Permanents of Doubly Stochastic Matrices
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Math 4571 (Advanced Linear Algebra) Lecture #27
Math 4571 (Advanced Linear Algebra) Lecture #27 Applications of Diagonalization and the Jordan Canonical Form (Part 1): Spectral Mapping and the Cayley-Hamilton Theorem Transition Matrices and Markov Chains The Spectral Theorem for Hermitian Operators This material represents x4.4.1 + x4.4.4 +x4.4.5 from the course notes. Overview In this lecture and the next, we discuss a variety of applications of diagonalization and the Jordan canonical form. This lecture will discuss three essentially unrelated topics: A proof of the Cayley-Hamilton theorem for general matrices Transition matrices and Markov chains, used for modeling iterated changes in systems over time The spectral theorem for Hermitian operators, in which we establish that Hermitian operators (i.e., operators with T ∗ = T ) are diagonalizable In the next lecture, we will discuss another fundamental application: solving systems of linear differential equations. Cayley-Hamilton, I First, we establish the Cayley-Hamilton theorem for arbitrary matrices: Theorem (Cayley-Hamilton) If p(x) is the characteristic polynomial of a matrix A, then p(A) is the zero matrix 0. The same result holds for the characteristic polynomial of a linear operator T : V ! V on a finite-dimensional vector space. Cayley-Hamilton, II Proof: Since the characteristic polynomial of a matrix does not depend on the underlying field of coefficients, we may assume that the characteristic polynomial factors completely over the field (i.e., that all of the eigenvalues of A lie in the field) by replacing the field with its algebraic closure. Then by our results, the Jordan canonical form of A exists. -
Lecture 4 1 the Permanent of a Matrix
Grafy a poˇcty - NDMI078 April 2009 Lecture 4 M. Loebl J.-S. Sereni 1 The permanent of a matrix 1.1 Minc's conjecture The set of permutations of f1; : : : ; ng is Sn. Let A = (ai;j)1≤i;j≤n be a square matrix with real non-negative entries. The permanent of the matrix A is n X Y perm(A) := ai,σ(i) : σ2Sn i=1 In 1973, Br`egman[4] proved M´ınc’sconjecture [18]. n×n Pn Theorem 1 (Br`egman,1973). Let A = (ai;j)1≤i;j≤n 2 f0; 1g . Set ri := j=1 ai;j. Then, n Y 1=ri perm(A) ≤ (ri!) : i=1 Further, if ri > 0 for every i 2 f1; 2; : : : ; ng, then there is equality if and only if up to permutations of rows and columns, A is a block-diagonal matrix, each block being a square matrix with all entries equal to 1. Several proofs of this result are known, the original being combinatorial. In 1978, Schrijver [22] found a neat and short proof. A probabilistic description of this proof is presented in the book of Alon and Spencer [3, Chapter 2]. The one we will see in Lecture 5 uses the concept of entropy, and was found by Radhakrishnan [20] in the late nineties. It is a nice illustration of the use of entropy to count combinatorial objects. 1.2 The van der Waerden conjecture A square matrix M = (mij)1≤i;j≤n of non-negative real numbers is doubly stochastic if the sum of the entries of every line is equal to 1, and the same holds for the sum of the entries of each column. -
Arxiv:1306.4805V3 [Math.OC] 6 Feb 2015 Used Greedy Techniques to Reorder Matrices
CONVEX RELAXATIONS FOR PERMUTATION PROBLEMS FAJWEL FOGEL, RODOLPHE JENATTON, FRANCIS BACH, AND ALEXANDRE D’ASPREMONT ABSTRACT. Seriation seeks to reconstruct a linear order between variables using unsorted, pairwise similarity information. It has direct applications in archeology and shotgun gene sequencing for example. We write seri- ation as an optimization problem by proving the equivalence between the seriation and combinatorial 2-SUM problems on similarity matrices (2-SUM is a quadratic minimization problem over permutations). The seriation problem can be solved exactly by a spectral algorithm in the noiseless case and we derive several convex relax- ations for 2-SUM to improve the robustness of seriation solutions in noisy settings. These convex relaxations also allow us to impose structural constraints on the solution, hence solve semi-supervised seriation problems. We derive new approximation bounds for some of these relaxations and present numerical experiments on archeological data, Markov chains and DNA assembly from shotgun gene sequencing data. 1. INTRODUCTION We study optimization problems written over the set of permutations. While the relaxation techniques discussed in what follows are applicable to a much more general setting, most of the paper is centered on the seriation problem: we are given a similarity matrix between a set of n variables and assume that the variables can be ordered along a chain, where the similarity between variables decreases with their distance within this chain. The seriation problem seeks to reconstruct this linear ordering based on unsorted, possibly noisy, pairwise similarity information. This problem has its roots in archeology [Robinson, 1951] and also has direct applications in e.g. -
Lecture 12 – the Permanent and the Determinant
Lecture 12 { The permanent and the determinant Uriel Feige Department of Computer Science and Applied Mathematics The Weizman Institute Rehovot 76100, Israel [email protected] June 23, 2014 1 Introduction Given an order n matrix A, its permanent is X Yn per(A) = aiσ(i) σ i=1 where σ ranges over all permutations on n elements. Its determinant is X Yn σ det(A) = (−1) aiσ(i) σ i=1 where (−1)σ is +1 for even permutations and −1 for odd permutations. A permutation is even if it can be obtained from the identity permutation using an even number of transpo- sitions (where a transposition is a swap of two elements), and odd otherwise. For those more familiar with the inductive definition of the determinant, obtained by developing the determinant by the first row of the matrix, observe that the inductive defini- tion if spelled out leads exactly to the formula above. The same inductive definition applies to the permanent, but without the alternating sign rule. The determinant can be computed in polynomial time by Gaussian elimination, and in time n! by fast matrix multiplication. On the other hand, there is no polynomial time algorithm known for computing the permanent. In fact, Valiant showed that the permanent is complete for the complexity class #P , which makes computing it as difficult as computing the number of solutions of NP-complete problems (such as SAT, Valiant's reduction was from Hamiltonicity). For 0/1 matrices, the matrix A can be thought of as the adjacency matrix of a bipartite graph (we refer to it as a bipartite adjacency matrix { technically, A is an off-diagonal block of the usual adjacency matrix), and then the permanent counts the number of perfect matchings. -
MATH 2370, Practice Problems
MATH 2370, Practice Problems Kiumars Kaveh Problem: Prove that an n × n complex matrix A is diagonalizable if and only if there is a basis consisting of eigenvectors of A. Problem: Let A : V ! W be a one-to-one linear map between two finite dimensional vector spaces V and W . Show that the dual map A0 : W 0 ! V 0 is surjective. Problem: Determine if the curve 2 2 2 f(x; y) 2 R j x + y + xy = 10g is an ellipse or hyperbola or union of two lines. Problem: Show that if a nilpotent matrix is diagonalizable then it is the zero matrix. Problem: Let P be a permutation matrix. Show that P is diagonalizable. Show that if λ is an eigenvalue of P then for some integer m > 0 we have λm = 1 (i.e. λ is an m-th root of unity). Hint: Note that P m = I for some integer m > 0. Problem: Show that if λ is an eigenvector of an orthogonal matrix A then jλj = 1. n Problem: Take a vector v 2 R and let H be the hyperplane orthogonal n n to v. Let R : R ! R be the reflection with respect to a hyperplane H. Prove that R is a diagonalizable linear map. Problem: Prove that if λ1; λ2 are distinct eigenvalues of a complex matrix A then the intersection of the generalized eigenspaces Eλ1 and Eλ2 is zero (this is part of the Spectral Theorem). 1 Problem: Let H = (hij) be a 2 × 2 Hermitian matrix. Use the Min- imax Principle to show that if λ1 ≤ λ2 are the eigenvalues of H then λ1 ≤ h11 ≤ λ2. -
Statistical Problems Involving Permutations with Restricted Positions
STATISTICAL PROBLEMS INVOLVING PERMUTATIONS WITH RESTRICTED POSITIONS PERSI DIACONIS, RONALD GRAHAM AND SUSAN P. HOLMES Stanford University, University of California and ATT, Stanford University and INRA-Biornetrie The rich world of permutation tests can be supplemented by a variety of applications where only some permutations are permitted. We consider two examples: testing in- dependence with truncated data and testing extra-sensory perception with feedback. We review relevant literature on permanents, rook polynomials and complexity. The statistical applications call for new limit theorems. We prove a few of these and offer an approach to the rest via Stein's method. Tools from the proof of van der Waerden's permanent conjecture are applied to prove a natural monotonicity conjecture. AMS subject classiήcations: 62G09, 62G10. Keywords and phrases: Permanents, rook polynomials, complexity, statistical test, Stein's method. 1 Introduction Definitive work on permutation testing by Willem van Zwet, his students and collaborators, has given us a rich collection of tools for probability and statistics. We have come upon a series of variations where randomization naturally takes place over a subset of all permutations. The present paper gives two examples of sets of permutations defined by restricting positions. Throughout, a permutation π is represented in two-line notation 1 2 3 ... n π(l) π(2) π(3) ••• τr(n) with π(i) referred to as the label at position i. The restrictions are specified by a zero-one matrix Aij of dimension n with Aij equal to one if and only if label j is permitted in position i. Let SA be the set of all permitted permutations. -
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. -
Doubly Stochastic Matrices Whose Powers Eventually Stop
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Linear Algebra and its Applications 330 (2001) 25–30 www.elsevier.com/locate/laa Doubly stochastic matrices whose powers eventually stopୋ Suk-Geun Hwang a,∗, Sung-Soo Pyo b aDepartment of Mathematics Education, Kyungpook National University, Taegu 702-701, South Korea bCombinatorial and Computational Mathematics Center, Pohang University of Science and Technology, Pohang, South Korea Received 22 June 2000; accepted 14 November 2000 Submitted by R.A. Brualdi Abstract In this note we characterize doubly stochastic matrices A whose powers A, A2,A3,... + eventually stop, i.e., Ap = Ap 1 =···for some positive integer p. The characterization en- ables us to determine the set of all such matrices. © 2001 Elsevier Science Inc. All rights reserved. AMS classification: 15A51 Keywords: Doubly stochastic matrix; J-potent 1. Introduction Let R denote the real field. For positive integers m, n,letRm×n denote the set of all m × n matrices with real entries. As usual let Rn denote the set Rn×1.We call the members of Rn the n-vectors. The n-vector of 1’s is denoted by e,andthe identity matrix of order n is denoted by In. For two matrices A, B of the same size, let A B denote that all the entries of A − B are nonnegative. A matrix A is called nonnegative if A O. A nonnegative square matrix is called a doubly stochastic matrix if all of its row sums and column sums equal 1. -
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. -
Lecture 2: Spectral Theorems
Lecture 2: Spectral Theorems This lecture introduces normal matrices. The spectral theorem will inform us that normal matrices are exactly the unitarily diagonalizable matrices. As a consequence, we will deduce the classical spectral theorem for Hermitian matrices. The case of commuting families of matrices will also be studied. All of this corresponds to section 2.5 of the textbook. 1 Normal matrices Definition 1. A matrix A 2 Mn is called a normal matrix if AA∗ = A∗A: Observation: The set of normal matrices includes all the Hermitian matrices (A∗ = A), the skew-Hermitian matrices (A∗ = −A), and the unitary matrices (AA∗ = A∗A = I). It also " # " # 1 −1 1 1 contains other matrices, e.g. , but not all matrices, e.g. 1 1 0 1 Here is an alternate characterization of normal matrices. Theorem 2. A matrix A 2 Mn is normal iff ∗ n kAxk2 = kA xk2 for all x 2 C : n Proof. If A is normal, then for any x 2 C , 2 ∗ ∗ ∗ ∗ ∗ 2 kAxk2 = hAx; Axi = hx; A Axi = hx; AA xi = hA x; A xi = kA xk2: ∗ n n Conversely, suppose that kAxk = kA xk for all x 2 C . For any x; y 2 C and for λ 2 C with jλj = 1 chosen so that <(λhx; (A∗A − AA∗)yi) = jhx; (A∗A − AA∗)yij, we expand both sides of 2 ∗ 2 kA(λx + y)k2 = kA (λx + y)k2 to obtain 2 2 ∗ 2 ∗ 2 ∗ ∗ kAxk2 + kAyk2 + 2<(λhAx; Ayi) = kA xk2 + kA yk2 + 2<(λhA x; A yi): 2 ∗ 2 2 ∗ 2 Using the facts that kAxk2 = kA xk2 and kAyk2 = kA yk2, we derive 0 = <(λhAx; Ayi − λhA∗x; A∗yi) = <(λhx; A∗Ayi − λhx; AA∗yi) = <(λhx; (A∗A − AA∗)yi) = jhx; (A∗A − AA∗)yij: n ∗ ∗ n Since this is true for any x 2 C , we deduce (A A − AA )y = 0, which holds for any y 2 C , meaning that A∗A − AA∗ = 0, as desired. -
Rotation Matrix - Wikipedia, the Free Encyclopedia Page 1 of 22
Rotation matrix - Wikipedia, the free encyclopedia Page 1 of 22 Rotation matrix From Wikipedia, the free encyclopedia In linear algebra, a rotation matrix is a matrix that is used to perform a rotation in Euclidean space. For example the matrix rotates points in the xy -Cartesian plane counterclockwise through an angle θ about the origin of the Cartesian coordinate system. To perform the rotation, the position of each point must be represented by a column vector v, containing the coordinates of the point. A rotated vector is obtained by using the matrix multiplication Rv (see below for details). In two and three dimensions, rotation matrices are among the simplest algebraic descriptions of rotations, and are used extensively for computations in geometry, physics, and computer graphics. Though most applications involve rotations in two or three dimensions, rotation matrices can be defined for n-dimensional space. Rotation matrices are always square, with real entries. Algebraically, a rotation matrix in n-dimensions is a n × n special orthogonal matrix, i.e. an orthogonal matrix whose determinant is 1: . The set of all rotation matrices forms a group, known as the rotation group or the special orthogonal group. It is a subset of the orthogonal group, which includes reflections and consists of all orthogonal matrices with determinant 1 or -1, and of the special linear group, which includes all volume-preserving transformations and consists of matrices with determinant 1. Contents 1 Rotations in two dimensions 1.1 Non-standard orientation