Appendices a Notation and Definitions
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Realizing Euclidean Distance Matrices by Sphere Intersection
Realizing Euclidean distance matrices by sphere intersection Jorge Alencara,∗, Carlile Lavorb, Leo Libertic aInstituto Federal de Educa¸c~ao,Ci^enciae Tecnologia do Sul de Minas Gerais, Inconfidentes, MG, Brazil bUniversity of Campinas (IMECC-UNICAMP), 13081-970, Campinas-SP, Brazil cCNRS LIX, Ecole´ Polytechnique, 91128 Palaiseau, France Abstract This paper presents the theoretical properties of an algorithm to find a re- alization of a (full) n × n Euclidean distance matrix in the smallest possible embedding dimension. Our algorithm performs linearly in n, and quadratically in the minimum embedding dimension, which is an improvement w.r.t. other algorithms. Keywords: Distance geometry, sphere intersection, euclidean distance matrix, embedding dimension. 2010 MSC: 00-01, 99-00 1. Introduction Euclidean distance matrices and sphere intersection have a strong mathe- matical importance [1, 2, 3, 4, 5, 6], in addition to many applications, such as navigation problems, molecular and nanostructure conformation, network 5 localization, robotics, as well as other problems of distance geometry [7, 8, 9]. Before defining precisely the problem of interest, we need the formal defini- tion for Euclidean distance matrices. Let D be a n × n symmetric hollow (i.e., with zero diagonal) matrix with non-negative elements. We say that D is a (squared) Euclidean Distance Matrix (EDM) if there are points x1; x2; : : : ; xn 2 RK (for a positive integer K) such that 2 D(i; j) = Dij = kxi − xjk ; i; j 2 f1; : : : ; ng; where k · k denotes the Euclidean norm. The smallest K for which such a set of points exists is called the embedding dimension of D, denoted by dim(D). -
Arxiv:1701.03378V1 [Math.RA] 12 Jan 2017 Setao Apihe Rusadfe Probability” Free and Groups T Lamplighter by on Supported Technology
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by TUGraz OPEN Library Linearizing the Word Problem in (some) Free Fields Konrad Schrempf∗ January 13, 2017 Abstract We describe a solution of the word problem in free fields (coming from non- commutative polynomials over a commutative field) using elementary linear algebra, provided that the elements are given by minimal linear representa- tions. It relies on the normal form of Cohn and Reutenauer and can be used more generally to (positively) test rational identities. Moreover we provide a construction of minimal linear representations for the inverse of non-zero elements. Keywords: word problem, minimal linear representation, linearization, realiza- tion, admissible linear system, rational series AMS Classification: 16K40, 16S10, 03B25, 15A22 Introduction arXiv:1701.03378v1 [math.RA] 12 Jan 2017 Free (skew) fields arise as universal objects when it comes to embed the ring of non-commutative polynomials, that is, polynomials in (a finite number of) non- commuting variables, into a skew field [Coh85, Chapter 7]. The notion of “free fields” goes back to Amitsur [Ami66]. A brief introduction can be found in [Coh03, Section 9.3], for details we refer to [Coh95, Section 6.4]. In the present paper we restrict the setting to commutative ground fields, as a special case. See also [Rob84]. In [CR94], Cohn and Reutenauer introduced a normal form for elements in free fields in order to extend results from the theory of formal languages. In particular they characterize minimality of linear representations in terms of linear independence of ∗Contact: [email protected], Department of Discrete Mathematics (Noncommutative Structures), Graz University of Technology. -
Seminar VII for the Course GROUP THEORY in PHYSICS Micael Flohr
Seminar VII for the course GROUP THEORY IN PHYSICS Mic~ael Flohr The classical Lie groups 25. January 2005 MATRIX LIE GROUPS Most Lie groups one ever encouters in physics are realized as matrix Lie groups and thus as subgroups of GL(n, R) or GL(n, C). This is the group of invertibel n × n matrices with coefficients in R or C, respectively. This is a Lie group, since it forms as an open subset of the vector space of n × n matrices a manifold. Matrix multiplication is certainly a differentiable map, as is taking the inverse via Cramer’s rule. The only condition defining the open 2 subset is that the determinat must not be zero, which implies that dimKGL(n, K) = n is the same as the one of the vector space Mn(K). However, GL(n, R) is not connected, because we cannot move continuously from a matrix with determinant less than zero to one with determinant larger than zero. It is worth mentioning that gl(n, K) is the vector space of all n × n matrices over the field K, equipped with the standard commutator as Lie bracket. We can describe most other Lie groups as subgroups of GL(n, K) for either K = R or K = C. There are two ways to do so. Firstly, one can give restricting equations to the coefficients of the matrices. Secondly, one can find subgroups of the automorphisms of V =∼ Kn, which conserve a given structure on Kn. In the following, we give some examples for this: SL(n, K). -
Inertia of the Matrix [(Pi + Pj) ]
isid/ms/2013/12 October 20, 2013 http://www.isid.ac.in/estatmath/eprints r Inertia of the matrix [(pi + pj) ] Rajendra Bhatia and Tanvi Jain Indian Statistical Institute, Delhi Centre 7, SJSS Marg, New Delhi{110 016, India r INERTIA OF THE MATRIX [(pi + pj) ] RAJENDRA BHATIA* AND TANVI JAIN** Abstract. Let p1; : : : ; pn be positive real numbers. It is well r known that for every r < 0 the matrix [(pi + pj) ] is positive def- inite. Our main theorem gives a count of the number of positive and negative eigenvalues of this matrix when r > 0: Connections with some other matrices that arise in Loewner's theory of oper- ator monotone functions and in the theory of spline interpolation are discussed. 1. Introduction Let p1; p2; : : : ; pn be distinct positive real numbers. The n×n matrix 1 C = [ ] is known as the Cauchy matrix. The special case pi = i pi+pj 1 gives the Hilbert matrix H = [ i+j ]: Both matrices have been studied by several authors in diverse contexts and are much used as test matrices in numerical analysis. The Cauchy matrix is known to be positive definite. It possessesh ai ◦r 1 stronger property: for each r > 0 the entrywise power C = r (pi+pj ) is positive definite. (See [4] for a proof.) The object of this paper is to study positivity properties of the related family of matrices r Pr = [(pi + pj) ]; r ≥ 0: (1) The inertia of a Hermitian matrix A is the triple In(A) = (π(A); ζ(A); ν(A)) ; in which π(A); ζ(A) and ν(A) stand for the number of positive, zero, and negative eigenvalues of A; respectively. -
Left Eigenvector of a Stochastic Matrix
Advances in Pure Mathematics, 2011, 1, 105-117 doi:10.4236/apm.2011.14023 Published Online July 2011 (http://www.SciRP.org/journal/apm) Left Eigenvector of a Stochastic Matrix Sylvain Lavalle´e Departement de mathematiques, Universite du Quebec a Montreal, Montreal, Canada E-mail: [email protected] Received January 7, 2011; revised June 7, 2011; accepted June 15, 2011 Abstract We determine the left eigenvector of a stochastic matrix M associated to the eigenvalue 1 in the commu- tative and the noncommutative cases. In the commutative case, we see that the eigenvector associated to the eigenvalue 0 is (,,NN1 n ), where Ni is the ith principal minor of NMI= n , where In is the 11 identity matrix of dimension n . In the noncommutative case, this eigenvector is (,P1 ,Pn ), where Pi is the sum in aij of the corresponding labels of nonempty paths starting from i and not passing through i in the complete directed graph associated to M . Keywords: Generic Stochastic Noncommutative Matrix, Commutative Matrix, Left Eigenvector Associated To The Eigenvalue 1, Skew Field, Automata 1. Introduction stochastic free field and that the vector 11 (,,PP1 n ) is fixed by our matrix; moreover, the sum 1 It is well known that 1 is one of the eigenvalue of a of the Pi is equal to 1, hence they form a kind of stochastic matrix (i.e. the sum of the elements of each noncommutative limiting probability. row is equal to 1) and its associated right eigenvector is These results have been proved in [1] but the proof the vector (1,1, ,1)T . -
Two-Graphs and Skew Two-Graphs in Finite Geometries
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Two-Graphs and Skew Two-Graphs in Finite Geometries G. Eric Moorhouse Department of Mathematics University of Wyoming Laramie Wyoming Dedicated to Professor J. J. Seidel Submitted by Aart Blokhuis ABSTRACT We describe the use of two-graphs and skew (oriented) two-graphs as isomor- phism invariants for translation planes and m-systems (including avoids and spreads) of polar spaces in odd characteristic. 1. INTRODUCTION Two-graphs were first introduced by G. Higman, as natural objects for the action of certain sporadic simple groups. They have since been studied extensively by Seidel, Taylor, and others, in relation to equiangular lines, strongly regular graphs, and other notions; see [26]-[28]. The analogous oriented two-graphs (which we call skew two-graphs) were introduced by Cameron [7], and there is some literature on the equivalent notion of switching classes of tournaments. Our exposition focuses on the use of two-graphs and skew two-graphs as isomorphism invariants of translation planes, and caps, avoids, and spreads of polar spaces in odd characteristic. The earliest precedent for using two-graphs to study ovoids and caps is apparently owing to Shult [29]. The degree sequences of these two-graphs and skew two-graphs yield the invariants known as fingerprints, introduced by J. H. Conway (see Chames [9, lo]). Two LZNEAR ALGEBRA AND ITS APPLZCATZONS 226-228:529-551 (1995) 0 Elsevier Science Inc., 1995 0024-3795/95/$9.50 655 Avenue of the Americas, New York, NY 10010 SSDI 0024-3795(95)00242-J 530 G. -
Hadamard and Conference Matrices
Hadamard and conference matrices Peter J. Cameron December 2011 with input from Dennis Lin, Will Orrick and Gordon Royle Now det(H) is equal to the volume of the n-dimensional parallelepiped spanned by the rows of H. By assumption, each row has Euclidean length at most n1/2, so that det(H) ≤ nn/2; equality holds if and only if I every entry of H is ±1; > I the rows of H are orthogonal, that is, HH = nI. A matrix attaining the bound is a Hadamard matrix. Hadamard's theorem Let H be an n × n matrix, all of whose entries are at most 1 in modulus. How large can det(H) be? A matrix attaining the bound is a Hadamard matrix. Hadamard's theorem Let H be an n × n matrix, all of whose entries are at most 1 in modulus. How large can det(H) be? Now det(H) is equal to the volume of the n-dimensional parallelepiped spanned by the rows of H. By assumption, each row has Euclidean length at most n1/2, so that det(H) ≤ nn/2; equality holds if and only if I every entry of H is ±1; > I the rows of H are orthogonal, that is, HH = nI. Hadamard's theorem Let H be an n × n matrix, all of whose entries are at most 1 in modulus. How large can det(H) be? Now det(H) is equal to the volume of the n-dimensional parallelepiped spanned by the rows of H. By assumption, each row has Euclidean length at most n1/2, so that det(H) ≤ nn/2; equality holds if and only if I every entry of H is ±1; > I the rows of H are orthogonal, that is, HH = nI. -
Fast Approximation Algorithms for Cauchy Matrices, Polynomials and Rational Functions
City University of New York (CUNY) CUNY Academic Works Computer Science Technical Reports CUNY Academic Works 2013 TR-2013011: Fast Approximation Algorithms for Cauchy Matrices, Polynomials and Rational Functions Victor Y. Pan How does access to this work benefit ou?y Let us know! More information about this work at: https://academicworks.cuny.edu/gc_cs_tr/386 Discover additional works at: https://academicworks.cuny.edu This work is made publicly available by the City University of New York (CUNY). Contact: [email protected] Fast Approximation Algorithms for Cauchy Matrices, Polynomials and Rational Functions ? Victor Y. Pan Department of Mathematics and Computer Science Lehman College and the Graduate Center of the City University of New York Bronx, NY 10468 USA [email protected], home page: http://comet.lehman.cuny.edu/vpan/ Abstract. The papers [MRT05], [CGS07], [XXG12], and [XXCBa] have combined the advanced FMM techniques with transformations of matrix structures (traced back to [P90]) in order to devise numerically stable algorithms that approximate the solutions of Toeplitz, Hankel, Toeplitz- like, and Hankel-like linear systems of equations in nearly linear arith- metic time, versus classical cubic time and quadratic time of the previous advanced algorithms. We show that the power of these approximation al- gorithms can be extended to yield similar results for computations with other matrices that have displacement structure, which includes Van- dermonde and Cauchy matrices, as well as to polynomial and rational evaluation and interpolation. The resulting decrease of the running time of the known approximation algorithms is again by order of magnitude, from quadratic to nearly linear. -
On Sign-Symmetric Signed Graphs∗
ISSN 1855-3966 (printed edn.), ISSN 1855-3974 (electronic edn.) ARS MATHEMATICA CONTEMPORANEA 19 (2020) 83–93 https://doi.org/10.26493/1855-3974.2161.f55 (Also available at http://amc-journal.eu) On sign-symmetric signed graphs∗ Ebrahim Ghorbani Department of Mathematics, K. N. Toosi University of Technology, P.O. Box 16765-3381, Tehran, Iran Willem H. Haemers Department of Econometrics and Operations Research, Tilburg University, Tilburg, The Netherlands Hamid Reza Maimani , Leila Parsaei Majd y Mathematics Section, Department of Basic Sciences, Shahid Rajaee Teacher Training University, P.O. Box 16785-163, Tehran, Iran Received 24 October 2019, accepted 27 March 2020, published online 10 November 2020 Abstract A signed graph is said to be sign-symmetric if it is switching isomorphic to its negation. Bipartite signed graphs are trivially sign-symmetric. We give new constructions of non- bipartite sign-symmetric signed graphs. Sign-symmetric signed graphs have a symmetric spectrum but not the other way around. We present constructions of signed graphs with symmetric spectra which are not sign-symmetric. This, in particular answers a problem posed by Belardo, Cioaba,˘ Koolen, and Wang in 2018. Keywords: Signed graph, spectrum. Math. Subj. Class. (2020): 05C22, 05C50 1 Introduction Let G be a graph with vertex set V and edge set E. All graphs considered in this paper are undirected, finite, and simple (without loops or multiple edges). A signed graph is a graph in which every edge has been declared positive or negative. In fact, a signed graph Γ is a pair (G; σ), where G = (V; E) is a graph, called the underlying ∗The authors would like to thank the anonymous referees for their helpful comments and suggestions. -
Pretty Good State Transfer in Discrete-Time Quantum Walks
Pretty good state transfer in discrete-time quantum walks Ada Chan and Hanmeng Zhan Department of Mathematics and Statistics, York University, Toronto, ON, Canada fssachan, [email protected] Abstract We establish the theory for pretty good state transfer in discrete- time quantum walks. For a class of walks, we show that pretty good state transfer is characterized by the spectrum of certain Hermitian adjacency matrix of the graph; more specifically, the vertices involved in pretty good state transfer must be m-strongly cospectral relative to this matrix, and the arccosines of its eigenvalues must satisfy some number theoretic conditions. Using normalized adjacency matrices, cyclic covers, and the theory on linear relations between geodetic an- gles, we construct several infinite families of walks that exhibits this phenomenon. 1 Introduction arXiv:2105.03762v1 [math.CO] 8 May 2021 A quantum walk with nice transport properties is desirable in quantum com- putation. For example, Grover's search algorithm [12] is a quantum walk on the looped complete graph, which sends the all-ones vector to a vector that almost \concentrate on" a vertex. In this paper, we study a slightly stronger notion of state transfer, where the target state can be approximated with arbitrary precision. This is called pretty good state transfer. Pretty good state transfer has been extensively studied in continuous-time quantum walks, especially on the paths [9, 22, 1, 5, 21]. However, not much 1 is known for the discrete-time analogues. The biggest difference between these two models is that in a continuous-time quantum walk, the evolution is completely determined by the adjacency or Laplacian matrix of the graph, while in a discrete-time quantum walk, the transition matrix depends on more than just the graph - usually, it is a product of two unitary matrices: U = SC; where S permutes the arcs of the graph, and C, called the coin matrix, send each arc to a linear combination of the outgoing arcs of the same vertex. -
Hadamard Matrices Include
Hadamard and conference matrices Peter J. Cameron University of St Andrews & Queen Mary University of London Mathematics Study Group with input from Rosemary Bailey, Katarzyna Filipiak, Joachim Kunert, Dennis Lin, Augustyn Markiewicz, Will Orrick, Gordon Royle and many happy returns . Happy Birthday, MSG!! Happy Birthday, MSG!! and many happy returns . Now det(H) is equal to the volume of the n-dimensional parallelepiped spanned by the rows of H. By assumption, each row has Euclidean length at most n1/2, so that det(H) ≤ nn/2; equality holds if and only if I every entry of H is ±1; > I the rows of H are orthogonal, that is, HH = nI. A matrix attaining the bound is a Hadamard matrix. This is a nice example of a continuous problem whose solution brings us into discrete mathematics. Hadamard's theorem Let H be an n × n matrix, all of whose entries are at most 1 in modulus. How large can det(H) be? A matrix attaining the bound is a Hadamard matrix. This is a nice example of a continuous problem whose solution brings us into discrete mathematics. Hadamard's theorem Let H be an n × n matrix, all of whose entries are at most 1 in modulus. How large can det(H) be? Now det(H) is equal to the volume of the n-dimensional parallelepiped spanned by the rows of H. By assumption, each row has Euclidean length at most n1/2, so that det(H) ≤ nn/2; equality holds if and only if I every entry of H is ±1; > I the rows of H are orthogonal, that is, HH = nI. -
An Accurate Computational Solution of Totally Positive Cauchy Linear Systems
AN ACCURATE COMPUTATIONAL SOLUTION OF TOTALLY POSITIVE CAUCHY LINEAR SYSTEMS Abdoulaye Cissé Andrew Anda Computer Science Department Computer Science Department St. Cloud State University St. Cloud State University St. Cloud, MN 56301 St. Cloud, MN 56301 [email protected] [email protected] Abstract ≈ ’ ∆ 1 The Cauchy linear systems C(x, y)V = b where C(x, y) = ∆ , x = (xi ) , y = ( yi ) , « xi − y j V = (vi ) , b = (bi ) can be solved very accurately regardless of condition number using Björck-Pereyra-type methods. We explain this phenomenon by the following facts. First, the Björck-Pereyra methods, written in matrix form, are essentially an accurate and efficient bidiagonal decomposition of the inverse of the matrix, as obtained by Neville Elimination with no pivoting. Second, each nontrivial entry of this bidiagonal decomposition is a product of two quotients of (initial) minors. We use this method to derive and implement a new method of accurately and efficiently solving totally positive Cauchy linear systems in time complexity O(n2). Keywords: Accuracy, Björck-Pereyra-type methods, Cauchy linear systems, floating point architectures, generalized Vandermonde linear systems, Neville elimination, totally positive, LDU decomposition. Note: In this paper we assume that the nodes xi and yj are pair wise distinct and C is totally positive 1 Background Definition 1.1 A Cauchy matrix is defined as: ≈ ’ ∆ 1 C(x, y) = ∆ , for i, j = 1,2,..., n (1.1) « xi − y j where the nodes xi and yj are assumed pair wise distinct. C(x,y) is totally positive (TP) if 2 2 0 < y1 < y2 < < yn−1 < yn < x1 < x2 < < xn−1 < xn .