Tropical Totally Positive Matrices 3
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Parametrizations of K-Nonnegative Matrices
Parametrizations of k-Nonnegative Matrices Anna Brosowsky, Neeraja Kulkarni, Alex Mason, Joe Suk, Ewin Tang∗ October 2, 2017 Abstract Totally nonnegative (positive) matrices are matrices whose minors are all nonnegative (positive). We generalize the notion of total nonnegativity, as follows. A k-nonnegative (resp. k-positive) matrix has all minors of size k or less nonnegative (resp. positive). We give a generating set for the semigroup of k-nonnegative matrices, as well as relations for certain special cases, i.e. the k = n − 1 and k = n − 2 unitriangular cases. In the above two cases, we find that the set of k-nonnegative matrices can be partitioned into cells, analogous to the Bruhat cells of totally nonnegative matrices, based on their factorizations into generators. We will show that these cells, like the Bruhat cells, are homeomorphic to open balls, and we prove some results about the topological structure of the closure of these cells, and in fact, in the latter case, the cells form a Bruhat-like CW complex. We also give a family of minimal k-positivity tests which form sub-cluster algebras of the total positivity test cluster algebra. We describe ways to jump between these tests, and give an alternate description of some tests as double wiring diagrams. 1 Introduction A totally nonnegative (respectively totally positive) matrix is a matrix whose minors are all nonnegative (respectively positive). Total positivity and nonnegativity are well-studied phenomena and arise in areas such as planar networks, combinatorics, dynamics, statistics and probability. The study of total positivity and total nonnegativity admit many varied applications, some of which are explored in “Totally Nonnegative Matrices” by Fallat and Johnson [5]. -
Diagonalizing a Matrix
Diagonalizing a Matrix Definition 1. We say that two square matrices A and B are similar provided there exists an invertible matrix P so that . 2. We say a matrix A is diagonalizable if it is similar to a diagonal matrix. Example 1. The matrices and are similar matrices since . We conclude that is diagonalizable. 2. The matrices and are similar matrices since . After we have developed some additional theory, we will be able to conclude that the matrices and are not diagonalizable. Theorem Suppose A, B and C are square matrices. (1) A is similar to A. (2) If A is similar to B, then B is similar to A. (3) If A is similar to B and if B is similar to C, then A is similar to C. Proof of (3) Since A is similar to B, there exists an invertible matrix P so that . Also, since B is similar to C, there exists an invertible matrix R so that . Now, and so A is similar to C. Thus, “A is similar to B” is an equivalence relation. Theorem If A is similar to B, then A and B have the same eigenvalues. Proof Since A is similar to B, there exists an invertible matrix P so that . Now, Since A and B have the same characteristic equation, they have the same eigenvalues. > Example Find the eigenvalues for . Solution Since is similar to the diagonal matrix , they have the same eigenvalues. Because the eigenvalues of an upper (or lower) triangular matrix are the entries on the main diagonal, we see that the eigenvalues for , and, hence, are . -
Crystals and Total Positivity on Orientable Surfaces 3
CRYSTALS AND TOTAL POSITIVITY ON ORIENTABLE SURFACES THOMAS LAM AND PAVLO PYLYAVSKYY Abstract. We develop a combinatorial model of networks on orientable surfaces, and study weight and homology generating functions of paths and cycles in these networks. Network transformations preserving these generating functions are investigated. We describe in terms of our model the crystal structure and R-matrix of the affine geometric crystal of products of symmetric and dual symmetric powers of type A. Local realizations of the R-matrix and crystal actions are used to construct a double affine geometric crystal on a torus, generalizing the commutation result of Kajiwara-Noumi- Yamada [KNY] and an observation of Berenstein-Kazhdan [BK07b]. We show that our model on a cylinder gives a decomposition and parametrization of the totally nonnegative part of the rational unipotent loop group of GLn. Contents 1. Introduction 3 1.1. Networks on orientable surfaces 3 1.2. Factorizations and parametrizations of totally positive matrices 4 1.3. Crystals and networks 5 1.4. Measurements and moves 6 1.5. Symmetric functions and loop symmetric functions 7 1.6. Comparison of examples 7 Part 1. Boundary measurements on oriented surfaces 9 2. Networks and measurements 9 2.1. Oriented networks on surfaces 9 2.2. Polygon representation of oriented surfaces 9 2.3. Highway paths and cycles 10 arXiv:1008.1949v1 [math.CO] 11 Aug 2010 2.4. Boundary and cycle measurements 10 2.5. Torus with one vertex 11 2.6. Flows and intersection products in homology 12 2.7. Polynomiality 14 2.8. Rationality 15 2.9. -
Chapter Four Determinants
Chapter Four Determinants In the first chapter of this book we considered linear systems and we picked out the special case of systems with the same number of equations as unknowns, those of the form T~x = ~b where T is a square matrix. We noted a distinction between two classes of T ’s. While such systems may have a unique solution or no solutions or infinitely many solutions, if a particular T is associated with a unique solution in any system, such as the homogeneous system ~b = ~0, then T is associated with a unique solution for every ~b. We call such a matrix of coefficients ‘nonsingular’. The other kind of T , where every linear system for which it is the matrix of coefficients has either no solution or infinitely many solutions, we call ‘singular’. Through the second and third chapters the value of this distinction has been a theme. For instance, we now know that nonsingularity of an n£n matrix T is equivalent to each of these: ² a system T~x = ~b has a solution, and that solution is unique; ² Gauss-Jordan reduction of T yields an identity matrix; ² the rows of T form a linearly independent set; ² the columns of T form a basis for Rn; ² any map that T represents is an isomorphism; ² an inverse matrix T ¡1 exists. So when we look at a particular square matrix, the question of whether it is nonsingular is one of the first things that we ask. This chapter develops a formula to determine this. (Since we will restrict the discussion to square matrices, in this chapter we will usually simply say ‘matrix’ in place of ‘square matrix’.) More precisely, we will develop infinitely many formulas, one for 1£1 ma- trices, one for 2£2 matrices, etc. -
Handout 9 More Matrix Properties; the Transpose
Handout 9 More matrix properties; the transpose Square matrix properties These properties only apply to a square matrix, i.e. n £ n. ² The leading diagonal is the diagonal line consisting of the entries a11, a22, a33, . ann. ² A diagonal matrix has zeros everywhere except the leading diagonal. ² The identity matrix I has zeros o® the leading diagonal, and 1 for each entry on the diagonal. It is a special case of a diagonal matrix, and A I = I A = A for any n £ n matrix A. ² An upper triangular matrix has all its non-zero entries on or above the leading diagonal. ² A lower triangular matrix has all its non-zero entries on or below the leading diagonal. ² A symmetric matrix has the same entries below and above the diagonal: aij = aji for any values of i and j between 1 and n. ² An antisymmetric or skew-symmetric matrix has the opposite entries below and above the diagonal: aij = ¡aji for any values of i and j between 1 and n. This automatically means the digaonal entries must all be zero. Transpose To transpose a matrix, we reect it across the line given by the leading diagonal a11, a22 etc. In general the result is a di®erent shape to the original matrix: a11 a21 a11 a12 a13 > > A = A = 0 a12 a22 1 [A ]ij = A : µ a21 a22 a23 ¶ ji a13 a23 @ A > ² If A is m £ n then A is n £ m. > ² The transpose of a symmetric matrix is itself: A = A (recalling that only square matrices can be symmetric). -
On the Eigenvalues of Euclidean Distance Matrices
“main” — 2008/10/13 — 23:12 — page 237 — #1 Volume 27, N. 3, pp. 237–250, 2008 Copyright © 2008 SBMAC ISSN 0101-8205 www.scielo.br/cam On the eigenvalues of Euclidean distance matrices A.Y. ALFAKIH∗ Department of Mathematics and Statistics University of Windsor, Windsor, Ontario N9B 3P4, Canada E-mail: [email protected] Abstract. In this paper, the notion of equitable partitions (EP) is used to study the eigenvalues of Euclidean distance matrices (EDMs). In particular, EP is used to obtain the characteristic poly- nomials of regular EDMs and non-spherical centrally symmetric EDMs. The paper also presents methods for constructing cospectral EDMs and EDMs with exactly three distinct eigenvalues. Mathematical subject classification: 51K05, 15A18, 05C50. Key words: Euclidean distance matrices, eigenvalues, equitable partitions, characteristic poly- nomial. 1 Introduction ( ) An n ×n nonzero matrix D = di j is called a Euclidean distance matrix (EDM) 1, 2,..., n r if there exist points p p p in some Euclidean space < such that i j 2 , ,..., , di j = ||p − p || for all i j = 1 n where || || denotes the Euclidean norm. i , ,..., Let p , i ∈ N = {1 2 n}, be the set of points that generate an EDM π π ( , ,..., ) D. An m-partition of D is an ordered sequence = N1 N2 Nm of ,..., nonempty disjoint subsets of N whose union is N. The subsets N1 Nm are called the cells of the partition. The n-partition of D where each cell consists #760/08. Received: 07/IV/08. Accepted: 17/VI/08. ∗Research supported by the Natural Sciences and Engineering Research Council of Canada and MITACS. -
3.3 Diagonalization
3.3 Diagonalization −4 1 1 1 Let A = 0 1. Then 0 1 and 0 1 are eigenvectors of A, with corresponding @ 4 −4 A @ 2 A @ −2 A eigenvalues −2 and −6 respectively (check). This means −4 1 1 1 −4 1 1 1 0 1 0 1 = −2 0 1 ; 0 1 0 1 = −6 0 1 : @ 4 −4 A @ 2 A @ 2 A @ 4 −4 A @ −2 A @ −2 A Thus −4 1 1 1 1 1 −2 −6 0 1 0 1 = 0−2 0 1 − 6 0 11 = 0 1 @ 4 −4 A @ 2 −2 A @ @ −2 A @ −2 AA @ −4 12 A We have −4 1 1 1 1 1 −2 0 0 1 0 1 = 0 1 0 1 @ 4 −4 A @ 2 −2 A @ 2 −2 A @ 0 −6 A 1 1 (Think about this). Thus AE = ED where E = 0 1 has the eigenvectors of A as @ 2 −2 A −2 0 columns and D = 0 1 is the diagonal matrix having the eigenvalues of A on the @ 0 −6 A main diagonal, in the order in which their corresponding eigenvectors appear as columns of E. Definition 3.3.1 A n × n matrix is A diagonal if all of its non-zero entries are located on its main diagonal, i.e. if Aij = 0 whenever i =6 j. Diagonal matrices are particularly easy to handle computationally. If A and B are diagonal n × n matrices then the product AB is obtained from A and B by simply multiplying entries in corresponding positions along the diagonal, and AB = BA. -
A Nonconvex Splitting Method for Symmetric Nonnegative Matrix Factorization: Convergence Analysis and Optimality
Electrical and Computer Engineering Publications Electrical and Computer Engineering 6-15-2017 A Nonconvex Splitting Method for Symmetric Nonnegative Matrix Factorization: Convergence Analysis and Optimality Songtao Lu Iowa State University, [email protected] Mingyi Hong Iowa State University Zhengdao Wang Iowa State University, [email protected] Follow this and additional works at: https://lib.dr.iastate.edu/ece_pubs Part of the Industrial Technology Commons, and the Signal Processing Commons The complete bibliographic information for this item can be found at https://lib.dr.iastate.edu/ ece_pubs/162. For information on how to cite this item, please visit http://lib.dr.iastate.edu/ howtocite.html. This Article is brought to you for free and open access by the Electrical and Computer Engineering at Iowa State University Digital Repository. It has been accepted for inclusion in Electrical and Computer Engineering Publications by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. A Nonconvex Splitting Method for Symmetric Nonnegative Matrix Factorization: Convergence Analysis and Optimality Abstract Symmetric nonnegative matrix factorization (SymNMF) has important applications in data analytics problems such as document clustering, community detection, and image segmentation. In this paper, we propose a novel nonconvex variable splitting method for solving SymNMF. The proposed algorithm is guaranteed to converge to the set of Karush-Kuhn-Tucker (KKT) points of the nonconvex SymNMF problem. Furthermore, it achieves a global sublinear convergence rate. We also show that the algorithm can be efficiently implemented in parallel. Further, sufficient conditions ear provided that guarantee the global and local optimality of the obtained solutions. -
Minimal Rank Decoupling of Full-Lattice CMV Operators With
MINIMAL RANK DECOUPLING OF FULL-LATTICE CMV OPERATORS WITH SCALAR- AND MATRIX-VALUED VERBLUNSKY COEFFICIENTS STEPHEN CLARK, FRITZ GESZTESY, AND MAXIM ZINCHENKO Abstract. Relations between half- and full-lattice CMV operators with scalar- and matrix-valued Verblunsky coefficients are investigated. In particular, the decoupling of full-lattice CMV oper- ators into a direct sum of two half-lattice CMV operators by a perturbation of minimal rank is studied. Contrary to the Jacobi case, decoupling a full-lattice CMV matrix by changing one of the Verblunsky coefficients results in a perturbation of twice the minimal rank. The explicit form for the minimal rank perturbation and the resulting two half-lattice CMV matrices are obtained. In addition, formulas relating the Weyl–Titchmarsh m-functions (resp., matrices) associated with the involved CMV operators and their Green’s functions (resp., matrices) are derived. 1. Introduction CMV operators are a special class of unitary semi-infinite or doubly-infinite five-diagonal matrices which received enormous attention in recent years. We refer to (2.8) and (3.18) for the explicit form of doubly infinite CMV operators on Z in the case of scalar, respectively, matrix-valued Verblunsky coefficients. For the corresponding half-lattice CMV operators we refer to (2.16) and (3.26). The actual history of CMV operators (with scalar Verblunsky coefficients) is somewhat intrigu- ing: The corresponding unitary semi-infinite five-diagonal matrices were first introduced in 1991 by Bunse–Gerstner and Elsner [15], and subsequently discussed in detail by Watkins [82] in 1993 (cf. the discussion in Simon [73]). They were subsequently rediscovered by Cantero, Moral, and Vel´azquez (CMV) in [17]. -
Random Matrices, Magic Squares and Matching Polynomials
Random Matrices, Magic Squares and Matching Polynomials Persi Diaconis Alex Gamburd∗ Departments of Mathematics and Statistics Department of Mathematics Stanford University, Stanford, CA 94305 Stanford University, Stanford, CA 94305 [email protected] [email protected] Submitted: Jul 22, 2003; Accepted: Dec 23, 2003; Published: Jun 3, 2004 MR Subject Classifications: 05A15, 05E05, 05E10, 05E35, 11M06, 15A52, 60B11, 60B15 Dedicated to Richard Stanley on the occasion of his 60th birthday Abstract Characteristic polynomials of random unitary matrices have been intensively studied in recent years: by number theorists in connection with Riemann zeta- function, and by theoretical physicists in connection with Quantum Chaos. In particular, Haake and collaborators have computed the variance of the coefficients of these polynomials and raised the question of computing the higher moments. The answer turns out to be intimately related to counting integer stochastic matrices (magic squares). Similar results are obtained for the moments of secular coefficients of random matrices from orthogonal and symplectic groups. Combinatorial meaning of the moments of the secular coefficients of GUE matrices is also investigated and the connection with matching polynomials is discussed. 1 Introduction Two noteworthy developments took place recently in Random Matrix Theory. One is the discovery and exploitation of the connections between eigenvalue statistics and the longest- increasing subsequence problem in enumerative combinatorics [1, 4, 5, 47, 59]; another is the outburst of interest in characteristic polynomials of Random Matrices and associated global statistics, particularly in connection with the moments of the Riemann zeta function and other L-functions [41, 14, 35, 36, 15, 16]. The purpose of this paper is to point out some connections between the distribution of the coefficients of characteristic polynomials of random matrices and some classical problems in enumerative combinatorics. -
R'kj.Oti-1). (3) the Object of the Present Article Is to Make This Estimate Effective
TRANSACTIONS OF THE AMERICAN MATHEMATICAL SOCIETY Volume 259, Number 2, June 1980 EFFECTIVE p-ADIC BOUNDS FOR SOLUTIONS OF HOMOGENEOUS LINEAR DIFFERENTIAL EQUATIONS BY B. DWORK AND P. ROBBA Dedicated to K. Iwasawa Abstract. We consider a finite set of power series in one variable with coefficients in a field of characteristic zero having a chosen nonarchimedean valuation. We study the growth of these series near the boundary of their common "open" disk of convergence. Our results are definitive when the wronskian is bounded. The main application involves local solutions of ordinary linear differential equations with analytic coefficients. The effective determination of the common radius of conver- gence remains open (and is not treated here). Let K be an algebraically closed field of characteristic zero complete under a nonarchimedean valuation with residue class field of characteristic p. Let D = d/dx L = D"+Cn_lD'-l+ ■ ■■ +C0 (1) be a linear differential operator with coefficients meromorphic in some neighbor- hood of the origin. Let u = a0 + a,jc + . (2) be a power series solution of L which converges in an open (/>-adic) disk of radius r. Our object is to describe the asymptotic behavior of \a,\rs as s —*oo. In a series of articles we have shown that subject to certain restrictions we may conclude that r'KJ.Oti-1). (3) The object of the present article is to make this estimate effective. At the same time we greatly simplify, and generalize, our best previous results [12] for the noneffective form. Our previous work was based on the notion of a generic disk together with a condition for reducibility of differential operators with unbounded solutions [4, Theorem 4]. -
Diagonalizable Matrix - Wikipedia, the Free Encyclopedia
Diagonalizable matrix - Wikipedia, the free encyclopedia http://en.wikipedia.org/wiki/Matrix_diagonalization Diagonalizable matrix From Wikipedia, the free encyclopedia (Redirected from Matrix diagonalization) In linear algebra, a square matrix A is called diagonalizable if it is similar to a diagonal matrix, i.e., if there exists an invertible matrix P such that P −1AP is a diagonal matrix. If V is a finite-dimensional vector space, then a linear map T : V → V is called diagonalizable if there exists a basis of V with respect to which T is represented by a diagonal matrix. Diagonalization is the process of finding a corresponding diagonal matrix for a diagonalizable matrix or linear map.[1] A square matrix which is not diagonalizable is called defective. Diagonalizable matrices and maps are of interest because diagonal matrices are especially easy to handle: their eigenvalues and eigenvectors are known and one can raise a diagonal matrix to a power by simply raising the diagonal entries to that same power. Geometrically, a diagonalizable matrix is an inhomogeneous dilation (or anisotropic scaling) — it scales the space, as does a homogeneous dilation, but by a different factor in each direction, determined by the scale factors on each axis (diagonal entries). Contents 1 Characterisation 2 Diagonalization 3 Simultaneous diagonalization 4 Examples 4.1 Diagonalizable matrices 4.2 Matrices that are not diagonalizable 4.3 How to diagonalize a matrix 4.3.1 Alternative Method 5 An application 5.1 Particular application 6 Quantum mechanical application 7 See also 8 Notes 9 References 10 External links Characterisation The fundamental fact about diagonalizable maps and matrices is expressed by the following: An n-by-n matrix A over the field F is diagonalizable if and only if the sum of the dimensions of its eigenspaces is equal to n, which is the case if and only if there exists a basis of Fn consisting of eigenvectors of A.