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Topological Divisors of Zero and Shilov Boundary
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Topology and its Applications 153 (2006) 1152–1163 www.elsevier.com/locate/topol Topological divisors of zero and Shilov boundary Alain Escassut Laboratoire de Mathématiques UMR 6620, Université Blaise Pascal, Clermont-Ferrand, Les Cézeaux, 63177 Aubiere cedex, France Received 17 February 2005; accepted 1 March 2005 Abstract Let L be a field complete for a non-trivial ultrametric absolute value and let (A, ·) be a com- mutative normed L-algebra with unity whose spectral semi-norm is ·si.LetMult(A, ·) be the set of continuous multiplicative semi-norms of A,letS be the Shilov boundary for (A, ·si) and let ψ ∈ Mult(A, ·si).Thenψ belongs to S if and only if for every neighborhood U of ψ in Mult(A, ·), there exists θ ∈ U and g ∈ A satisfying gsi = θ(g) and γ(g)<gsi ∀γ ∈ S \ U. Suppose A is uniform, let f ∈ A and let Z(f ) ={φ ∈ Mult(A, ·) | φ(f)= 0}.Thenf is a topo- logical divisor of zero if and only if there exists ψ ∈ S such that ψ(f) = 0. Suppose now A is complete. If f is not a divisor of zero, then it is a topological divisor of zero if and only if the ideal fAis not closed in A. Suppose A is ultrametric, complete and Noetherian. All topological divisors of zero are divisors of zero. This applies to affinoid algebras. Let A be a Krasner algebra H(D)without non-trivial idempotents: an element f ∈ H(D)is a topological divisor of zero if and only if fH(D) is not a closed ideal; moreover, H(D) is a principal ideal ring if and only if it has no topological divisors of zero but 0 (this new condition adds to the well-known set of equivalent conditions found in 1969). -
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. -
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. -
Book of Abstracts for the 3Rd Conference of Settat on Operator Algebras and Applications, November 1–5, 2011
Book of Abstracts for the 3rd Conference of Settat on Operator Algebras and Applications, November 1{5, 2011 1. Speaker: Ahmed Al-Rawashdeh (United Arab Emirates University, Al Ain) Title: On Certain Projections of C*-Matrix Algebras ∗ Abstract: H. Dye defined the projections Pi;j(a) of a C -matrix algebra by ∗ −1 ∗ −1 Pi;j(a) = (1 + aa ) ⊗ Ei;i + (1 + aa ) a ⊗ Ei;j ∗ ∗ −1 ∗ ∗ −1 + a (1 + aa ) ⊗ Ej;i + a (1 + aa ) a ⊗ Ej;j; and he used it to show that in the case of factors not of type I2n, the unitary group determines the algebraic type of that factor. We study these projections and we show that in M2(C), the set of such projections includes all the projections. For infinite C∗-algebra A, having a system of matrix units, including the Cuntz algebra On, we have A ' Mn(A). M. Leen proved that in a simple, ∗ purely infinite C -algebra A, the ∗-symmetries generate U0(A). We revise and modify Leen's proof to show that part of such ∗-isometry factors are of the form 1−2Pi;j(!);! 2 U(A). In simple, unital ∗ purely infinite C -algebras having trivial K1-group, we prove that all Pi;j(!) have trivial K0-class. In particular, if u 2 U(On), then u can be factorized as a product of ∗-symmetries, where eight of them are of the form 1 − 2Pi;j(!). 2. Speaker: Pere Ara (Universitat Aut`onomade Barcelona, Spain) Title:(m; n)-dynamical systems and their C*-algebras Abstract: Given positive integers n and m, we consider dynamical systems in which n copies of a topological space is homeomorphic to m copies of that same space. -
Geometry of the Shilov Boundary of a Bounded Symmetric Domain Jean-Louis Clerc
Geometry of the Shilov Boundary of a Bounded Symmetric Domain Jean-Louis Clerc To cite this version: Jean-Louis Clerc. Geometry of the Shilov Boundary of a Bounded Symmetric Domain. journal of geometry and symmetry in physics, 2009, Vol. 13, pp. 25-74. hal-00381665 HAL Id: hal-00381665 https://hal.archives-ouvertes.fr/hal-00381665 Submitted on 6 May 2009 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. Geometry of the Shilov Boundary of a Bounded Symmetric Domain Jean-Louis Clerc today Abstract In the first part, the theory of bounded symmetric domains is pre- sented along two main approaches : as special cases of Riemannian symmetric spaces of the noncompact type on one hand, as unit balls in positive Hermitian Jordan triple systems on the other hand. In the second part, an invariant for triples in the Shilov boundary of such a domain is constructed. It generalizes an invariant constructed by E. Cartan for the unit sphere in C2 and also the triple Maslov index on the Lagrangian manifold. 1 Introduction The present paper is an outgrowth of the cycle of conferences delivred by the author at the Tenth International Conference on Geometry, Integrability and Quantization, held in Varna in June 2008. -
Extension of Characters and Generalized Shilov Boundaries
Revista de la 211 Union Matem~tica Argentina . Volumen 32, 1986. EXTENSION OF CHARACTERS AND GENERALIZED SHILOV BOUNDARIES Gustavo Corach and Alejandra M~estripieri ABSTRACT. We prove in a very simple way Shilov extension theorem (if A is a commutative Banach algebra, h a character in the Shilov boundary of A and B a commutative superalgebra of A then h admits an extension to B which can be chosen in the Shilov boundary of B) and use this proof t~ extend similar results to higher dimensional Shilov boundaries. 1. I NTRODUCT ION. Let A be a (complex commutative) Banach algebra with unity and X(A) the spectrum space of characters of A. The Shilov boundary ro(A) is a compact subset of X(A) which plays an important role in various problems, in particular in the search of existence of I-dimensional analytic structure on X(A). For higher dimensions ro(A) seems to be too small and Basener [2] and S;ibony [11} have defined a bigger subset of X(A), rn(A), which is an appropiate generalized Shilov boundary (see als0 [1,5,6,7,11] for more information on rnCA)). In this paper we are concerned w;ith the following property of r 0 (A), ca.lled Shilov extension theo'l'em: if A is a closed subalgebra of a Banach algebra B then every· chal'acter in r 0 (A) admits an extension to some character k of B. Moreover k can be chosen in ro(B), by a result of; Rickart [9,(3.3.25)]. -
Class Notes, Functional Analysis 7212
Class notes, Functional Analysis 7212 Ovidiu Costin Contents 1 Banach Algebras 2 1.1 The exponential map.....................................5 1.2 The index group of B = C(X) ...............................6 1.2.1 p1(X) .........................................7 1.3 Multiplicative functionals..................................7 1.3.1 Multiplicative functionals on C(X) .........................8 1.4 Spectrum of an element relative to a Banach algebra.................. 10 1.5 Examples............................................ 19 1.5.1 Trigonometric polynomials............................. 19 1.6 The Shilov boundary theorem................................ 21 1.7 Further examples....................................... 21 1.7.1 The convolution algebra `1(Z) ........................... 21 1.7.2 The return of Real Analysis: the case of L¥ ................... 23 2 Bounded operators on Hilbert spaces 24 2.1 Adjoints............................................ 24 2.2 Example: a space of “diagonal” operators......................... 30 2.3 The shift operator on `2(Z) ................................. 32 2.3.1 Example: the shift operators on H = `2(N) ................... 38 3 W∗-algebras and measurable functional calculus 41 3.1 The strong and weak topologies of operators....................... 42 4 Spectral theorems 46 4.1 Integration of normal operators............................... 51 4.2 Spectral projections...................................... 51 5 Bounded and unbounded operators 54 5.1 Operations.......................................... -
Noncommutative Functional Analysis for Undergraduates"
N∞M∞T Lecture Course Canisius College Noncommutative functional analysis David P. Blecher October 22, 2004 2 Chapter 1 Preliminaries: Matrices = operators 1.1 Introduction Functional analysis is one of the big fields in mathematics. It was developed throughout the 20th century, and has several major strands. Some of the biggest are: “Normed vector spaces” “Operator theory” “Operator algebras” We’ll talk about these in more detail later, but let me give a micro-summary. Normed (vector) spaces were developed most notably by the mathematician Banach, who not very subtly called them (B)-spaces. They form a very general framework and tools to attack a wide range of problems: in fact all a normed (vector) space is, is a vector space X on which is defined a measure of the ‘length’ of each ‘vector’ (element of X). They have a huge theory. Operator theory and operator algebras grew partly out of the beginnings of the subject of quantum mechanics. In operator theory, you prove important things about ‘linear functions’ (also known as operators) T : X → X, where X is a normed space (indeed usually a Hilbert space (defined below). Such operators can be thought of as matrices, as we will explain soon. Operator algebras are certain collections of operators, and they can loosely be thought of as ‘noncommutative number fields’. They fall beautifully within the trend in mathematics towards the ‘noncommutative’, linked to discovery in quantum physics that we live in a ‘noncommutative world’. You can study a lot of ‘noncommutative mathematics’ in terms of operator algebras. The three topics above are functional analysis. -
Notes on Birkhoff-Von Neumann Decomposition of Doubly Stochastic Matrices Fanny Dufossé, Bora Uçar
Notes on Birkhoff-von Neumann decomposition of doubly stochastic matrices Fanny Dufossé, Bora Uçar To cite this version: Fanny Dufossé, Bora Uçar. Notes on Birkhoff-von Neumann decomposition of doubly stochastic matri- ces. Linear Algebra and its Applications, Elsevier, 2016, 497, pp.108–115. 10.1016/j.laa.2016.02.023. hal-01270331v6 HAL Id: hal-01270331 https://hal.inria.fr/hal-01270331v6 Submitted on 23 Apr 2016 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. Notes on Birkhoff-von Neumann decomposition of doubly stochastic matrices Fanny Dufoss´ea, Bora U¸carb,∗ aInria Lille, Nord Europe, 59650, Villeneuve d'Ascq, France bLIP, UMR5668 (CNRS - ENS Lyon - UCBL - Universit´ede Lyon - INRIA), Lyon, France Abstract Birkhoff-von Neumann (BvN) decomposition of doubly stochastic matrices ex- presses a double stochastic matrix as a convex combination of a number of permutation matrices. There are known upper and lower bounds for the num- ber of permutation matrices that take part in the BvN decomposition of a given doubly stochastic matrix. We investigate the problem of computing a decom- position with the minimum number of permutation matrices and show that the associated decision problem is strongly NP-complete. -
On Matrices with a Doubly Stochastic Pattern
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector JOURNAL OF MATHEMATICALANALYSISAND APPLICATIONS 34,648-652(1971) On Matrices with a Doubly Stochastic Pattern DAVID LONDON Technion-Israel Institute of Technology, Haifa Submitted by Ky Fan 1. INTRODUCTION In [7] Sinkhorn proved that if A is a positive square matrix, then there exist two diagonal matrices D, = {@r,..., d$) and D, = {dj2),..., di2r) with positive entries such that D,AD, is doubly stochastic. This problem was studied also by Marcus and Newman [3], Maxfield and Mint [4] and Menon [5]. Later Sinkhorn and Knopp [8] considered the same problem for A non- negative. Using a limit process of alternately normalizing the rows and columns sums of A, they obtained a necessary and sufficient condition for the existence of D, and D, such that D,AD, is doubly stochastic. Brualdi, Parter and Schneider [l] obtained the same theorem by a quite different method using spectral properties of some nonlinear operators. In this note we give a new proof of the same theorem. We introduce an extremal problem, and from the existence of a solution to this problem we derive the existence of D, and D, . This method yields also a variational characterization for JJTC1(din dj2’), which can be applied to obtain bounds for this quantity. We note that bounds for I7y-r (d,!‘) dj”)) may be of interest in connection with inequalities for the permanent of doubly stochastic matrices [3]. 2. PRELIMINARIES Let A = (aij) be an 12x n nonnegative matrix; that is, aij > 0 for i,j = 1,***, n. -
Banach Function Algebras with Dense Invertible Group
PROCEEDINGS OF THE AMERICAN MATHEMATICAL SOCIETY Volume 136, Number 4, April 2008, Pages 1295–1304 S 0002-9939(07)09044-2 Article electronically published on December 21, 2007 BANACH FUNCTION ALGEBRAS WITH DENSE INVERTIBLE GROUP H. G. DALES AND J. F. FEINSTEIN (Communicated by N. Tomczak-Jaegermann) Abstract. In 2003 Dawson and Feinstein asked whether or not a Banach function algebra with dense invertible group can have a proper Shilov bound- ary. We give an example of a uniform algebra showing that this can happen, and investigate the properties of such algebras. We make some remarks on the topological stable rank of commutative, unital Banach algebras. In particular, we prove that tsr(A) ≥ tsr(C(ΦA)) whenever A is approximately regular. 1. Introduction Let A be a commutative, unital Banach algebra. The character space of A is denoted by ΦA,asin[8].Fora ∈ A, we denote the Gel’fand transform of a by a.WesaythatΦA contains analytic structure if there is a continuous injection τ from the open unit disk D to ΦA such that, for all a ∈ A, a ◦ τ is analytic on D. In [16], Stolzenberg gave a counter-example to the conjecture that, whenever a uniform algebra has proper Shilov boundary, its character space must contain analytic structure (see also [17, Theorem 29.19], [19] and [1]). Cole gave an even more extreme example in his thesis [3], where the Shilov boundary is proper and yet every Gleason part is trivial. It is elementary to show that the invertible group of A cannot be dense in A whenever ΦA contains analytic structure. -
An Inequality for Doubly Stochastic Matrices*
JOURNAL OF RESEARCH of the National Bureau of Standards-B. Mathematical Sciences Vol. 80B, No.4, October-December 1976 An Inequality for Doubly Stochastic Matrices* Charles R. Johnson** and R. Bruce Kellogg** Institute for Basic Standards, National Bureau of Standards, Washington, D.C. 20234 (June 3D, 1976) Interrelated inequalities involving doubly stochastic matrices are presented. For example, if B is an n by n doubly stochasti c matrix, x any nonnegative vector and y = Bx, the n XIX,· •• ,x" :0:::; YIY" •• y ... Also, if A is an n by n nonnegotive matrix and D and E are positive diagonal matrices such that B = DAE is doubly stochasti c, the n det DE ;:::: p(A) ... , where p (A) is the Perron· Frobenius eigenvalue of A. The relationship between these two inequalities is exhibited. Key words: Diagonal scaling; doubly stochasti c matrix; P erron·Frobenius eigenvalue. n An n by n entry·wise nonnegative matrix B = (b i;) is called row (column) stochastic if l bi ; = 1 ;= 1 for all i = 1,. ',n (~l bij = 1 for all j = 1,' . ',n ). If B is simultaneously row and column stochastic then B is said to be doubly stochastic. We shall denote the Perron·Frobenius (maximal) eigenvalue of an arbitrary n by n entry·wise nonnegative matrix A by p (A). Of course, if A is stochastic, p (A) = 1. It is known precisely which n by n nonnegative matrices may be diagonally scaled by positive diagonal matrices D, E so that (1) B=DAE is doubly stochastic. If there is such a pair D, E, we shall say that A has property (").