Tensor valuations on lattice polytopes
Monika Ludwig, Laura Silverstein

TL;DR
This paper extends classical lattice polytope valuation theories to tensor valuations, classifies low-rank equivariant tensor valuations, and identifies a rank threshold where the classification no longer applies.
Contribution
It provides a complete classification of tensor valuations of rank up to eight on lattice polytopes, extending Ehrhart theory to tensor valuations and identifying the limits of this classification.
Findings
Classified tensor valuations of rank up to eight on lattice polytopes.
Established that all such valuations are linear combinations of Ehrhart tensors.
Discovered that the classification breaks down at rank nine.
Abstract
The Ehrhart polynomial and the reciprocity theorems by Ehrhart \& Macdonald are extended to tensor valuations on lattice polytopes. A complete classification is established of tensor valuations of rank up to eight that are equivariant with respect to the special linear group over the integers and translation covariant. Every such valuation is a linear combination of the Ehrhart tensors which is shown to no longer hold true for rank nine.
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Tensor valuations on lattice polytopes
Monika Ludwig and Laura Silverstein
Abstract
The Ehrhart polynomial and the reciprocity theorems by Ehrhart & Macdonald are extended to tensor valuations on lattice polytopes. A complete classification is established of tensor valuations of rank up to eight that are equivariant with respect to the special linear group over the integers and translation covariant. Every such valuation is a linear combination of the Ehrhart tensors which is shown to no longer hold true for rank nine.
2010 AMS subject classification: 52B20, 52B45
1 Introduction and statement of results
Tensor valuations on convex bodies have attracted increasing attention in recent years (see, e.g., [26, 7, 23]). They were introduced by McMullen in [37] and Alesker subsequently obtained a complete classification of continuous and isometry equivariant tensor valuations on convex bodies (based on [3] but completed in [4]). Tensor valuations have found applications in different fields and subjects; in particular, in Stochastic Geometry and Imaging (see [26]). The aim of this article is to begin to develop the theory of tensor valuations on lattice polytopes.
Let denote the set of lattice polytopes in ; that is, the set of convex polytopes with vertices in the integer lattice . In general, a full-dimensional lattice in is an image of by an invertible linear transformation and, therefore, all results can easily be translated to the general situation of polytopes with vertices in an arbitrary lattice. A function defined on with values in an abelian semigroup is a valuation if
[TABLE]
whenever and .
For , the lattice point enumerator, , is defined as
[TABLE]
Hence, is the number of lattice points in and is a valuation on . A function defined on is invariant if for all and where is the special linear group over the integers; that is, the group of transformations that can be described by matrices of determinant 1 with integer coefficients. A function is translation invariant on if for all and . It is -homogeneous if for all and where is the set of non-negative integers.
A fundamental result on lattice polytopes by Ehrhart [14] introduces the so-called Ehrhart polynomial and was the beginning of what is now known as Ehrhart Theory (see [5, 6]).
Theorem** (Ehrhart).**
There exist for such that
[TABLE]
for every and . For each , the functional is an and translation invariant valuation that is homogeneous of degree .
Note that is the -dimensional volume, , and the Euler characteristic of , that is, for non-empty and . Also note that for with , where is the dimension of the affine hull of .
Extending the definition of the lattice point enumerator (1), for and a non-negative integer , we define the discrete moment tensor of rank by
[TABLE]
where denotes the -fold symmetric tensor product of . Let denote the vector space of symmetric tensors of rank on . We then have and . For , we obtain the discrete moment vector, which was introduced in [11]. For , discrete moment tensors were introduced in [12]. The discrete moment tensor is a natural discretization of the moment tensor of rank of which is defined to be
[TABLE]
For and , respectively, this is the -dimensional volume, , and the moment vector. See [42, Section 5.4] for more information on moment tensors and [19, 18, 31, 30, 17, 33, 32] for some recent results.
Corresponding to the theorem of Ehrhart, we establish the existence of a homogeneous decomposition for the discrete moment tensors for integers .
Theorem 1**.**
There exist for such that
[TABLE]
for every and . For each , the function is an equivariant, translation covariant and -homogeneous valuation.
For the definition of equivariance and translation covariance, see Section 2. The coefficients yield new valuations that we introduce here as Ehrhart tensors. Note that is the moment tensor of and that for (see Section 7). The existence of the homogeneous decomposition is proved for general tensor valuations in Section 3. The proof is based on results by Khovanskiĭ & Pukhlikov [25].
A second fundamental result on lattice polytopes is the reciprocity theorem of Ehrhart [14] and Macdonald [34].
Theorem** (Ehrhart & Macdonald).**
For , the relation
[TABLE]
holds where .
Here, we write for the relative interior of with respect to the affine hull of . We establish a reciprocity result corresponding to the Ehrhart-Macdonald Theorem for the discrete moment tensor.
Theorem 2**.**
For , the relation
[TABLE]
holds where .
In Section 4, we establish reciprocity theorems for general tensor valuations; the above theorem is a special case. We follow the approach of McMullen [35].
A third fundamental result on lattice polytopes is the Betke & Kneser Theorem [10]. It provides a complete classification of and translation invariant real-valued valuations on and a characterization of the Ehrhart coefficients.
Theorem** (Betke & Kneser).**
A functional is an and translation invariant valuation if and only if there are such that
[TABLE]
for every .
The above result was established by Betke [9] and first published in [10]. In both papers, it was assumed that the functional is invariant with respect to unimodular transformations where these are defined to be a combination of translations by integral vectors and transformations; that is, linear transformations with integer coefficients and determinant . The proofs remain unchanged for the case (see [11]).
The Betke & Kneser Theorem is a discrete analogue of what is presumably the most celebrated result in the geometric theory of valuations, Hadwiger’s Characterization Theorem [20]. Let denote the space of convex bodies (that is, compact convex sets) on equipped with the topology coming from the Hausdorff metric.
Theorem** (Hadwiger).**
A functional is a continuous and rigid motion invariant valuation if and only if there are such that
[TABLE]
for every .
Here are the intrinsic volumes of , which are classically defined through the Steiner polynomial. That is, for ,
[TABLE]
where is the -dimensional Euclidean unit ball with volume and
[TABLE]
The Hadwiger Theorem has powerful applications within Integral Geometry and Geometric Probability (see [20, 27]).
Hadwiger’s theorem was extended to vector valuations by Hadwiger & Schneider [21].
Theorem** (Hadwiger & Schneider).**
A function is a continuous, rotation equivariant, and translation covariant valuation if and only if there are such that
[TABLE]
for every .
Here are the intrinsic vectors of (see (3) below). The key ingredient in the proof is a characterization of the Steiner point by Schneider [41].
Correspondingly, we obtain the following classification theorem for .
Theorem 3**.**
A function is an equivariant and translation covariant valuation if and only if there are such that
[TABLE]
for every .
The proof is based on a characterization [11] of the discrete Steiner point. For , the characterization follows from the Betke & Kneser Theorem as only translation covariance has to be considered. Therefore, we assume and for the remainder of the paper.
The theorems by Hadwiger and Hadwiger & Schneider were extended by Alesker [4, 2] (based on [3]) to a classification of continuous, rotation equivariant, and translation covariant tensor valuations on involving extensions of the intrinsic volumes. Just as the intrinsic volumes can be obtained from the Steiner polynomial, the moment tensor satisfies the Steiner formula
[TABLE]
for and . The coefficients are called the Minkowski tensors (see [42, Section 5.4]). Let be the metric tensor, that is, for .
Theorem** (Alesker).**
A function is a continuous, rotation equivariant, and translation covariant valuation if and only if can be written as linear combination of the symmetric tensor products with .
We remark that there are linear relations, called syzygies, between the tensors described above and that the dimension of the space of continuous, rotation equivariant and translation covariant matrix valuations is for (see [3]).
For tensor valuations of rank up to eight, we obtain the following complete classification. For , symbolic computation is used in the proof to show that certain matrices are non-singular.
Theorem 4**.**
For , a function is an equivariant and translation covariant valuation if and only if there are such that
[TABLE]
for every .
While the Betke & Kneser Theorem looks similar to the Hadwiger Theorem and Theorem 3 looks similar to the Hadwiger & Schneider Theorem, the similarity between the discrete and continuous cases breaks down for rank , as corresponding spaces have even different dimensions. For and , there exists a new equivariant and translation invariant valuation which is not a linear combination of the Ehrhart tensors; it is described in Section 8.2. Hence, we do not expect that a classification similar to Theorem 4 continues to hold for .
Additionally, we obtain a classification of translation covariant and -homogeneous tensor valuations on for in Theorem 27 which provides a characterization of the moment tensor. The scalar case of this result corresponds to Hadwiger’s classification [20, Satz XIV] of translation invariant and -homogeneous valuations on convex polytopes while the case of tensors of general rank corresponds to McMullen’s classification [37] of continuous, translation covariant, and -homogeneous tensor valuations on convex bodies.
2 Preliminaries
For quick later reference, we aggregate most of the basics into this section and refer the reader, for more general reference, to [6, 5, 16].
Our setting will be the -dimensional Euclidean space, , equipped with the scalar product , for , to identify with its dual space. The identification of with its dual space allows us to regard each symmetric -tensor as a symmetric -linear functional on . Let denote the vector space of symmetric tensors of rank on . We will also write this as if we want to stress the vector space in which we are working.
The symmetric tensor product of tensors for is
[TABLE]
for where , the ordinary tensor product is denoted by , and we sum over all of the permutations of . We use the abbreviated notation . Specifically, the -fold symmetric tensor product of will be written as
[TABLE]
Symmetry is inherent here; so this is equal to the -fold tensor product. Note that, for , its -fold symmetric tensor product is
[TABLE]
for . We also define whenever .
Applying this to the discrete moment tensor, in particular, gives us
[TABLE]
for . For the discrete moment tensor , the action of is observed to be
[TABLE]
for and where is the transpose of . In general, a function is said to be * equivariant* if
[TABLE]
for , , and . We will write this as . We use the term equivariance in order to stay consistent with the vector-valued case and note that for , we have
[TABLE]
for .
Using the standard orthonormal basis of , which we denote as , we show that an equivariant tensor valuation defined on a lower dimensional lattice polytope is completely determined by its lower dimensional coordinates. The precise statement is given as the following lemma. For and with , we write for with appearing times for . We identify the subspace of lattice polytopes lying in the span of with and set .
Lemma 5**.**
If is equivariant, then
[TABLE]
for every whenever and .
Proof.
If , then we consider that maps to and to for . For , we have
[TABLE]
yielding the result.
So, let . The proof is by induction on . Consider the linear transformation that maps to and maps to for all . Any lattice polytope is invariant with respect to the map yielding
[TABLE]
for any integers with . Hence we have proved the statement for .
Let and suppose the statement holds for . Then the equation
[TABLE]
shows that , which completes the proof by induction. ∎
Next, we look at the behavior of the discrete moment tensor with respect to translations. For , we have
[TABLE]
where on the right side we sum over symmetric tensor products. In accordance with McMullen [37], a valuation is called translation covariant if there exist associated functions for such that
[TABLE]
for all and .
Certain essential properties of are inherited by its associated functions. These can be seen by a comparison of the coefficients in the polynomial expansion of evaluated at a translated lattice polytope. The following proposition gives the first of these properties. It was proven in [37] for tensor valuations on convex bodies and is included here for completeness.
Proposition 6**.**
If is a translation covariant valuation with associated functions , then, for , the associated function is a translation covariant valuation with the same associated functions as , that is,
[TABLE]
for all and .
Proof.
We compare coefficients in the polynomial expansion in the translation vector . Since is a valuation, we have
[TABLE]
Hence the associated functions of are valuations.
For , observe that
[TABLE]
Therefore
[TABLE]
that is, we obtain the same associated functions as before. ∎
We require further results on the associated functions.
Proposition 7**.**
Let be a translation covariant valuation. If is equivariant, then its associated functions are also equivariant. If is -homogeneous, then its associated function vanishes for and otherwise is -homogeneous.
Proof.
If is equivariant, then, for any , we can deduce that
[TABLE]
It follows that the associated functions are also equivariant.
Now suppose is -homogeneous and let . For and , we have
[TABLE]
Furthermore, if we first consider the homogeneity of the valuation, we obtain
[TABLE]
As these equations hold for any , a comparison of the two shows that for
[TABLE]
Hence, if the valuation is -homogeneous, then is -homogeneous for and vanishes for . ∎
The inclusion-exclusion principle is a fundamental property of valuations on lattice polytopes that was first established by Betke but left unpublished. The first published proof was given by McMullen in [38] where the following more general extension property was also established. For , we write for and given lattice polytopes . Let denote the number of elements in and let be an abelian group.
Theorem 8** ( McMullen [38]).**
If is a valuation, then there exists an extension of , also denoted by , to finite unions of lattice polytopes such that
[TABLE]
whenever for all .
In particular, Theorem 8 can be used to define valuations on the relative interior of lattice polytopes. We write for the relative boundary of and further set . Expressing as the union of its faces, we obtain
[TABLE]
for where we sum over all non-empty faces of .
Betke & Kneser [10] proved their classification theorem by using suitable dissections and complementations of lattice polytopes by lattice simplices. Let be the standard -dimensional simplex, that is, the convex hull of the origin and the vectors . We call a -dimensional simplex unimodular if there are and such that . We require the following results.
Proposition 9** (Betke & Kneser [10]).**
For every , there exist unimodular simplices and integers such that
[TABLE]
for all valuations on with values in an abelian group.
The following statement is a direct consequence of this proposition.
Corollary 10**.**
If are equivariant and translation invariant valuations such that
[TABLE]
then on .
A function is Minkowski additive if for any . The following is proved as [42, Remark 6.3.3].
Proposition 11**.**
Every 1-homogeneous, translation invariant valuation is Minkowski additive.
3 Ehrhart tensor polynomials
We now apply results on translative polynomial valuations to show that the evaluation of the discrete moment tensor on dilated lattice polytopes yields a homogeneous decomposition in which the coefficients themselves are new tensor valuations. In analogy to Ehrhart’s celebrated result, we call this expansion the Ehrhart tensor polynomial of .
We now consider valuations that take values in a rational vector space which we denote by . A valuation is translative polynomial of degree at most if, for every , the function defined on by is a polynomial of degree at most . McMullen [35] considered translative polynomial valuations of degree at most one and Khovanskiĭ & Pukhlikov [25] proved Theorem 13 in the general case. Another proof, following the approach of [35], is due to Alesker [3]. These papers assume that the valuation on satisfies the inclusion-exclusion principle, which holds by Theorem 8.
Theorem 12** (Khovanskiĭ & Pukhlikov [25]).**
Let be a valuation which is translative polynomial of degree at most and let be given. For any , the function is a polynomial in of total degree at most . Moreover, the coefficient of is an -homogeneous valuation in which is translative polynomial of degree at most .
Here we only require a special case of the result by Khovanskiĭ & Pukhlikov.
Theorem 13**.**
If is a valuation that is translative polynomial of degree at most , then there exist for such that
[TABLE]
for every and . For each , the function is a translative polynomial and -homogeneous valuation.
Since is -homogeneous, the function is an -homogeneous polynomial. As a consequence, the function is translative polynomial of degree . Note that this result contains the translation invariant case by setting .
Let be a translation covariant tensor valuation. For given , we associate the real-valued valuation with the tensor valuation . Since
[TABLE]
the real-valued valuation is translative polynomial of degree at most . Therefore, we immediately obtain the following consequence of Theorem 13.
Theorem 14**.**
If is a translation covariant valuation, then there exist for such that
[TABLE]
for every and . For each , the function is a translation covariant and -homogeneous valuation.
Note that if the tensor valuation is equivariant, then so are the homogeneous components .
The homogeneous components have a translation property that agrees with the covariance of . The translation covariance of together with its decomposition from Theorem 14 yields
[TABLE]
By the homogeneous decomposition of Theorem 14, we also have
[TABLE]
A comparison of the coefficients of these polynomials in gives
[TABLE]
where we set for . Furthermore, if is equivariant, then Lemma 5 implies that
[TABLE]
for with , and hence, is translation invariant for .
We apply the homogeneous decomposition of Theorem 14 to the discrete moment tensor to obtain the following corollary.
Corollary 15**.**
There exist for such that
[TABLE]
for every and . For each , the function is an equivariant, translation covariant, and -homogeneous valuation.
Theorem 1 is then an implication of Corollary 15 and (6). We remark that, within Ehrhart Theory, further bases for the space of real-valued valuations on are also important (see [12, 24] for more information).
4 Reciprocity
The reciprocity theorem of Ehrhart and Macdonald [14, 34] is a widely used tool in combinatorics. We provide an extension of their result.
Given a function , we define the function as
[TABLE]
where the sum extends over all non-empty faces of the lattice polytope . Sallee [40] showed that is a valuation and that . Furthermore, if is translation invariant or translation covariant, then has the same translation property. The latter case can be seen from the equation
[TABLE]
for any , where we have also shown that the associated tensor is equal to for every applicable .
The following reciprocity theorem was established by McMullen [35]; see [24] for a different proof. Let be a rational vector space.
Theorem 16** (McMullen [35]).**
If is an -homogeneous and translation invariant valuation, then
[TABLE]
for .
This result applies to any rational vector space and, therefore, includes tensor valuations with the aforementioned properties. We now use this result to prove an analogous reciprocity theorem for translation covariant tensor valuations.
Theorem 17**.**
If is an -homogeneous and translation covariant valuation, then
[TABLE]
for .
Proof.
We prove this by induction on where the case is covered by Theorem 16. By the translation behavior of and and by the induction hypothesis, we have for and
[TABLE]
Recall here that by Proposition 7 the associated tensor is -homogeneous as is -homogeneous and that .
Let . Then is an -homogeneous and translation invariant valuation. From Theorem 16, we obtain
[TABLE]
Thus
[TABLE]
yielding which proves the theorem. ∎
The homogeneous decomposition of tensor valuations from Theorem 14 allows to consider reciprocity without the assumption of homogeneity. Since is also a translation covariant valuation if is, the following result is a simple consequence of Theorem 17.
Corollary 18**.**
If is a translation covariant valuation, then
[TABLE]
for .
Combined with (4), this gives the following result.
Corollary 19**.**
If is a translation covariant valuation, then
[TABLE]
for .
So, in particular, using that and that for , which is shown in Lemma 26 below, we obtain Theorem 2. Note that the results in this section for translation covariant tensor valuations also hold for translative polynomial valuations on lattice polytopes taking values in a rational vector space. The proofs remain the same.
5 Vector valuations
For a lattice polytope , the discrete Steiner point was introduced in [11]. The valuation has a translation property that we refer to as translation equivariance. In general, is called translation equivariant if for and .
Theorem 20** (Böröczky & Ludwig [11]).**
A function is an and translation equivariant valuation if and only if .
This result is the key ingredient in the classification of equivariant and translation covariant vector valuations, Theorem 3.
5.1 Proof of Theorem 3
Since is translation covariant, there is such that
[TABLE]
for and . It follows that is an and translation invariant valuation. By the Betke & Kneser Theorem, there are constants such that
[TABLE]
Set
[TABLE]
Note that (5) applied to gives . Therefore, we obtain
[TABLE]
Hence is translation invariant and is and translation equivariant. Thus, Theorem 20 shows that for all .
6 Translation invariant valuations
The classification of equivariant and translation invariant tensor valuations turns out to be the main tool in our classification of equivariant and translation covariant tensor valuations. In this section, we show that the only -homogeneous, translation invariant tensor valuation that intertwines is the trivial tensor; that is, the tensor that vanishes identically. We also offer some definitions and include key lemmas on general translation invariant valuations here that will be applied to tensor valuations.
The following result corresponds to Hadwiger’s result [20, Satz XIV] on polytopes and is a direct consequence of a result by McMullen [36, Theorem 1].
Theorem 21**.**
If is a translation invariant and -homogeneous valuation, then there exists such that
[TABLE]
for every .
The argument can easily be modified for tensor valuations by substituting a tensor for the constant . Therefore, we immediately obtain the following corollary of Theorem 21.
Corollary 22**.**
If is a translation invariant and -homogeneous valuation, then there exists such that
[TABLE]
for every .
As volume is invariant, Corollary 22 makes it natural to expect that the only valuation that is translation invariant, -homogeneous, and, additionally, equivariant is the trivial valuation. We show that this is the case.
Proposition 23**.**
Let and . If is an equivariant, translation invariant, and -homogeneous valuation, then for every .
Proof.
By Corollary 22, there exists such that on . For any and , this implies that
[TABLE]
as volume is invariant and is equivariant.
We are left to show that the only fixed point of the action of on the space of tensors is trivial. Let and . If is a basis of and , then, by setting and for , we obtain a map . As volume is invariant with respect to transformations, for , we have
[TABLE]
Since is arbitrary, this implies that
[TABLE]
completing the proof. ∎
The Minkowski sum of is . For , a polytope is called a -cylinder if there are proper independent linear subspaces of and lattice polytopes such that . We denote by the class of -cylinders and note that . Observe that an -cylinder is an -dimensional parallelotope.
The following lemma can be found for convex polytopes in [28] and for lattice polytopes in [35, Lemma 4]. A valuation is called simple if it vanishes on lower dimensional sets.
Lemma 24**.**
If is a simple, translation invariant, -homogeneous valuation, then for every when .
The following lemma can be found in [42] for valuations on convex polytopes. Here, we provide a proof for lattice polytopes. Let be a rational vector space.
Lemma 25**.**
Let be a translation invariant valuation that is -homogeneous for some . If and , then .
Proof.
Let be an -dimensional lattice subspace of . The restriction of to polytopes in is a valuation on polytopes with vertices in the lattice which is invariant under the translations of into itself. The homogeneous decomposition from Theorem 13 states that this restricted is a sum of valuations homogeneous of degrees . However, the valuation is -homogeneous implying that for . The translation invariance of together with the arbitrary choice of implies that for every such that . ∎
7 Properties of the Ehrhart tensors
The Ehrhart tensors include, for , and expand upon the Ehrhart coefficients. They are also the discrete analogues of the Minkowski tensors. By Propositions 6 and 7, the Ehrhart tensors are equivariant and translation covariant valuations. In this section, we derive further properties of these tensors and give a characterization for the leading Ehrhart tensor.
Recall that the discrete moment tensor is the discrete analogue of the moment tensor defined in (2). On lattice polytopes, the moment tensor coincides with the leading Ehrhart tensor. The following result is well-known for (where ).
Lemma 26**.**
For ,
[TABLE]
and for . Moreover, is not simple for .
Proof.
By the definition of the Riemann integral, we have
[TABLE]
This proves the statement for and shows that for . The statement for follows by considering the affine hull of since is again proportional (with a positive factor) to the moment tensor calculated in this subspace. This also implies that is not simple for . ∎
Although our main interest in this article is the classification of equivariant and translation covariant tensor valuations, we also obtain a characterization of translation covariant and -homogeneous tensor valuations. In fact, by Lemma 26, they are equal to the moment tensor up to a scalar. In this simple result, which is analogous to Alesker’s result on tensor valuations on convex bodies in [4], no equivariance is assumed.
Theorem 27**.**
If is a translation covariant, -homogeneous valuation, then there is such that
[TABLE]
for every .
Proof.
For , the statement is the same as Theorem 21. Suppose the assumption is true for all translation covariant and -homogeneous valuations that take values in tensors of rank . Let for be the associated functions of . By Proposition 7, the associated function is homogeneous of degree . Hence, by the induction assumption, there is such that . Note that it follows from Proposition 6 that
[TABLE]
for . Consider the translation covariant and -homogeneous valuation . For , by the translation covariance of and and (8), we obtain
[TABLE]
Therefore, the valuation is translation invariant. Theorem 13 implies that as non-trivial translation invariant valuations cannot be homogeneous of degree greater than . ∎
The characterization of the first Ehrhart tensor is the key element in the classification of tensor valuations. We show, in Lemma 28, that it can only be simple in the planar case. Faulhaber’s formula oftentimes appears in the calculation of the discrete moment tensor of a lattice polytope as it does in Lemma 28. The formula was given by Bernoulli in Ars Conjectandi which was translated in [8] although he fully attributed it to Faulhaber due to his formulas for sums of integral powers up to the 17th power [15]. With the convention that and that for , the formula is stated as
[TABLE]
where are the Bernoulli numbers. We will use the following convolution identity for Bernoulli polynomials (see, e.g., [1]) which, interestingly enough, is usually attributed to Leonhard Euler. For , the identity is
[TABLE]
Lemma 28**.**
For , the valuation is non-trivial. For and odd, it is simple.
Proof.
It suffices to prove that and, by Lemma 5 for and odd, that .
For any , we have
[TABLE]
where the sum of the first powers of can be expressed through Faulhaber’s formula (9). By its homogeneous decomposition, Corollary 15, the first Ehrhart tensor is the coefficient of , where in (9), implying that . As for where , we obtain the second statement of the lemma.
Similarly to (11), for any , we have
[TABLE]
Applying Faulhaber’s formula (9) twice, the discrete moment tensor of is
[TABLE]
The value of is equal to the coefficient of in (7); precisely the value when we set . Hence
[TABLE]
Euler’s identity (10) together with equation (13) and then gives
[TABLE]
as and, for any , and . ∎
8 The classification of tensor valuations
The main aim of this section is to prove Theorem 4. We also obtain characterization results for the one-homogeneous component of the discrete moment tensor in Corollaries 42 and 43 and construct a new equivariant and translation invariant valuation in Section 8.2. We start with a discussion of simple tensor valuations in the planar case.
8.1 Simple tensor valuations on
We make the following elementary observation for valuations that vanish on the square .
Lemma 29**.**
Let be even and let be a simple, equivariant, and translation invariant valuation. If , then .
Proof.
The square can be dissected into and a translate of . Therefore, we obtain
[TABLE]
which implies that . ∎
We also require the following result.
Lemma 30**.**
Let be odd and let be a simple, equivariant, and translation invariant valuation. If , then there exists such that .
Proof.
Let . Following [19], a valuation is called --equivariant, if
[TABLE]
for all and , where stands for determinant.
Let be the transform that swaps with and hence has . Defining, as in [19], the valuations and for by
[TABLE]
we see that is --equivariant with and that is --equivariant with . Indeed, if and , then
[TABLE]
If with and , then
[TABLE]
The proof for is similar. Moreover, note that .
Let for . We set for and . Then
[TABLE]
or . If we set for and , then
[TABLE]
or . Thus, in each case, we have to determine only coordinates of .
Let be the map sending to and to . We have . For , the translation invariance of implies
[TABLE]
First, we look at . Note that gives us and that we have a system of equations involving . That is, for odd, we have
[TABLE]
and, for even,
[TABLE]
It is easily checked that, for odd, this system of equations combined with has rank . Hence vanishes and we have . Yet, equations (14) and (15) remain the same for with the replacement of each by . It is easy to see that for odd, this system of equations combined with has rank . As the tensor is non-trivial by Lemma 28, any solution is a multiple of concluding the proof. ∎
We remark that the above lemma fails to hold for odd. In particular, the system of equations that determine the coordinates with has rank for and rank for ; there exist new tensor valuations in these cases. For , we describe the construction of this new valuation in the following section.
Proposition 31**.**
Let . If is a simple, equivariant, and translation invariant valuation, then for even and there is such that for odd.
Proof.
We only need to consider the statement for being in addition -homogeneous by Theorem 13. If , then is trivial due to Proposition 23. If , then Lemma 24 implies that vanishes on which gives . By Lemma 29, we have for even. By Lemma 30, there is such that for odd. Since is simple, Corollary 10 implies in both cases the result. ∎
8.2 A new tensor valuation on
We now define a new simple, 1-homogeneous, equivariant, and translation invariant tensor valuation . The basic step is to set ; that is, to use the threefold symmetric tensor product of . Note that is simple, equivariant, translation invariant, and, by Lemma 28, non-trivial. So, for , we have
[TABLE]
Also note that by Lemma 24.
More precisely, we set for with and for a two-dimensional lattice polygon we choose a dissection into translates of triangles with for . Here is said to be dissected into the triangles if where and have disjoint interiors for every ; this is written as . By Theorem 8, a simple valuation then has the property that . We set
[TABLE]
Note that (8.2) implies that is equivariant.
We need to show that is well-defined. To do this, we use the following definition and theorem. Every lattice polygon has a unimodular triangulation; that is, a dissection into unimodular triangles (see, e.g., [13]). If the union of two unimodular triangles in such a triangulation is a convex quadrilateral , then replacing the diagonal of given by the edges of the adjacent triangles with the opposite diagonal produces a new unimodular triangulation. This process is called a flip.
Theorem 32** ( Lawson [29]).**
Given any two unimodular triangulations and of a lattice polygon , there exists a finite sequence of flips transforming into .
We now show that the definition (17) does not depend on the choice of the triangulation.
Lemma 33**.**
Let and be unimodular triangles. If
[TABLE]
then
[TABLE]
Proof.
By Theorem 32, there is a sequence of flips that transforms any triangulation of a given polygon to any other triangulation of P. Therefore, it suffices to check that the value of is not changed by any flip, as vanishes on lower dimensional polygons. So if and is a translate of an image of , we have to show that
[TABLE]
This is easily seen. Indeed, since is a translate of an image of , we have
[TABLE]
by the equivariance as and the same holds for . ∎
Lemma 33 also shows that is a valuation. Indeed, if are such that and is a triangulation of , then we perform a sequence of flips on until the subset of the triangulation of that minimally covers is fully contained in . Now, the valuation property of follows immediately from the definition. Thus, we have shown that is a simple, 1-homogeneous, equivariant, and translation invariant valuation. Elementary calculations show that is non-trivial and not a multiple of .
We remark that for odd, we can define new valuations in a similar way using symmetric tensors products of for with odd and . In general, there are linear dependencies among these new valuations.
8.3 Simple tensor valuations on
Let . For the classification of simple tensor valuations, we use the following dissection of the the 2-cylinder into simplices . Let . We set and
[TABLE]
Note that each is -dimensional and unimodular (see, for example, [22, Section 2.1]).
Let be a simple, equivariant, and translation invariant valuation. Applying the dissection (18), we make use of the translation invariance of and consider for all . Define , for , by for , for , and . Let be the identity matrix. Then for all and
[TABLE]
for any with . For , this is a system of linear and homogeneous equations for the coordinates of the tensor . In addition, if is an even permutation of , then and we can also make use of these symmetries. We checked directly that the corresponding matrix has full rank and that, therefore, all coordinates vanish by using a computer algebra system (namely, SageMath [39]) in the following cases.
Lemma 34**.**
Let be a simple, equivariant, and translation invariant valuation such that . If , then .
For , we also require the following variants of the above lemma. The calculations were again performed with a computer algebra system.
Lemma 35**.**
Let be a simple, equivariant, and translation invariant valuation. If
[TABLE]
for odd and , then .
Lemma 36**.**
Let be a simple, equivariant, and translation invariant valuation. If
[TABLE]
for even and , then .
For more information, see [43].
The dissection (18) is also used in the proof of the following result.
Lemma 37**.**
Let be a simple, equivariant, and translation invariant valuation such that . If and , then .
Proof.
As in (19), we have
[TABLE]
as is a simple, translation invariant valuation that vanishes on . Thus, for any coordinate of where , we have the equations
[TABLE]
For such that , the corresponding coordinate of is . As is invariant under permutations, the permutations of the ’s are irrelevant. Without loss of generality, we may then assume and drop when from our notation. Set where and each .
We define a total order on the coordinates by saying that if or if and . Therefore, the coordinates are ordered in the following way from the biggest to smallest:
[TABLE]
where is the largest integer less or equal to and is the smallest integer greater or equal to .
We claim that the equations (8.3) imply that the coordinates of that involve at most of all vanish. One can see this by noticing that, for given with , the linear equation (8.3) only involves and coordinates that are smaller than this coordinate in the ordering defined above. Thus, for , we have an equation for each coordinate and the system of equations can be regarded as an upper-triangular matrix that, therefore, has full-rank. Thus, each coordinate vanishes implying that for .
Additionally, for , we have
[TABLE]
Thus , as by the first step. As the first step also shows that all further coordinates vanish, this completes the proof. ∎
We now establish the three-dimensional case first and then, using this result, the general case.
Lemma 38**.**
Let be a simple, equivariant, and translation invariant valuation. If , then .
Proof.
We only need to consider the statement for being in addition -homogeneous by Theorem 13. If is -homogeneous, then it is trivial due to Proposition 23. Lemma 24 implies that if . Hence, Lemma 34 and Lemma 37 imply that for . Therefore, let be -homogeneous.
Since is simple and -homogeneous, Theorem 16 implies that , that is, is odd. Using that is odd, translation invariant, and equivariant, we obtain
[TABLE]
First, let be odd. Then (8.3) implies that for odd,
[TABLE]
We can therefore apply Lemma 35 and obtain that . This implies the statement of the lemma for odd.
Second, let be even. Then (8.3) implies that for even,
[TABLE]
Applying Lemma 36 gives . This completes the proof of the lemma. ∎
Proposition 39**.**
Let be a simple, equivariant, and translation invariant valuation. If and , then .
Proof.
For , we have by Lemma 38 and the result follows from Corollary 10.
Let and suppose that the statement is true in dimension . Let be a simple, equivariant, and translation invariant valuation for . We only need to consider the statement for being in addition -homogeneous by Theorem 13. If is -homogeneous, then it is trivial due to Proposition 23. So, let .
Define by setting for
[TABLE]
where and . Then is a simple, equivariant, and translation invariant valuation. Furthermore, is -homogeneous as
[TABLE]
by the simplicity and translation invariance of .
For , the induction assumption implies that . If , then is real-valued and and translation invariant. Since it is simple, the Betke & Kneser Theorem implies that it is a multiple of the -dimensional volume as, by Lemma 26, the only simple Ehrhart coefficient is volume. Hence, is also -homogeneous and must vanish. If , then is vector-valued and equivariant and translation invariant. Since it is simple, Theorem 3 implies that it is a multiple of the moment vector, as by Lemma 26 the only simple Ehrhart tensor of rank one is the moment tensor. Thus is also -homogeneous, which implies that it vanishes.
In particular, we obtain . Hence, Lemma 34 and Lemma 37 imply that . The result now follows from Corollary 10. ∎
8.4 General tensor valuations
Let . The translation property (5) combined with (6) gives
[TABLE]
that is, is translation invariant. We show that every equivariant and translation invariant valuation is a multiple of for . We start with the case of 1-homogeneous valuations.
Proposition 40**.**
Let be an equivariant and translation invariant valuation. If is -homogeneous and , then there exists such that .
Proof.
We use induction on the dimension . The case is elementary (and also follows from the Betke & Kneser Theorem) and states that, for a 1-homogeneous and translation invariant valuation , we have for some .
Assume the statement holds for . Restrict to lattice polytopes with vertices in . By Lemma 5, we may view this restricted valuation as a function . Since is an equivariant and translation invariant valuation on , by the induction hypothesis, there is such that for . By Lemma 5, for ,
[TABLE]
where and . Hence for .
Set
[TABLE]
Note that vanishes on . By the equivariance and translation invariance of , this implies that vanishes on lattice polytopes in any -dimensional lattice hyperplane as we have for some and . In other words, is simple and the statement follows from Propositions 31 and 39. ∎
We can now extend Proposition 23 to -homogeneous valuations with .
Proposition 41**.**
Let be an equivariant and translation invariant valuation. If is -homogeneous with and , then .
Proof.
Lemma 25 implies that the valuation vanishes on all lattice polytopes of dimension . We use induction on and show that also vanishes on all -dimensional lattice polytopes for .
First, let . Restrict to lattice polytopes in . By Lemma 5, we may view this restricted valuation as a function . Since is invariant under translations of into itself and equivariant, Proposition 23 implies that vanishes on . Thus, by Lemma 5, we obtain that also vanishes on lattice polytopes with vertices in . Now, let be a general -dimensional lattice polytope. Let be the -dimensional subspace of that is parallel to the affine hull of . There exists such that and there exists such that . Since vanishes on and is equivariant and translation invariant, we obtain that .
Next, for , suppose that for all with . By Lemma 5, we may view the restriction of to lattice polytopes in as a function . Since is a simple, equivariant and translation invariant valuation and , Proposition 39 implies that vanishes on . As in the previous step, this implies that for any -dimensional lattice polytope in and, by induction, we have . ∎
The characterization of follows immediately from the combination of Theorem 13, Proposition 40, and Proposition 41.
Corollary 42**.**
For , a function is an equivariant and translation invariant valuation if and only if there exists such that .
Together with Proposition 11, we obtain the following consequence of Corollary 42.
Corollary 43**.**
For , a function is equivariant, translation invariant, and Minkowski additive if and only if there exists such that .
8.5 Proof of Theorem 4
The classification is now obtained by an inductive proof on the rank . Recall that the Betke & Kneser Theorem gives the characterization for the case and Theorem 3 for . The induction assumption gives that
[TABLE]
for some constants . Furthermore, for any , this characterization together with Proposition 6 applied to and to yields
[TABLE]
A comparison of the coefficients of the polynomial expansion in gives
[TABLE]
Consider the equivariant valuation
[TABLE]
For , by Proposition 6 and the induction assumption, we obtain
[TABLE]
Consequently, the function is translation invariant and Corollary 42 implies that proving the theorem.
Acknowledgments
The work of both authors was supported, in part, by the Austrian Science Fund (FWF) Projects P25515-N25 and I3017-N35.
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