Linear Algebra: Invariant Subspaces
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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. -
Solution to Homework 5
Solution to Homework 5 Sec. 5.4 1 0 2. (e) No. Note that A = is in W , but 0 2 0 1 1 0 0 2 T (A) = = 1 0 0 2 1 0 is not in W . Hence, W is not a T -invariant subspace of V . 3. Note that T : V ! V is a linear operator on V . To check that W is a T -invariant subspace of V , we need to know if T (w) 2 W for any w 2 W . (a) Since we have T (0) = 0 2 f0g and T (v) 2 V; so both of f0g and V to be T -invariant subspaces of V . (b) Note that 0 2 N(T ). For any u 2 N(T ), we have T (u) = 0 2 N(T ): Hence, N(T ) is a T -invariant subspace of V . For any v 2 R(T ), as R(T ) ⊂ V , we have v 2 V . So, by definition, T (v) 2 R(T ): Hence, R(T ) is also a T -invariant subspace of V . (c) Note that for any v 2 Eλ, λv is a scalar multiple of v, so λv 2 Eλ as Eλ is a subspace. So we have T (v) = λv 2 Eλ: Hence, Eλ is a T -invariant subspace of V . 4. For any w in W , we know that T (w) is in W as W is a T -invariant subspace of V . Then, by induction, we know that T k(w) is also in W for any k. k Suppose g(T ) = akT + ··· + a1T + a0, we have k g(T )(w) = akT (w) + ··· + a1T (w) + a0(w) 2 W because it is just a linear combination of elements in W . -
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. -
Orthogonal Complements (Revised Version)
Orthogonal Complements (Revised Version) Math 108A: May 19, 2010 John Douglas Moore 1 The dot product You will recall that the dot product was discussed in earlier calculus courses. If n x = (x1: : : : ; xn) and y = (y1: : : : ; yn) are elements of R , we define their dot product by x · y = x1y1 + ··· + xnyn: The dot product satisfies several key axioms: 1. it is symmetric: x · y = y · x; 2. it is bilinear: (ax + x0) · y = a(x · y) + x0 · y; 3. and it is positive-definite: x · x ≥ 0 and x · x = 0 if and only if x = 0. The dot product is an example of an inner product on the vector space V = Rn over R; inner products will be treated thoroughly in Chapter 6 of [1]. Recall that the length of an element x 2 Rn is defined by p jxj = x · x: Note that the length of an element x 2 Rn is always nonnegative. Cauchy-Schwarz Theorem. If x 6= 0 and y 6= 0, then x · y −1 ≤ ≤ 1: (1) jxjjyj Sketch of proof: If v is any element of Rn, then v · v ≥ 0. Hence (x(y · y) − y(x · y)) · (x(y · y) − y(x · y)) ≥ 0: Expanding using the axioms for dot product yields (x · x)(y · y)2 − 2(x · y)2(y · y) + (x · y)2(y · y) ≥ 0 or (x · x)(y · y)2 ≥ (x · y)2(y · y): 1 Dividing by y · y, we obtain (x · y)2 jxj2jyj2 ≥ (x · y)2 or ≤ 1; jxj2jyj2 and (1) follows by taking the square root. -
Does Geometric Algebra Provide a Loophole to Bell's Theorem?
Discussion Does Geometric Algebra provide a loophole to Bell’s Theorem? Richard David Gill 1 1 Leiden University, Faculty of Science, Mathematical Institute; [email protected] Version October 30, 2019 submitted to Entropy Abstract: Geometric Algebra, championed by David Hestenes as a universal language for physics, was used as a framework for the quantum mechanics of interacting qubits by Chris Doran, Anthony Lasenby and others. Independently of this, Joy Christian in 2007 claimed to have refuted Bell’s theorem with a local realistic model of the singlet correlations by taking account of the geometry of space as expressed through Geometric Algebra. A series of papers culminated in a book Christian (2014). The present paper first explores Geometric Algebra as a tool for quantum information and explains why it did not live up to its early promise. In summary, whereas the mapping between 3D geometry and the mathematics of one qubit is already familiar, Doran and Lasenby’s ingenious extension to a system of entangled qubits does not yield new insight but just reproduces standard QI computations in a clumsy way. The tensor product of two Clifford algebras is not a Clifford algebra. The dimension is too large, an ad hoc fix is needed, several are possible. I further analyse two of Christian’s earliest, shortest, least technical, and most accessible works (Christian 2007, 2011), exposing conceptual and algebraic errors. Since 2015, when the first version of this paper was posted to arXiv, Christian has published ambitious extensions of his theory in RSOS (Royal Society - Open Source), arXiv:1806.02392, and in IEEE Access, arXiv:1405.2355. -
Spectral Coupling for Hermitian Matrices
Spectral coupling for Hermitian matrices Franc¸oise Chatelin and M. Monserrat Rincon-Camacho Technical Report TR/PA/16/240 Publications of the Parallel Algorithms Team http://www.cerfacs.fr/algor/publications/ SPECTRAL COUPLING FOR HERMITIAN MATRICES FRANC¸OISE CHATELIN (1);(2) AND M. MONSERRAT RINCON-CAMACHO (1) Cerfacs Tech. Rep. TR/PA/16/240 Abstract. The report presents the information processing that can be performed by a general hermitian matrix when two of its distinct eigenvalues are coupled, such as λ < λ0, instead of λ+λ0 considering only one eigenvalue as traditional spectral theory does. Setting a = 2 = 0 and λ0 λ 6 e = 2− > 0, the information is delivered in geometric form, both metric and trigonometric, associated with various right-angled triangles exhibiting optimality properties quantified as ratios or product of a and e. The potential optimisation has a triple nature which offers two j j e possibilities: in the case λλ0 > 0 they are characterised by a and a e and in the case λλ0 < 0 a j j j j by j j and a e. This nature is revealed by a key generalisation to indefinite matrices over R or e j j C of Gustafson's operator trigonometry. Keywords: Spectral coupling, indefinite symmetric or hermitian matrix, spectral plane, invariant plane, catchvector, antieigenvector, midvector, local optimisation, Euler equation, balance equation, torus in 3D, angle between complex lines. 1. Spectral coupling 1.1. Introduction. In the work we present below, we focus our attention on the coupling of any two distinct real eigenvalues λ < λ0 of a general hermitian or symmetric matrix A, a coupling called spectral coupling. -
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. -
Section 18.1-2. in the Next 2-3 Lectures We Will Have a Lightning Introduction to Representations of finite Groups
Section 18.1-2. In the next 2-3 lectures we will have a lightning introduction to representations of finite groups. For any vector space V over a field F , denote by L(V ) the algebra of all linear operators on V , and by GL(V ) the group of invertible linear operators. Note that if dim(V ) = n, then L(V ) = Matn(F ) is isomorphic to the algebra of n×n matrices over F , and GL(V ) = GLn(F ) to its multiplicative group. Definition 1. A linear representation of a set X in a vector space V is a map φ: X ! L(V ), where L(V ) is the set of linear operators on V , the space V is called the space of the rep- resentation. We will often denote the representation of X by (V; φ) or simply by V if the homomorphism φ is clear from the context. If X has any additional structures, we require that the map φ is a homomorphism. For example, a linear representation of a group G is a homomorphism φ: G ! GL(V ). Definition 2. A morphism of representations φ: X ! L(V ) and : X ! L(U) is a linear map T : V ! U, such that (x) ◦ T = T ◦ φ(x) for all x 2 X. In other words, T makes the following diagram commutative φ(x) V / V T T (x) U / U An invertible morphism of two representation is called an isomorphism, and two representa- tions are called isomorphic (or equivalent) if there exists an isomorphism between them. Example. (1) A representation of a one-element set in a vector space V is simply a linear operator on V . -
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. -
Signing a Linear Subspace: Signature Schemes for Network Coding
Signing a Linear Subspace: Signature Schemes for Network Coding Dan Boneh1?, David Freeman1?? Jonathan Katz2???, and Brent Waters3† 1 Stanford University, {dabo,dfreeman}@cs.stanford.edu 2 University of Maryland, [email protected] 3 University of Texas at Austin, [email protected]. Abstract. Network coding offers increased throughput and improved robustness to random faults in completely decentralized networks. In contrast to traditional routing schemes, however, network coding requires intermediate nodes to modify data packets en route; for this reason, standard signature schemes are inapplicable and it is a challenge to provide resilience to tampering by malicious nodes. Here, we propose two signature schemes that can be used in conjunction with network coding to prevent malicious modification of data. In particular, our schemes can be viewed as signing linear subspaces in the sense that a signature σ on V authenticates exactly those vectors in V . Our first scheme is homomorphic and has better performance, with both public key size and per-packet overhead being constant. Our second scheme does not rely on random oracles and uses weaker assumptions. We also prove a lower bound on the length of signatures for linear subspaces showing that both of our schemes are essentially optimal in this regard. 1 Introduction Network coding [1, 25] refers to a general class of routing mechanisms where, in contrast to tra- ditional “store-and-forward” routing, intermediate nodes modify data packets in transit. Network coding has been shown to offer a number of advantages with respect to traditional routing, the most well-known of which is the possibility of increased throughput in certain network topologies (see, e.g., [21] for measurements of the improvement network coding gives even for unicast traffic). -
Quantification of Stability in Systems of Nonlinear Ordinary Differential Equations Jason Terry
University of New Mexico UNM Digital Repository Mathematics & Statistics ETDs Electronic Theses and Dissertations 2-14-2014 Quantification of Stability in Systems of Nonlinear Ordinary Differential Equations Jason Terry Follow this and additional works at: https://digitalrepository.unm.edu/math_etds Recommended Citation Terry, Jason. "Quantification of Stability in Systems of Nonlinear Ordinary Differential Equations." (2014). https://digitalrepository.unm.edu/math_etds/48 This Dissertation is brought to you for free and open access by the Electronic Theses and Dissertations at UNM Digital Repository. It has been accepted for inclusion in Mathematics & Statistics ETDs by an authorized administrator of UNM Digital Repository. For more information, please contact [email protected]. Candidate Department This dissertation is approved, and it is acceptable in quality and form for publication: Approved by the Dissertation Committee: , Chairperson Quantification of Stability in Systems of Nonlinear Ordinary Differential Equations by Jason Terry B.A., Mathematics, California State University Fresno, 2003 B.S., Computer Science, California State University Fresno, 2003 M.A., Interdiscipline Studies, California State University Fresno, 2005 M.S., Applied Mathematics, University of New Mexico, 2009 DISSERTATION Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Mathematics The University of New Mexico Albuquerque, New Mexico December, 2013 c 2013, Jason Terry iii Dedication To my mom. iv Acknowledgments I would like