jr12, 0knr12, and let V,W𝐑r×n be vector configurations such that V is k-coneighborly and W is j-neighborly. Then

gj,k(VW)=(nkr+jj)(k+r1jk)(nkr+j1j1)(k+rjk)>0 (17)

3.2 The 𝒈-Matrix and the 𝒈-Matrix

We are now ready to define the g-matrix and the g-matrix of a vector configuration.

Definition 29 (g-matrix and g-matrix).

Let V be a configuration of n vectors in r. Set

gj,k(V):=gj,k(Vcocyclic(n,r)V),andgj,k(V):=gj,k(VVcyclic(n,r))

for 0jr and 0knr. We call g(V)=[gj,k(V)] and g(V)=[gj,k(V)] the g-matrix and the g-matrix of V, respectively. By the skew-symmetries (2), both matrices are determined by their “upper left” quadrants indexed by 0jr12 and 0knr12, which we call the small g-matrix and the small g-matrix, respectively.

By Gale duality, we get gj,k(V)=gk,j(V), i.e., the g-matrix of V is the transpose of the g-matrix of the Gale dual V. Moreover, by Lemma 28,

gj,k(V)+gj,k(V)=(nkr+jj)(k+r1jk)(nkr+j1j1)(k+rjk)>0 (18)

for 0jr12 and 0knr12.

The 0-th column [gj,0(V)0jr] of the g-matrix corresponds to the classical g-vector of a simple polytope. The Generalized Lower Bound Theorem (first proved by Stanley [39], as part of a full characterization of g-vectors, and hence f-vectors of simple polytopes, see [48, Sec. 8.6]), asserts that gj,0(V)0 for 0jr12.

The 0-th row [g0,k0knr] corresponds to a Gale dual version of the g-vector of convex polytopes studied by Lee [25] and Welzl [46]. The following is implicit in [46, Sec. 4] (and implies McMullen’s Upper Bound Theorem):

Theorem 30.

Let V𝐑r be a configuration of n vectors in general position. Then

g0,k(V)(k+r1r1)(0knr12)

Equivalently, by (18), g0,k0 for 0knr12. Equality holds if V is coneighborly.

As a common generalization of the Upper Bound Theorem, the Generalized Lower Bound Theorem, and the Eckhoff–Linhart–Welzl Conjecture (Conj. 5), we propose the following:

Conjecture 31 (Nonnegativity of the Small g-Matrix).

Let V𝐑r be a configuration of n vectors in general position. Then gj,k(V)0 for 0jr12 and 0knr12.

Our main result (which implies Theorems 9 and 6) is the following:

Theorem 32.

Let V𝐑3 be a configuration of n vectors in general position. Then

g1,k(V)(k+1)n3(k+22)(0kn42)

Equivalently, g1,k(V)0 for 0kn42. Equality holds if V is coneighborly.

4 The Space 𝕲𝒏,𝒓 Spanned by 𝒈-Matrices

Here, we prove Theorem 17. (The proof of the corresponding result for pointed configurations – Theorem 18 – uses similar ideas but is more involved, see [40]). By Theorem 10, the description of the space 𝔉n,r follows from the description of the space 𝔊n,r, so it remains to prove the latter. Recall that 𝒱n,r is the set of all vector configurations V𝐑r×n in general position.

By Theorem 10, the g-matrix g=g(VW) of any pair V,W𝒱n,r satisfies the skew-symmetries gj,k=grj,k=gj,nrk=grj,nrk in (2). Thus, in order to prove Theorem 17, it remains to show that 𝔊n,r=lin{g(VW):V,W𝒱n,r} has dimension r+12nr+12. To see this, consider a generic continuous deformation from a coneighborly configuration V0 to a neighborly configuration VN, and let Vt, 0iN, be the intermediate vector configurations, i.e., Vt and Vt1 differ by a mutation. Thus, the g-matrices g(V0Vt) and g(V0Vt1) differ by the g-matrix of a mutation, i.e., their first quadrants (small g-matrices) differ in at most one coordinate, by +1 or 1. Moreover, g(V0V0) is identically zero, and every entry of the first quadrant of g(V0VN) is strictly positive by Lemma 28. Thus, the proof of Theorem 17 is completed by the following lemma:

Lemma 33.

Let X0,X1,,XN be vectors in 𝐑m such that

  1. 1.

    X0=0;

  2. 2.

    Xt and Xt1 differ in one coordinate, by +1 or 1;

  3. 3.

    All coordinates of XN are non-zero.

Then there is a subset Xt1,,Xtm of vectors that form a basis of 𝐑m.

Proof.

For 1im, let ti be the smallest t{1,,N} such that the i-th coordinate of Xti is non-zero; the index ti exists by Properties 1 and 3. Moreover, by Property 2, no two coordinates can become non-zero at the same time, i.e., the indices ti are pairwise distinct. Up to re-labeling the coordinates, we may assume t1<t2<<tm. Then, for 1im, the vector Xti is linearly independent from the vectors Xt1,Xti1, since all of the latter vectors have i-th coordinate zero. Thus, the Xti form a basis.

5 Bounding the Spherical Arc Crossing Number

In this section, we outline the proof of Theorem 32 and explain how it implies Theorems 6 and 9. By Gale duality, Theorem 6 is equivalent to the following:

Theorem 34.

Let V={v1,,vn}𝐑3 be a vector configuration in general position. Then, for all sn, the numbers fs,0(V) and fs,1(V):=fs,0(V)+fs,1(V) are maximized if V is coneighborly.

Theorem 26 and the skew-symmetries of the g-matrix imply the following (see [41]):

Lemma 35.

Let V𝐑3×n be a vector configuration in general position and let s4. Then there are non-negative integers α(n,s,k) and β(n,s,k), 0kn42, such that

fs,0(V)=k=0n42α(n,k,s)g0,k(V)

and

fs,1(V)=k=0n42α(n,k,s)g1,k(V)+k=0n42β(n,s,k)g0,k(V)

Specifically, α(n,k,s)=(n3ks3)(ks3) and β(n,s,k)=(n3ks4)k(ks4)(n3k).

Proof of Theorem 34.

By Lemma 35, for s4, fs,0(V) and fs,1(V) are non-negative linear combinations of the numbers g0,k(V) and g1,k(V), 0kn42. Hence, Theorem 34 follows from Theorems 30 and 32.

Proof of Theorem 9.

Let V𝐑3 be a configuration of n vectors in general position. There are three combinatorial types of quadruples WV, which we call Type 0, Type 1, and Type 2, respectively, see Fig. 3. Each quadruple of Type i supports exactly two dependency patterns F,F(V) with four non-zero signs, one with |F|=i negative signs, and the other one with |F+|=4i negative signs.

Figure 3: The three combinatorial types of four vectors in general position in 𝐑3.

It follows that

f4,0(V)+f4,1(V)+12f4,2(V)=(n4)

Moreover, if all the vectors in V have unit length, then the number of crossings in the induced spherical arc drawing of Kn equals cr(V)=12f4,2(V). Thus Theorem 9 is equivalent to the statement that f4,2(V)2X(n), equivalently, f4,0(V)+f4,1(V)(n4)X(n), where equality holds in both bounds if V is coneighborly. By the special case s=4 of Theorem 34,

f4,1(V)k=0n42(n32k)((k+1)n3(k+22))=:Y(n), (19)

with equality if V is coneighborly. By a direct calculation (see [41]), Y(n)=(n4)X(n).

 Remark 36.

We mention a connection to geometric probability. Let μ be a probability distribution on 𝐑3; we assume that μ is non-degenerate in the sense that every plane through the origin has μ-measure zero. A beautiful argument due to Wendel [47] shows that if μ is centrally symmetric and W={w1,w2,w3,w4}𝐑3 is a set of four independent μ-random vectors then the probability that W is of Type i{0,1,2} (Fig. 3) equals qi, where q0=(40)+(44)24=18, q1=(41)+(43)24=12, and q2=(42)24=38; it follows that for any set V𝐑3 of n independent μ-random vectors, the expected number of quadruples of type i equals qi(n4). In particular, if the vectors in V are chosen independently and uniformly at random from S2 then the expected number of crossings in the spherical arc drawing given by V is 38(n4), which was also independently shown by Moon [32]. Theorem 9, together with a well-known limiting argument [38] implies that for an arbitrary (not necessarily centrally symmetric) non-degenerate probability distribution μ on S2, the expected number of crossings in the spherical arc drawing given by n independent μ-random points is at least 38(n4).

To conclude, we outline some of the main ideas for the proof of Theorem 32 (see [41] for the details). Let V={v1,,vn}𝐑3 be a configuration of n vectors in general position. We will view V as the set So of differences vi=pio between a point set S={p1,,pn}𝐑3 (the tips of the vectors) and another point o𝐑3 (the origin).

Choose a line in 𝐑3 through o in general position. By positively rescaling the vectors vi, we may assume that S is a subset of the cylinder Z consisting of the points in 𝐑3 at Euclidean distance 1 from ; in particular, S is a point set in convex position.

The point set S𝐑3 corresponds to a neighborly configuration U={u1,,un} of vectors in 𝐑4, and the origin o𝐑3 corresponds to an additional vector u0𝐑4. Let 𝒜({u0}U)={H0+,H1+,,Hn+} be the polar dual arrangement of hemispheres in S3. The intersection 𝒜(U)H0 is a spherical arrangment in the equatorial 2-sphere H0S2, which is combinatorially isomorphic to the arrangement 𝒜(V).

Let C=HiHj be the great circle in S3 formed by the intersection of two great 2-spheres of the arrangement 𝒜(U), 1i<jn. For kn42, consider the subgraph of 𝒜(U) consisting of the vertices and edges of 𝒜(U) contained in C and at sublevel (k). This subgraph cannot cover the entire circle C, since C contains at least one pair of antipodal vertices at levels n22,n22. Thus, the connected components of this subgraph form closed intervals, which we call k-arcs of 𝒜(U). We define λk(U,u0) as the number of k-arcs of 𝒜(U) that are completely contained in the negative open hemisphere H0.

Suppose we move the origin o continuously along the line , while keeping the set S fixed. Consider the corresponding continuous motions of the vector configurations V=So in 𝐑3 and {u0}U in 𝐑4. A careful analysis of how λk(U,u0) and g1,k(V) change during this continuous motion yields

g1,k(V)=λk(U,u0)0

for 0kn42, which proves Theorem 32.

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