The Spectra of Nonnegative Integer Matrices Via Formal Power Series
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CONSTRUCTING INTEGER MATRICES with INTEGER EIGENVALUES CHRISTOPHER TOWSE,∗ Scripps College
Applied Probability Trust (25 March 2016) CONSTRUCTING INTEGER MATRICES WITH INTEGER EIGENVALUES CHRISTOPHER TOWSE,∗ Scripps College ERIC CAMPBELL,∗∗ Pomona College Abstract In spite of the proveable rarity of integer matrices with integer eigenvalues, they are commonly used as examples in introductory courses. We present a quick method for constructing such matrices starting with a given set of eigenvectors. The main feature of the method is an added level of flexibility in the choice of allowable eigenvalues. The method is also applicable to non-diagonalizable matrices, when given a basis of generalized eigenvectors. We have produced an online web tool that implements these constructions. Keywords: Integer matrices, Integer eigenvalues 2010 Mathematics Subject Classification: Primary 15A36; 15A18 Secondary 11C20 In this paper we will look at the problem of constructing a good problem. Most linear algebra and introductory ordinary differential equations classes include the topic of diagonalizing matrices: given a square matrix, finding its eigenvalues and constructing a basis of eigenvectors. In the instructional setting of such classes, concrete “toy" examples are helpful and perhaps even necessary (at least for most students). The examples that are typically given to students are, of course, integer-entry matrices with integer eigenvalues. Sometimes the eigenvalues are repeated with multipicity, sometimes they are all distinct. Oftentimes, the number 0 is avoided as an eigenvalue due to the degenerate cases it produces, particularly when the matrix in question comes from a linear system of differential equations. Yet in [10], Martin and Wong show that “Almost all integer matrices have no integer eigenvalues," let alone all integer eigenvalues. -
Fast Computation of the Rank Profile Matrix and the Generalized Bruhat Decomposition Jean-Guillaume Dumas, Clement Pernet, Ziad Sultan
Fast Computation of the Rank Profile Matrix and the Generalized Bruhat Decomposition Jean-Guillaume Dumas, Clement Pernet, Ziad Sultan To cite this version: Jean-Guillaume Dumas, Clement Pernet, Ziad Sultan. Fast Computation of the Rank Profile Matrix and the Generalized Bruhat Decomposition. Journal of Symbolic Computation, Elsevier, 2017, Special issue on ISSAC’15, 83, pp.187-210. 10.1016/j.jsc.2016.11.011. hal-01251223v1 HAL Id: hal-01251223 https://hal.archives-ouvertes.fr/hal-01251223v1 Submitted on 5 Jan 2016 (v1), last revised 14 May 2018 (v2) HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Fast Computation of the Rank Profile Matrix and the Generalized Bruhat Decomposition Jean-Guillaume Dumas Universit´eGrenoble Alpes, Laboratoire LJK, umr CNRS, BP53X, 51, av. des Math´ematiques, F38041 Grenoble, France Cl´ement Pernet Universit´eGrenoble Alpes, Laboratoire de l’Informatique du Parall´elisme, Universit´ede Lyon, France. Ziad Sultan Universit´eGrenoble Alpes, Laboratoire LJK and LIG, Inria, CNRS, Inovall´ee, 655, av. de l’Europe, F38334 St Ismier Cedex, France Abstract The row (resp. column) rank profile of a matrix describes the stair-case shape of its row (resp. -
Ratner's Work on Unipotent Flows and Impact
Ratner’s Work on Unipotent Flows and Its Impact Elon Lindenstrauss, Peter Sarnak, and Amie Wilkinson Dani above. As the name suggests, these theorems assert that the closures, as well as related features, of the orbits of such flows are very restricted (rigid). As such they provide a fundamental and powerful tool for problems connected with these flows. The brilliant techniques that Ratner in- troduced and developed in establishing this rigidity have been the blueprint for similar rigidity theorems that have been proved more recently in other contexts. We begin by describing the setup for the group of 푑×푑 matrices with real entries and determinant equal to 1 — that is, SL(푑, ℝ). An element 푔 ∈ SL(푑, ℝ) is unipotent if 푔−1 is a nilpotent matrix (we use 1 to denote the identity element in 퐺), and we will say a group 푈 < 퐺 is unipotent if every element of 푈 is unipotent. Connected unipotent subgroups of SL(푑, ℝ), in particular one-parameter unipo- Ratner presenting her rigidity theorems in a plenary tent subgroups, are basic objects in Ratner’s work. A unipo- address to the 1994 ICM, Zurich. tent group is said to be a one-parameter unipotent group if there is a surjective homomorphism defined by polyno- In this note we delve a bit more into Ratner’s rigidity theo- mials from the additive group of real numbers onto the rems for unipotent flows and highlight some of their strik- group; for instance ing applications, expanding on the outline presented by Elon Lindenstrauss is Alice Kusiel and Kurt Vorreuter professor of mathemat- 1 푡 푡2/2 1 푡 ics at The Hebrew University of Jerusalem. -
Integer Matrix Approximation and Data Mining
Noname manuscript No. (will be inserted by the editor) Integer Matrix Approximation and Data Mining Bo Dong · Matthew M. Lin · Haesun Park Received: date / Accepted: date Abstract Integer datasets frequently appear in many applications in science and en- gineering. To analyze these datasets, we consider an integer matrix approximation technique that can preserve the original dataset characteristics. Because integers are discrete in nature, to the best of our knowledge, no previously proposed technique de- veloped for real numbers can be successfully applied. In this study, we first conduct a thorough review of current algorithms that can solve integer least squares problems, and then we develop an alternative least square method based on an integer least squares estimation to obtain the integer approximation of the integer matrices. We discuss numerical applications for the approximation of randomly generated integer matrices as well as studies of association rule mining, cluster analysis, and pattern extraction. Our computed results suggest that our proposed method can calculate a more accurate solution for discrete datasets than other existing methods. Keywords Data mining · Matrix factorization · Integer least squares problem · Clustering · Association rule · Pattern extraction The first author's research was supported in part by the National Natural Science Foundation of China under grant 11101067 and the Fundamental Research Funds for the Central Universities. The second author's research was supported in part by the National Center for Theoretical Sciences of Taiwan and by the Ministry of Science and Technology of Taiwan under grants 104-2115-M-006-017-MY3 and 105-2634-E-002-001. The third author's research was supported in part by the Defense Advanced Research Projects Agency (DARPA) XDATA program grant FA8750-12-2-0309 and NSF grants CCF-0808863, IIS-1242304, and IIS-1231742. -
Alternating Sign Matrices, Extensions and Related Cones
See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/311671190 Alternating sign matrices, extensions and related cones Article in Advances in Applied Mathematics · May 2017 DOI: 10.1016/j.aam.2016.12.001 CITATIONS READS 0 29 2 authors: Richard A. Brualdi Geir Dahl University of Wisconsin–Madison University of Oslo 252 PUBLICATIONS 3,815 CITATIONS 102 PUBLICATIONS 1,032 CITATIONS SEE PROFILE SEE PROFILE Some of the authors of this publication are also working on these related projects: Combinatorial matrix theory; alternating sign matrices View project All content following this page was uploaded by Geir Dahl on 16 December 2016. The user has requested enhancement of the downloaded file. All in-text references underlined in blue are added to the original document and are linked to publications on ResearchGate, letting you access and read them immediately. Alternating sign matrices, extensions and related cones Richard A. Brualdi∗ Geir Dahly December 1, 2016 Abstract An alternating sign matrix, or ASM, is a (0; ±1)-matrix where the nonzero entries in each row and column alternate in sign, and where each row and column sum is 1. We study the convex cone generated by ASMs of order n, called the ASM cone, as well as several related cones and polytopes. Some decomposition results are shown, and we find a minimal Hilbert basis of the ASM cone. The notion of (±1)-doubly stochastic matrices and a generalization of ASMs are introduced and various properties are shown. For instance, we give a new short proof of the linear characterization of the ASM polytope, in fact for a more general polytope. -
K-Theory of Furstenberg Transformation Group C ∗-Algebras
Canad. J. Math. Vol. 65 (6), 2013 pp. 1287–1319 http://dx.doi.org/10.4153/CJM-2013-022-x c Canadian Mathematical Society 2013 K-theory of Furstenberg Transformation Group C∗-algebras Kamran Reihani Abstract. This paper studies the K-theoretic invariants of the crossed product C∗-algebras associated n with an important family of homeomorphisms of the tori T called Furstenberg transformations. Using the Pimsner–Voiculescu theorem, we prove that given n, the K-groups of those crossed products whose corresponding n × n integer matrices are unipotent of maximal degree always have the same rank an. We show using the theory developed here that a claim made in the literature about the torsion subgroups of these K-groups is false. Using the representation theory of the simple Lie algebra sl(2; C), we show that, remarkably, an has a combinatorial significance. For example, every a2n+1 is just the number of ways that 0 can be represented as a sum of integers between −n and n (with no repetitions). By adapting an argument of van Lint (in which he answered a question of Erdos),˝ a simple explicit formula for the asymptotic behavior of the sequence fang is given. Finally, we describe the order structure of the K0-groups of an important class of Furstenberg crossed products, obtaining their complete Elliott invariant using classification results of H. Lin and N. C. Phillips. 1 Introduction Furstenberg transformations were introduced in [9] as the first examples of homeo- morphisms of the tori that under some necessary and sufficient conditions are mini- mal and uniquely ergodic. -
Package 'Matrixcalc'
Package ‘matrixcalc’ July 28, 2021 Version 1.0-5 Date 2021-07-27 Title Collection of Functions for Matrix Calculations Author Frederick Novomestky <[email protected]> Maintainer S. Thomas Kelly <[email protected]> Depends R (>= 2.0.1) Description A collection of functions to support matrix calculations for probability, econometric and numerical analysis. There are additional functions that are comparable to APL functions which are useful for actuarial models such as pension mathematics. This package is used for teaching and research purposes at the Department of Finance and Risk Engineering, New York University, Polytechnic Institute, Brooklyn, NY 11201. Horn, R.A. (1990) Matrix Analysis. ISBN 978-0521386326. Lancaster, P. (1969) Theory of Matrices. ISBN 978-0124355507. Lay, D.C. (1995) Linear Algebra: And Its Applications. ISBN 978-0201845563. License GPL (>= 2) Repository CRAN Date/Publication 2021-07-28 08:00:02 UTC NeedsCompilation no R topics documented: commutation.matrix . .3 creation.matrix . .4 D.matrix . .5 direct.prod . .6 direct.sum . .7 duplication.matrix . .8 E.matrices . .9 elimination.matrix . 10 entrywise.norm . 11 1 2 R topics documented: fibonacci.matrix . 12 frobenius.matrix . 13 frobenius.norm . 14 frobenius.prod . 15 H.matrices . 17 hadamard.prod . 18 hankel.matrix . 19 hilbert.matrix . 20 hilbert.schmidt.norm . 21 inf.norm . 22 is.diagonal.matrix . 23 is.idempotent.matrix . 24 is.indefinite . 25 is.negative.definite . 26 is.negative.semi.definite . 28 is.non.singular.matrix . 29 is.positive.definite . 31 is.positive.semi.definite . 32 is.singular.matrix . 34 is.skew.symmetric.matrix . 35 is.square.matrix . 36 is.symmetric.matrix . -
Chapter 2 a Short Review of Matrix Algebra
Chapter 2 A short review of matrix algebra 2.1 Vectors and vector spaces Definition 2.1.1. A vector a of dimension n is a collection of n elements typically written as ⎛ ⎞ ⎜ a1 ⎟ ⎜ ⎟ ⎜ ⎟ ⎜ a2 ⎟ a = ⎜ ⎟ =(ai)n. ⎜ . ⎟ ⎝ . ⎠ an Vectors of length 2 (two-dimensional vectors) can be thought of points in 33 BIOS 2083 Linear Models Abdus S. Wahed the plane (See figures). Chapter 2 34 BIOS 2083 Linear Models Abdus S. Wahed Figure 2.1: Vectors in two and three dimensional spaces (-1.5,2) (1, 1) (1, -2) x1 (2.5, 1.5, 0.95) x2 (0, 1.5, 0.95) x3 Chapter 2 35 BIOS 2083 Linear Models Abdus S. Wahed • A vector with all elements equal to zero is known as a zero vector and is denoted by 0. • A vector whose elements are stacked vertically is known as column vector whereas a vector whose elements are stacked horizontally will be referred to as row vector. (Unless otherwise mentioned, all vectors will be referred to as column vectors). • A row vector representation of a column vector is known as its trans- T pose. We will use⎛ the⎞ notation ‘ ’or‘ ’ to indicate a transpose. For ⎜ a1 ⎟ ⎜ ⎟ ⎜ a2 ⎟ ⎜ ⎟ T instance, if a = ⎜ ⎟ and b =(a1 a2 ... an), then we write b = a ⎜ . ⎟ ⎝ . ⎠ an or a = bT . • Vectors of same dimension are conformable to algebraic operations such as additions and subtractions. Sum of two or more vectors of dimension n results in another n-dimensional vector with elements as the sum of the corresponding elements of summand vectors. -
Generalized Spectral Characterizations of Almost Controllable Graphs
Generalized spectral characterizations of almost controllable graphs Wei Wanga Fenjin Liub∗ Wei Wangc aSchool of Mathematics, Physics and Finance, Anhui Polytechnic University, Wuhu 241000, P.R. China bSchool of Science, Chang’an University, Xi’an 710064, P.R. China bSchool of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an 710049, P.R. China Abstract Characterizing graphs by their spectra is an important topic in spectral graph theory, which has attracted a lot of attention of researchers in recent years. It is generally very hard and challenging to show a given graph to be determined by its spectrum. In Wang [J. Combin. Theory, Ser. B, 122 (2017): 438-451], the author gave a simple arithmetic condition for a family of graphs being determined by their generalized spectra. However, the method applies only to a family of the so called controllable graphs; it fails when the graphs are non-controllable. In this paper, we introduce a class of non-controllable graphs, called almost con- trollable graphs, and prove that, for any pair of almost controllable graphs G and H that are generalized cospectral, there exist exactly two rational orthogonal matrices Q with constant row sums such that QTA(G)Q = A(H), where A(G) and A(H) are the adjacency matrices of G and H, respectively. The main ingredient of the proof is a use of the Binet-Cauchy formula. As an application, we obtain a simple criterion for an almost controllable graph G to be determined by its generalized spectrum, which in some sense extends the corresponding result for controllable graphs. -
Facts from Linear Algebra
Appendix A Facts from Linear Algebra Abstract We introduce the notation of vector and matrices (cf. Section A.1), and recall the solvability of linear systems (cf. Section A.2). Section A.3 introduces the spectrum σ(A), matrix polynomials P (A) and their spectra, the spectral radius ρ(A), and its properties. Block structures are introduced in Section A.4. Subjects of Section A.5 are orthogonal and orthonormal vectors, orthogonalisation, the QR method, and orthogonal projections. Section A.6 is devoted to the Schur normal form (§A.6.1) and the Jordan normal form (§A.6.2). Diagonalisability is discussed in §A.6.3. Finally, in §A.6.4, the singular value decomposition is explained. A.1 Notation for Vectors and Matrices We recall that the field K denotes either R or C. Given a finite index set I, the linear I space of all vectors x =(xi)i∈I with xi ∈ K is denoted by K . The corresponding square matrices form the space KI×I . KI×J with another index set J describes rectangular matrices mapping KJ into KI . The linear subspace of a vector space V spanned by the vectors {xα ∈V : α ∈ I} is denoted and defined by α α span{x : α ∈ I} := aαx : aα ∈ K . α∈I I×I T Let A =(aαβ)α,β∈I ∈ K . Then A =(aβα)α,β∈I denotes the transposed H matrix, while A =(aβα)α,β∈I is the adjoint (or Hermitian transposed) matrix. T H Note that A = A holds if K = R . Since (x1,x2,...) indicates a row vector, T (x1,x2,...) is used for a column vector. -
On the Distance Spectra of Graphs
On the distance spectra of graphs Ghodratollah Aalipour∗ Aida Abiad† Zhanar Berikkyzy‡ Jay Cummings§ Jessica De Silva¶ Wei Gaok Kristin Heysse‡ Leslie Hogben‡∗∗ Franklin H.J. Kenter†† Jephian C.-H. Lin‡ Michael Tait§ October 5, 2015 Abstract The distance matrix of a graph G is the matrix containing the pairwise distances between vertices. The distance eigenvalues of G are the eigenvalues of its distance matrix and they form the distance spectrum of G. We determine the distance spectra of halved cubes, double odd graphs, and Doob graphs, completing the determination of distance spectra of distance regular graphs having exactly one positive distance eigenvalue. We characterize strongly regular graphs having more positive than negative distance eigenvalues. We give examples of graphs with few distinct distance eigenvalues but lacking regularity properties. We also determine the determinant and inertia of the distance matrices of lollipop and barbell graphs. Keywords. distance matrix, eigenvalue, distance regular graph, Kneser graph, double odd graph, halved cube, Doob graph, lollipop graph, barbell graph, distance spectrum, strongly regular graph, optimistic graph, determinant, inertia, graph AMS subject classifications. 05C12, 05C31, 05C50, 15A15, 15A18, 15B48, 15B57 1 Introduction The distance matrix (G) = [d ] of a graph G is the matrix indexed by the vertices of G where d = d(v , v ) D ij ij i j is the distance between the vertices vi and vj , i.e., the length of a shortest path between vi and vj . Distance matrices were introduced in the study of a data communication problem in [16]. This problem involves finding appropriate addresses so that a message can move efficiently through a series of loops from its origin to its destination, choosing the best route at each switching point. -
Computing Rational Forms of Integer Matrices
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector J. Symbolic Computation (2002) 34, 157–172 doi:10.1006/jsco.2002.0554 Available online at http://www.idealibrary.com on Computing Rational Forms of Integer Matrices MARK GIESBRECHT† AND ARNE STORJOHANN‡ School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1 n×n A new algorithm is presented for finding the Frobenius rational form F ∈ Z of any n×n 4 3 A ∈ Z which requires an expected number of O(n (log n + log kAk) + n (log n + log kAk)2) word operations using standard integer and matrix arithmetic (where kAk = maxij |Aij |). This substantially improves on the fastest previously known algorithms. The algorithm is probabilistic of the Las Vegas type: it assumes a source of random bits but always produces the correct answer. Las Vegas algorithms are also presented for computing a transformation matrix to the Frobenius form, and for computing the rational Jordan form of an integer matrix. c 2002 Elsevier Science Ltd. All rights reserved. 1. Introduction In this paper we present new algorithms for exactly computing the Frobenius and rational Jordan normal forms of an integer matrix which are substantially faster than those previously known. We show that the Frobenius form F ∈ Zn×n of any A ∈ Zn×n can be computed with an expected number of O(n4(log n+log kAk)+n3(log n+log kAk)2) word operations using standard integer and matrix arithmetic. Here and throughout this paper, kAk = maxij |Aij|.