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Symmetric Tensor Decompositions On Varieties

Jiawang Nie Jiawang Nie, Department of Mathematics, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, USA, 92093. [email protected] Ke Ye Ke Ye and Lihong Zhi, KLMM, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China [email protected], [email protected]  and  Lihong Zhi
Abstract.

This paper discusses the problem of symmetric tensor decomposition on a given variety XX: decomposing a symmetric tensor into the sum of tensor powers of vectors contained in XX. In this paper, we first study geometric and algebraic properties of such decomposable tensors, which are crucial to the practical computations of such decompositions. For a given tensor, we also develop a criterion for the existence of a symmetric decomposition on XX. Secondly and most importantly, we propose a method for computing symmetric tensor decompositions on an arbitrary XX. As a specific application, Vandermonde decompositions for nonsymmetric tensors can be computed by the proposed algorithm.

Key words and phrases:
symmetric tensor, numerical algorithm, decomposition, generating polynomial, generating matrix
2010 Mathematics Subject Classification:
15A69, 65F99

1. Introduction

Let n,d>0n,d>0 be integers and \mathbb{C} be the complex field. Denote by Td(n+1)\operatorname{T}^{d}(\mathbb{C}^{n+1}) the space of (n+1)(n+1)-dimensional complex tensors of order dd. For 𝒜Td(n+1)\mathcal{A}\in\operatorname{T}^{d}(\mathbb{C}^{n+1}) and an integral tuple (i1,,id)(i_{1},\ldots,i_{d}), 𝒜i1id\mathcal{A}_{i_{1}\dots i_{d}} denotes the (i1,,id)(i_{1},\ldots,i_{d})th entry of 𝒜\mathcal{A}, where 0i1,,imn0\leq i_{1},\ldots,i_{m}\leq n. The tensor 𝒜\mathcal{A} is symmetric if

𝒜i1id=𝒜j1jd\mathcal{A}_{i_{1}\ldots i_{d}}=\mathcal{A}_{j_{1}\ldots j_{d}}

whenever (j1,,jd)(j_{1},\ldots,j_{d}) is a permutation of (i1,,id)(i_{1},\ldots,i_{d}). Let Sd(n+1)\operatorname{S}^{d}(\mathbb{C}^{n+1}) be the subspace of all symmetric tensors in Td(n+1)\operatorname{T}^{d}(\mathbb{C}^{n+1}). For a vector uu, denote by udu^{\otimes d} the ddth tensor power of uu, i.e., udu^{\otimes d} is the tensor such that (ud)i1,,id=ui1uid(u^{\otimes d})_{i_{1},\dots,i_{d}}=u_{i_{1}}\cdots u_{i_{d}}. As shown in [9], for each 𝒜Sd(n+1)\mathcal{A}\in\operatorname{S}^{d}(\mathbb{C}^{n+1}), there exist vectors u1,,urn+1u_{1},\ldots,u_{r}\in\mathbb{C}^{n+1} such that

(1.1) 𝒜=(u1)d++(ur)d.\mathcal{A}=(u_{1})^{\otimes d}+\cdots+(u_{r})^{\otimes d}.

The above is called a symmetric tensor decomposition (STD) for 𝒜\mathcal{A}. The smallest such rr is called the symmetric rank of 𝒜\mathcal{A}, for which we denote as srank(𝒜)\operatorname{srank}(\mathcal{A}). If srank(𝒜)=r\operatorname{srank}(\mathcal{A})=r, 𝒜\mathcal{A} is called a rank-rr tensor and (1.1) is called a symmetric rank decomposition, which is also called a Waring decomposition in some references. The rank of a generic symmetric tensor is given by a formula in Alexander-Hirschowitz [2]. We refer to [9] for symmetric tensors and their symmetric ranks, and refer to [27, 30] for general tensors and their ranks.

This paper concerns symmetric tensor decompositions on a given set. Let Xn+1X\subseteq\mathbb{C}^{n+1} be a homogeneous set, i.e., txXtx\in X for all tt\in\mathbb{C} and xXx\in X. If each ujXu_{j}\in X, (1.1) is called a a symmetric tensor decomposition on XX (STDX) for 𝒜\mathcal{A}. The STDX problem has been studied in applications for various choices of XX. Symmetric tensor decompositions have broad applications in quantum physics [50], algebraic complexity theory [7, 26, 46, 51], numerical analysis [34, 52]. More tensor applications can be found in [24].

When X=n+1X=\mathbb{C}^{n+1}, the STDX is just the classical symmetric tensor decomposition, which has been studied extensively in the literature. Binary tensor (i.e., n=1n=1) decomposition problems were discussed in [8]. For higher dimensional tensors, the Catalecticant type methods [23] are often used when their ranks are low. For general symmetric tensors, Brachat et al. [5] proposed a method by using Hankel (and truncated Hankel) operators. It is equivalent to computing a new tensor whose order is higher but the rank is the same as the original one. Oeding and Ottaviani [37] proposed to compute symmetric decompositions by Koszul flattening, tensor eigenvectors and vector bundles. Other related work on computing symmetric tensor decompositions can be found in [3, 4]. For generic tensors of certain ranks, the symmetric tensor decomposition is unique. As shown in [16], a generic 𝒜Sm(n+1)\mathcal{A}\in\operatorname{S}^{m}(\mathbb{C}^{n+1}) has a unique Waring decomposition if and only if

(n,m,r){(1,2k1,k),(3,3,5),(2,5,7)}.(n,m,r)\in\big{\{}(1,2k-1,k),\,(3,3,5),\,(2,5,7)\big{\}}.

When 𝒜Sm(n+1)\mathcal{A}\in\operatorname{S}^{m}(\mathbb{C}^{n+1}) is a generic tensor of a subgeneric rank rr (i.e., rr is smaller than the value given by the Alexander-Hirschowitz formula; see [2, 9].) and m3m\geq 3, the Waring decomposition is unique, with only three exceptions [6].

When X={(an,an1b,,abn1,bn):a,b}n+1X=\{(a^{n},a^{n-1}b,\ldots,ab^{n-1},b^{n}):\,a,b\in\mathbb{C}\}\subseteq\mathbb{C}^{n+1}, i.e., XX is the affine cone of a rational normal curve in the projective space n\mathbb{P}^{n}, the STDX becomes a Vandermonde decomposition for symmetric tensors. It only exists for Hankel tensors, which were introduced in [38] for studying the harmonic retrieval problem. Hankel tensors were discussed in [39]. Relations among various ranks of Hankel tensors were studied in [35]. More applications of Hankel tensors can be found in [44, 49].

When X={a1ak:a1,,akm}n+1X=\{a_{1}\otimes\cdots\otimes a_{k}:a_{1},\ldots,a_{k}\in\mathbb{C}^{m}\}\subseteq\mathbb{C}^{n+1} with n+1=mkn+1=m^{k}, i.e., XX is a Segre variety, the STDX becomes a Vandermonde decomposition for nonsymmetric tensors. This has broad applications in signal processing [12, 29, 36, 47]. Vandermonde decompositions for nonsymmetric tensors are closely related to secant varieties of Segre-Veronese varieties, which has been studied vastly [1, 25, 40]. In the subsection 3.3, we will discuss this question with more details. Interesting, it can be shown that every nonsymmetric tensor has a Vandermonde decomposition, which is different from the symmetric case.

Contributions

This paper focuses on computing symmetric tensor decompositions on a given set XX. We assume that Xn+1X\subseteq\mathbb{C}^{n+1} is a variety that is given by homogeneous polynomial equations. Generally, symmetric tensor decompositions on XX can be theoretically studied by secant varieties of the Veronese embedding of XX [28, 42, 43]. From this view, one may expect to get polynomials which characterize the symmetric XX-rank of a given symmetric tensor. In this paper, we give a method for computing symmetric XX-rank decompositions. It is based on the tool of generating polynomials that were recently introduced in [33]. The work [33] only discussed the case X=n+1X=\mathbb{C}^{n+1}. When XX is a variety, i.e., the method in [33] does not work, because uiXu_{i}\in X is required in (1.1). We need to modify the approach in [33], by posing additional conditions on generating polynomials. For this purpose, we give a unified framework for computing symmetric tensor decompositions on XX, which sheds light on the study of both theoretical and computational aspects of tensor decompositions.

The paper is organized as follows. Section 2 gives some basics for tensor decompositions. Section 3 studies some properties of symmetric tensor decompositons on XX. Section 4 defines generating polynomials and generating matrices. It gives conditions ensuring that the computed vectors belong to the given set in symmetric tensor decompositions. Section 5 presents a unified framework to do the computation. Last, Section 6 gives various examples to show how the proposed method works.

2. Preliminaries

Notation The symbol \mathbb{N} (resp., \mathbb{R}, \mathbb{C}) denotes the set of nonnegative integers (resp., real, complex numbers). For α:=(α1,,αn)n\alpha:=(\alpha_{1},\ldots,\alpha_{n})\in\mathbb{N}^{n}, define |α|:=α1++αn|\alpha|:=\alpha_{1}+\cdots+\alpha_{n}. For a degree d>0d>0, denote the index set

dn={α:=(α1,,αn)n|α|d}.\mathbb{N}^{n}_{d}=\{\alpha:=(\alpha_{1},\ldots,\alpha_{n})\in\mathbb{N}^{n}\mid|\alpha|\leq d\}.

For a real number tt\in\mathbb{R}, we denote by t\lceil t\rceil the smallest integer nn such that ntn\geq t. Let x:=(x0,,xn)x:=(x_{0},\dots,x_{n}) and [x]:=[x0,,xn]\mathbb{C}[x]:=\mathbb{C}[x_{0},\dots,x_{n}] denote the ring of polynomials in xx and with complex coefficients. For a degree mm, [x]m\mathbb{C}[x]_{m} denotes the subset of polynomials whose degrees are less than or equal to mm, and [x]mh\mathbb{C}[x]_{m}^{h} denotes the subset of forms whose degrees are equal to mm. The cardinality of a finite set TT is denoted as |T||T|. For a finite set 𝔹[x]\mathbb{B}\subseteq\mathbb{C}[x] and a vector vnv\in\mathbb{C}^{n}, denote

(2.1) [v]𝔹:=(p(v))p𝔹,[v]_{\mathbb{B}}:=\big{(}p(v)\big{)}_{p\in\mathbb{B}},

the vector of polynomials in 𝔹\mathbb{B} evaluated at vv. For a complex matrix AA, ATA^{T} denotes its transpose and AA^{*} denotes its conjugate transpose. For a complex vector uu, u2=uu\|u\|_{2}=\sqrt{u^{*}u} denotes the standard Euclidean norm. The eie_{i} denotes the standard ii-th unit vector in n\mathbb{N}^{n}. For two square matrices X,YX,Y of the same dimension, their commutator is [X,Y]:=XYYX[X,Y]:=XY-YX.

2.1. Equivalent descriptions for symmetric tensors

There is a one-to-one correspondence between symmetric tensors in Sd(n+1)\operatorname{S}^{d}(\mathbb{C}^{n+1}) and homogeneous polynomials of degree dd and in (n+1)(n+1) variables. When 𝒜Sd(n+1)\mathcal{A}\in\operatorname{S}^{d}(\mathbb{C}^{n+1}) is symmetric, we can equivalently use α=(α1,,αn)dn\alpha=(\alpha_{1},\dots,\alpha_{n})\in\mathbb{N}_{d}^{n} to label 𝒜\mathcal{A} in the way that

(2.2) 𝒜α𝒜j1,,jd,ifx0d|α|x1α1xnαn=xj1xjd.\mathcal{A}_{\alpha}\coloneqq\mathcal{A}_{j_{1},\dots,j_{d}},\quad\mbox{if}\quad x_{0}^{d-|\alpha|}x_{1}^{\alpha_{1}}\cdots x_{n}^{\alpha_{n}}=x_{j_{1}}\cdots x_{j_{d}}.

The symmetry guarantees that the labelling 𝒜α\mathcal{A}_{\alpha} is well-defined. For 𝒜\mathcal{A}, define the homogeneous polynomial (i.e., a form) in x:=(x0,,xn)x:=(x_{0},\dots,x_{n}) and of degree dd:

(2.3) 𝒜(x):=j1,,jd=0n𝒜j1,,jdxj1xjd.\mathcal{A}(x)\,:=\,{\sum}_{j_{1},\ldots,j_{d}=0}^{n}\mathcal{A}_{j_{1},\dots,j_{d}}x_{j_{1}}\cdots x_{j_{d}}.

If 𝒜\mathcal{A} is labeled as in (2.2), then

𝒜(x)=αdn(dd|α|,α1,,αn)𝒜αx0d|α|x1α1xnαn\mathcal{A}(x)={\sum}_{\alpha\in\mathbb{N}_{d}^{n}}\binom{d}{d-|\alpha|,\alpha_{1},\dots,\alpha_{n}}\mathcal{A}_{\alpha}x_{0}^{d-|\alpha|}x_{1}^{\alpha_{1}}\cdots x_{n}^{\alpha_{n}}

where (dα0,,αn)d!α0!αn!.\binom{d}{\alpha_{0},\dots,\alpha_{n}}\coloneqq\frac{d!}{\alpha_{0}!\cdots\alpha_{n}!}. The decomposition (1.1) is equivalent to

𝒜(x)=j=1r((uj)0x0++(uj)nxn)d.\mathcal{A}(x)={\sum}_{j=1}^{r}\big{(}(u_{j})_{0}x_{0}+\cdots+(u_{j})_{n}x_{n}\big{)}^{d}.

For a polynomial p=αdnpαy1α1ynαnp=\sum_{\alpha\in\mathbb{N}^{n}_{d}}p_{\alpha}y_{1}^{\alpha_{1}}\cdots y_{n}^{\alpha_{n}} and 𝒜Sd(n+1)\mathcal{A}\in\operatorname{S}^{d}(\mathbb{C}^{n+1}), define the operation

(2.4) p,𝒜=αdnpα𝒜α\langle p,\mathcal{A}\rangle={\sum}_{\alpha\in\mathbb{N}^{n}_{d}}p_{\alpha}\mathcal{A}_{\alpha}

where 𝒜\mathcal{A} is labeled as in (2.2). For fixed pp, p,\langle p,\cdot\rangle is a linear functional on Sd(n+1)\operatorname{S}^{d}(\mathbb{C}^{n+1}), while for fixed 𝒜Sd(n+1)\mathcal{A}\in\operatorname{S}^{d}(\mathbb{C}^{n+1}), ,𝒜\langle\cdot,\mathcal{A}\rangle is a linear functional on [y1,,yn]d\mathbb{C}[y_{1},\ldots,y_{n}]_{d}.

2.2. Algebraic varieties

A set I[x]:=[x0,,xn]I\subseteq\mathbb{C}[x]:=\mathbb{C}[x_{0},\dots,x_{n}] is an ideal if I[x]II\cdot\mathbb{C}[x]\subseteq I and I+III+I\subseteq I. For polynomials f1,,fs[x]f_{1},\dots,f_{s}\in\mathbb{C}[x], let f1,,fs\left\langle f_{1},\dots,f_{s}\right\rangle denote the smallest ideal that contains f1,,fsf_{1},\ldots,f_{s}. A subset Xn+1X\subseteq\mathbb{C}^{n+1} is called an affine variety if XX is the set of common zeros of some polynomials in [x]\mathbb{C}[x]. The vanishing ideal of XX is the ideal consisting of all polynomials identically vanish on XX.

Two nonzero vectors in n+1\mathbb{C}^{n+1} are equivalent if they are parallel to each other. Denote by [u][u] the set of all nonzero vectors that are equivalent to uu; the set [u][u] is called the equivalent class of uu. The set of all equivalent classes [u][u] with 0un+10\neq u\in\mathbb{C}^{n+1} is the projective space n\mathbb{P}^{n}, or equivalently, n={[u]: 0un+1}\mathbb{P}^{n}=\{[u]:\,0\neq u\in\mathbb{C}^{n+1}\}. A subset ZnZ\subseteq\mathbb{P}^{n} is said to be a projective variety if there are homogeneous polynomials h1,,ht[x]h_{1},\ldots,h_{t}\in\mathbb{C}[x] such that

Z={[u]n:h1(u)==ht(u)=0}.Z=\{[u]\in\mathbb{P}^{n}:\,h_{1}(u)=\cdots=h_{t}(u)=0\}.

The vanishing ideal (Z)\mathcal{I}(Z) is defined to be the ideal consisting of all polynomials identically vanish on ZZ. For each nonnegative integer mm, we denote by m(Z)\mathcal{I}_{m}(Z) the linear subspace of polynomials of degree mm in (Z)\mathcal{I}(Z). A projective variety ZnZ\subseteq\mathbb{P}^{n} is said to be nondegenerate if ZZ is not contained in any proper linear subspace of n\mathbb{P}^{n}, i.e., 1(Z)={0}\mathcal{I}_{1}(Z)=\{0\}.

In the Zariski topology for n+1\mathbb{C}^{n+1} and n\mathbb{P}^{n}, the closed sets are varieties and the open sets are complements of varieties. For a projective variety ZnZ\subseteq\mathbb{P}^{n}, its Hilbert function hZ:h_{Z}:\mathbb{N}\to\mathbb{N} is defined as hZ(d)dim[Z]d.h_{Z}(d)\,\coloneqq\,\dim\mathbb{C}[Z]_{d}. As in [11, 20, 21], when dd is sufficiently large, hZ(d)h_{Z}(d) is a polynomial and

hZ(d)=em!dm+O(dm1),e=deg(Z),m=dim(Z),h_{Z}(d)=\frac{e}{m!}d^{m}+O(d^{m-1}),\quad e=\deg(Z),\quad m=\dim(Z),

where O(dm1)O(d^{m-1}) denotes terms of order at most m1m-1.

For an affine variety Xn+1X\subseteq\mathbb{C}^{n+1}, we denote by X\mathbb{P}X the projective set of equivalent classes of nonzero vectors in XX, i.e., X={[u]: 0uX}.\mathbb{P}X=\{[u]:\,0\neq u\in X\}. If X=Z\mathbb{P}X=Z, then XX is called the affine cone of ZZ. Clearly, the vanishing ideal of XX is the same as that of X\mathbb{P}X, i.e., (X)=(X)\mathcal{I}(X)=\mathcal{I}(\mathbb{P}X). For a degree m>0m>0, the set

m(X):=[x]mh(X)\mathcal{I}_{m}(X):=\mathbb{C}[x]^{h}_{m}\cap\mathcal{I}(X)

is the space of all forms of degree mm vanishing on XX.

Veronese maps

For an affine variety Xn+1X\subseteq\mathbb{C}^{n+1}, let vd(X)Sd(n+1)v_{d}(X)\subseteq\operatorname{S}^{d}(\mathbb{C}^{n+1}) be the image of XX under the Veronese map:

vd:n+1Sd(n+1),uud.v_{d}:\mathbb{C}^{n+1}\to\operatorname{S}^{d}(\mathbb{C}^{n+1}),\quad u\mapsto u^{\otimes d}.

Note that vd1(ud)={ωiu:i=0,,d1}v_{d}^{-1}(u^{\otimes d})=\{\omega^{i}u:i=0,\dots,d-1\}, where ω\omega is a primitive dd-th root of 11. Therefore, the dimension of vd(X)v_{d}(X) is the same as that of XX. In particular, for

C:={(x0,x1,,xn)n+1:xixj=xkxl,i+j=k+l},C:=\{(x_{0},x_{1},\ldots,x_{n})\in\mathbb{C}^{n+1}:x_{i}x_{j}=x_{k}x_{l},\,\forall\,i+j=k+l\},

the set vd(C)v_{d}(C) is a variety of tensors 𝒜Sd(2)\mathcal{A}\in\operatorname{S}^{d}(\mathbb{C}^{2}) that is defined by the equations

𝒜α𝒜β𝒜γ𝒜τ=0(α+β=γ+τ,α,β,γ,τd2).\mathcal{A}_{\alpha}\mathcal{A}_{\beta}-\mathcal{A}_{\gamma}\mathcal{A}_{\tau}=0\,\quad(\alpha+\beta=\gamma+\tau,\,\alpha,\beta,\gamma,\tau\in\mathbb{N}_{d}^{2}).

In the above, the tensors in Sd(2)\operatorname{S}^{d}(\mathbb{C}^{2}) are labelled by vectors in d2\mathbb{N}_{d}^{2}. The projectivization vd(C)\mathbb{P}v_{d}(C) is called the rational normal curve in the projective space Sd(2)d\mathbb{P}\operatorname{S}^{d}(\mathbb{C}^{2})\simeq\mathbb{P}^{d}. For a projective variety ZnZ\subseteq\mathbb{P}^{n}, the Veronese embedding map vdv_{d} is defined in the same way as

vd:nSd(n+1),[u][ud].v_{d}:\mathbb{P}^{n}\to\mathbb{P}\operatorname{S}^{d}(\mathbb{C}^{n+1}),\quad[u]\mapsto[u^{\otimes d}].

Note that vd(X)=vd(X)\mathbb{P}v_{d}(X)=v_{d}(\mathbb{P}X) for every affine variety XX.

Segre varieties

For projective spaces n1,,nk\mathbb{P}^{n_{1}},\ldots,\mathbb{P}^{n_{k}}, their Segre product, denoted as Seg(n1××nk)\operatorname{Seg}(\mathbb{P}^{n_{1}}\times\cdots\times\mathbb{P}^{n_{k}}), is the image of the Segre map:

Seg:([u1],,[uk])[u1uk].\operatorname{Seg}:\,\quad([u_{1}],\ldots,[u_{k}])\mapsto[u_{1}\otimes\cdots\otimes u_{k}].

The dimension of Seg(n1××nk)\operatorname{Seg}(\mathbb{P}^{n_{1}}\times\cdots\times\mathbb{P}^{n_{k}}) is the sum n1++nkn_{1}+\cdots+n_{k}. The Segre product n1,,nk\mathbb{P}^{n_{1}},\dots,\mathbb{P}^{n_{k}} is defined by equations of the form

𝒜α𝒜β𝒜γ𝒜γ=0,(α+β=γ+τ,α,β,γ,τj=1k{0,,nj}).\mathcal{A}_{\alpha}\mathcal{A}_{\beta}-\mathcal{A}_{\gamma}\mathcal{A}_{\gamma}=0\,,\quad(\alpha+\beta=\gamma+\tau,\,\alpha,\beta,\gamma,\tau\in{\prod}_{j=1}^{k}\{0,\dots,n_{j}\}).

Here, tensors in (j=1knj+1)\mathbb{P}(\bigotimes_{j=1}^{k}\mathbb{C}^{n_{j}+1}) are labelled by integral tuples in j=1k{0,,nj}\prod_{j=1}^{k}\{0,\dots,n_{j}\}.

Secant varieties

Let Xn+1X\subseteq\mathbb{C}^{n+1} be an affine variety and let vd(X)v_{d}(X) be its image under the dd-th Veronese map vdv_{d}. Define the set

σr(vd(X)):={(u1)d++(ur)d:u1,,urX}.\sigma^{\circ}_{r}(v_{d}(X))\,:=\,\big{\{}(u_{1})^{\otimes d}+\cdots+(u_{r})^{\otimes d}:u_{1},\dots,u_{r}\in X\big{\}}.

The Zariski closure of σr(vd(X))\sigma_{r}^{\circ}(v_{d}(X)), which we denote as σr(vd(X))n+1\sigma_{r}(v_{d}(X))\subseteq\mathbb{C}^{n+1}, is called the rrth secant variety of vd(X)v_{d}(X). The closure σr(vd(X))\sigma_{r}(v_{d}(X)) is an affine variety in Sd(n+1)\operatorname{S}^{d}(\mathbb{C}^{n+1}), while σr(vd(X))\sigma_{r}^{\circ}(v_{d}(X)) is usually not. However, it holds that

dimσr(vd(X))=dimσr(vd(X)),\dim\sigma_{r}^{\circ}(v_{d}(X))=\dim\sigma_{r}(v_{d}(X)),

because σr(vd(X))\sigma^{\circ}_{r}(v_{d}(X)) is a dense subset of σr(vd(X))\sigma_{r}(v_{d}(X)) in the Zariski topology. When vd(X)v_{d}(X) is replaced by a general variety YY, the sets σr(Y)\sigma_{r}^{\circ}(Y) and σr(Y)\sigma_{r}(Y) can be defined in the same way. We refer to [27] for secant varieties.

3. Properties of STDX

Let Xn+1X\subseteq\mathbb{C}^{n+1} be a set that is given by homogeneous polynomial equations. For a given tensor 𝒜Sd(n+1)\mathcal{A}\in\operatorname{S}^{d}(\mathbb{C}^{n+1}), a symmetric XX-decomposition on XX is

(3.1) 𝒜=(u1)d++(ur)d,u1,,urX.\mathcal{A}=(u_{1})^{\otimes d}+\cdots+(u_{r})^{\otimes d},\quad u_{1},\ldots,u_{r}\in X.

The smallest such rr is called the symmetric XX-rank of 𝒜\mathcal{A}, which we denote as srankX(𝒜)\operatorname{srank}_{X}(\mathcal{A}), or equivalently,

(3.2) srankX(𝒜)=min{r:𝒜=i=1r(ui)d,uiX}.\operatorname{srank}_{X}(\mathcal{A})=\min\{r:\,\mathcal{A}=\sum_{i=1}^{r}(u_{i})^{\otimes d},\,u_{i}\in\mathbb{C}^{X}\}.

When rr is the smallest, (3.1) is called a rank-retaining symmetric XX-decomposition for 𝒜\mathcal{A}. It is possible that the decomposition (3.1) does not exist; for such a case, we define srankX(𝒜)=+\operatorname{srank}_{X}(\mathcal{A})=+\infty. For instance, a symmetric tensor 𝒜\mathcal{A} admits a Vandermonde decomposition if and only if 𝒜\mathcal{A} is a Hankel tensor. So, if 𝒜\mathcal{A} is not Hankel, then srankX(𝒜)=+\operatorname{srank}_{X}(\mathcal{A})=+\infty. Interested readers are referred to [35, 39] for more details. We denote by Sd(X)\operatorname{S}^{d}(X) the subspace of tensors which admit a symmetric XX-decomposition as in (3.1). As a counterpart for symmetric border rank, the symmetric border XX-rank of 𝒜\mathcal{A} is similarly defined as

(3.3) sbrankX(𝒜)min{r:𝒜σr(vd(X))},\operatorname{sbrank}_{X}(\mathcal{A})\,\coloneqq\,\min\{r:\mathcal{A}\in\sigma_{r}(v_{d}(X))\},

where σr(vd(X))\sigma_{r}(v_{d}(X)) is the secant variety of vd(X)v_{d}(X), defined in Subsection 2. When X\mathbb{P}X is an irreducible variety, sbrankX(𝒜)\operatorname{sbrank}_{X}(\mathcal{A}) is also equal to the smallest integer rr such that 𝒜\mathcal{A} is the limit of a sequence of tensors whose symmetric XX-rank is rr (see [27, Sec. 5.1.1] or [32, Theorem 2.33]). The generic symmetric XX-rank of Sd(X)\operatorname{S}^{d}(X) is the smallest rr such that σr(vd(X))=Sd(X)\sigma_{r}(v_{d}(X))=\operatorname{S}^{d}(X). When X=n+1X=\mathbb{C}^{n+1}, the symmetric XX-rank becomes the usual symmetric rank (or Waring rank). If X=vd(1)\mathbb{P}X=v_{d}(\mathbb{P}^{1}) is the rational normal curve, the symmetric XX-rank becomes the Vandermonde rank for Hankel tensors [35, 39]. How to characterize tensors that has a symmetric XX-decomposition as in (3.1)? How to tell srankX(𝒜)<+\operatorname{srank}_{X}(\mathcal{A})<+\infty or srankX(𝒜)=+\operatorname{srank}_{X}(\mathcal{A})=+\infty? These questions are the focus of this section.

3.1. Existence of symmetric XX-decompositions

Let Xn\mathbb{P}X\subseteq\mathbb{P}^{n} be the projectivization of XX. Its vanishing ideal is (X)\mathcal{I}(X), the ideal of polynomials that are identically zero on XX. The section of degree-dd forms in (X)\mathcal{I}(X) is

d(X)(X)[x]dh.\mathcal{I}_{d}(X)\coloneqq\mathcal{I}(X)\cap\mathbb{C}[x]^{h}_{d}.

Let c:=dimd(X)c:=\dim\mathcal{I}_{d}(X). Choose a basis {f1,,fc}\{f_{1},\ldots,f_{c}\} for d(X)\mathcal{I}_{d}(X). Let l1,,lcl_{1},\dots,l_{c} be linear functionals on Sd(n+1)\operatorname{S}^{d}(\mathbb{C}^{n+1}) such that

(3.4) li(j(uj)d)=jfi(uj).l_{i}\Big{(}{\sum}_{j}(u_{j})^{\otimes d}\Big{)}={\sum}_{j}f_{i}(u_{j}).

They are linearly independent functions on Sd(X)\operatorname{S}^{d}(X). If we use f^i\hat{f}_{i} to denote the dehomogenization of fif_{i}, i.e., f^i(x1,,xn):=fi(1,x1,,xn)\hat{f}_{i}(x_{1},\ldots,x_{n}):=f_{i}(1,x_{1},\ldots,x_{n}), then li(𝒜)=f^i,𝒜l_{i}(\mathcal{A})=\langle\hat{f}_{i},\mathcal{A}\rangle for all 𝒜Sd(n+1)\mathcal{A}\in\operatorname{S}^{d}(\mathbb{C}^{n+1}). See (2.4) for the definition of the operation ,\langle\cdot,\cdot\rangle.

Proposition 3.1.

Let X,c,fi,liX,c,f_{i},l_{i} be as above. Then, a tensor 𝒜Sd(n+1)\mathcal{A}\in\operatorname{S}^{d}(\mathbb{C}^{n+1}) belongs to Sd(X)\operatorname{S}^{d}(X) if and only if l1(𝒜)==lc(𝒜)=0l_{1}(\mathcal{A})=\cdots=l_{c}(\mathcal{A})=0. Consequently, the codimension of Sd(X)\operatorname{S}^{d}(X) is cc, i.e., dimSd(X)=(n+dd)c.\dim\operatorname{S}^{d}(X)=\binom{n+d}{d}-c.

Proof.

Clearly, if 𝒜Sd(X)\mathcal{A}\in\operatorname{S}^{d}(X), then l1(𝒜)==lc(𝒜)=0l_{1}(\mathcal{A})=\cdots=l_{c}(\mathcal{A})=0. Next, we prove the converse is also true. Suppose 𝒜Sd(n+1)\mathcal{A}\in\operatorname{S}^{d}(\mathbb{C}^{n+1}) is such that l1(𝒜)==lc(𝒜)=0l_{1}(\mathcal{A})=\cdots=l_{c}(\mathcal{A})=0. To show 𝒜Sd(X)\mathcal{A}\in\operatorname{S}^{d}(X), it is enough to show that: if ll is a linear function vanishing on Sd(X)\operatorname{S}^{d}(X) then l(𝒜)=0l(\mathcal{A})=0. Each lil_{i} vanishes on vd(X)v_{d}(X) and l1,,lcl_{1},\dots,l_{c} are linearly independent as vectors in Sd(n+1)\operatorname{S}^{d}(\mathbb{C}^{n+1})^{\ast}. (The superscript denotes the dual space.) Note that lSd(n+1)l\in\operatorname{S}^{d}(\mathbb{C}^{n+1})^{\ast} and it vanishes on vd(X)v_{d}(X). So, there is a form f[x]df\in\mathbb{C}[x]_{d} such that l(ud)=f(u)l(u^{\otimes d})=f(u) for all un+1u\in\mathbb{C}^{n+1}. Since l0l\equiv 0 on vd(X)v_{d}(X), ff also vanishes on XX. So, ff is a linear combination of f1,,fcf_{1},\dots,f_{c}, and hence ll is a linear combination of l1,,lcl_{1},\dots,l_{c}. This implies that l(𝒜)=0l(\mathcal{A})=0 and 𝒜Sd(X)\mathcal{A}\in\operatorname{S}^{d}(X).

Since l1,,lcl_{1},\dots,l_{c} are linearly independent, the subspace Sd(X)\operatorname{S}^{d}(X) are defined by cc linearly independent linear equations. So its codimension is cc. Since the dimension of Sd(n+1)\operatorname{S}^{d}(\mathbb{C}^{n+1}) is (n+dd)\binom{n+d}{d}, the dimension of Sd(X)\operatorname{S}^{d}(X) follows from the codimension. ∎

The first part of Proposition 3.1 is a high dimensional analogue of the apolarity lemma, which can be found in [23, Theorem 5.3], [41, Section 1.3], and [48, Section 3]). For convenience of referencing, we state this result here and give a straightforward proof. We would like to thank Zach Teitler for pointing out the relationship between Proposition 3.1 and the apolarity Lemma.

By Proposition 3.1, we get the following algorithm for checking if 𝒜Sd(X)\mathcal{A}\in\operatorname{S}^{d}(X) or not. Suppose the vanishing ideal (X)\mathcal{I}(X) is generated by the forms f1,,fsf_{1},\dots,f_{s}, with degrees d1dsd_{1}\leq\dots\leq d_{s} respectively.

Algorithm 3.2.

For a given tensor 𝒜Sd(n+1)\mathcal{A}\in\operatorname{S}^{d}(\mathbb{C}^{n+1}), do the following:

  • Step 1:

    Find the integer k0k\geq 0 such that dkd<dk+1.d_{k}\leq d<d_{k+1}.111Here we adopt the convention that d0=0d_{0}=0 and ds+1=d_{s+1}=\infty.

  • Step 2:

    For each t=1,,kt=1,\dots,k and βddtn+1\beta\in\mathbb{N}^{n+1}_{d-d_{t}}, let ft,β:=ftx0β0xnβn.f_{t,\beta}:=f_{t}\cdot x_{0}^{\beta_{0}}\cdots x_{n}^{\beta_{n}}.

  • Step 3:

    Check whether or not ft,β,𝒜=0\langle f_{t,\beta},\mathcal{A}\rangle=0 for all tt and β\beta in Step 2. If it is, then 𝒜Sd(X)\mathcal{A}\in\operatorname{S}^{d}(X); otherwise, 𝒜Sd(X)\mathcal{A}\not\in\operatorname{S}^{d}(X).

The above algorithm can be easily applied to detect tensors in Sd(X)\operatorname{S}^{d}(X). For instance, if XX is defined by linear equations j=0nfijxj=0\sum_{j=0}^{n}f_{ij}x_{j}=0 (i=1,,ci=1,\ldots,c), then 𝒜Sd(X)\mathcal{A}\in\operatorname{S}^{d}(X) if and only if for all 1ic1\leq i\leq c and 0k2,,kmn0\leq k_{2},\ldots,k_{m}\leq n,

j=0nfij𝒜jk2km=0.\sum_{j=0}^{n}f_{ij}\mathcal{A}_{jk_{2}\ldots k_{m}}=0.

If XX is a hypersurface defined by a single homogeneous polynomial f(x)=0f(x)=0 with deg(f)m\deg(f)\leq m, then 𝒜Sd(X)\mathcal{A}\in\operatorname{S}^{d}(X) if and only if

fxα,𝒜=0\langle f\cdot x^{\alpha},\mathcal{A}\rangle=0

for all monomials xαx^{\alpha} with deg(f)+|α|=m\deg(f)+|\alpha|=m.

When does Sd(X)=Sd(n+1)\operatorname{S}^{d}(X)=\operatorname{S}^{d}(\mathbb{C}^{n+1}), i.e., when does every tensor admit a symmetric XX-decomposition? By Proposition 3.1, we get the following characterization.

Corollary 3.3.

Let Xn+1X\subseteq\mathbb{C}^{n+1} be as above. Then, the equality Sd(X)=Sd(n+1)\operatorname{S}^{d}(X)=\operatorname{S}^{d}(\mathbb{C}^{n+1}) holds if and only if d(X)={0}\mathcal{I}_{d}(X)=\{0\}, which is equivalent to that XX is not contained in any hypersurface of degree dd.

The above corollary implies that if XX is a hypersurface of degree bigger than dd, then every tensor in Sd(n+1)\operatorname{S}^{d}(\mathbb{C}^{n+1}) has a symmetric XX-decomposition. Moreover, if X=n+1X=\mathbb{C}^{n+1}, then obviously we have d(X)={0}\mathcal{I}_{d}(X)=\{0\} for any d1d\geq 1, which implies the well known fact [9] that every symmetric tensor admits a symmetric decomposition.

3.2. The dimension and expected rank

By Proposition 3.1, dimSd(X)=hX(d),\dim\operatorname{S}^{d}(X)=h_{\mathbb{P}X}(d), where hX()h_{\mathbb{P}X}(\cdot) is the Hilbert function for the projective variety X\mathbb{P}X. For the Veronese map vdv_{d}, we have dimvd(X)=dimX\dim v_{d}(X)=\dim X. Therefore, the expected dimension of the secant variety σr(vd(X))\sigma_{r}(v_{d}(X)):

exp.dimσr(vd(X))=min{rdimX,hX(d)}.\operatorname{exp.dim}\sigma_{r}(v_{d}(X))=\min\left\{r\dim X,h_{\mathbb{P}X}(d)\right\}.

The expected generic symmetric XX-rank of Sd(X)\operatorname{S}^{d}(X) is therefore

(3.5) exp.grank=hX(d)/dimX.\operatorname{exp.grank}=\left\lceil{h_{\mathbb{P}X}(d)}/{\dim X}\right\rceil.
Example 3.4.

(i) If X=Seg(n1××nk)\mathbb{P}X=\operatorname{Seg}(\mathbb{P}^{n_{1}}\times\cdots\times\mathbb{P}^{n_{k}}), the Segre variety, then dimX=n1++nk+1\dim X=n_{1}+\cdots+n_{k}+1. Its Hilbert function hX(d)=j=1k(nj+dnj)h_{\mathbb{P}X}(d)=\prod_{j=1}^{k}\binom{n_{j}+d}{n_{j}} [20, Example 18.15]. So

dimSd(X)=j=1k(nj+dnj),exp.grank=j=1k(nj+dnj)j=1knj+1.\dim\operatorname{S}^{d}(X)=\prod_{j=1}^{k}\binom{n_{j}+d}{n_{j}},\quad\operatorname{exp.grank}=\left\lceil\frac{\prod_{j=1}^{k}\binom{n_{j}+d}{n_{j}}}{\sum_{j=1}^{k}n_{j}+1}\right\rceil.

(ii) If Xn\mathbb{P}X\subseteq\mathbb{P}^{n} is a hypersurface defined by a form of degree tt, the Hilbert function of X\mathbb{P}X is

hX(d)={(n+dn),ifd<t,(n+dn)(n+dtn),otherwise.h_{\mathbb{P}X}(d)=\begin{cases}\binom{n+d}{n},~{}\text{if}~{}d<t,\\ \binom{n+d}{n}-\binom{n+d-t}{n},~{}\text{otherwise}.\end{cases}

Then, dimSd(X)=hX(d)\dim\operatorname{S}^{d}(X)=h_{\mathbb{P}{X}}(d) and the exp.grank\operatorname{exp.grank} can be obtained accordingly.

When X\mathbb{P}X is a curve, we can get the dimension of σr(vd(X))\sigma_{r}(v_{d}(X)) as follows.

Proposition 3.5.

If X\mathbb{P}X is a non-degenerate curve (i.e., dimX=1\dim\mathbb{P}X=1 and X\mathbb{P}X is not contained in any proper linear subspace of n\mathbb{P}^{n}), then

dimσr(vd(X))=min{2r,hX(d)}.\dim\sigma_{r}(v_{d}(X))=\min\left\{2r,h_{\mathbb{P}X}(d)\right\}.

Therefore, the generic symmetric XX-rank is hX(d)2\lceil\frac{h_{X}(d)}{2}\rceil. Moreover, if X\mathbb{P}X is nonsingular of genus gg and degree tt, then there exists an integer d0d_{0} such that for all dd0d\geq d_{0},

dimσr(vd(X))=min{2r,dtg+1},grank=dtg+12.\displaystyle\dim\sigma_{r}(v_{d}(X))=\min\{2r,dt-g+1\},\quad\operatorname{grank}=\lceil\frac{dt-g+1}{2}\rceil.
Proof.

The first part follows directly from [20, Example 11.30]. The “moreover” part follows from Riemann-Roch theorem [21, Chapter 4]. ∎

By [20, Example 13.7], if X\mathbb{P}X is a space curve of degree ee, i.e., X\mathbb{P}X is a curve in 3\mathbb{P}^{3} which intersects a generic plane in ee points, then d0=e2d_{0}=e-2 in Proposition 3.5. For an arbitrary curve X\mathbb{P}X, however, not much is known about d0d_{0}. The Hilbert functions are also known for Veronese varieties, Grassmann varieties and flag varieties, see [20, 19]. Hence the expected value of the generic rank for them may be calculated by (3.5).

3.3. Vandermonde decompositions for nonsymmetric tensors

A nonsymmetric tensor 𝒜(d+1)k\mathcal{A}\in(\mathbb{C}^{d+1})^{\otimes k} is said to admit a Vandermonde decomposition if there exist vectors (s=1,,ks=1,\ldots,k, j=1,,rj=1,\ldots,r)

vs(j):=(asj)d,(asj)d1bsj,,asj(bsj)d1,(bsj)d)d+1v_{s}^{(j)}:=\Big{(}a_{sj})^{d},(a_{sj})^{d-1}b_{sj},\ldots,a_{sj}(b_{sj})^{d-1},(b_{sj})^{d}\Big{)}\in\mathbb{C}^{d+1}

such that

𝒜=j=1rv1(j)vk(j).\mathcal{A}={\sum}_{j=1}^{r}v_{1}^{(j)}\otimes\cdots\otimes v_{k}^{(j)}.

The smallest integer rr in the above is called the Vandermonde rank of 𝒜\mathcal{A}, which we denote as vrank(𝒜)\operatorname{vrank}(\mathcal{A}). Since vs(j)=(asj,bsj)dSd2d+1,v_{s}^{(j)}=(a_{sj},b_{sj})^{\otimes d}\in\operatorname{S}^{d}\mathbb{C}^{2}\simeq\mathbb{C}^{d+1}, we can rewrite (3.6) equivalently as

(3.6) 𝒜=j=1r((a1j,b1j)(akj,bkj))d.\mathcal{A}={\sum}_{j=1}^{r}\Big{(}(a_{1j},b_{1j})\otimes\cdots\otimes(a_{kj},b_{kj})\Big{)}^{\otimes d}.

The Vandermonde decomposition can be thought of as a symmetric tensor decomposition on the set X2kX\subseteq\mathbb{C}^{2^{k}} such that

X1××1 (k times).\mathbb{P}X\coloneqq\mathbb{P}^{1}\times\cdots\times\mathbb{P}^{1}\quad\mbox{\, ($k$ times)}.

The variety σr(vd(X))Sd(2k)\sigma_{r}(v_{d}(X))\subseteq\mathbb{P}\operatorname{S}^{d}(\mathbb{C}^{2^{k}}) is exactly the Zariski closure of tensors whose Vandermonde ranks at most rr. The vanishing ideal of the Segre variety n1××nk\mathbb{P}^{n_{1}}\times\cdots\times\mathbb{P}^{n_{k}} is generated by 2×22\times 2 minors of its flattenings [18]. So d(n1××nk)0\mathcal{I}_{d}(\mathbb{P}^{n_{1}}\times\cdots\times\mathbb{P}^{n_{k}})\neq 0 for all d2d\geq 2. By Corollary 3.3, Sd(X)\operatorname{S}^{d}(X) is a proper subspace of Sd(2k)\operatorname{S}^{d}(\mathbb{C}^{2^{k}}). However, every 𝒜(d+1)k\mathcal{A}\in(\mathbb{C}^{d+1})^{\otimes k} has a Vandermonde decomposition.

Theorem 3.6.

Every tensor in (d+1)k(\mathbb{C}^{d+1})^{\otimes k} has a Vandermonde decomposition.

Proof.

Each 𝒜(d+1)k\mathcal{A}\in(\mathbb{C}^{d+1})^{\otimes k} admits a general tensor decomposition, say,

𝒜=j=1Luj,1uj,k\mathcal{A}={\sum}_{j=1}^{L}u_{j,1}\otimes\cdots\otimes u_{j,k}

for vectors uj,id+1u_{j,i}\in\mathbb{C}^{d+1}. Choose distinct numbers t0,t1,,tdt_{0},t_{1},\ldots,t_{d} and let

vl:=(1,tl,tl2,,tld),v_{l}:=(1,t_{l},t_{l}^{2},\ldots,t_{l}^{d}),

for l=0,1,,dl=0,1,\ldots,d. Clearly, v0,v1,,vdv_{0},v_{1},\ldots,v_{d} are linearly independent and they span d+1\mathbb{C}^{d+1}. For each uj,iu_{j,i}, there exists numbers λj,i,0,,λj,i,d\lambda_{j,i,0},\ldots,\lambda_{j,i,d} such that

uj,i=λj,i,0v0++λj,i,dvl.u_{j,i}=\lambda_{j,i,0}v_{0}+\cdots+\lambda_{j,i,d}v_{l}.

Plugging the above expression of uj,iu_{j,i} in the decomposition for 𝒜\mathcal{A}, we get

𝒜=j1,,jk=0lcj1,,jkvj1vjk,\mathcal{A}={\sum}_{j_{1},\ldots,j_{k}=0}^{l}c_{j_{1},\ldots,j_{k}}v_{j_{1}}\otimes\cdots\otimes v_{j_{k}},

for some scalars cj1,,jkc_{j_{1},\ldots,j_{k}}. ∎

4. Generating polynomials

Assume that Xn+1X\subseteq\mathbb{C}^{n+1} is a set defined by homogeneous polynomial equations. For a given tensor 𝒜Sd(n+1)\mathcal{A}\in\operatorname{S}^{d}(\mathbb{C}^{n+1}) with srankX(𝒜)=r\operatorname{srank}_{X}(\mathcal{A})=r, we discuss how to compute the symmetric XX-decomposition

(4.1) 𝒜=(u1)d++(ur)d,u1,,urX.\mathcal{A}=(u_{1})^{\otimes d}+\cdots+(u_{r})^{\otimes d},\quad u_{1},\dots,u_{r}\in X.

Suppose XX is given as

(4.2) X={xn+1:hi(x)=0,i=1,,N},X=\{x\in\mathbb{C}^{n+1}:\,h_{i}(x)=0,i=1,\ldots,N\},

with homogeneous polynomials hih_{i} in x:=(x0,x1,,xn)x:=(x_{0},x_{1},\ldots,x_{n}). Denote y:=(y1,,yn)y:=(y_{1},\ldots,y_{n}). The dehomogenization of XX is the set

(4.3) Y={yn:hi(1,y1,,yn)=0,i=1,,N}.Y=\{y\in\mathbb{C}^{n}:\,h_{i}(1,y_{1},\ldots,y_{n})=0,i=1,\ldots,N\}.

If each (uj)00(u_{j})_{0}\neq 0, then the decomposition (4.1) is equivalent to that

(4.4) 𝒜=λ1(1,v1)d++λr(1,vr)d,\mathcal{A}=\lambda_{1}(1,v_{1})^{\otimes d}+\cdots+\lambda_{r}(1,v_{r})^{\otimes d},

for scalars λj\lambda_{j}\in\mathbb{C} and vectors vjYv_{j}\in Y. In fact, they are

λj=((uj)0)d,vj=1(uj)0((uj)1,,(uj)n)𝖳.\lambda_{j}=((u_{j})_{0})^{d},\quad v_{j}=\frac{1}{(u_{j})_{0}}\big{(}(u_{j})_{1},\dots,(u_{j})_{n}\big{)}^{\mathsf{T}}.

The assumption that (uj)00(u_{j})_{0}\neq 0 is generic. For the rare case that it is zero, we can apply a generic coordinate change for 𝒜\mathcal{A} so that this assumption holds. Throughout this section, we assume the rank rr is given.

4.1. Generating polynomials

Denote the quotient ring C[Y]:=[y]/I(Y)C[Y]:=\mathbb{C}[y]/\operatorname{I}(Y), where I(Y)I(Y) is the vanishing ideal of YY. The C[Y]C[Y] is also called the coordinate ring of YY. We list monomials in [y]\mathbb{C}[y] with respect to the graded lexicographic order, i.e.,

1<y1<<yn<y12<y1y2<.1<y_{1}<\cdots<y_{n}<y_{1}^{2}<y_{1}y_{2}<\cdots.

We choose B0B_{0} to be the set of first rr monomials, whose images in [Y]\mathbb{C}[Y] are linearly independent, say,

B0={yβ1,,yβr}.B_{0}=\{y^{\beta_{1}},\dots,y^{\beta_{r}}\}.

The border set of B0B_{0} is B1:=B0y1B0ynB0.B_{1}:=B_{0}\cup y_{1}B_{0}\cup\cdots\cup y_{n}B_{0}. The boundary of B1B_{1} is

B1:=(B0y1B0ynB0)B0.\partial B_{1}:=\big{(}B_{0}\cup y_{1}B_{0}\cup\cdots\cup y_{n}B_{0}\big{)}\setminus B_{0}.

For a matrix Gr×|B1|G\in\mathbb{C}^{r\times\lvert\partial B_{1}\rvert}, we label it as

G=(ci,α)i[r],αB1.G=(c_{i,\alpha})_{i\in[r],\alpha\in\partial B_{1}}.

For each αB1\alpha\in\partial B_{1}, denote the polynomial in yy

(4.5) φ[G,α]:=i=1rci,αyβiyα.\varphi[G,\alpha]:={\sum}_{i=1}^{r}c_{i,\alpha}y^{\beta_{i}}-y^{\alpha}.

Denote the tuple of all such polynomials as

(4.6) φ[G]:=(φ[G,α](y):αB1).\varphi[G]\,:=\,\big{(}\varphi[G,\alpha](y):\alpha\in\partial B_{1}\big{)}.

Let JGJ_{G} be the ideal generated by φ[G]\varphi[G]. The set φ[G]\varphi[G] is called a border basis of JGJ_{G} with respect to B0B_{0}. We refer to [45, 14] for border sets and border bases.

In the following, we give the definition of generating polynomials which were introduced in [33]. For αB1\alpha\in\partial B_{1}, the polynomial φ[G,α]\varphi[G,\alpha] is called a generating polynomial for 𝒜Sd(n+1)\mathcal{A}\in\operatorname{S}^{d}(\mathbb{C}^{n+1}) if

(4.7) yγφ[G,α],𝒜=0γd|α|n.\langle y^{\gamma}\varphi[G,\alpha],\mathcal{A}\rangle=0\quad\forall\,\gamma\in\mathbb{N}^{n}_{d-\lvert\alpha\rvert}.

(See (2.4) for the operation ,\langle,\rangle.) If φ[G,α]\varphi[G,\alpha] is a generating polynomial for all αB1\alpha\in\partial B_{1}, then GG is called a generating matrix for 𝒜\mathcal{A}. The set of all generating matrices for 𝒜\mathcal{A} is denoted as 𝒢(𝒜)\mathcal{G}(\mathcal{A}). The condition (4.7) is equivalent to that

(4.8) i=1rci,α𝒜βi+γ=𝒜α+γ.{\sum}_{i=1}^{r}c_{i,\alpha}\mathcal{A}_{\beta_{i}+\gamma}=\mathcal{A}_{\alpha+\gamma}.

We use G(:,α)G(:,\alpha) to denote the α\alphath column of GG. Then (4.8) can be rewritten as

A[𝒜,α]G(:,α)=b[𝒜,α]A[\mathcal{A},\alpha]G(:,\alpha)=b[\mathcal{A},\alpha]

where A[𝒜,α],b[𝒜,α]A[\mathcal{A},\alpha],b[\mathcal{A},\alpha] are given as

(A[𝒜,α])γ,β=𝒜β+γ,(b[𝒜,α])γ=𝒜α+γ(βB0,γd|α|n).\big{(}A[\mathcal{A},\alpha])_{\gamma,\beta}=\mathcal{A}_{\beta+\gamma},\quad\big{(}b[\mathcal{A},\alpha])_{\gamma}=\mathcal{A}_{\alpha+\gamma}\quad\big{(}\beta\in B_{0},\,\gamma\in\mathbb{N}^{n}_{d-\lvert\alpha\rvert}\big{)}.

The solutions to (4.8) can be parameterized as cα+Nαwαc_{\alpha}+N_{\alpha}w_{\alpha}, where cαc_{\alpha} is a solution to (4.8), NαN_{\alpha} is a basis matrix for the nullspace, and wαw_{\alpha} is the vector of free parameters. So, every generating matrix can be parameterized as

(4.9) G(w)=C+N(w),G(w)=C+N(w),

where CC is a constant matrix and N(w)[r]×B1N(w)\in\mathbb{C}^{[r]\times\partial B_{1}}. The following is an example of parameterizing G(w)G(w).

Example 4.1.

Consider the tensor 𝒜S3(4)\mathcal{A}\in\operatorname{S}^{3}(\mathbb{C}^{4}) such that

𝒜(1,y1,y2,y3)= 32y3312y32y2+126y32y148y3y22+36y3y2y1+150y3y1220y2318y22y1+42y2y12+51y13+18y3236y3y2+84y3y130y22+12y2y1+45y12+6y36y2+9y11.\mathcal{A}(1,y_{1},y_{2},y_{3})\,=\,32\,{y_{{3}}}^{3}-12\,{y_{{3}}}^{2}y_{{2}}+126\,{y_{{3}}}^{2}y_{{1}}-48\,y_{{3}}{y_{{2}}}^{2}+36\,y_{{3}}y_{{2}}y_{{1}}\\ +150\,y_{{3}}{y_{{1}}}^{2}-20\,{y_{{2}}}^{3}-18\,{y_{{2}}}^{2}y_{{1}}+42\,y_{{2}}{y_{{1}}}^{2}+51\,{y_{{1}}}^{3}+18\,{y_{{3}}}^{2}-36\,y_{{3}}y_{{2}}\\ +84\,y_{{3}}y_{{1}}-30\,{y_{{2}}}^{2}+12\,y_{{2}}y_{{1}}+45\,{y_{{1}}}^{2}+6\,y_{{3}}-6\,y_{{2}}+9\,y_{{1}}-1.

For r=3r=3, if we choose B0={1,y1,y2}B_{0}=\{1,y_{1},y_{2}\}, then

B1={y3,y12,y1y2,y22,y1y3,y2y3},|B1|=6,\partial B_{1}=\{y_{3},y_{1}^{2},y_{1}y_{2},y_{2}^{2},y_{1}y_{3},y_{2}y_{3}\},\quad\lvert\partial B_{1}\rvert=6,
α1\displaystyle\alpha_{1} =(0,0,1),α2=(2,0,0),α3=(1,1,0),\displaystyle=(0,0,1),\quad\alpha_{2}=(2,0,0),\quad\alpha_{3}=(1,1,0),
α4\displaystyle\alpha_{4} =(0,2,0),α5=(1,0,1),α6=(0,1,1).\displaystyle=(0,2,0),\quad\alpha_{5}=(1,0,1),\quad\alpha_{6}=(0,1,1).

The A[𝒜,αi]A[\mathcal{A},\alpha_{i}] and b[𝒜,αi]b[\mathcal{A},\alpha_{i}] can be formulated accordingly. For instance,

A[𝒜,α1]= [\@arstrut(0,0,0)(1,0,0)(0,1,0)\\(0,0,0)-13-2\\(1,0,0)3152\\(0,1,0)-22-10\\(0,0,1)214-6\\(2,0,0)155114\\(1,1,0)214-6\\(0,2,0)-10-6-20\\(1,0,1)14506\\(0,1,1)-66-16\\(0,0,2)642-4\\] ,b[𝒜,α1]= [\@arstrut\\(0,0,0)2\\(1,0,0)14\\(0,1,0)-6\\(0,0,1)6\\(2,0,0)50\\(1,1,0)6\\(0,2,0)-16\\(1,0,1)42\\(0,1,1)-4\\(0,0,2)32\\] .A[\mathcal{A},\alpha_{1}]=\hbox{}\vbox{\kern 0.86108pt\hbox{$\kern 0.0pt\kern 2.5pt\kern-2.5pt\left[\kern 0.0pt\kern-2.5pt\kern-5.55557pt\vbox{\kern-0.86108pt\vbox{\vbox{ \halign{\kern\arraycolsep\hfil\@arstrut$\kbcolstyle#$\hfil\kern\arraycolsep& \kern\arraycolsep\hfil$\@kbrowstyle#$\ifkbalignright\relax\else\hfil\fi\kern\arraycolsep&& \kern\arraycolsep\hfil$\@kbrowstyle#$\ifkbalignright\relax\else\hfil\fi\kern\arraycolsep\cr 2.5pt\hfil\@arstrut$\scriptstyle$\hfil\kern 2.5pt&2.5pt\hfil$\scriptstyle(0,0,0)$\hfil\kern 2.5pt&2.5pt\hfil$\scriptstyle(1,0,0)$\hfil\kern 2.5pt&2.5pt\hfil$\scriptstyle(0,1,0)\\(0,0,0)$\hfil\kern 2.5pt&2.5pt\hfil$\scriptstyle-1$\hfil\kern 2.5pt&2.5pt\hfil$\scriptstyle 3$\hfil\kern 2.5pt&2.5pt\hfil$\scriptstyle-2\\(1,0,0)$\hfil\kern 2.5pt&2.5pt\hfil$\scriptstyle 3$\hfil\kern 2.5pt&2.5pt\hfil$\scriptstyle 15$\hfil\kern 2.5pt&2.5pt\hfil$\scriptstyle 2\\(0,1,0)$\hfil\kern 2.5pt&2.5pt\hfil$\scriptstyle-2$\hfil\kern 2.5pt&2.5pt\hfil$\scriptstyle 2$\hfil\kern 2.5pt&2.5pt\hfil$\scriptstyle-10\\(0,0,1)$\hfil\kern 2.5pt&2.5pt\hfil$\scriptstyle 2$\hfil\kern 2.5pt&2.5pt\hfil$\scriptstyle 14$\hfil\kern 2.5pt&2.5pt\hfil$\scriptstyle-6\\(2,0,0)$\hfil\kern 2.5pt&2.5pt\hfil$\scriptstyle 15$\hfil\kern 2.5pt&2.5pt\hfil$\scriptstyle 51$\hfil\kern 2.5pt&2.5pt\hfil$\scriptstyle 14\\(1,1,0)$\hfil\kern 2.5pt&2.5pt\hfil$\scriptstyle 2$\hfil\kern 2.5pt&2.5pt\hfil$\scriptstyle 14$\hfil\kern 2.5pt&2.5pt\hfil$\scriptstyle-6\\(0,2,0)$\hfil\kern 2.5pt&2.5pt\hfil$\scriptstyle-10$\hfil\kern 2.5pt&2.5pt\hfil$\scriptstyle-6$\hfil\kern 2.5pt&2.5pt\hfil$\scriptstyle-20\\(1,0,1)$\hfil\kern 2.5pt&2.5pt\hfil$\scriptstyle 14$\hfil\kern 2.5pt&2.5pt\hfil$\scriptstyle 50$\hfil\kern 2.5pt&2.5pt\hfil$\scriptstyle 6\\(0,1,1)$\hfil\kern 2.5pt&2.5pt\hfil$\scriptstyle-6$\hfil\kern 2.5pt&2.5pt\hfil$\scriptstyle 6$\hfil\kern 2.5pt&2.5pt\hfil$\scriptstyle-16\\(0,0,2)$\hfil\kern 2.5pt&2.5pt\hfil$\scriptstyle 6$\hfil\kern 2.5pt&2.5pt\hfil$\scriptstyle 42$\hfil\kern 2.5pt&2.5pt\hfil$\scriptstyle-4\\$\hfil\kern 2.5pt\crcr}}}}\right]$}},\quad b[\mathcal{A},\alpha_{1}]=\hbox{}\vbox{\kern 0.86108pt\hbox{$\kern 0.0pt\kern 2.5pt\kern-2.5pt\left[\kern 0.0pt\kern-2.5pt\kern-5.55557pt\vbox{\kern-0.86108pt\vbox{\vbox{ \halign{\kern\arraycolsep\hfil\@arstrut$\kbcolstyle#$\hfil\kern\arraycolsep& \kern\arraycolsep\hfil$\@kbrowstyle#$\ifkbalignright\relax\else\hfil\fi\kern\arraycolsep&& \kern\arraycolsep\hfil$\@kbrowstyle#$\ifkbalignright\relax\else\hfil\fi\kern\arraycolsep\cr 2.5pt\hfil\@arstrut$\scriptstyle$\hfil\kern 2.5pt&2.5pt\hfil$\scriptstyle~{}\\(0,0,0)$\hfil\kern 2.5pt&2.5pt\hfil$\scriptstyle 2\\(1,0,0)$\hfil\kern 2.5pt&2.5pt\hfil$\scriptstyle 14\\(0,1,0)$\hfil\kern 2.5pt&2.5pt\hfil$\scriptstyle-6\\(0,0,1)$\hfil\kern 2.5pt&2.5pt\hfil$\scriptstyle 6\\(2,0,0)$\hfil\kern 2.5pt&2.5pt\hfil$\scriptstyle 50\\(1,1,0)$\hfil\kern 2.5pt&2.5pt\hfil$\scriptstyle 6\\(0,2,0)$\hfil\kern 2.5pt&2.5pt\hfil$\scriptstyle-16\\(1,0,1)$\hfil\kern 2.5pt&2.5pt\hfil$\scriptstyle 42\\(0,1,1)$\hfil\kern 2.5pt&2.5pt\hfil$\scriptstyle-4\\(0,0,2)$\hfil\kern 2.5pt&2.5pt\hfil$\scriptstyle 32\\$\hfil\kern 2.5pt\crcr}}}}\right]$}}.

The generating matrix is uniquely determined, i.e.,

G(w)=[131832046320141272047201019101910].G(w)=\left[\begin{array}[]{cccccc}-1&-3&-1&{\frac{83}{20}}&-4&{\frac{63}{20}}\\ \vskip 6.0pt plus 2.0pt minus 2.0pt\cr 1&4&1&-{\frac{27}{20}}&4&-{\frac{7}{20}}\\ \vskip 6.0pt plus 2.0pt minus 2.0pt\cr 1&0&1&{\frac{9}{10}}&1&{\frac{9}{10}}\end{array}\right].

For r=4r=4, if we choose B0={1,y1,y2,y3},B_{0}=\{1,y_{1},y_{2},y_{3}\}, then B1={y12,y1y2,y22,y1y3,y2y3,y32}.\partial B_{1}=\{y_{1}^{2},y_{1}y_{2},y_{2}^{2},y_{1}y_{3},y_{2}y_{3},y_{3}^{2}\}. The generating matrix has 66 parameters, i.e.,

G(w)=[3+w11+w283/20+w34+w463/20+w53/20+w64w11w227/20w34w47/20w553/20w6w11w29/10w31w49/10w59/10w63+w1w2w3w4w5w6].G(w)=\begin{bmatrix}-3+w_{1}&-1+w_{2}&83/20+w_{3}&-4+w_{4}&63/20+w_{5}&3/20+w_{6}\\ 4-w_{1}&1-w_{2}&-27/20-w_{3}&4-w_{4}&-7/20-w_{5}&53/20-w_{6}\\ -w_{1}&1-w_{2}&9/10-w_{3}&1-w_{4}&9/10-w_{5}&9/10-w_{6}\\ -3+w_{1}&w_{2}&w_{3}&w_{4}&w_{5}&w_{6}\\ \end{bmatrix}.

4.2. Polynomial division by φ[G]\varphi[G]

From now on, suppose the vanishing ideal

I(Y)=g1,,gN,\operatorname{I}(Y)=\left\langle g_{1},\dots,g_{N}\right\rangle,

generated by g1,,gN[y]g_{1},\dots,g_{N}\in\mathbb{C}[y]. For p[y]p\in\mathbb{C}[y], denote by NF(p;G)\mbox{NF}(p;G) the normal form of pp with respect to φ[G]\varphi[G], i.e., NF(p;G)\mbox{NF}(p;G) is the remainder of pp divided by φ[G]\varphi[G], obtained by the Border Division Algorithm [22, Proposition 6.4.11]. Here we use the Formally, NF(p;G)\mbox{NF}(p;G) is the polynomial such that pNF(p;G)JGp-\mbox{NF}(p;G)\in J_{G}, the ideal generated by polynomials in φ[G]\varphi[G]. Note that NF(p;G)\mbox{NF}(p;G) is a polynomial in y:=(y1,,yn)y:=(y_{1},\ldots,y_{n}) whose coefficients are parametrized by the entries of GG.

Proposition 4.2.

Suppose B0B_{0} is the set of first rr monomials222We remark that instead of the first rr monomials, one may choose any rr monomials which are connected to one. Interested readers are referred to [31] for a detailed discussion. For simplicity, we use in this paper the set of first rr monomials, which is obviously connected to one. This choice of basis will be convenient in practical computations like those in Section 6. yβ1,,yβry^{\beta_{1}},\dots,y^{\beta_{r}} in the graded lexicographic ordering such that their images in [y]/I(Y)\mathbb{C}[y]/\operatorname{I}(Y) are linearly independent. Then, a polynomial p[y]p\in\mathbb{C}[y] belongs to JGJ_{G} if and only if NF(p;G)=0\mbox{NF}(p;G)=0.

Proof.

By the construction, φ[G]\varphi[G] is a border basis of JGJ_{G}, so it contains a Gröbner basis (say, SS) of JGJ_{G} with respect to the graded lexicographic ordering. Indeed, those elements in GG which are associated to the corner of B0B_{0} form a Gröbner basis. See [45, Proposition 2.30] or [14, Proposition 4.3.9] for definition of the corner of B0B_{0} and more details of the proof. Therefore, pJGp\in J_{G} if and only if NF(p;G)=0\mbox{NF}(p;G)=0. Since Sφ[G]S\subseteq\varphi[G], the normal form of pp with respect to SS is the same as the normal form with respect to φ[G]\varphi[G], which is NF(y;G)\mbox{NF}(y;G). Therefore, pJGp\in J_{G} if and only if NF(p;G)=0\mbox{NF}(p;G)=0. ∎

The condition NF(p;G)=0\mbox{NF}(p;G)=0 is equivalent to that its coefficients are zeros identically. The coefficients of NF(p;G)\mbox{NF}(p;G) are polynomials in ci,αc_{i,\alpha}. Their degrees can be bounded as follows. For a degree k>1k>1, define the set BkB_{k} recursively as

Bk:=Bk1y1Bk1ynBk1.B_{k}\,:=\,B_{k-1}\cup y_{1}B_{k-1}\cup\cdots\cup y_{n}B_{k-1}.

The monomials in BkB_{k} generate a subspace, which we denote as SpanBk\mbox{Span}\,B_{k}.

Lemma 4.3.

Let Bk,p(y)B_{k},p(y) and NF(p;G)\mbox{NF}(p;G) be as above. Write p=p1+p2p=p_{1}+p_{2}, where p1i1SpanBip_{1}\in\cup_{i\geq 1}\mbox{Span}\,B_{i} and p2p_{2} is a polynomial whose monomials are not contained in any BiB_{i}. If p1SpanBkp_{1}\in\mbox{Span}\,B_{k}, then the coefficients of NF(p;G)\mbox{NF}(p;G) are polynomials of degree at most kk in GG.

Proof.

Note that NF(p;G)=NF(p1;G)+p2\mbox{NF}(p;G)=\mbox{NF}(p_{1};G)+p_{2}, because p2p_{2} is not reducible by φ[G]\varphi[G]. The coefficients of p2p_{2} do not depend on GG.

For the case k=1k=1, we can write p1SpanB1p_{1}\in\operatorname{Span}B_{1} as

p1=βB1aβyβ+γB1aγyγ,p_{1}={\sum}_{\beta\in\partial B_{1}}a_{\beta}y^{\beta}+{\sum}_{\gamma\in B_{1}}a_{\gamma}y^{\gamma},

with coefficients aβ,aγa_{\beta},a_{\gamma}\in\mathbb{C}. The reduction of pp by φ[G]\varphi[G] is equivalent to that

NF(p1;G)=p1+βB1aβφ[G,β]=γB1aγ(G)yγ,\mbox{NF}(p_{1};G)\,=\,p_{1}+{\sum}_{\beta\in\partial B_{1}}a_{\beta}\varphi[G,\beta]={\sum}_{\gamma\in B_{1}}a^{\prime}_{\gamma}(G)y^{\gamma},

where aγ(G)a^{\prime}_{\gamma}(G) is affine linear in the entries of GG and are affine linear in the coefficients of pp. So, the coefficients of NF(p;G)\mbox{NF}(p;G) are affine linear in GG.

For the case k>1k>1, we can write each p1p_{1} as

p1=p0+βBkaβyβ,p_{1}=p_{0}+{\sum}_{\beta\in\partial B_{k}}a_{\beta}y^{\beta},

with p0SpanBk1p_{0}\in\operatorname{Span}B_{k-1} and coefficients aβa_{\beta}\in\mathbb{C}. For each βBk\beta\in\partial B_{k}, denote by i(β)i(\beta) the smallest i[n]i\in[n] such that βyiBk1\beta\in y_{i}B_{k-1}. Then

p1=p0+βBkaβyi(β)yβei(β),p_{1}=p_{0}+{\sum}_{\beta\in\partial B_{k}}a_{\beta}y_{i(\beta)}y^{\beta-e_{i(\beta)}},
NF(p1;G)=NF(p0;G)+βBkaβNF(yi(β)NF(yβei(β);G);G).\mbox{NF}(p_{1};G)=\mbox{NF}(p_{0};G)+{\sum}_{\beta\in\partial B_{k}}a_{\beta}\mbox{NF}\Big{(}y_{i(\beta)}\mbox{NF}(y^{\beta-e_{i(\beta)}};G);G\Big{)}.

By induction, the coeffieints of NF(p1;G)\mbox{NF}(p_{1};G) are of degree k\leq k in GG. ∎

Example 4.4.

Suppose that XX is the Segre variety 1×13\mathbb{P}^{1}\times\mathbb{P}^{1}\subset\mathbb{P}^{3}. Then YY is the surface in 3\mathbb{C}^{3} defined by g(y)y3y1y2=0,g(y)\coloneqq y_{3}-y_{1}y_{2}=0, where y1,y2y_{1},y_{2} and y3y_{3} are coordinates333 If we use the notation in Proposition 4.3, coordinates of 3\mathbb{C}^{3} should be y1,0,y0,1y_{1,0},y_{0,1} and y1,1y_{1,1}. of 3\mathbb{C}^{3}. If we choose B1={1,y1,y2,y3},B_{1}=\{1,y_{1},y_{2},y_{3}\}, then

B1={y12,y1y2,y22,y1y3,y2y3,y32}.\partial B_{1}=\{y_{1}^{2},y_{1}y_{2},y_{2}^{2},y_{1}y_{3},y_{2}y_{3},y_{3}^{2}\}.

As in Example 4.1, we can get

φ[G,(2,0,0)]\displaystyle\varphi[G,(2,0,0)] =(3+w1)+(4w1)y1w1y2+w1y3y12,\displaystyle=(-3+w_{1})+(4-w_{1})y_{1}-w_{1}y_{2}+w_{1}y_{3}-y_{1}^{2},
φ[G,(1,1,0)]\displaystyle\varphi[G,(1,1,0)] =(1+w2)+(1w2)y1+(1w2)y2+w2y3y1y2,\displaystyle=(-1+w_{2})+(1-w_{2})y_{1}+(1-w_{2})y_{2}+w_{2}y_{3}-y_{1}y_{2},
φ[G,(0,2,0)]\displaystyle\varphi[G,(0,2,0)] =(83/20+w4)+(27/20w4)y1+(9/10w4)y2+w4y3y22,\displaystyle=(83/20+w_{4})+(-27/20-w_{4})y_{1}+(9/10-w_{4})y_{2}+w_{4}y_{3}-y_{2}^{2},
φ[G,(1,0,1)]\displaystyle\varphi[G,(1,0,1)] =(4+w3)+(4w3)y1+(1w3)y2+w3y3y1y3,\displaystyle=(-4+w_{3})+(4-w_{3})y_{1}+(1-w_{3})y_{2}+w_{3}y_{3}-y_{1}y_{3},
φ[G,(0,1,1)]\displaystyle\varphi[G,(0,1,1)] =(63/20+w5)+(7/20w5)y1+(9/10w5)y2+w5y3y2y3,\displaystyle=(63/20+w_{5})+(-7/20-w_{5})y_{1}+(9/10-w_{5})y_{2}+w_{5}y_{3}-y_{2}y_{3},
φ[G,(0,0,2)]\displaystyle\varphi[G,(0,0,2)] =(3/20+w6)+(53/20w6)y1+(9/10w6)y2+w6y3y2y3.\displaystyle=(3/20+w_{6})+(-53/20-w_{6})y_{1}+(9/10-w_{6})y_{2}+w_{6}y_{3}-y_{2}y_{3}.

The normal form NF(g;G)NF(g;G) of gg is

NF(g;G)=(1w2)(1w2)y1(1w2)y2+(1w2)y3.NF(g;G)=(1-w_{2})-(1-w_{2})y_{1}-(1-w_{2})y_{2}+(1-w_{2})y_{3}.

The condition NF(g;G)=0NF(g;G)=0 requires that 1w2=01-w_{2}=0.

4.3. Commutativity conditions

For each 1in1\leq i\leq n and G[r]×B1G\in\mathbb{C}^{[r]\times\partial B_{1}}, define the matrix Mi(G)r×rM_{i}(G)\in\mathbb{C}^{r\times r} as

(Mi(G))s,t={1,ifβs=βt+eiB0,0,ifβsβt+eiB0,cs,βt+ei,ifβt+eiB1.(M_{i}(G))_{s,t}=\begin{cases}1,\quad~{}\text{if}~{}\beta_{s}=\beta_{t}+e_{i}\in B_{0},\\ 0,\quad~{}\text{if}~{}\beta_{s}\neq\beta_{t}+e_{i}\in B_{0},\\ c_{s,\beta_{t}+e_{i}},\quad~{}\text{if}~{}\beta_{t}+e_{i}\in\partial B_{1}.\end{cases}

They are also called multiplication matrices for the ideal JGJ_{G}.

Theorem 4.5.

Let B0B_{0} be the set of first rr monomials whose images in [Y]\mathbb{C}[Y] are linearly independent. Then, for G[r]×B1G\in\mathbb{C}^{[r]\times\partial B_{1}}, the polynomial system

(4.10) φ[G](y)=0\varphi[G](y)=0

has rr solutions (counting multipliciites) and they belong to YY if and only if

(4.11) [Mi(G),Mj(G)]=0(1i<jn),\big{[}M_{i}(G),M_{j}(G)\big{]}=0\quad(1\leq i<j\leq n),
(4.12) NF(gi;G)=0(i=1,,N),\mbox{NF}(g_{i};G)=0\quad(i=1,\dots,N),

where g1,,gN=I(Y)\left\langle g_{1},\ldots,g_{N}\right\rangle=I(Y).

Proof.

By Theorem 2.4 of [33], (4.10) has rr solutions if and only if (4.11) holds. All the solutions are contained in YY if and only if I(Y)JG\operatorname{I}(Y)\subseteq J_{G}, which is equivalent to that each normal form NF(gi;G)=0\mbox{NF}(g_{i};G)=0, by Proposition 4.2. ∎

5. An algorithm for computing STDXs

Let X,YX,Y be the varieties as in (4.2) and (4.3). This section discusses how to compute a symmetric tensor decomposition on XX for a given tensor 𝒜Sd(n+1)\mathcal{A}\in\operatorname{S}^{d}(\mathbb{C}^{n+1}) whose symmetric XX-rank is rr. To compute (4.1), it is enough to compute

(5.1) 𝒜=λ1(1,v1)d++λr(1,vr)d\mathcal{A}=\lambda_{1}(1,v_{1})^{\otimes d}+\cdots+\lambda_{r}(1,v_{r})^{\otimes d}

for vectors v1,,vrYv_{1},\ldots,v_{r}\in Y and scalars λ1,,λr\lambda_{1},\ldots,\lambda_{r}.

Choose B0={xβ1,,xβr}B_{0}=\{x^{\beta_{1}},\ldots,x^{\beta_{r}}\} to be the set of first rr monomials that are linearly independent in C[Y]C[Y]. For a point vnv\in\mathbb{C}^{n}, denote by B0(v)rB_{0}(v)\in\mathbb{C}^{r} the column vector whose iith entry is vβiv^{\beta_{i}}. Denote the Zariski open subset DD of (n)r(\mathbb{C}^{n})^{r}

D={(v1,,vr)(n)r:det[B0(v1)B0(vr)]0}.D=\{(v_{1},\ldots,v_{r})\in(\mathbb{C}^{n})^{r}:\,\det\begin{bmatrix}B_{0}(v_{1})&\cdots B_{0}(v_{r})\end{bmatrix}\neq 0\}.

Recall that φ[G]\varphi[G] is the tuple of generating polynomials as in (4.6) and 𝒢(𝒜)\mathcal{G}(\mathcal{A}) denotes the set of generating matrices for 𝒜\mathcal{A}.

Theorem 5.1.

Let X,Y,DX,Y,D be as above. For each 𝒜Sd(n+1)\mathcal{A}\in\operatorname{S}^{d}(\mathbb{C}^{n+1}), we have:

  • If G𝒢(𝒜)G\in\mathcal{G}(\mathcal{A}) and v1,,vrYv_{1},\dots,v_{r}\in Y are distinct zeros of φ[G]\varphi[G], then there exist scalars λ1,,λr\lambda_{1},\ldots,\lambda_{r} satisfying (5.1).

  • If the decomposition (5.1) holds for 𝒜\mathcal{A} and (v1,,vr)D(v_{1},\dots,v_{r})\in D, then there exists a unique G𝒢(𝒜)G\in\mathcal{G}(\mathcal{A}) such that v1,,vrv_{1},\dots,v_{r} are zeros of φ[G]\varphi[G].

Proof.

The conclusions mostly follow from Theorem 3.2 of [33]. The difference is that we additionally require the points v1,,vrYv_{1},\ldots,v_{r}\in Y. ∎

According to Theorem 5.1, to compute a symmetric XX-rank decomposition for 𝒜\mathcal{A}, we need to find a generating matrix G𝒢(𝒜)G\in\mathcal{G}(\mathcal{A}) such that

  1. (1)

    φ[G]\varphi[G] has rr distinct zeros.

  2. (2)

    The zeros of φ[G]\varphi[G] are contained in YY, i.e., I(Y)JG\operatorname{I}(Y)\subseteq J_{G}.

Conditions (1)(1) and (2)(2) are equivalent to (4.11) and (4.12) by Theorem 5.1. Suppose the vanishing ideal I(Y)=g1,,gNI(Y)=\left\langle g_{1},\dots,g_{N}\right\rangle. We have the following algorithm.

Algorithm 5.2.

For a given 𝒜Sd(n+1)\mathcal{A}\in\operatorname{S}^{d}(\mathbb{C}^{n+1}) with srankX(𝒜)r\operatorname{srank}_{X}(\mathcal{A})\leq r, do the following:

  • Step 0

    Choose the set of first rr monomials yβ1,,yβry^{\beta_{1}},\dots,y^{\beta_{r}} that are linearly independent in the quotient ring [Y]:=[y]/I(Y)\mathbb{C}[Y]:=\mathbb{C}[y]/\operatorname{I}(Y), with respect to the graded lexicographic monomial order.

  • Step 1

    Parameterize the generating matrix G(w)=C+N(w)G(w)=C+N(w) as in (4.9).

  • Step 2

    For each gig_{i}, compute NF(gi;G(w))\mbox{NF}(g_{i};G(w)) with respect to φ[G(w)]\varphi[G(w)].

  • Step 3

    Compute a solution of the polynomial system

    (5.2) {[Mi(G(w)),Mj(G(w))]=0(1i<jn),NF(gi;G(w))=0(1iN).\left\{\begin{array}[]{rl}\big{[}M_{i}(G(w)),M_{j}(G(w))\big{]}&=0\quad(1\leq i<j\leq n),\\ \mbox{NF}(g_{i};G(w))&=0\quad(1\leq i\leq N).\end{array}\right.
  • Step 4

    Compute rr zeros v1,,vrnv_{1},\dots,v_{r}\in\mathbb{C}^{n} of the polynomial system

    φ[G(w),α]=0(αB1).\varphi[G(w),\alpha]=0\quad(\alpha\in\partial B_{1}).
  • Step 5

    Determine scalars λ1,,λr\lambda_{1},\dots,\lambda_{r} satisfying (5.1).

The major task of Algorithm 5.2 is in Step 3. It requires to solve a set of polynomial system, which is given by commutative equations and normal forms. The commutative equations are quadratic in the parameter ww. The equations NF(gi;G)=0\mbox{NF}(g_{i};G)=0 are polynomial in ww, whose degrees are bounded in Lemma 4.3. One can apply the existing symbolic or numerical methods for solving polynomial equations. In Step 4, the polynomials φ[G(w),α]\varphi[G(w),\alpha] have special structures. One can get a Gröbner basis quickly, hence their zeros can be computed efficiently. We refer to [10] and [33, Sec. 2.4] for how to compute their common zeros.

Remark 5.3.

In Algorithm 5.2, we need to know a value of rr, with rsrankX(𝒜)r\geq\operatorname{srank}_{X}(\mathcal{A}). Typically, such a rr is not known. In practice, we can choose rr heuristically. For instance, we can choose rr to be the expected generic rank given in the subsection 3.2. If the flattening matrices of 𝒜\mathcal{A} have low ranks, we can choose rr to be the maximum of their ranks. For any case, if the system (5.2) cannot be solved, we can increase the value of rr and repeat the algorithm.

We conclude this section with an example of applying Algorithm 5.2.

Example 5.4.

Let 𝒜S3(4)\mathcal{A}\in\operatorname{S}^{3}(\mathbb{C}^{4}) be the same tensor as in Example 4.1. Let X4X\subseteq\mathbb{C}^{4} be the set whose dehomogenization Y3Y\subseteq\mathbb{C}^{3} is the surface whose defining ideal is I(Y)=y3y1y2I(Y)=\langle y_{3}-y_{1}y_{2}\rangle. The maximum rank of flattening matrices of 𝒜\mathcal{A} is 33, so we apply Algorithm 5.2 with r=3r=3. Choose B0={1,y1,y2}B_{0}=\{1,y_{1},y_{2}\}. From the calculation in Example 4.1, we can get

M1(G(w))\displaystyle M_{1}(G(w)) =[031141001],M2(G(w))=[01832001272011910],\displaystyle=\left[\begin{array}[]{rrr}0&-3&-1\\ \vskip 6.0pt plus 2.0pt minus 2.0pt\cr 1&4&1\\ \vskip 6.0pt plus 2.0pt minus 2.0pt\cr 0&0&1\end{array}\right],\quad M_{2}(G(w))=\left[\begin{array}[]{rrr}0&-1&{\frac{83}{20}}\\ \vskip 6.0pt plus 2.0pt minus 2.0pt\cr 0&1&-{\frac{27}{20}}\\ \vskip 6.0pt plus 2.0pt minus 2.0pt\cr 1&1&{\frac{9}{10}}\end{array}\right],
M3(G(w))\displaystyle M_{3}(G(w)) =[1463201472011910].\displaystyle=\left[\begin{array}[]{rrr}-1&-4&{\frac{63}{20}}\\ \vskip 6.0pt plus 2.0pt minus 2.0pt\cr 1&4&-{\frac{7}{20}}\\ \vskip 6.0pt plus 2.0pt minus 2.0pt\cr 1&1&{\frac{9}{10}}\end{array}\right].

The matrices M1(G(w)),M2(G(w))M_{1}(G(w)),M_{2}(G(w)) and M3(G(w))M_{3}(G(w)) commute. The generating polynomials are

φ[G,(0,0,1)]\displaystyle\varphi[G,(0,0,1)] =(1+y1+y2)y3,\displaystyle=(-1+y_{1}+y_{2})-y_{3},
φ[G,(2,0,0)]\displaystyle\varphi[G,(2,0,0)] =(3+4y1)y12,\displaystyle=(-3+4y_{1})-y_{1}^{2},
φ[G,(1,1,0)]\displaystyle\varphi[G,(1,1,0)] =(1+y1+y2)y1y2,\displaystyle=(-1+y_{1}+y_{2})-y_{1}y_{2},
φ[G,(0,2,0)]\displaystyle\varphi[G,(0,2,0)] =(83/2027/20y1+9/10y2)y22,\displaystyle=(83/20-27/20y_{1}+9/10y_{2})-y_{2}^{2},
φ[G,(1,0,1)]\displaystyle\varphi[G,(1,0,1)] =(4+4y1+y2)y1y3,\displaystyle=(-4+4y_{1}+y_{2})-y_{1}y_{3},
φ[G,(0,1,1)]\displaystyle\varphi[G,(0,1,1)] =(63/207/20y1+9/10y2)y2y3.\displaystyle=(63/20-7/20y_{1}+9/10y_{2})-y_{2}y_{3}.

They have 33 common zeros. There are no radical formulae for them, but they can be numerically evaluated as

(3,1,3),(1,1.283,1.283),(1,2.183,2.183).(3,1,3),\quad(1,-1.283,-1.283),\quad(1,2.183,2.183).

The given symmetric XX-decomposition for 𝒜\mathcal{A} as

𝒜~(y)\displaystyle\widetilde{\mathcal{A}}(y) 2(1+3y1+y2+3y3)30.7353(1+y11.283y21.283y3)3\displaystyle\coloneqq 2\,\left(1+3\,y_{{1}}+\,y_{{2}}+3\,y_{{3}}\right)^{3}-0.7353\,\left(1+\,y_{{1}}-1.283\,y_{{2}}-1.283\,y_{{3}}\right)^{3}
2.265(1+y1+2.183y2+2.183y3)3.\displaystyle-2.265\,\left(1+\,y_{{1}}+2.183\,y_{{2}}+2.183\,y_{{3}}\right)^{3}.

Because of numerical errors, we do not have 𝒜~=𝒜\widetilde{\mathcal{A}}=\mathcal{A} exactly, but the round-off error 𝒜~𝒜6.841016\lVert\widetilde{\mathcal{A}}-\mathcal{A}\rVert\approx 6.84\cdot 10^{-16}.

6. Numerical experiments

In this section, we present examples of applying Algorithm 5.2 to compute symmetrix XX-rank decompositions. The computation is implemented in a laptop with a 2.5 GHz Intel Core i7 processor. The software for carrying out numerical experiments is Maple 2017. We solve the system (5.2) by the built-in function fsolve directly. The algorithm returns a decomposition

𝒜~λ~1(1,v~1)d++λ~r(1,v~r)d.\widetilde{\mathcal{A}}\coloneqq\widetilde{\lambda}_{1}(1,\widetilde{v}_{1})^{\otimes d}+\cdots+\widetilde{\lambda}_{r}(1,\widetilde{v}_{r})^{\otimes d}.

Because of round-off errors, the equation 𝒜~=𝒜\widetilde{\mathcal{A}}=\mathcal{A} does not hold exactly. We use the absolute error 𝒜𝒜~\lVert\mathcal{A}-\widetilde{\mathcal{A}}\rVert or the relative one 𝒜𝒜~/𝒜\lVert\mathcal{A}-\widetilde{\mathcal{A}}\rVert/\lVert\mathcal{A}\rVert to verify the correctness. Here, the Hilbert-Schmidt norm of 𝒜\mathcal{A} is used, i.e.,

𝒜=(i1,,im|𝒜i1im|2)1/2.\|\mathcal{A}\|=\Big{(}{\sum}_{i_{1},\ldots,i_{m}}|\mathcal{A}_{i_{1}\ldots i_{m}}|^{2}\Big{)}^{1/2}.

We display the computed decompositions by showing

V~=[v~1v~r],Λ~=[λ~1λ~r].\widetilde{V}=\begin{bmatrix}\widetilde{v}_{1}\\ \vdots\\ \widetilde{v}_{r}\end{bmatrix},\quad\widetilde{\Lambda}=\begin{bmatrix}\widetilde{\lambda}_{1}\\ \vdots\\ \widetilde{\lambda}_{r}\end{bmatrix}.

For neatness, only four decimal digits are shown, and ii denotes the unit pure imaginary number. If the real or imaginary part of a complex number is smaller than 101010^{-10}, we treat it as zero and do not display it, for cleanness of the paper. To apply Algorithm 5.2, we need a value for the rank rr. This issue is discussed in Remark 5.3. In our computation, we initially choose rr to be the maximum rank of the flattening matrices of the given tensor 𝒜\mathcal{A}. If the equations (4.8) or (5.2) are inconsistent, we need to increase the value of rr by one, until Algorithm 5.2 successfully returns a decomposition.

For the set YY dehomogenized from XX as in (4.3), we need generators of its vanishing ideal I(Y)I(Y). For some YY, it is easy to compute the generators of I(Y)I(Y); for some YY, it may be difficult to compute them. This is a classical, standard problem in symbolic computation. We refer to [13, 15, 17] for the related work. So, we do not focus on how to compute generators of I(Y)I(Y) in this paper. In our examples, the generators of I(Y)I(Y) are known or can be computed easily.

First we illustrate how to apply Algorithm 3.2 to detect the existence of the STDX for a given 𝒜\mathcal{A} and X\mathbb{P}X.

Example 6.1.

We consider 𝒜S3(4)\mathcal{A}\in\operatorname{S}^{3}(\mathbb{C}^{4}) that is given as

𝒜ijk=i+j+k\mathcal{A}_{ijk}=i+j+k

and X3X\subseteq\mathbb{P}^{3} that is defined by x22=x12+x02x_{2}^{2}=x_{1}^{2}+x_{0}^{2}, x32=x22+x12x_{3}^{2}=x_{2}^{2}+x_{1}^{2}. According to Algorithm 3.2, we have

f1=x22x12x02,f2=x32x22x12.f_{1}=x_{2}^{2}-x_{1}^{2}-x_{0}^{2},\quad f_{2}=x_{3}^{2}-x_{2}^{2}-x_{1}^{2}.

We let β14\beta\in\mathbb{N}^{4}_{1} be β=(1,0,0,0)\beta=(1,0,0,0) and hence we have f1,β=f1x0f_{1,\beta}=f_{1}x_{0}. It is straightforward to verify that f1,β,𝒜0\langle f_{1,\beta},\mathcal{A}\rangle\neq 0 and hence 𝒜\mathcal{A} has no STDX for XX. Another example is

𝒜S4(3),𝒜ijkl=(i+j+k+l)2(i2+j2+k2+l2)\mathcal{A}\in\operatorname{S}^{4}(\mathbb{C}^{3}),\quad\mathcal{A}_{ijkl}=(i+j+k+l)^{2}-(i^{2}+j^{2}+k^{2}+l^{2})

and X2X\in\mathbb{P}^{2} is defined by x0x1+x1x2+x0x2=0,x02x1+x12x2+x22x0=0x_{0}x_{1}+x_{1}x_{2}+x_{0}x_{2}=0,x_{0}^{2}x_{1}+x_{1}^{2}x_{2}+x_{2}^{2}x_{0}=0. By Algorithm 3.2 again, we can show easily that 𝒜\mathcal{A} does not admit an STDX for such XX.

The resting examples in this section are devoted to exhibit the validity and efficiency of Algorithm 5.2. To this end, we make the following convention on the representations of tensors. Recall that a tensor 𝒜Sd(n+1)\mathcal{A}\in\operatorname{S}^{d}(\mathbb{C}^{n+1}) is an array of numbers whose elements are indexed by (i1,,id)(i_{1},\dots,i_{d}), i.e., 𝒜=(𝒜i1,,id)\mathcal{A}=(\mathcal{A}_{i_{1},\dots,i_{d}}), where 0i1,,idn0\leq i_{1},\dots,i_{d}\leq n. As in Section 2.1, we can equivalently represent 𝒜\mathcal{A} by 𝒜α\mathcal{A}_{\alpha}’s, where α=(α0,,αn)dn+1\alpha=(\alpha_{0},\dots,\alpha_{n})\in\mathbb{N}_{d}^{n+1} satisfies |α|=d\lvert\alpha\rvert=d. To be more precise, for each element in {(i1,,id):0i1,,idn}\{(i_{1},\dots,i_{d}):0\leq i_{1},\dots,i_{d}\leq n\}, we have

𝒜i1,,id=𝒜α,\mathcal{A}_{i_{1},\dots,i_{d}}=\mathcal{A}_{\alpha},

where αdn+1\alpha\in\mathbb{N}_{d}^{n+1} is the sequence such that |α|=d\lvert\alpha\rvert=d and x0α0xnαn=xi1xidx_{0}^{\alpha_{0}}\cdots x_{n}^{\alpha_{n}}=x_{i_{1}}\cdots x_{i_{d}}. We may list the entries 𝒜α\mathcal{A}_{\alpha} with respect to the lexicographic order, i.e., 𝒜α\mathcal{A}_{\alpha} precedes 𝒜β\mathcal{A}_{\beta} if and only if the most left nonzero entry of αβ\alpha-\beta is positive. For instance, a binary cubic tensor 𝒜=S3(2)\mathcal{A}=\in\operatorname{S}^{3}(\mathbb{C}^{2}) can be displayed as 𝒜30,𝒜21,𝒜12,𝒜03\mathcal{A}_{30},\mathcal{A}_{21},\mathcal{A}_{12},\mathcal{A}_{03}. In the following examples, we will represent a symmetric tensor 𝒜\mathcal{A} in this way.

Example 6.2.

Let X2\mathbb{P}X\subseteq\mathbb{P}^{2} be the parabola defined by x22x0x1+x02=0x_{2}^{2}-x_{0}x_{1}+x_{0}^{2}=0. Let 𝒜S3(3)\mathcal{A}\in\operatorname{S}^{3}(\mathbb{C}^{3}) be the symmetric tensor such that

𝒜300\displaystyle\mathcal{A}_{300} =15,𝒜210=81,𝒜201=6,𝒜120=621,𝒜111=108,𝒜102=66,\displaystyle=15,\mathcal{A}_{210}=81,\mathcal{A}_{201}=-6,\mathcal{A}_{120}=621,\mathcal{A}_{111}=-108,\mathcal{A}_{102}=66,
𝒜030\displaystyle\mathcal{A}_{030} =5541,𝒜021=1296,𝒜012=540,𝒜003=102.\displaystyle=5541,\mathcal{A}_{021}=-1296,\mathcal{A}_{012}=540,\mathcal{A}_{003}=-102.

The vanishing ideal of Y2Y\subseteq\mathbb{C}^{2} is

I(Y)=y22y1+1.I(Y)=\langle y_{2}^{2}-y_{1}+1\rangle.

The maximum rank of flattening matrices of 𝒜\mathcal{A} is 33. When we run Algorithm 5.2 with r=3r=3, it does not give a desired tensor decomposition. So, we apply Algorithm 5.2 with r=4r=4 and the symmetric XX-decomposition with

V~=[9.8740.002786i2.979+0.0004677i1.1510.006293i0.3886+0.008096i4.124+0.02027i1.768+0.005734i16.62+1.251i3.956+0.1581i],Λ~=[5.184+0.0036i3.694+0.0184i6.0980.0154i0.02460.0066i].\widetilde{V}=\left[\begin{array}[]{cc}9.874-0.002786\,i&-2.979+0.0004677\,i\\ \vskip 6.0pt plus 2.0pt minus 2.0pt\cr 1.151-0.006293\,i&-0.3886+0.008096\,i\\ \vskip 6.0pt plus 2.0pt minus 2.0pt\cr 4.124+0.02027\,i&1.768+0.005734\,i\\ \vskip 6.0pt plus 2.0pt minus 2.0pt\cr 16.62+1.251\,i&3.956+0.1581\,i\end{array}\right],\quad\widetilde{\Lambda}=\left[\begin{array}[]{c}5.184+0.0036\,i\\ \vskip 6.0pt plus 2.0pt minus 2.0pt\cr 3.694+0.0184\,i\\ \vskip 6.0pt plus 2.0pt minus 2.0pt\cr 6.098-0.0154\,i\\ \vskip 6.0pt plus 2.0pt minus 2.0pt\cr 0.0246-0.0066\,i\end{array}\right].

We have 𝒜=7241.79\lVert{\mathcal{A}}\rVert=7241.79 and the error 𝒜𝒜~=81014\lVert\mathcal{A}-\widetilde{\mathcal{A}}\rVert=8\cdot 10^{-14}.

Example 6.3.

Let X2\mathbb{P}X\subseteq\mathbb{P}^{2} be the nodal curve defined by x13+x0x12x0x22=0.x_{1}^{3}+x_{0}x_{1}^{2}-x_{0}x_{2}^{2}=0. The vanishing ideal of Y2Y\subseteq\mathbb{C}^{2} is

I(Y)=y13+y12y22.I(Y)=\langle y_{1}^{3}+y_{1}^{2}-y_{2}^{2}\rangle.

Let 𝒜S3(3)\mathcal{A}\in\operatorname{S}^{3}(\mathbb{C}^{3}) be the symmetric tensor such that

𝒜300\displaystyle\mathcal{A}_{300} =3,𝒜210=24,𝒜201=72,𝒜120=144,𝒜111=456,𝒜102=1224,\displaystyle=3,\mathcal{A}_{210}=24,\mathcal{A}_{201}=72,\mathcal{A}_{120}=144,\mathcal{A}_{111}=456,\mathcal{A}_{102}=1224,
𝒜030\displaystyle\mathcal{A}_{030} =1080,𝒜021=3288,𝒜012=9432,𝒜003=28512.\displaystyle=1080,\mathcal{A}_{021}=3288,\mathcal{A}_{012}=9432,\mathcal{A}_{003}=28512.

The maximum rank of flattening matrices of 𝒜\mathcal{A} is 33. When we run Algorithm 5.2 with r=3,4r=3,4, it fails to give a desired tensor decomposition. So, we apply Algorithm 5.2 with r=5r=5 and get the symmetric XX-decomposition with

V~=[3.0290.07505i6.079+0.2073i1.047+0.1114i0.2338+0.2814i2.6150.09868i4.9700.2555i7.9270.008981i23.680.03874i5.6853.079i10.8410.80i],Λ~=[0.97690.06497i1.46500.1107i3.3470+0.1587i2.0980+0.01425i0.002438+0.002715i].\widetilde{V}=\left[\begin{array}[]{cc}3.029-0.07505\,i&-6.079+0.2073\,i\\ \vskip 6.0pt plus 2.0pt minus 2.0pt\cr-1.047+0.1114\,i&0.2338+0.2814\,i\\ \vskip 6.0pt plus 2.0pt minus 2.0pt\cr 2.615-0.09868\,i&4.970-0.2555\,i\\ \vskip 6.0pt plus 2.0pt minus 2.0pt\cr 7.927-0.008981\,i&23.68-0.03874\,i\\ \vskip 6.0pt plus 2.0pt minus 2.0pt\cr-5.685-3.079\,i&10.84-10.80\,i\end{array}\right],\quad\widetilde{\Lambda}=\left[\begin{array}[]{c}-0.9769-0.06497\,i\\ \vskip 6.0pt plus 2.0pt minus 2.0pt\cr-1.4650-0.1107\,i\\ \vskip 6.0pt plus 2.0pt minus 2.0pt\cr 3.3470+0.1587\,i\\ \vskip 6.0pt plus 2.0pt minus 2.0pt\cr 2.0980+0.01425\,i\\ \vskip 6.0pt plus 2.0pt minus 2.0pt\cr-0.002438+0.002715\,i\end{array}\right].

We have 𝒜=41632.56\lVert{\mathcal{A}}\rVert=41632.56 and the error 𝒜𝒜~=41013\lVert\mathcal{A}-\widetilde{\mathcal{A}}\rVert=4\cdot 10^{-13}.

Example 6.4.

Let X3\mathbb{P}X\subseteq\mathbb{P}^{3} be the union of two planes defined by (x3x2)(x1x0)=0(x_{3}-x_{2})(x_{1}-x_{0})=0. Let 𝒜S3(4)\mathcal{A}\in\operatorname{S}^{3}(\mathbb{C}^{4}) be the symmetric tensor such that

𝒜3000\displaystyle\mathcal{A}_{3000} =2,𝒜2100=1,𝒜2010=5,𝒜2001=3,𝒜1200=5,𝒜1110=10,𝒜1020=9,\displaystyle=2,\mathcal{A}_{2100}=1,\mathcal{A}_{2010}=5,\mathcal{A}_{2001}=-3,\mathcal{A}_{1200}=5,\mathcal{A}_{1110}=10,\mathcal{A}_{1020}=9,
𝒜1101\displaystyle\mathcal{A}_{1101} =2,𝒜1011=7,𝒜1002=5,𝒜0300=5,𝒜0210=2,𝒜0120=8,𝒜0030=29,\displaystyle=2,\mathcal{A}_{1011}=7,\mathcal{A}_{1002}=5,\mathcal{A}_{0300}=-5,\mathcal{A}_{0210}=2,\mathcal{A}_{0120}=8,\mathcal{A}_{0030}=29,
𝒜0201\displaystyle\mathcal{A}_{0201} =6,𝒜0111=6,𝒜0021=7,𝒜0102=4,𝒜0012=5,𝒜0003=9.\displaystyle=-6,\mathcal{A}_{0111}=6,\mathcal{A}_{0021}=7,\mathcal{A}_{0102}=4,\mathcal{A}_{0012}=5,\mathcal{A}_{0003}=-9.

The vanishing ideal of Y3Y\subseteq\mathbb{C}^{3} is

I(Y)=(y3y2)(y11).I(Y)=\langle(y_{3}-y_{2})(y_{1}-1)\rangle.

The maximum rank of flattening matrices of 𝒜\mathcal{A} is 44. When we run Algorithm 5.2 with r=4r=4, it fails to give a desired tensor decomposition. So, we use r=5r=5 and apply Algorithm 5.2. It returns the symmetric XX-decomposition

V~=[2.01.01.01.01.4871.01.02.2871.01.01.01.01.002.0],Λ~=[1.01.2492.2491.01.0].\widetilde{V}=\left[\begin{array}[]{ccc}-2.0&-1.0&-1.0\\ \vskip 6.0pt plus 2.0pt minus 2.0pt\cr 1.0&-1.487&1.0\\ \vskip 6.0pt plus 2.0pt minus 2.0pt\cr 1.0&2.287&1.0\\ \vskip 6.0pt plus 2.0pt minus 2.0pt\cr-1.0&1.0&1.0\\ \vskip 6.0pt plus 2.0pt minus 2.0pt\cr 1.0&0&-2.0\end{array}\right],\quad\widetilde{\Lambda}=\left[\begin{array}[]{c}1.0\\ \vskip 6.0pt plus 2.0pt minus 2.0pt\cr-1.249\\ \vskip 6.0pt plus 2.0pt minus 2.0pt\cr 2.249\\ \vskip 6.0pt plus 2.0pt minus 2.0pt\cr-1.0\\ \vskip 6.0pt plus 2.0pt minus 2.0pt\cr 1.0\end{array}\right].

We have 𝒜=104.86\lVert\mathcal{A}\rVert=104.86 and the error 𝒜𝒜~=1015\lVert\mathcal{A}-\widetilde{\mathcal{A}}\rVert=10^{-15}.

Example 6.5.

Let X3\mathbb{P}X\subseteq\mathbb{P}^{3} be the surface defined by 3x1x22+x13x02x3=0-3x_{1}x_{2}^{2}+x_{1}^{3}-x_{0}^{2}x_{3}=0. Then Y3Y\subseteq\mathbb{C}^{3} is the monkey saddle surface whose vanishing idea is

I(Y)=3y1y22+y13y3.I(Y)=\langle-3y_{1}y_{2}^{2}+y_{1}^{3}-y_{3}\rangle.

Let 𝒜S3(4)\mathcal{A}\in\operatorname{S}^{3}(\mathbb{C}^{4}) be the symmetric tensor such that

𝒜3000\displaystyle\mathcal{A}_{3000} =5,𝒜2100=1,𝒜2010=6,𝒜2001=13,𝒜1200=9,𝒜1110=8,𝒜1020=6,\displaystyle=5,\mathcal{A}_{2100}=-1,\mathcal{A}_{2010}=6,\mathcal{A}_{2001}=-13,\mathcal{A}_{1200}=9,\mathcal{A}_{1110}=8,\mathcal{A}_{1020}=6,
𝒜1101\displaystyle\mathcal{A}_{1101} =33,𝒜1011=16,𝒜1002=87,𝒜0300=17,𝒜0210=24,𝒜0120=10,𝒜0030=12,\displaystyle=-33,\mathcal{A}_{1011}=-16,\mathcal{A}_{1002}=87,\mathcal{A}_{0300}=17,\mathcal{A}_{0210}=24,\mathcal{A}_{0120}=10,\mathcal{A}_{0030}=12,
𝒜0201\displaystyle\mathcal{A}_{0201} =91,𝒜0111=54,𝒜0021=38,𝒜0102=233,𝒜0012=5,𝒜0003=739.\displaystyle=-91,\mathcal{A}_{0111}=-54,\mathcal{A}_{0021}=-38,\mathcal{A}_{0102}=233,\mathcal{A}_{0012}=5,\mathcal{A}_{0003}=-739.

The maximum rank of flattening matrices of 𝒜\mathcal{A} is 44. When we run Algorithm 5.2 with r=4,5r=4,5, it fails to give a desired tensor decomposition. So, we apply Algorithm 5.2 with r=6r=6 and get the symmetric XX-decomposition

V~\displaystyle\widetilde{V} =[5.9360.8124i3.5820.4510i19.62+2.978i0.41150.5223i1.801+1.185i9.2262.511i2.0420.01150i1.031+0.008333i2.0080.001995i0.64220.02100i0.9440+0.001783i1.453+0.03664i2.1570.08836i1.5760.04318i6.043+0.3063i1.01.5051018i3.16310181.0041018i1.0+1.5191018i],\displaystyle=\left[\begin{array}[]{ccc}5.936-0.8124\,i&3.582-0.4510\,i&-19.62+2.978\,i\\ \vskip 6.0pt plus 2.0pt minus 2.0pt\cr-0.4115-0.5223\,i&-1.801+1.185\,i&9.226-2.511\,i\\ \vskip 6.0pt plus 2.0pt minus 2.0pt\cr 2.042-0.01150\,i&-1.031+0.008333\,i&2.008-0.001995\,i\\ \vskip 6.0pt plus 2.0pt minus 2.0pt\cr-0.6422-0.02100\,i&0.9440+0.001783\,i&1.453+0.03664\,i\\ \vskip 6.0pt plus 2.0pt minus 2.0pt\cr 2.157-0.08836\,i&1.576-0.04318\,i&-6.043+0.3063\,i\\ \vskip 6.0pt plus 2.0pt minus 2.0pt\cr-1.0-{1.505\cdot 10^{-18}}\,i&-{3.163\cdot 10^{-18}}-{1.004\cdot 10^{-18}}\,i&-1.0+{1.519\cdot 10^{-18}}\,i\end{array}\right],
Λ~\displaystyle\widetilde{\Lambda} =[0.03454+0.02496i0.001235+0.00440i0.95640.01231i2.04800.05064i1.8730+0.03358i2.0+1.85601017i],𝒜=1275.93,𝒜𝒜~=21015,𝒜𝒜~𝒜~=21018.\displaystyle=\left[\begin{array}[]{c}0.03454+0.02496\,i\\ \vskip 6.0pt plus 2.0pt minus 2.0pt\cr 0.001235+0.00440\,i\\ \vskip 6.0pt plus 2.0pt minus 2.0pt\cr-0.9564-0.01231\,i\\ \vskip 6.0pt plus 2.0pt minus 2.0pt\cr 2.0480-0.05064\,i\\ \vskip 6.0pt plus 2.0pt minus 2.0pt\cr 1.8730+0.03358\,i\\ \vskip 6.0pt plus 2.0pt minus 2.0pt\cr 2.0+{1.8560\cdot 10^{-17}}\,i\end{array}\right],\quad\lVert\mathcal{A}\rVert=1275.93,\quad\lVert\mathcal{A}-\widetilde{\mathcal{A}}\rVert=2\cdot 10^{-15},\quad\frac{\lVert\mathcal{A}-\widetilde{\mathcal{A}}\rVert}{\lVert\widetilde{\mathcal{A}}\rVert}=2\cdot 10^{-18}.

In the next two examples, we still display the tensor entries 𝒜α\mathcal{A}_{\alpha} according to the lexicographic order, but we drop the labelling indices, for cleanness of the paper.

Example 6.6.

Let X4\mathbb{P}X\subseteq\mathbb{P}^{4} be the curve defined by

x1x3x0x2x0x1+x0x3\displaystyle x_{1}x_{3}-x_{0}x_{2}-x_{0}x_{1}+x_{0}x_{3} =0,x32x0x1x02=0,\displaystyle=0,\,x_{3}^{2}-x_{0}x_{1}-x_{0}^{2}=0,
x1x4+4x1x3x1x2x12+5x1\displaystyle x_{1}x_{4}+4x_{1}x_{3}-x_{1}x_{2}-x_{1}^{2}+5x_{1} =0.\displaystyle=0.

The vanishing ideal of Y4Y\subseteq\mathbb{C}^{4} is then

I(Y)=y1y3y2y2+y3,y32y11,y1y4+4y1y3y1y2y12+5y1.I(Y)=\langle y_{1}y_{3}-y_{2}-y_{2}+y_{3},y_{3}^{2}-y_{1}-1,y_{1}y_{4}+4y_{1}y_{3}-y_{1}y_{2}-y_{1}^{2}+5y_{1}\rangle.

Let 𝒜S3(5)\mathcal{A}\in\operatorname{S}^{3}(\mathbb{C}^{5}) be the symmetric tensor whose 3535 entries are

7,2,87,25,20,26,334,233,60,45,9,130,144,74,182,406,1754,1150,13647,300,156,2353,24,421,85,830,610,6500,60,1050,150,550,3630,880,250.-7,-2,87,25,20,26,334,-233,60,-45,-9,130,-144,-74,182,406,1754,1150,\\ 13647,300,156,2353,24,421,85,830,610,6500,60,1050,150,550,3630,880,-250.

The maximum rank of flattening matrices of 𝒜\mathcal{A} is 55. When we run Algorithm 5.2 with r=5r=5, it fails to give a desired tensor decomposition. So, we apply Algorithm 5.2 with r=6r=6 and get the symmetric XX-decomposition

V~=[680.917130.026.1117700.07.71718.022.9528.92601.01.010.528.239i0.22720.45210.87912.1630.75261.5681.3247.9752.94510.781.9864.891],Λ~=[01.172006.7803.8715.263].\widetilde{V}=\left[\begin{array}[]{cccc}680.9&17130.0&26.11&17700.0\\ 7.717&18.02&2.952&8.926\\ 0&-1.0&-1.0&-10.52-8.239\,i\\ -0.2272&-0.4521&-0.8791&-2.163\\ 0.7526&1.568&1.324&-7.975\\ 2.945&-10.78&-1.986&-4.891\end{array}\right],\quad\widetilde{\Lambda}=\left[\begin{array}[]{c}0\\ 1.1720\\ 0\\ -6.780\\ 3.871\\ -5.263\end{array}\right].

We have the norm 𝒜=29222.16\lVert{\mathcal{A}}\rVert=29222.16 and the error 𝒜𝒜~=1010\lVert\mathcal{A}-\widetilde{\mathcal{A}}\rVert=10^{-10}.

Example 6.7.

Let X4\mathbb{P}X\subseteq\mathbb{P}^{4} be the surface defined by

x32+x42x0x1=0,x3x4x0x2=0.\displaystyle x_{3}^{2}+x_{4}^{2}-x_{0}x_{1}=0,\quad x_{3}x_{4}-x_{0}x_{2}=0.

Then the vanishing ideal of the variety Y4Y\subseteq\mathbb{C}^{4} is

I(Y)=y32+y42y1,y3y4y2.I(Y)=\langle y_{3}^{2}+y_{4}^{2}-y_{1},y_{3}y_{4}-y_{2}\rangle.

Let 𝒜S4(5)\mathcal{A}\in\operatorname{S}^{4}(\mathbb{C}^{5}) be the symmetric tensor whose 7070 entries are respectively

22,38,89,6,34,220,490,79,119,65,32,165,71,89,6,2216,3044,686,653,1029,490,239,195,173,48,1111,574,257,490,79,65,25,317,71,100,21424,20440,6028,4570,1615,8033,3788,1918,929,1553,1162,415,455,233,116,8187,4316,1954,1007,3044,686,653,490,239,173,663,1882,271,574,257,79,621,335,317,54.22,38,89,6,34,220,490,79,119,65,32,165,71,89,6,2216,3044,686,653,1029,\\ 490,239,195,173,48,1111,574,257,490,79,65,25,317,71,100,21424,20440,\\ 6028,4570,1615,8033,3788,1918,929,1553,1162,415,455,233,116,8187,4316,1954,\\ 1007,3044,686,653,490,239,173,663,1882,271,574,257,79,621,335,317,-54.

The maximum rank of flattening matrices of 𝒜\mathcal{A} is 1010. When we run Algorithm 5.2 with r=10r=10, we get the symmetric XX-decomposition

V~\displaystyle\widetilde{V} =[5.02.01.02.00.7596+0.2348i0.2187+0.5369i1.00.2187+0.5369i1.937+0.1658i0.9718+0.08532i1.00.9718+0.08532i2.01.01.01.01.0001.02.065+0.07655i1.0330.03706i1.01.0330.03706i5.003+0.006546i2.0010.001636i1.02.0010.001636i2.01.01.01.04.999+0.01109i2.0+0.002774i1.02.0+0.002774i8.04.02.02.0],\displaystyle=\left[\begin{array}[]{cccc}5.0&-2.0&-1.0&2.0\\ 0.7596+0.2348\,i&0.2187+0.5369\,i&1.0&0.2187+0.5369\,i\\ 1.937+0.1658\,i&0.9718+0.08532\,i&1.0&0.9718+0.08532\,i\\ 2.0&1.0&-1.0&-1.0\\ 1.0&0&0&1.0\\ 2.065+0.07655\,i&-1.033-0.03706\,i&1.0&-1.033-0.03706\,i\\ 5.003+0.006546\,i&-2.001-0.001636\,i&1.0&-2.001-0.001636\,i\\ 2.0&-1.0&-1.0&1.0\\ 4.999+0.01109\,i&2.0+0.002774\,i&1.0&2.0+0.002774\,i\\ 8.0&4.0&2.0&2.0\end{array}\right],
Λ~\displaystyle\widetilde{\Lambda} =[10.00.505+0.297i3.2320.413i11.011.03.778+0.132i7.970+0.053i5.06.010.069i7.0],𝒜=147394.37,𝒜𝒜~=81011.\displaystyle=\left[\begin{array}[]{c}-10.0\\ 0.505+0.297\,i\\ 3.232-0.413\,i\\ 11.0\\ 11.0\\ -3.778+0.132\,i\\ -7.970+0.053\,i\\ 5.0\\ 6.01-0.069\,i\\ 7.0\end{array}\right],\quad\lVert{\mathcal{A}}\rVert=147394.37,\quad\lVert\mathcal{A}-\widetilde{\mathcal{A}}\rVert=8\cdot 10^{-11}.

We conclude this section by considering various examples on Segre varieties.

Example 6.8.

(Vandermonde decompositions of nonsymmetric tensors) Each tensor 𝒜(d+1)k\mathcal{A}\in(\mathbb{C}^{d+1})^{\otimes k} has a Vandermonde decomposition as in (3.6), which is proved in Theorem 3.6. We can view 𝒜\mathcal{A} as a tensor in Sd(2k)\operatorname{S}^{d}(\mathbb{C}^{2^{k}}) with the set X2kX\subseteq\mathbb{C}^{2^{k}} such that X=1××1\mathbb{P}X=\mathbb{P}^{1}\times\cdots\times\mathbb{P}^{1} (1\mathbb{P}^{1} is repeated kk times). Let

n=2k1.n=2^{k}-1.

A vector x2kx\in\mathbb{C}^{2^{k}} can be labelled as x=(xν)x=(x_{\nu}), with binary vectors ν{0,1}k\nu\in\{0,1\}^{k}. Under this labelling, the set XX is defined by the homogeneous equations (c.f. [20, Example 2.11]) or [27, Section 4.3.5])

(6.1) xμxνxηxθ=0x_{\mu}x_{\nu}-x_{\eta}x_{\theta}=0

for all μ,ν,η,θ{0,1}k\mu,\nu,\eta,\theta\in\{0,1\}^{k} such that for some 1i<jk1\leq i<j\leq k,

μl=νl=ηl=θl(li,j),μi+νj=ηj+θi.\mu_{l}=\nu_{l}=\eta_{l}=\theta_{l}\,(l\neq i,j),\quad\mu_{i}+\nu_{j}=\eta_{j}+\theta_{i}.

Under the dehomogenization x00=1x_{0\ldots 0}=1, the corresponding affine variety Y2k1Y\subseteq\mathbb{C}^{2^{k}-1} consists of vectors yy, labelled as y=(xμ)y=(x_{\mu}) with 0μ{0,1}k0\neq\mu\in\{0,1\}^{k}, defined by the vanishing ideal

I(Y)=yμyνyηyθ,I(Y)=\langle y_{\mu}y_{\nu}-y_{\eta}y_{\theta}\rangle,

where μ,ν,η,θ{0,1}k\mu,\nu,\eta,\theta\in\{0,1\}^{k} are as above and y00=1y_{0\ldots 0}=1.

We apply Algorithm 5.2 to compute Vandermonde decompositions for random 𝒜(d+1)k\mathcal{A}\in(\mathbb{C}^{d+1})^{\otimes k} whose entries are randomly generated (obeying the normal distribution). For all the instances, Algorithm 5.2 successfully got Vandermonde rank decompositions. The relative errors 𝒜𝒜~𝒜\frac{\lVert\mathcal{A}-\widetilde{\mathcal{A}}\rVert}{\lVert\mathcal{A}\rVert} are all in the magnitude of O(1016)O(10^{-16}). The consumed computtaional time (in seconds) is also reported. The results are displayed in Table 1.

Table 1. Computational results on symmetric XX-decompositions on Segre varieties
kk nn dd rr time kk nn dd rr time
2 3 3 4 7.07 3 7 3 7 7.45
2 3 4 4 7.08 3 7 3 9 9.74
2 3 4 5 7.11 3 7 3 10 19.04
2 3 4 6 7.31 3 7 4 8 6.85
2 3 4 7 7.45 3 7 4 9 6.88
2 3 4 8 8.11 3 7 4 10 6.54
2 3 5 5 7.10 3 7 4 11 8.35
2 3 5 6 7.08 3 7 4 12 11.37
2 3 5 7 7.22 3 7 4 13 22.84
2 3 5 8 7.17 3 7 4 14 41.45
2 3 5 9 7.37 3 7 4 15 69.26
2 3 5 10 7.76 3 7 4 16 134.47
2 3 5 11 8.87 3 7 4 17 224.97
2 3 6 12 7.84 3 7 4 18 382.00
2 3 6 13 7.97 3 7 5 19 8.35
2 3 6 14 8.50 3 7 5 20 8.45
2 3 6 15 9.26 3 7 5 21 8.65
2 3 6 16 12.33 3 7 5 22 8.65
2 3 7 17 8.27 3 7 5 23 8.80
2 3 7 18 12.11 3 7 5 24 9.08
2 3 7 19 34.24 3 7 5 25 8.96
2 3 7 20 722.98 3 7 6 26 9.10

7. Conclusion

In this paper, we discuss how to compute symmetric XX-decompositions of symmetric tensors on a given variety XX. The tool of generating polynomial is used to do the computation. Based on that, give an algorithm for computing symmetric XX-decompositions. Various examples are given to demonstrate the correctness and efficiency of the proposed method.

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