Simultaneous Orthogonalization of Inner Products Over Arbitrary Fields
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Orthogonal Projection, Low Rank Approximation, and Orthogonal Bases
Week 11 Orthogonal Projection, Low Rank Approximation, and Orthogonal Bases 11.1 Opening Remarks 11.1.1 Low Rank Approximation * View at edX 463 Week 11. Orthogonal Projection, Low Rank Approximation, and Orthogonal Bases 464 11.1.2 Outline 11.1. Opening Remarks..................................... 463 11.1.1. Low Rank Approximation............................. 463 11.1.2. Outline....................................... 464 11.1.3. What You Will Learn................................ 465 11.2. Projecting a Vector onto a Subspace........................... 466 11.2.1. Component in the Direction of ............................ 466 11.2.2. An Application: Rank-1 Approximation...................... 470 11.2.3. Projection onto a Subspace............................. 474 11.2.4. An Application: Rank-2 Approximation...................... 476 11.2.5. An Application: Rank-k Approximation...................... 478 11.3. Orthonormal Bases.................................... 481 11.3.1. The Unit Basis Vectors, Again........................... 481 11.3.2. Orthonormal Vectors................................ 482 11.3.3. Orthogonal Bases.................................. 485 11.3.4. Orthogonal Bases (Alternative Explanation).................... 488 11.3.5. The QR Factorization................................ 492 11.3.6. Solving the Linear Least-Squares Problem via QR Factorization......... 493 11.3.7. The QR Factorization (Again)........................... 494 11.4. Change of Basis...................................... 498 11.4.1. The Unit Basis Vectors, -
3 Bilinear Forms & Euclidean/Hermitian Spaces
3 Bilinear Forms & Euclidean/Hermitian Spaces Bilinear forms are a natural generalisation of linear forms and appear in many areas of mathematics. Just as linear algebra can be considered as the study of `degree one' mathematics, bilinear forms arise when we are considering `degree two' (or quadratic) mathematics. For example, an inner product is an example of a bilinear form and it is through inner products that we define the notion of length in n p 2 2 analytic geometry - recall that the length of a vector x 2 R is defined to be x1 + ... + xn and that this formula holds as a consequence of Pythagoras' Theorem. In addition, the `Hessian' matrix that is introduced in multivariable calculus can be considered as defining a bilinear form on tangent spaces and allows us to give well-defined notions of length and angle in tangent spaces to geometric objects. Through considering the properties of this bilinear form we are able to deduce geometric information - for example, the local nature of critical points of a geometric surface. In this final chapter we will give an introduction to arbitrary bilinear forms on K-vector spaces and then specialise to the case K 2 fR, Cg. By restricting our attention to thse number fields we can deduce some particularly nice classification theorems. We will also give an introduction to Euclidean spaces: these are R-vector spaces that are equipped with an inner product and for which we can `do Euclidean geometry', that is, all of the geometric Theorems of Euclid will hold true in any arbitrary Euclidean space. -
Notes on Symmetric Bilinear Forms
Symmetric bilinear forms Joel Kamnitzer March 14, 2011 1 Symmetric bilinear forms We will now assume that the characteristic of our field is not 2 (so 1 + 1 6= 0). 1.1 Quadratic forms Let H be a symmetric bilinear form on a vector space V . Then H gives us a function Q : V → F defined by Q(v) = H(v,v). Q is called a quadratic form. We can recover H from Q via the equation 1 H(v, w) = (Q(v + w) − Q(v) − Q(w)) 2 Quadratic forms are actually quite familiar objects. Proposition 1.1. Let V = Fn. Let Q be a quadratic form on Fn. Then Q(x1,...,xn) is a polynomial in n variables where each term has degree 2. Con- versely, every such polynomial is a quadratic form. Proof. Let Q be a quadratic form. Then x1 . Q(x1,...,xn) = [x1 · · · xn]A . x n for some symmetric matrix A. Expanding this out, we see that Q(x1,...,xn) = Aij xixj 1 Xi,j n ≤ ≤ and so it is a polynomial with each term of degree 2. Conversely, any polynomial of degree 2 can be written in this form. Example 1.2. Consider the polynomial x2 + 4xy + 3y2. This the quadratic 1 2 form coming from the bilinear form HA defined by the matrix A = . 2 3 1 We can use this knowledge to understand the graph of solutions to x2 + 2 1 0 4xy + 3y = 1. Note that HA has a diagonal matrix with respect to 0 −1 the basis (1, 0), (−2, 1). -
Embeddings of Integral Quadratic Forms Rick Miranda Colorado State
Embeddings of Integral Quadratic Forms Rick Miranda Colorado State University David R. Morrison University of California, Santa Barbara Copyright c 2009, Rick Miranda and David R. Morrison Preface The authors ran a seminar on Integral Quadratic Forms at the Institute for Advanced Study in the Spring of 1982, and worked on a book-length manuscript reporting on the topic throughout the 1980’s and early 1990’s. Some new results which are proved in the manuscript were announced in two brief papers in the Proceedings of the Japan Academy of Sciences in 1985 and 1986. We are making this preliminary version of the manuscript available at this time in the hope that it will be useful. Still to do before the manuscript is in final form: final editing of some portions, completion of the bibliography, and the addition of a chapter on the application to K3 surfaces. Rick Miranda David R. Morrison Fort Collins and Santa Barbara November, 2009 iii Contents Preface iii Chapter I. Quadratic Forms and Orthogonal Groups 1 1. Symmetric Bilinear Forms 1 2. Quadratic Forms 2 3. Quadratic Modules 4 4. Torsion Forms over Integral Domains 7 5. Orthogonality and Splitting 9 6. Homomorphisms 11 7. Examples 13 8. Change of Rings 22 9. Isometries 25 10. The Spinor Norm 29 11. Sign Structures and Orientations 31 Chapter II. Quadratic Forms over Integral Domains 35 1. Torsion Modules over a Principal Ideal Domain 35 2. The Functors ρk 37 3. The Discriminant of a Torsion Bilinear Form 40 4. The Discriminant of a Torsion Quadratic Form 45 5. -
Preliminary Material for M382d: Differential Topology
PRELIMINARY MATERIAL FOR M382D: DIFFERENTIAL TOPOLOGY DAVID BEN-ZVI Differential topology is the application of differential and integral calculus to study smooth curved spaces called manifolds. The course begins with the assumption that you have a solid working knowledge of calculus in flat space. This document sketches some important ideas and given you some problems to jog your memory and help you review the material. Don't panic if some of this material is unfamiliar, as I expect it will be for most of you. Simply try to work through as much as you can. [In the name of intellectual honesty, I mention that this handout, like much of the course, is cribbed from excellent course materials developed in previous semesters by Dan Freed.] Here, first, is a brief list of topics I expect you to know: Linear algebra: abstract vector spaces and linear maps, dual space, direct sum, subspaces and quotient spaces, bases, change of basis, matrix compu- tations, trace and determinant, bilinear forms, diagonalization of symmetric bilinear forms, inner product spaces Calculus: directional derivative, differential, (higher) partial derivatives, chain rule, second derivative as a symmetric bilinear form, Taylor's theo- rem, inverse and implicit function theorem, integration, change of variable formula, Fubini's theorem, vector calculus in 3 dimensions (div, grad, curl, Green and Stokes' theorems), fundamental theorem on ordinary differential equations You may or may not have studied all of these topics in your undergraduate years. Most of the topics listed here will come up at some point in the course. As for references, I suggest first that you review your undergraduate texts. -
Inner Product Spaces Isaiah Lankham, Bruno Nachtergaele, Anne Schilling (March 2, 2007)
MAT067 University of California, Davis Winter 2007 Inner Product Spaces Isaiah Lankham, Bruno Nachtergaele, Anne Schilling (March 2, 2007) The abstract definition of vector spaces only takes into account algebraic properties for the addition and scalar multiplication of vectors. For vectors in Rn, for example, we also have geometric intuition which involves the length of vectors or angles between vectors. In this section we discuss inner product spaces, which are vector spaces with an inner product defined on them, which allow us to introduce the notion of length (or norm) of vectors and concepts such as orthogonality. 1 Inner product In this section V is a finite-dimensional, nonzero vector space over F. Definition 1. An inner product on V is a map ·, · : V × V → F (u, v) →u, v with the following properties: 1. Linearity in first slot: u + v, w = u, w + v, w for all u, v, w ∈ V and au, v = au, v; 2. Positivity: v, v≥0 for all v ∈ V ; 3. Positive definiteness: v, v =0ifandonlyifv =0; 4. Conjugate symmetry: u, v = v, u for all u, v ∈ V . Remark 1. Recall that every real number x ∈ R equals its complex conjugate. Hence for real vector spaces the condition about conjugate symmetry becomes symmetry. Definition 2. An inner product space is a vector space over F together with an inner product ·, ·. Copyright c 2007 by the authors. These lecture notes may be reproduced in their entirety for non- commercial purposes. 2NORMS 2 Example 1. V = Fn n u =(u1,...,un),v =(v1,...,vn) ∈ F Then n u, v = uivi. -
Basics of Euclidean Geometry
This is page 162 Printer: Opaque this 6 Basics of Euclidean Geometry Rien n'est beau que le vrai. |Hermann Minkowski 6.1 Inner Products, Euclidean Spaces In a±ne geometry it is possible to deal with ratios of vectors and barycen- ters of points, but there is no way to express the notion of length of a line segment or to talk about orthogonality of vectors. A Euclidean structure allows us to deal with metric notions such as orthogonality and length (or distance). This chapter and the next two cover the bare bones of Euclidean ge- ometry. One of our main goals is to give the basic properties of the transformations that preserve the Euclidean structure, rotations and re- ections, since they play an important role in practice. As a±ne geometry is the study of properties invariant under bijective a±ne maps and projec- tive geometry is the study of properties invariant under bijective projective maps, Euclidean geometry is the study of properties invariant under certain a±ne maps called rigid motions. Rigid motions are the maps that preserve the distance between points. Such maps are, in fact, a±ne and bijective (at least in the ¯nite{dimensional case; see Lemma 7.4.3). They form a group Is(n) of a±ne maps whose corresponding linear maps form the group O(n) of orthogonal transformations. The subgroup SE(n) of Is(n) corresponds to the orientation{preserving rigid motions, and there is a corresponding 6.1. Inner Products, Euclidean Spaces 163 subgroup SO(n) of O(n), the group of rotations. -
Limit Roots of Lorentzian Coxeter Systems
UNIVERSITY OF CALIFORNIA Santa Barbara Limit Roots of Lorentzian Coxeter Systems A Thesis submitted in partial satisfaction of the requirements for the degree of Master of Arts in Mathematics by Nicolas R. Brody Committee in Charge: Professor Jon McCammond, Chair Professor Darren Long Professor Stephen Bigelow June 2016 The Thesis of Nicolas R. Brody is approved: Professor Darren Long Professor Stephen Bigelow Professor Jon McCammond, Committee Chairperson May 2016 Limit Roots of Lorentzian Coxeter Systems Copyright c 2016 by Nicolas R. Brody iii Acknowledgements My time at UC Santa Barbara has been inundated with wonderful friends, teachers, mentors, and mathematicians. I am especially indebted to Jon McCammond, Jordan Schettler, and Maribel Cachadia. A very special thanks to Jean-Phillippe Labb for sharing his code which was used to produce Figures 7.1, 7.3, and 7.4. This thesis is dedicated to my three roommates: Viki, Luna, and Layla. I hope we can all be roommates again soon. iv Abstract Limit Roots of Lorentzian Coxeter Systems Nicolas R. Brody A reflection in a real vector space equipped with a positive definite symmetric bilinear form is any automorphism that sends some nonzero vector v to its negative and pointwise fixes its orthogonal complement, and a finite reflection group is a group generated by such transformations that acts properly on the sphere of unit vectors. We note two important classes of groups which occur as finite reflection groups: for a 2-dimensional vector space, we recover precisely the finite dihedral groups as reflection groups, and permuting basis vectors in an n-dimensional vector space gives a way of viewing a symmetric group as a reflection group. -
Linear Algebra Chapter 5: Norms, Inner Products and Orthogonality
MATH 532: Linear Algebra Chapter 5: Norms, Inner Products and Orthogonality Greg Fasshauer Department of Applied Mathematics Illinois Institute of Technology Spring 2015 [email protected] MATH 532 1 Outline 1 Vector Norms 2 Matrix Norms 3 Inner Product Spaces 4 Orthogonal Vectors 5 Gram–Schmidt Orthogonalization & QR Factorization 6 Unitary and Orthogonal Matrices 7 Orthogonal Reduction 8 Complementary Subspaces 9 Orthogonal Decomposition 10 Singular Value Decomposition 11 Orthogonal Projections [email protected] MATH 532 2 Vector Norms Vector[0] Norms 1 Vector Norms 2 Matrix Norms Definition 3 Inner Product Spaces Let x; y 2 Rn (Cn). Then 4 Orthogonal Vectors n T X 5 Gram–Schmidt Orthogonalization & QRx Factorizationy = xi yi 2 R i=1 6 Unitary and Orthogonal Matrices n X ∗ = ¯ 2 7 Orthogonal Reduction x y xi yi C i=1 8 Complementary Subspaces is called the standard inner product for Rn (Cn). 9 Orthogonal Decomposition 10 Singular Value Decomposition 11 Orthogonal Projections [email protected] MATH 532 4 Vector Norms Definition Let V be a vector space. A function k · k : V! R≥0 is called a norm provided for any x; y 2 V and α 2 R 1 kxk ≥ 0 and kxk = 0 if and only if x = 0, 2 kαxk = jαj kxk, 3 kx + yk ≤ kxk + kyk. Remark The inequality in (3) is known as the triangle inequality. [email protected] MATH 532 5 Vector Norms Remark Any inner product h·; ·i induces a norm via (more later) p kxk = hx; xi: We will show that the standard inner product induces the Euclidean norm (cf. -
Krammer Representations for Complex Braid Groups
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Journal of Algebra 371 (2012) 175–206 Contents lists available at SciVerse ScienceDirect Journal of Algebra www.elsevier.com/locate/jalgebra Krammer representations for complex braid groups Ivan Marin Institut de Mathématiques de Jussieu, Université Paris 7, 175 rue du Chevaleret, F-75013 Paris, France article info abstract Article history: Let B be the generalized braid group associated to some finite Received 5 April 2012 complex reflection group W . We define a representation of B Availableonline30August2012 of dimension the number of reflections of the corresponding Communicated by Michel Broué reflection group, which generalizes the Krammer representation of the classical braid groups, and is thus a good candidate in view MSC: 20C99 of proving the linearity of B. We decompose this representation 20F36 in irreducible components and compute its Zariski closure, as well as its restriction to parabolic subgroups. We prove that it is Keywords: faithful when W is a Coxeter group of type ADE and odd dihedral Braid groups types, and conjecture its faithfulness when W has a single class Complex reflection groups of reflections. If true, this conjecture would imply various group- Linear representations theoretic properties for these groups, that we prove separately to Knizhnik–Zamolodchikov systems be true for the other groups. Krammer representation © 2012 Elsevier Inc. All rights reserved. 1. Introduction Let k be a field of characteristic 0 and E a finite-dimensional k-vector space. A reflection in E is an element s ∈ GL(E) such that s2 = 1 and Ker(s − 1) is a hyperplane of E. -
On the Classification of Integral Quadratic Forms
15 On the Classification of Integral Quadratic Forms J. H. Conway and N. J. A. Sloane This chapter gives an account of the classification of integral quadratic forms. It is particularly designed for readers who wish to be able to do explicit calculations. Novel features include an elementary system of rational invariants (defined without using the Hilbert norm residue symbol), an improved notation for the genus of a form, an efficient way to compute the number of spinor genera in a genus, and some conditions which imply that there is only one class in a genus. We give tables of the binary forms with - 100 ~ det ~ 50, the indecomposable ternary forms with Idetl ~ 50, the genera of forms with Idetl ~ 11, the genera of p elementary forms for all p, and the positive definite forms with determinant 2 up to dimension 18 and determinant 3 up to dimension 17. 1. Introduction The project of classifying integral quadratic forms has a long history, to which many mathematicians have contributed. The binary (or two dimensional) forms were comprehensively discussed by Gauss. Gauss and later workers also made substantial inroads into the problem of ternary and higher-dimensional forms. The greatest advances since then have been the beautiful development of the theory of rational quadratic forms (Minkowski, Hasse, Witt), and Eichler's complete classification of indefinite forms in dimension 3 or higher in terms of the notion of spinor genus. Definite forms correspond to lattices in Euclidean space. For small dimensions they can be classified using Minkowski's generalization of Gauss's notion of reduced form, but this method rapidly becomes impracticable when the dimension reaches 6 or 7. -
Contents 5 Bilinear and Quadratic Forms
Linear Algebra (part 5): Bilinear and Quadratic Forms (by Evan Dummit, 2020, v. 1.00) Contents 5 Bilinear and Quadratic Forms 1 5.1 Bilinear Forms . 1 5.1.1 Denition, Associated Matrices, Basic Properties . 1 5.1.2 Symmetric Bilinear Forms and Diagonalization . 3 5.2 Quadratic Forms . 5 5.2.1 Denition and Basic Properties . 6 5.2.2 Quadratic Forms Over Rn: Diagonalization of Quadratic Varieties . 7 5.2.3 Quadratic Forms Over Rn: The Second Derivatives Test . 9 5.2.4 Quadratic Forms Over Rn: Sylvester's Law of Inertia . 11 5 Bilinear and Quadratic Forms In this chapter, we will discuss bilinear and quadratic forms. Bilinear forms are simply linear transformations that are linear in more than one variable, and they will allow us to extend our study of linear phenomena. They are closely related to quadratic forms, which are (classically speaking) homogeneous quadratic polynomials in multiple variables. Despite the fact that quadratic forms are not linear, we can (perhaps surprisingly) still use many of the tools of linear algebra to study them. 5.1 Bilinear Forms • We begin by discussing basic properties of bilinear forms on an arbitrary vector space. 5.1.1 Denition, Associated Matrices, Basic Properties • Let V be a vector space over the eld F . • Denition: A function Φ: V × V ! F is a bilinear form on V if it is linear in each variable when the other variable is xed. Explicitly, this means Φ(v1 + αv2; y) = Φ(v1; w) + αΦ(v2; w) and Φ(v; w1 + αw2) = Φ(v; w1) + αΦ(v; w2) for arbitrary vi; wi 2 V and α 2 F .