Hyperdeterminants
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Multilinear Algebra and Applications July 15, 2014
Multilinear Algebra and Applications July 15, 2014. Contents Chapter 1. Introduction 1 Chapter 2. Review of Linear Algebra 5 2.1. Vector Spaces and Subspaces 5 2.2. Bases 7 2.3. The Einstein convention 10 2.3.1. Change of bases, revisited 12 2.3.2. The Kronecker delta symbol 13 2.4. Linear Transformations 14 2.4.1. Similar matrices 18 2.5. Eigenbases 19 Chapter 3. Multilinear Forms 23 3.1. Linear Forms 23 3.1.1. Definition, Examples, Dual and Dual Basis 23 3.1.2. Transformation of Linear Forms under a Change of Basis 26 3.2. Bilinear Forms 30 3.2.1. Definition, Examples and Basis 30 3.2.2. Tensor product of two linear forms on V 32 3.2.3. Transformation of Bilinear Forms under a Change of Basis 33 3.3. Multilinear forms 34 3.4. Examples 35 3.4.1. A Bilinear Form 35 3.4.2. A Trilinear Form 36 3.5. Basic Operation on Multilinear Forms 37 Chapter 4. Inner Products 39 4.1. Definitions and First Properties 39 4.1.1. Correspondence Between Inner Products and Symmetric Positive Definite Matrices 40 4.1.1.1. From Inner Products to Symmetric Positive Definite Matrices 42 4.1.1.2. From Symmetric Positive Definite Matrices to Inner Products 42 4.1.2. Orthonormal Basis 42 4.2. Reciprocal Basis 46 4.2.1. Properties of Reciprocal Bases 48 4.2.2. Change of basis from a basis to its reciprocal basis g 50 B B III IV CONTENTS 4.2.3. -
Multilinear Algebra
Appendix A Multilinear Algebra This chapter presents concepts from multilinear algebra based on the basic properties of finite dimensional vector spaces and linear maps. The primary aim of the chapter is to give a concise introduction to alternating tensors which are necessary to define differential forms on manifolds. Many of the stated definitions and propositions can be found in Lee [1], Chaps. 11, 12 and 14. Some definitions and propositions are complemented by short and simple examples. First, in Sect. A.1 dual and bidual vector spaces are discussed. Subsequently, in Sects. A.2–A.4, tensors and alternating tensors together with operations such as the tensor and wedge product are introduced. Lastly, in Sect. A.5, the concepts which are necessary to introduce the wedge product are summarized in eight steps. A.1 The Dual Space Let V be a real vector space of finite dimension dim V = n.Let(e1,...,en) be a basis of V . Then every v ∈ V can be uniquely represented as a linear combination i v = v ei , (A.1) where summation convention over repeated indices is applied. The coefficients vi ∈ R arereferredtoascomponents of the vector v. Throughout the whole chapter, only finite dimensional real vector spaces, typically denoted by V , are treated. When not stated differently, summation convention is applied. Definition A.1 (Dual Space)Thedual space of V is the set of real-valued linear functionals ∗ V := {ω : V → R : ω linear} . (A.2) The elements of the dual space V ∗ are called linear forms on V . © Springer International Publishing Switzerland 2015 123 S.R. -
Arxiv:1810.05857V3 [Math.AG] 11 Jun 2020
HYPERDETERMINANTS FROM THE E8 DISCRIMINANT FRED´ ERIC´ HOLWECK AND LUKE OEDING Abstract. We find expressions of the polynomials defining the dual varieties of Grass- mannians Gr(3, 9) and Gr(4, 8) both in terms of the fundamental invariants and in terms of a generic semi-simple element. We restrict the polynomial defining the dual of the ad- joint orbit of E8 and obtain the polynomials of interest as factors. To find an expression of the Gr(4, 8) discriminant in terms of fundamental invariants, which has 15, 942 terms, we perform interpolation with mod-p reductions and rational reconstruction. From these expressions for the discriminants of Gr(3, 9) and Gr(4, 8) we also obtain expressions for well-known hyperdeterminants of formats 3 × 3 × 3 and 2 × 2 × 2 × 2. 1. Introduction Cayley’s 2 × 2 × 2 hyperdeterminant is the well-known polynomial 2 2 2 2 2 2 2 2 ∆222 = x000x111 + x001x110 + x010x101 + x100x011 + 4(x000x011x101x110 + x001x010x100x111) − 2(x000x001x110x111 + x000x010x101x111 + x000x100x011x111+ x001x010x101x110 + x001x100x011x110 + x010x100x011x101). ×3 It generates the ring of invariants for the group SL(2) ⋉S3 acting on the tensor space C2×2×2. It is well-studied in Algebraic Geometry. Its vanishing defines the projective dual of the Segre embedding of three copies of the projective line (a toric variety) [13], and also coincides with the tangential variety of the same Segre product [24, 28, 33]. On real tensors it separates real ranks 2 and 3 [9]. It is the unique relation among the principal minors of a general 3 × 3 symmetric matrix [18]. -
Vector Calculus and Differential Forms
Vector Calculus and Differential Forms John Terilla Math 208 Spring 2015 1 Vector fields Definition 1. Let U be an open subest of Rn.A tangent vector in U is a pair (p; v) 2 U × Rn: We think of (p; v) as consisting of a vector v 2 Rn lying at the point p 2 U. Often, we denote a tangent vector by vp instead of (p; v). For p 2 U, the set of all tangent vectors at p is denoted by Tp(U). The set of all tangent vectors in U is denoted T (U) For a fixed point p 2 U, the set Tp(U) is a vector space with addition and scalar multiplication defined by vp + wp = (v + w)p and αvp = (αv)p: Note n that as a vector space, Tp(U) is isomorphic to R . Definition 2. A vector field on U is a function X : U ! T (Rn) satisfying X(p) 2 Tp(U). Remark 1. Notice that any function f : U ! Rn defines a vector field X by the rule X(p) = f(p)p: Denote the set of vector fields on U by Vect(U). Note that Vect(U) is a vector space with addition and scalar multiplication defined pointwise (which makes sense since Tp(U) is a vector space): (X + Y )(p) := X(p) + Y (p) and (αX)(p) = α(X(p)): Definition 3. Denote the set of functions on the set U by Fun(U) = ff : U ! Rg. Let C1(U) be the subset of Fun(U) consisting of functions with continuous derivatives and let C1(U) be the subset of Fun(U) consisting of smooth functions, i.e., infinitely differentiable functions. -
Ring (Mathematics) 1 Ring (Mathematics)
Ring (mathematics) 1 Ring (mathematics) In mathematics, a ring is an algebraic structure consisting of a set together with two binary operations usually called addition and multiplication, where the set is an abelian group under addition (called the additive group of the ring) and a monoid under multiplication such that multiplication distributes over addition.a[›] In other words the ring axioms require that addition is commutative, addition and multiplication are associative, multiplication distributes over addition, each element in the set has an additive inverse, and there exists an additive identity. One of the most common examples of a ring is the set of integers endowed with its natural operations of addition and multiplication. Certain variations of the definition of a ring are sometimes employed, and these are outlined later in the article. Polynomials, represented here by curves, form a ring under addition The branch of mathematics that studies rings is known and multiplication. as ring theory. Ring theorists study properties common to both familiar mathematical structures such as integers and polynomials, and to the many less well-known mathematical structures that also satisfy the axioms of ring theory. The ubiquity of rings makes them a central organizing principle of contemporary mathematics.[1] Ring theory may be used to understand fundamental physical laws, such as those underlying special relativity and symmetry phenomena in molecular chemistry. The concept of a ring first arose from attempts to prove Fermat's last theorem, starting with Richard Dedekind in the 1880s. After contributions from other fields, mainly number theory, the ring notion was generalized and firmly established during the 1920s by Emmy Noether and Wolfgang Krull.[2] Modern ring theory—a very active mathematical discipline—studies rings in their own right. -
Chapter IX. Tensors and Multilinear Forms
Notes c F.P. Greenleaf and S. Marques 2006-2016 LAII-s16-quadforms.tex version 4/25/2016 Chapter IX. Tensors and Multilinear Forms. IX.1. Basic Definitions and Examples. 1.1. Definition. A bilinear form is a map B : V V C that is linear in each entry when the other entry is held fixed, so that × → B(αx, y) = αB(x, y)= B(x, αy) B(x + x ,y) = B(x ,y)+ B(x ,y) for all α F, x V, y V 1 2 1 2 ∈ k ∈ k ∈ B(x, y1 + y2) = B(x, y1)+ B(x, y2) (This of course forces B(x, y)=0 if either input is zero.) We say B is symmetric if B(x, y)= B(y, x), for all x, y and antisymmetric if B(x, y)= B(y, x). Similarly a multilinear form (aka a k-linear form , or a tensor− of rank k) is a map B : V V F that is linear in each entry when the other entries are held fixed. ×···×(0,k) → We write V = V ∗ . V ∗ for the set of k-linear forms. The reason we use V ∗ here rather than V , and⊗ the⊗ rationale for the “tensor product” notation, will gradually become clear. The set V ∗ V ∗ of bilinear forms on V becomes a vector space over F if we define ⊗ 1. Zero element: B(x, y) = 0 for all x, y V ; ∈ 2. Scalar multiple: (αB)(x, y)= αB(x, y), for α F and x, y V ; ∈ ∈ 3. Addition: (B + B )(x, y)= B (x, y)+ B (x, y), for x, y V . -
Tensor Complexes: Multilinear Free Resolutions Constructed from Higher Tensors
Tensor complexes: Multilinear free resolutions constructed from higher tensors C. Berkesch, D. Erman, M. Kummini and S. V. Sam REPORT No. 7, 2010/2011, spring ISSN 1103-467X ISRN IML-R- -7-10/11- -SE+spring TENSOR COMPLEXES: MULTILINEAR FREE RESOLUTIONS CONSTRUCTED FROM HIGHER TENSORS CHRISTINE BERKESCH, DANIEL ERMAN, MANOJ KUMMINI, AND STEVEN V SAM Abstract. The most fundamental complexes of free modules over a commutative ring are the Koszul complex, which is constructed from a vector (i.e., a 1-tensor), and the Eagon– Northcott and Buchsbaum–Rim complexes, which are constructed from a matrix (i.e., a 2-tensor). The subject of this paper is a multilinear analogue of these complexes, which we construct from an arbitrary higher tensor. Our construction provides detailed new examples of minimal free resolutions, as well as a unifying view on a wide variety of complexes including: the Eagon–Northcott, Buchsbaum– Rim and similar complexes, the Eisenbud–Schreyer pure resolutions, and the complexes used by Gelfand–Kapranov–Zelevinsky and Weyman to compute hyperdeterminants. In addition, we provide applications to the study of pure resolutions and Boij–S¨oderberg theory, including the construction of infinitely many new families of pure resolutions, and the first explicit description of the differentials of the Eisenbud–Schreyer pure resolutions. 1. Introduction In commutative algebra, the Koszul complex is the mother of all complexes. David Eisenbud The most fundamental complex of free modules over a commutative ring R is the Koszul a complex, which is constructed from a vector (i.e., a 1-tensor) f = (f1, . , fa) R . The next most fundamental complexes are likely the Eagon–Northcott and Buchsbaum–Rim∈ complexes, which are constructed from a matrix (i.e., a 2-tensor) ψ Ra Rb. -
Cross Products, Automorphisms, and Gradings 3
CROSS PRODUCTS, AUTOMORPHISMS, AND GRADINGS ALBERTO DAZA-GARC´IA, ALBERTO ELDUQUE, AND LIMING TANG Abstract. The affine group schemes of automorphisms of the multilinear r- fold cross products on finite-dimensional vectors spaces over fields of character- istic not two are determined. Gradings by abelian groups on these structures, that correspond to morphisms from diagonalizable group schemes into these group schemes of automorphisms, are completely classified, up to isomorphism. 1. Introduction Eckmann [Eck43] defined a vector cross product on an n-dimensional real vector space V , endowed with a (positive definite) inner product b(u, v), to be a continuous map X : V r −→ V (1 ≤ r ≤ n) satisfying the following axioms: b X(v1,...,vr), vi =0, 1 ≤ i ≤ r, (1.1) bX(v1,...,vr),X(v1,...,vr) = det b(vi, vj ) , (1.2) There are very few possibilities. Theorem 1.1 ([Eck43, Whi63]). A vector cross product exists in precisely the following cases: • n is even, r =1, • n ≥ 3, r = n − 1, • n =7, r =2, • n =8, r =3. Multilinear vector cross products X on vector spaces V over arbitrary fields of characteristic not two, relative to a nondegenerate symmetric bilinear form b(u, v), were classified by Brown and Gray [BG67]. These are the multilinear maps X : V r → V (1 ≤ r ≤ n) satisfying (1.1) and (1.2). The possible pairs (n, r) are again those in Theorem 1.1. The exceptional cases: (n, r) = (7, 2) and (8, 3), are intimately related to the arXiv:2006.10324v1 [math.RT] 18 Jun 2020 octonion, or Cayley, algebras. -
Singularities of Hyperdeterminants Annales De L’Institut Fourier, Tome 46, No 3 (1996), P
ANNALES DE L’INSTITUT FOURIER JERZY WEYMAN ANDREI ZELEVINSKY Singularities of hyperdeterminants Annales de l’institut Fourier, tome 46, no 3 (1996), p. 591-644 <http://www.numdam.org/item?id=AIF_1996__46_3_591_0> © Annales de l’institut Fourier, 1996, tous droits réservés. L’accès aux archives de la revue « Annales de l’institut Fourier » (http://annalif.ujf-grenoble.fr/) implique l’accord avec les conditions gé- nérales d’utilisation (http://www.numdam.org/conditions). Toute utilisa- tion commerciale ou impression systématique est constitutive d’une in- fraction pénale. Toute copie ou impression de ce fichier doit conte- nir la présente mention de copyright. Article numérisé dans le cadre du programme Numérisation de documents anciens mathématiques http://www.numdam.org/ Ann. Inst. Fourier, Grenoble 46, 3 (1996), 591-644 SINGULARITIES OF HYPERDETERMINANTS by J. WEYMAN (1) and A. ZELEVINSKY (2) Contents. 0. Introduction 1. Symmetric matrices with diagonal lacunae 2. Cusp type singularities 3. Eliminating special node components 4. The generic node component 5. Exceptional cases: the zoo of three- and four-dimensional matrices 6. Decomposition of the singular locus into cusp and node parts 7. Multi-dimensional "diagonal" matrices and the Vandermonde matrix 0. Introduction. In this paper we continue the study of hyperdeterminants recently undertaken in [4], [5], [12]. The hyperdeterminants are analogs of deter- minants for multi-dimensional "matrices". Their study was initiated by (1) Partially supported by the NSF (DMS-9102432). (2) Partially supported by the NSF (DMS- 930424 7). Key words: Hyperdeterminant - Singular locus - Cusp type singularities - Node type singularities - Projectively dual variety - Segre embedding. Math. classification: 14B05. -
Arxiv:1404.6852V1 [Quant-Ph] 28 Apr 2014 Asnmes 36.A 22.J 03.65.-W 02.20.Hj, 03.67.-A, Numbers: PACS Given
SLOCC Invariants for Multipartite Mixed States Naihuan Jing1,5,∗ Ming Li2,3, Xianqing Li-Jost2, Tinggui Zhang2, and Shao-Ming Fei2,4 1School of Sciences, South China University of Technology, Guangzhou 510640, China 2Max-Planck-Institute for Mathematics in the Sciences, 04103 Leipzig, Germany 3Department of Mathematics, China University of Petroleum , Qingdao 266555, China 4School of Mathematical Sciences, Capital Normal University, Beijing 100048, China 5Department of Mathematics, North Carolina State University, Raleigh, NC 27695, USA Abstract We construct a nontrivial set of invariants for any multipartite mixed states under the SLOCC symmetry. These invariants are given by hyperdeterminants and independent from basis change. In particular, a family of d2 invariants for arbitrary d-dimensional even partite mixed states are explicitly given. PACS numbers: 03.67.-a, 02.20.Hj, 03.65.-w arXiv:1404.6852v1 [quant-ph] 28 Apr 2014 ∗Electronic address: [email protected] 1 I. INTRODUCTION Classification of multipartite states under stochastic local operations and classical commu- nication (SLOCC) has been a central problem in quantum communication and computation. Recently advances have been made for the classification of pure multipartite states under SLOCC [1, 2] and the dimension of the space of homogeneous SLOCC-invariants in a fixed degree is given as a function of the number of qudits. In this work we present a general method to construct polynomial invariants for mixed states under SLOCC. In particular we also derive general invariants under local unitary (LU) symmetry for mixed states. Polynomial invariants have been investigated in [3–6], which allow in principle to deter- mine all the invariants of local unitary transformations. -
Hyperdeterminants As Integrable Discrete Systems
Takustraße 7 Konrad-Zuse-Zentrum D-14195 Berlin-Dahlem f¨ur Informationstechnik Berlin Germany SERGEY P. TSAREV 1, THOMAS WOLF 2 Hyperdeterminants as integrable discrete systems 1Siberian Federal University, Krasnoyarsk, Russia 2Department of Mathematics, Brock University, St.Catharines, Canada and ZIB Fellow ZIB-Report 09-17 (May 2009) Hyperdeterminants as integrable discrete systems S.P. Tsarev‡ and T. Wolf Siberian Federal University, Svobodnyi avenue, 79, 660041, Krasnoyarsk, Russia and Department of Mathematics, Brock University 500 Glenridge Avenue, St.Catharines, Ontario, Canada L2S 3A1 e-mails: [email protected] [email protected] Abstract We give the basic definitions and some theoretical results about hyperdeter- minants, introduced by A. Cayley in 1845. We prove integrability (understood as 4d-consistency) of a nonlinear difference equation defined by the 2×2×2 - hy- perdeterminant. This result gives rise to the following hypothesis: the difference equations defined by hyperdeterminants of any size are integrable. We show that this hypothesis already fails in the case of the 2 × 2 × 2 × 2 - hyperdeterminant. 1 Introduction Discrete integrable equations have become a very vivid topic in the last decade. A number of important results on the classification of different classes of such equations, based on the notion of consistency [3], were obtained in [1, 2, 17] (cf. also references to earlier publications given there). As a rule, discrete equations describe relations on the C Zn scalar field variables fi1...in ∈ associated with the points of a lattice with vertices n at integer points in the n-dimensional space R = {(x1,...,xn)|xs ∈ R}. If we take the elementary cubic cell Kn = {(i1,...,in) | is ∈{0, 1}} of this lattice and the field n variables fi1...in associated to its 2 vertices, an n-dimensional discrete system of the type considered here is given by an equation of the form Qn(f)=0. -
Lectures on Algebraic Statistics
Lectures on Algebraic Statistics Mathias Drton, Bernd Sturmfels, Seth Sullivant September 27, 2008 2 Contents 1 Markov Bases 7 1.1 Hypothesis Tests for Contingency Tables . 7 1.2 Markov Bases of Hierarchical Models . 17 1.3 The Many Bases of an Integer Lattice . 26 2 Likelihood Inference 35 2.1 Discrete and Gaussian Models . 35 2.2 Likelihood Equations for Implicit Models . 46 2.3 Likelihood Ratio Tests . 54 3 Conditional Independence 67 3.1 Conditional Independence Models . 67 3.2 Graphical Models . 75 3.3 Parametrizations of Graphical Models . 85 4 Hidden Variables 95 4.1 Secant Varieties in Statistics . 95 4.2 Factor Analysis . 105 5 Bayesian Integrals 113 5.1 Information Criteria and Asymptotics . 113 5.2 Exact Integration for Discrete Models . 122 6 Exercises 131 6.1 Markov Bases Fixing Subtable Sums . 131 6.2 Quasi-symmetry and Cycles . 136 6.3 A Colored Gaussian Graphical Model . 139 6.4 Instrumental Variables and Tangent Cones . 143 6.5 Fisher Information for Multivariate Normals . 150 6.6 The Intersection Axiom and Its Failure . 152 6.7 Primary Decomposition for CI Inference . 155 6.8 An Independence Model and Its Mixture . 158 7 Open Problems 165 4 Contents Preface Algebraic statistics is concerned with the development of techniques in algebraic geometry, commutative algebra, and combinatorics, to address problems in statis- tics and its applications. On the one hand, algebra provides a powerful tool set for addressing statistical problems. On the other hand, it is rarely the case that algebraic techniques are ready-made to address statistical challenges, and usually new algebraic results need to be developed.