MULTILINEAR ALGEBRA 1. Tensor and Symmetric Algebra Let K Be A
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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. -
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. -
Lecture 21: Symmetric Products and Algebras
LECTURE 21: SYMMETRIC PRODUCTS AND ALGEBRAS Symmetric Products The last few lectures have focused on alternating multilinear functions. This one will focus on symmetric multilinear functions. Recall that a multilinear function f : U ×m ! V is symmetric if f(~v1; : : : ;~vi; : : : ;~vj; : : : ;~vm) = f(~v1; : : : ;~vj; : : : ;~vi; : : : ;~vm) for any i and j, and for any vectors ~vk. Just as with the exterior product, we can get the universal object associated to symmetric multilinear functions by forming various quotients of the tensor powers. Definition 1. The mth symmetric power of V , denoted Sm(V ), is the quotient of V ⊗m by the subspace generated by ~v1 ⊗ · · · ⊗ ~vi ⊗ · · · ⊗ ~vj ⊗ · · · ⊗ ~vm − ~v1 ⊗ · · · ⊗ ~vj ⊗ · · · ⊗ ~vi ⊗ · · · ⊗ ~vm where i and j and the vectors ~vk are arbitrary. Let Sym(U ×m;V ) denote the set of symmetric multilinear functions U ×m to V . The following is immediate from our construction. Lemma 1. We have an natural bijection Sym(U ×m;V ) =∼ L(Sm(U);V ): n We will denote the image of ~v1 ⊗: : :~vm in S (V ) by ~v1 ·····~vm, the usual notation for multiplication to remind us that the terms here can be rearranged. Unlike with the exterior product, it is easy to determine a basis for the symmetric powers. Theorem 1. Let f~v1; : : : ;~vmg be a basis for V . Then _ f~vi1 · :::~vin ji1 ≤ · · · ≤ ing is a basis for Sn(V ). Before we prove this, we note a key difference between this and the exterior product. For the exterior product, we have strict inequalities. For the symmetric product, we have non-strict inequalities. -
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. -
Left-Symmetric Algebras of Derivations of Free Algebras
LEFT-SYMMETRIC ALGEBRAS OF DERIVATIONS OF FREE ALGEBRAS Ualbai Umirbaev1 Abstract. A structure of a left-symmetric algebra on the set of all derivations of a free algebra is introduced such that its commutator algebra becomes the usual Lie algebra of derivations. Left and right nilpotent elements of left-symmetric algebras of deriva- tions are studied. Simple left-symmetric algebras of derivations and Novikov algebras of derivations are described. It is also proved that the positive part of the left-symmetric al- gebra of derivations of a free nonassociative symmetric m-ary algebra in one free variable is generated by one derivation and some right nilpotent derivations are described. Mathematics Subject Classification (2010): Primary 17D25, 17A42, 14R15; Sec- ondary 17A36, 17A50. Key words: left-symmetric algebras, free algebras, derivations, Jacobian matrices. 1. Introduction If A is an arbitrary algebra over a field k, then the set DerkA of all k-linear derivations of A forms a Lie algebra. If A is a free algebra, then it is possible to define a multiplication · on DerkA such that it becomes a left-symmetric algebra and its commutator algebra becomes the Lie algebra DerkA of all derivations of A. The language of the left-symmetric algebras of derivations is more convenient to describe some combinatorial properties of derivations. Recall that an algebra B over k is called left-symmetric [4] if B satisfies the identity (1) (xy)z − x(yz)=(yx)z − y(xz). This means that the associator (x, y, z) := (xy)z −x(yz) is symmetric with respect to two left arguments, i.e., (x, y, z)=(y, x, z). -
WOMP 2001: LINEAR ALGEBRA Reference Roman, S. Advanced
WOMP 2001: LINEAR ALGEBRA DAN GROSSMAN Reference Roman, S. Advanced Linear Algebra, GTM #135. (Not very good.) 1. Vector spaces Let k be a field, e.g., R, Q, C, Fq, K(t),. Definition. A vector space over k is a set V with two operations + : V × V → V and · : k × V → V satisfying some familiar axioms. A subspace of V is a subset W ⊂ V for which • 0 ∈ W , • If w1, w2 ∈ W , a ∈ k, then aw1 + w2 ∈ W . The quotient of V by the subspace W ⊂ V is the vector space whose elements are subsets of the form (“affine translates”) def v + W = {v + w : w ∈ W } (for which v + W = v0 + W iff v − v0 ∈ W , also written v ≡ v0 mod W ), and whose operations +, · are those naturally induced from the operations on V . Exercise 1. Verify that our definition of the vector space V/W makes sense. Given a finite collection of elements (“vectors”) v1, . , vm ∈ V , their span is the subspace def hv1, . , vmi = {a1v1 + ··· amvm : a1, . , am ∈ k}. Exercise 2. Verify that this is a subspace. There may sometimes be redundancy in a spanning set; this is expressed by the notion of linear dependence. The collection v1, . , vm ∈ V is said to be linearly dependent if there is a linear combination a1v1 + ··· + amvm = 0, some ai 6= 0. This is equivalent to being able to express at least one of the vi as a linear combination of the others. Exercise 3. Verify this equivalence. Theorem. Let V be a vector space over a field k. -
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. -
Universal Enveloping Algebras and Some Applications in Physics
Universal enveloping algebras and some applications in physics Xavier BEKAERT Institut des Hautes Etudes´ Scientifiques 35, route de Chartres 91440 – Bures-sur-Yvette (France) Octobre 2005 IHES/P/05/26 IHES/P/05/26 Universal enveloping algebras and some applications in physics Xavier Bekaert Institut des Hautes Etudes´ Scientifiques Le Bois-Marie, 35 route de Chartres 91440 Bures-sur-Yvette, France [email protected] Abstract These notes are intended to provide a self-contained and peda- gogical introduction to the universal enveloping algebras and some of their uses in mathematical physics. After reviewing their abstract definitions and properties, the focus is put on their relevance in Weyl calculus, in representation theory and their appearance as higher sym- metries of physical systems. Lecture given at the first Modave Summer School in Mathematical Physics (Belgium, June 2005). These lecture notes are written by a layman in abstract algebra and are aimed for other aliens to this vast and dry planet, therefore many basic definitions are reviewed. Indeed, physicists may be unfamiliar with the daily- life terminology of mathematicians and translation rules might prove to be useful in order to have access to the mathematical literature. Each definition is particularized to the finite-dimensional case to gain some intuition and make contact between the abstract definitions and familiar objects. The lecture notes are divided into four sections. In the first section, several examples of associative algebras that will be used throughout the text are provided. Associative and Lie algebras are also compared in order to motivate the introduction of enveloping algebras. The Baker-Campbell- Haussdorff formula is presented since it is used in the second section where the definitions and main elementary results on universal enveloping algebras (such as the Poincar´e-Birkhoff-Witt) are reviewed in details. -
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.