Symmetry Groups and Group Invariant Solutions of Partial Differential Equations Peter J
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The Invariant Theory of Matrices
UNIVERSITY LECTURE SERIES VOLUME 69 The Invariant Theory of Matrices Corrado De Concini Claudio Procesi American Mathematical Society The Invariant Theory of Matrices 10.1090/ulect/069 UNIVERSITY LECTURE SERIES VOLUME 69 The Invariant Theory of Matrices Corrado De Concini Claudio Procesi American Mathematical Society Providence, Rhode Island EDITORIAL COMMITTEE Jordan S. Ellenberg Robert Guralnick William P. Minicozzi II (Chair) Tatiana Toro 2010 Mathematics Subject Classification. Primary 15A72, 14L99, 20G20, 20G05. For additional information and updates on this book, visit www.ams.org/bookpages/ulect-69 Library of Congress Cataloging-in-Publication Data Names: De Concini, Corrado, author. | Procesi, Claudio, author. Title: The invariant theory of matrices / Corrado De Concini, Claudio Procesi. Description: Providence, Rhode Island : American Mathematical Society, [2017] | Series: Univer- sity lecture series ; volume 69 | Includes bibliographical references and index. Identifiers: LCCN 2017041943 | ISBN 9781470441876 (alk. paper) Subjects: LCSH: Matrices. | Invariants. | AMS: Linear and multilinear algebra; matrix theory – Basic linear algebra – Vector and tensor algebra, theory of invariants. msc | Algebraic geometry – Algebraic groups – None of the above, but in this section. msc | Group theory and generalizations – Linear algebraic groups and related topics – Linear algebraic groups over the reals, the complexes, the quaternions. msc | Group theory and generalizations – Linear algebraic groups and related topics – Representation theory. msc Classification: LCC QA188 .D425 2017 | DDC 512.9/434–dc23 LC record available at https://lccn. loc.gov/2017041943 Copying and reprinting. Individual readers of this publication, and nonprofit libraries acting for them, are permitted to make fair use of the material, such as to copy select pages for use in teaching or research. -
Algebraic Geometry, Moduli Spaces, and Invariant Theory
Algebraic Geometry, Moduli Spaces, and Invariant Theory Han-Bom Moon Department of Mathematics Fordham University May, 2016 Han-Bom Moon Algebraic Geometry, Moduli Spaces, and Invariant Theory Part I Algebraic Geometry Han-Bom Moon Algebraic Geometry, Moduli Spaces, and Invariant Theory Algebraic geometry From wikipedia: Algebraic geometry is a branch of mathematics, classically studying zeros of multivariate polynomials. Han-Bom Moon Algebraic Geometry, Moduli Spaces, and Invariant Theory Algebraic geometry in highschool The zero set of a two-variable polynomial provides a plane curve. Example: 2 2 2 f(x; y) = x + y 1 Z(f) := (x; y) R f(x; y) = 0 − , f 2 j g a unit circle ··· Han-Bom Moon Algebraic Geometry, Moduli Spaces, and Invariant Theory Algebraic geometry in college calculus The zero set of a three-variable polynomial give a surface. Examples: 2 2 2 3 f(x; y; z) = x + y z 1 Z(f) := (x; y; z) R f(x; y; z) = 0 − − , f 2 j g 2 2 2 3 g(x; y; z) = x + y z Z(g) := (x; y; z) R g(x; y; z) = 0 − , f 2 j g Han-Bom Moon Algebraic Geometry, Moduli Spaces, and Invariant Theory Algebraic geometry in college calculus The common zero set of two three-variable polynomials gives a space curve. Example: f(x; y; z) = x2 + y2 + z2 1; g(x; y; z) = x + y + z − Z(f; g) := (x; y; z) R3 f(x; y; z) = g(x; y; z) = 0 , f 2 j g Definition An algebraic variety is a common zero set of some polynomials. -
Reflection Invariant and Symmetry Detection
1 Reflection Invariant and Symmetry Detection Erbo Li and Hua Li Abstract—Symmetry detection and discrimination are of fundamental meaning in science, technology, and engineering. This paper introduces reflection invariants and defines the directional moments(DMs) to detect symmetry for shape analysis and object recognition. And it demonstrates that detection of reflection symmetry can be done in a simple way by solving a trigonometric system derived from the DMs, and discrimination of reflection symmetry can be achieved by application of the reflection invariants in 2D and 3D. Rotation symmetry can also be determined based on that. Also, if none of reflection invariants is equal to zero, then there is no symmetry. And the experiments in 2D and 3D show that all the reflection lines or planes can be deterministically found using DMs up to order six. This result can be used to simplify the efforts of symmetry detection in research areas,such as protein structure, model retrieval, reverse engineering, and machine vision etc. Index Terms—symmetry detection, shape analysis, object recognition, directional moment, moment invariant, isometry, congruent, reflection, chirality, rotation F 1 INTRODUCTION Kazhdan et al. [1] developed a continuous measure and dis- The essence of geometric symmetry is self-evident, which cussed the properties of the reflective symmetry descriptor, can be found everywhere in nature and social lives, as which was expanded to 3D by [2] and was augmented in shown in Figure 1. It is true that we are living in a spatial distribution of the objects asymmetry by [3] . For symmetric world. Pursuing the explanation of symmetry symmetry discrimination [4] defined a symmetry distance will provide better understanding to the surrounding world of shapes. -
Molecular Symmetry
Molecular Symmetry Symmetry helps us understand molecular structure, some chemical properties, and characteristics of physical properties (spectroscopy) – used with group theory to predict vibrational spectra for the identification of molecular shape, and as a tool for understanding electronic structure and bonding. Symmetrical : implies the species possesses a number of indistinguishable configurations. 1 Group Theory : mathematical treatment of symmetry. symmetry operation – an operation performed on an object which leaves it in a configuration that is indistinguishable from, and superimposable on, the original configuration. symmetry elements – the points, lines, or planes to which a symmetry operation is carried out. Element Operation Symbol Identity Identity E Symmetry plane Reflection in the plane σ Inversion center Inversion of a point x,y,z to -x,-y,-z i Proper axis Rotation by (360/n)° Cn 1. Rotation by (360/n)° Improper axis S 2. Reflection in plane perpendicular to rotation axis n Proper axes of rotation (C n) Rotation with respect to a line (axis of rotation). •Cn is a rotation of (360/n)°. •C2 = 180° rotation, C 3 = 120° rotation, C 4 = 90° rotation, C 5 = 72° rotation, C 6 = 60° rotation… •Each rotation brings you to an indistinguishable state from the original. However, rotation by 90° about the same axis does not give back the identical molecule. XeF 4 is square planar. Therefore H 2O does NOT possess It has four different C 2 axes. a C 4 symmetry axis. A C 4 axis out of the page is called the principle axis because it has the largest n . By convention, the principle axis is in the z-direction 2 3 Reflection through a planes of symmetry (mirror plane) If reflection of all parts of a molecule through a plane produced an indistinguishable configuration, the symmetry element is called a mirror plane or plane of symmetry . -
Arxiv:1910.10745V1 [Cond-Mat.Str-El] 23 Oct 2019 2.2 Symmetry-Protected Time Crystals
A Brief History of Time Crystals Vedika Khemania,b,∗, Roderich Moessnerc, S. L. Sondhid aDepartment of Physics, Harvard University, Cambridge, Massachusetts 02138, USA bDepartment of Physics, Stanford University, Stanford, California 94305, USA cMax-Planck-Institut f¨urPhysik komplexer Systeme, 01187 Dresden, Germany dDepartment of Physics, Princeton University, Princeton, New Jersey 08544, USA Abstract The idea of breaking time-translation symmetry has fascinated humanity at least since ancient proposals of the per- petuum mobile. Unlike the breaking of other symmetries, such as spatial translation in a crystal or spin rotation in a magnet, time translation symmetry breaking (TTSB) has been tantalisingly elusive. We review this history up to recent developments which have shown that discrete TTSB does takes place in periodically driven (Floquet) systems in the presence of many-body localization (MBL). Such Floquet time-crystals represent a new paradigm in quantum statistical mechanics — that of an intrinsically out-of-equilibrium many-body phase of matter with no equilibrium counterpart. We include a compendium of the necessary background on the statistical mechanics of phase structure in many- body systems, before specializing to a detailed discussion of the nature, and diagnostics, of TTSB. In particular, we provide precise definitions that formalize the notion of a time-crystal as a stable, macroscopic, conservative clock — explaining both the need for a many-body system in the infinite volume limit, and for a lack of net energy absorption or dissipation. Our discussion emphasizes that TTSB in a time-crystal is accompanied by the breaking of a spatial symmetry — so that time-crystals exhibit a novel form of spatiotemporal order. -
TASI 2008 Lectures: Introduction to Supersymmetry And
TASI 2008 Lectures: Introduction to Supersymmetry and Supersymmetry Breaking Yuri Shirman Department of Physics and Astronomy University of California, Irvine, CA 92697. [email protected] Abstract These lectures, presented at TASI 08 school, provide an introduction to supersymmetry and supersymmetry breaking. We present basic formalism of supersymmetry, super- symmetric non-renormalization theorems, and summarize non-perturbative dynamics of supersymmetric QCD. We then turn to discussion of tree level, non-perturbative, and metastable supersymmetry breaking. We introduce Minimal Supersymmetric Standard Model and discuss soft parameters in the Lagrangian. Finally we discuss several mech- anisms for communicating the supersymmetry breaking between the hidden and visible sectors. arXiv:0907.0039v1 [hep-ph] 1 Jul 2009 Contents 1 Introduction 2 1.1 Motivation..................................... 2 1.2 Weylfermions................................... 4 1.3 Afirstlookatsupersymmetry . .. 5 2 Constructing supersymmetric Lagrangians 6 2.1 Wess-ZuminoModel ............................... 6 2.2 Superfieldformalism .............................. 8 2.3 VectorSuperfield ................................. 12 2.4 Supersymmetric U(1)gaugetheory ....................... 13 2.5 Non-abeliangaugetheory . .. 15 3 Non-renormalization theorems 16 3.1 R-symmetry.................................... 17 3.2 Superpotentialterms . .. .. .. 17 3.3 Gaugecouplingrenormalization . ..... 19 3.4 D-termrenormalization. ... 20 4 Non-perturbative dynamics in SUSY QCD 20 4.1 Affleck-Dine-Seiberg -
Observability and Symmetries of Linear Control Systems
S S symmetry Article Observability and Symmetries of Linear Control Systems Víctor Ayala 1,∗, Heriberto Román-Flores 1, María Torreblanca Todco 2 and Erika Zapana 2 1 Instituto de Alta Investigación, Universidad de Tarapacá, Casilla 7D, Arica 1000000, Chile; [email protected] 2 Departamento Académico de Matemáticas, Universidad, Nacional de San Agustín de Arequipa, Calle Santa Catalina, Nro. 117, Arequipa 04000, Peru; [email protected] (M.T.T.); [email protected] (E.Z.) * Correspondence: [email protected] Received: 7 April 2020; Accepted: 6 May 2020; Published: 4 June 2020 Abstract: The goal of this article is to compare the observability properties of the class of linear control systems in two different manifolds: on the Euclidean space Rn and, in a more general setup, on a connected Lie group G. For that, we establish well-known results. The symmetries involved in this theory allow characterizing the observability property on Euclidean spaces and the local observability property on Lie groups. Keywords: observability; symmetries; Euclidean spaces; Lie groups 1. Introduction For general facts about control theory, we suggest the references, [1–5], and for general mathematical issues, the references [6–8]. In the context of this article, a general control system S can be modeled by the data S = (M, D, h, N). Here, M is the manifold of the space states, D is a family of differential equations on M, or if you wish, a family of vector fields on the manifold, h : M ! N is a differentiable observation map, and N is a manifold that contains all the known information that you can see of the system through h. -
A New Invariant Under Congruence of Nonsingular Matrices
A new invariant under congruence of nonsingular matrices Kiyoshi Shirayanagi and Yuji Kobayashi Abstract For a nonsingular matrix A, we propose the form Tr(tAA−1), the trace of the product of its transpose and inverse, as a new invariant under congruence of nonsingular matrices. 1 Introduction Let K be a field. We denote the set of all n n matrices over K by M(n,K), and the set of all n n nonsingular matrices× over K by GL(n,K). For A, B M(n,K), A is similar× to B if there exists P GL(n,K) such that P −1AP = B∈, and A is congruent to B if there exists P ∈ GL(n,K) such that tP AP = B, where tP is the transpose of P . A map f∈ : M(n,K) K is an invariant under similarity of matrices if for any A M(n,K), f(P→−1AP ) = f(A) holds for all P GL(n,K). Also, a map g : M∈ (n,K) K is an invariant under congruence∈ of matrices if for any A M(n,K), g(→tP AP ) = g(A) holds for all P GL(n,K), and h : GL(n,K) ∈ K is an invariant under congruence of nonsingular∈ matrices if for any A →GL(n,K), h(tP AP ) = h(A) holds for all P GL(n,K). ∈ ∈As is well known, there are many invariants under similarity of matrices, such as trace, determinant and other coefficients of the minimal polynomial. In arXiv:1904.04397v1 [math.RA] 8 Apr 2019 case the characteristic is zero, rank is also an example. -
Moduli Spaces and Invariant Theory
MODULI SPACES AND INVARIANT THEORY JENIA TEVELEV CONTENTS §1. Syllabus 3 §1.1. Prerequisites 3 §1.2. Course description 3 §1.3. Course grading and expectations 4 §1.4. Tentative topics 4 §1.5. Textbooks 4 References 4 §2. Geometry of lines 5 §2.1. Grassmannian as a complex manifold. 5 §2.2. Moduli space or a parameter space? 7 §2.3. Stiefel coordinates. 8 §2.4. Complete system of (semi-)invariants. 8 §2.5. Plücker coordinates. 9 §2.6. First Fundamental Theorem 10 §2.7. Equations of the Grassmannian 11 §2.8. Homogeneous ideal 13 §2.9. Hilbert polynomial 15 §2.10. Enumerative geometry 17 §2.11. Transversality. 19 §2.12. Homework 1 21 §3. Fine moduli spaces 23 §3.1. Categories 23 §3.2. Functors 25 §3.3. Equivalence of Categories 26 §3.4. Representable Functors 28 §3.5. Natural Transformations 28 §3.6. Yoneda’s Lemma 29 §3.7. Grassmannian as a fine moduli space 31 §4. Algebraic curves and Riemann surfaces 37 §4.1. Elliptic and Abelian integrals 37 §4.2. Finitely generated fields of transcendence degree 1 38 §4.3. Analytic approach 40 §4.4. Genus and meromorphic forms 41 §4.5. Divisors and linear equivalence 42 §4.6. Branched covers and Riemann–Hurwitz formula 43 §4.7. Riemann–Roch formula 45 §4.8. Linear systems 45 §5. Moduli of elliptic curves 47 1 2 JENIA TEVELEV §5.1. Curves of genus 1. 47 §5.2. J-invariant 50 §5.3. Monstrous Moonshine 52 §5.4. Families of elliptic curves 53 §5.5. The j-line is a coarse moduli space 54 §5.6. -
The Symmetry Group of a Finite Frame
Technical Report 3 January 2010 The symmetry group of a finite frame Richard Vale and Shayne Waldron Department of Mathematics, Cornell University, 583 Malott Hall, Ithaca, NY 14853-4201, USA e–mail: [email protected] Department of Mathematics, University of Auckland, Private Bag 92019, Auckland, New Zealand e–mail: [email protected] ABSTRACT We define the symmetry group of a finite frame as a group of permutations on its index set. This group is closely related to the symmetry group of [VW05] for tight frames: they are isomorphic when the frame is tight and has distinct vectors. The symmetry group is the same for all similar frames, in particular for a frame, its dual and canonical tight frames. It can easily be calculated from the Gramian matrix of the canonical tight frame. Further, a frame and its complementary frame have the same symmetry group. We exploit this last property to construct and classify some classes of highly symmetric tight frames. Key Words: finite frame, geometrically uniform frame, Gramian matrix, harmonic frame, maximally symmetric frames partition frames, symmetry group, tight frame, AMS (MOS) Subject Classifications: primary 42C15, 58D19, secondary 42C40, 52B15, 0 1. Introduction Over the past decade there has been a rapid development of the theory and application of finite frames to areas as diverse as signal processing, quantum information theory and multivariate orthogonal polynomials, see, e.g., [KC08], [RBSC04] and [W091]. Key to these applications is the construction of frames with desirable properties. These often include being tight, and having a high degree of symmetry. Important examples are the harmonic or geometrically uniform frames, i.e., tight frames which are the orbit of a single vector under an abelian group of unitary transformations (see [BE03] and [HW06]). -
Introduction to Supersymmetry
Introduction to Supersymmetry Pre-SUSY Summer School Corpus Christi, Texas May 15-18, 2019 Stephen P. Martin Northern Illinois University [email protected] 1 Topics: Why: Motivation for supersymmetry (SUSY) • What: SUSY Lagrangians, SUSY breaking and the Minimal • Supersymmetric Standard Model, superpartner decays Who: Sorry, not covered. • For some more details and a slightly better attempt at proper referencing: A supersymmetry primer, hep-ph/9709356, version 7, January 2016 • TASI 2011 lectures notes: two-component fermion notation and • supersymmetry, arXiv:1205.4076. If you find corrections, please do let me know! 2 Lecture 1: Motivation and Introduction to Supersymmetry Motivation: The Hierarchy Problem • Supermultiplets • Particle content of the Minimal Supersymmetric Standard Model • (MSSM) Need for “soft” breaking of supersymmetry • The Wess-Zumino Model • 3 People have cited many reasons why extensions of the Standard Model might involve supersymmetry (SUSY). Some of them are: A possible cold dark matter particle • A light Higgs boson, M = 125 GeV • h Unification of gauge couplings • Mathematical elegance, beauty • ⋆ “What does that even mean? No such thing!” – Some modern pundits ⋆ “We beg to differ.” – Einstein, Dirac, . However, for me, the single compelling reason is: The Hierarchy Problem • 4 An analogy: Coulomb self-energy correction to the electron’s mass A point-like electron would have an infinite classical electrostatic energy. Instead, suppose the electron is a solid sphere of uniform charge density and radius R. An undergraduate problem gives: 3e2 ∆ECoulomb = 20πǫ0R 2 Interpreting this as a correction ∆me = ∆ECoulomb/c to the electron mass: 15 0.86 10− meters m = m + (1 MeV/c2) × . -
Group Symmetry and Covariance Regularization
Group Symmetry and Covariance Regularization Parikshit Shah and Venkat Chandrasekaran email: [email protected]; [email protected] Abstract Statistical models that possess symmetry arise in diverse settings such as ran- dom fields associated to geophysical phenomena, exchangeable processes in Bayesian statistics, and cyclostationary processes in engineering. We formalize the notion of a symmetric model via group invariance. We propose projection onto a group fixed point subspace as a fundamental way of regularizing covariance matrices in the high- dimensional regime. In terms of parameters associated to the group we derive precise rates of convergence of the regularized covariance matrix and demonstrate that signif- icant statistical gains may be expected in terms of the sample complexity. We further explore the consequences of symmetry on related model-selection problems such as the learning of sparse covariance and inverse covariance matrices. We also verify our results with simulations. 1 Introduction An important feature of many modern data analysis problems is the small number of sam- ples available relative to the dimension of the data. Such high-dimensional settings arise in a range of applications in bioinformatics, climate studies, and economics. A fundamental problem that arises in the high-dimensional regime is the poor conditioning of sample statis- tics such as sample covariance matrices [9] ,[8]. Accordingly, a fruitful and active research agenda over the last few years has been the development of methods for high-dimensional statistical inference and modeling that take into account structure in the underlying model. Some examples of structural assumptions on statistical models include models with a few latent factors (leading to low-rank covariance matrices) [18], models specified by banded or sparse covariance matrices [9], [8], and Markov or graphical models [25, 31, 27].