Notes for Math 535A: Differential Geometry
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DIFFERENTIAL GEOMETRY Contents 1. Introduction 2 2. Differentiation 3
DIFFERENTIAL GEOMETRY FINNUR LARUSSON´ Lecture notes for an honours course at the University of Adelaide Contents 1. Introduction 2 2. Differentiation 3 2.1. Review of the basics 3 2.2. The inverse function theorem 4 3. Smooth manifolds 7 3.1. Charts and atlases 7 3.2. Submanifolds and embeddings 8 3.3. Partitions of unity and Whitney’s embedding theorem 10 4. Tangent spaces 11 4.1. Germs, derivations, and equivalence classes of paths 11 4.2. The derivative of a smooth map 14 5. Differential forms and integration on manifolds 16 5.1. Introduction 16 5.2. A little multilinear algebra 17 5.3. Differential forms and the exterior derivative 18 5.4. Integration of differential forms on oriented manifolds 20 6. Stokes’ theorem 22 6.1. Manifolds with boundary 22 6.2. Statement and proof of Stokes’ theorem 24 6.3. Topological applications of Stokes’ theorem 26 7. Cohomology 28 7.1. De Rham cohomology 28 7.2. Cohomology calculations 30 7.3. Cechˇ cohomology and de Rham’s theorem 34 8. Exercises 36 9. References 42 Last change: 26 September 2008. These notes were originally written in 2007. They have been classroom-tested twice. Address: School of Mathematical Sciences, University of Adelaide, Adelaide SA 5005, Australia. E-mail address: [email protected] Copyright c Finnur L´arusson 2007. 1 1. Introduction The goal of this course is to acquire familiarity with the concept of a smooth manifold. Roughly speaking, a smooth manifold is a space on which we can do calculus. Manifolds arise in various areas of mathematics; they have a rich and deep theory with many applications, for example in physics. -
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. -
Lecture 10: Tubular Neighborhood Theorem
LECTURE 10: TUBULAR NEIGHBORHOOD THEOREM 1. Generalized Inverse Function Theorem We will start with Theorem 1.1 (Generalized Inverse Function Theorem, compact version). Let f : M ! N be a smooth map that is one-to-one on a compact submanifold X of M. Moreover, suppose dfx : TxM ! Tf(x)N is a linear diffeomorphism for each x 2 X. Then f maps a neighborhood U of X in M diffeomorphically onto a neighborhood V of f(X) in N. Note that if we take X = fxg, i.e. a one-point set, then is the inverse function theorem we discussed in Lecture 2 and Lecture 6. According to that theorem, one can easily construct neighborhoods U of X and V of f(X) so that f is a local diffeomor- phism from U to V . To get a global diffeomorphism from a local diffeomorphism, we will need the following useful lemma: Lemma 1.2. Suppose f : M ! N is a local diffeomorphism near every x 2 M. If f is invertible, then f is a global diffeomorphism. Proof. We need to show f −1 is smooth. Fix any y = f(x). The smoothness of f −1 at y depends only on the behaviour of f −1 near y. Since f is a diffeomorphism from a −1 neighborhood of x onto a neighborhood of y, f is smooth at y. Proof of Generalized IFT, compact version. According to Lemma 1.2, it is enough to prove that f is one-to-one in a neighborhood of X. We may embed M into RK , and consider the \"-neighborhood" of X in M: X" = fx 2 M j d(x; X) < "g; where d(·; ·) is the Euclidean distance. -
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. -
Tangent Spaces
CHAPTER 4 Tangent spaces So far, the fact that we dealt with general manifolds M and not just embedded submanifolds of Euclidean spaces did not pose any serious problem: we could make sense of everything right away, by moving (via charts) to opens inside Euclidean spaces. However, when trying to make sense of "tangent vectors", the situation is quite different. Indeed, already intuitively, when trying to draw a "tangent space" to a manifold M, we are tempted to look at M as part of a bigger (Euclidean) space and draw planes that are "tangent" (in the intuitive sense) to M. The fact that the tangent spaces of M are intrinsic to M and not to the way it sits inside a bigger ambient space is remarkable and may seem counterintuitive at first. And it shows that the notion of tangent vector is indeed of a geometric, intrinsic nature. Given our preconception (due to our intuition), the actual general definition may look a bit abstract. For that reason, we describe several different (but equivalent) approaches to tangent m spaces. Each one of them starts with one intuitive perception of tangent vectors on R , and then proceeds by concentrating on the main properties (to be taken as axioms) of the intuitive perception. However, before proceeding, it is useful to clearly state what we expect: • tangent spaces: for M-manifold, p 2 M, have a vector space TpM. And, of course, m~ for embedded submanifolds M ⊂ R , these tangent spaces should be (canonically) isomorphic to the previously defined tangent spaces TpM. • differentials: for smooths map F : M ! N (between manifolds), p 2 M, have an induced linear map (dF )p : TpM ! TF (p)N; m called the differential of F at p. -
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. -
MAT 531: Topology&Geometry, II Spring 2011
MAT 531: Topology&Geometry, II Spring 2011 Solutions to Problem Set 1 Problem 1: Chapter 1, #2 (10pts) Let F be the (standard) differentiable structure on R generated by the one-element collection of ′ charts F0 = {(R, id)}. Let F be the differentiable structure on R generated by the one-element collection of charts ′ 3 F0 ={(R,f)}, where f : R−→R, f(t)= t . Show that F 6=F ′, but the smooth manifolds (R, F) and (R, F ′) are diffeomorphic. ′ ′ (a) We begin by showing that F 6=F . Since id∈F0 ⊂F, it is sufficient to show that id6∈F , i.e. the overlap map id ◦ f −1 : f(R∩R)=R −→ id(R∩R)=R ′ from f ∈F0 to id is not smooth, in the usual (i.e. calculus) sense: id ◦ f −1 R R f id R ∩ R Since f(t)=t3, f −1(s)=s1/3, and id ◦ f −1 : R −→ R, id ◦ f −1(s)= s1/3. This is not a smooth map. (b) Let h : R −→ R be given by h(t)= t1/3. It is immediate that h is a homeomorphism. We will show that the map h:(R, F) −→ (R, F ′) is a diffeomorphism, i.e. the maps h:(R, F) −→ (R, F ′) and h−1 :(R, F ′) −→ (R, F) are smooth. To show that h is smooth, we need to show that it induces smooth maps between the ′ charts in F0 and F0. In this case, there is only one chart in each. So we need to show that the map f ◦ h ◦ id−1 : id h−1(R)∩R =R −→ R is smooth: −1 f ◦ h ◦ id R R id f h (R, F) (R, F ′) Since f ◦ h ◦ id−1(t)= f h(t) = f(t1/3)= t1/3 3 = t, − this map is indeed smooth, and so is h.