The Characteristic Masses of Niemeier Lattices Gaetan Chenevier
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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. -
Classification of Positive Definite Lattices. 22 Feb 2000 Richard E
Classification of positive definite lattices. 22 Feb 2000 Richard E. Borcherds, ∗ Mathematics department, Evans Hall #3840, University of California at Berkeley, CA 94720-3840 U.S.A. e-mail: [email protected] www home page www.math.berkeley.edu/˜reb Contents. 1. An algorithm for classifying vectors in some Lorentzian lattices. 2. Vectors in the lattice II1,25. 3. Lattices with no roots. Table 0: Primitive norm 0 vectors in II1,25. Table 1: Norm 2 vectors in II1,25. Table 2: Norm 4 vectors in II1,25. 1. Classification of positive norm vectors. In this paper we describe an algorithm for classifying orbits of vectors in Lorentzian lattices. The main point of this is that isomorphism classes of positive definite lattices in some genus often correspond to orbits of vectors in some Lorentzian lattice, so we can classify some positive definite lattices. Section 1 gives an overview of this algorithm, and in section 2 we describe this algorithm more precisely for the case of II1,25, and as an application we give the classification of the 665 25-dimensional unimodular positive definite lattices and the 121 even 25 dimensional positive definite lattices of determinant 2 (see tables 1 and 2). In section 3 we use this algorithm to show that there is a unique 26 dimensional unimodular positive definite lattice with no roots. Most of the results of this paper are taken from the unpublished manuscript [B], which contains more details and examples. For general facts about lattices used in this paper see [C-S], especially chapters 15–18 and 23–28. -
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. -
Automorphism Groups of the Holomorphic Vertex Operator
AUTOMORPHISM GROUPS OF THE HOLOMORPHIC VERTEX OPERATOR ALGEBRAS ASSOCIATED WITH NIEMEIER LATTICES AND THE 1-ISOMETRIES − HIROKI SHIMAKURA Abstract. In this article, we determine the automorphism groups of 14 holomorphic vertex operator algebras of central charge 24 obtained by applying the Z2-orbifold con- struction to the Niemeier lattice vertex operator algebras and lifts of the 1-isometries. − 1. Introduction Recently, (strongly regular) holomorphic vertex operator algebras (VOAs) of central charge 24 with non-zero weight one spaces are classified; there exist exactly 70 such VOAs (up to isomorphism) and they are uniquely determined by the Lie algebra structures on the weight one spaces. The remaining case is the famous conjecture in [FLM88]: a (strongly regular) holomorphic VOA of central charge 24 is isomorphic to the moonshine VOA if the weight one space is zero. The determination of the automorphism groups of vertex operator algebras is one of fundamental problems in VOA theory; it is natural to ask what the automorphism groups of holomorphic VOAs of central charge 24 are. For example, the automorphism group of the moonshine VOA is the Monster ([FLM88]) and those of Niemeier lattice VOAs were determined in [DN99]. However, the other cases have not been determined yet. The purpose of this article is to determine the automorphism group of the holomorphic orb(θ) VOA VN of central charge 24 obtained in [DGM96] by applying the Z2-orbifold con- arXiv:1811.05119v1 [math.QA] 13 Nov 2018 struction to the lattice VOA VN associated with a Niemeier lattice N and a lift θ of the orb(θ) 1-isometry of N. -
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. -
String Theory Moonshine
Strings 2014, Princeton Umbral Moonshine and String Theory Miranda Cheng University of Amsterdam* *: on leave from CNRS, France. A Myseros Story Abot Strings on K3 Finite Moonshine Modular Groups Objects symmetries of interesting objects functions with special symmetries K3 Sigma-Model 2d sigma models: use strings to probe the geometry. M = K3 Σ N=(4,4) superconformal Elliptic Genus of 2d SCFT In a 2d N>=(2,2) SCFT, susy states are counted by the elliptic genus: q = e2⇡i⌧ ,y = e2⇡iz • holomorphic [Schellekens–Warner, Witten ’87] • modular SL(2,Z) •topological EG = EG ⇣ ⌘ ⇣ ⌘ K3 Sigma-Model 2d sigma model on K3 is a N=(4,4) SCFT. ⇒ The spectrum fall into irred. representations of the N=4 SCA. 4 2 J +J¯ J L c/24 L c/24 ✓i(⌧,z) EG(⌧,z; K3) = Tr ( 1) 0 0 y 0 q 0− q¯ 0− =8 HRR − ✓ (⌧, 0) i=2 i ⇣ ⌘ X ✓ ◆ = 24 massless multiplets + tower of massive multiplets 1 2 ✓ (⌧,z) 1/8 2 3 = 1 24 µ(⌧,z)+2 q− ( 1 + 45 q + 231 q + 770 q + ...) ⌘3(⌧) − “Appell–Lerch⇣ sum” numbers of massive N=4 multiplets ⌘ also dimensions of irreps of M24, ! an interesting finite group with ~108 elements [Eguchi–Ooguri–Tachikawa ’10] Why EG(K3) ⟷ M24? Q: Is there a K3 surface M whose symmetry (that preserves the hyperKähler structure) is M24? [Mukai ’88, Kondo ’98] No! M24 elements symmetries of M2 symmetries of M1 Q: Is there a K3 sigma model whose symmetry is M24? [Gaberdiel–Hohenegger–Volpato ’11] No! M24 elements possible symmetries of K3 sigma models 3. -
4 Exterior Algebra
4 Exterior algebra 4.1 Lines and 2-vectors The time has come now to develop some new linear algebra in order to handle the space of lines in a projective space P (V ). In the projective plane we have seen that duality can deal with this but lines in higher dimensional spaces behave differently. From the point of view of linear algebra we are looking at 2-dimensional vector sub- spaces U ⊂ V . To motivate what we shall do, consider how in Euclidean geometry we describe a 2-dimensional subspace of R3. We could describe it through its unit normal n, which is also parallel to u×v where u and v are linearly independent vectors in the space and u×v is the vector cross product. The vector product has the following properties: • u×v = −v×u • (λ1u1 + λ2u2)×v = λ1u1×v + λ2u2×v We shall generalize these properties to vectors in any vector space V – the difference is that the product will not be a vector in V , but will lie in another associated vector space. Definition 12 An alternating bilinear form on a vector space V is a map B : V × V → F such that • B(v, w) = −B(w, v) • B(λ1v1 + λ2v2, w) = λ1B(v1, w) + λ2B(v2, w) This is the skew-symmetric version of the symmetric bilinear forms we used to define quadrics. Given a basis {v1, . , vn}, B is uniquely determined by the skew symmetric matrix B(vi, vj). We can add alternating forms and multiply by scalars so they form a vector space, isomorphic to the space of skew-symmetric n × n matrices.