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The Many Faces of Alternating-Sign Matrices
The many faces of alternating-sign matrices James Propp Department of Mathematics University of Wisconsin – Madison, Wisconsin, USA [email protected] August 15, 2002 Abstract I give a survey of different combinatorial forms of alternating-sign ma- trices, starting with the original form introduced by Mills, Robbins and Rumsey as well as corner-sum matrices, height-function matrices, three- colorings, monotone triangles, tetrahedral order ideals, square ice, gasket- and-basket tilings and full packings of loops. (This article has been pub- lished in a conference edition of the journal Discrete Mathematics and Theo- retical Computer Science, entitled “Discrete Models: Combinatorics, Com- putation, and Geometry,” edited by R. Cori, J. Mazoyer, M. Morvan, and R. Mosseri, and published in July 2001 in cooperation with le Maison de l’Informatique et des Mathematiques´ Discretes,` Paris, France: ISSN 1365- 8050, http://dmtcs.lori.fr.) 1 Introduction An alternating-sign matrix of order n is an n-by-n array of 0’s, 1’s and 1’s with the property that in each row and each column, the non-zero entries alter- nate in sign, beginning and ending with a 1. For example, Figure 1 shows an Supported by grants from the National Science Foundation and the National Security Agency. 1 alternating-sign matrix (ASM for short) of order 4. 0 100 1 1 10 0001 0 100 Figure 1: An alternating-sign matrix of order 4. Figure 2 exhibits all seven of the ASMs of order 3. 001 001 0 10 0 10 0 10 100 001 1 1 1 100 0 10 100 0 10 0 10 100 100 100 001 0 10 001 0 10 001 Figure 2: The seven alternating-sign matrices of order 3. -
The Many Faces of Alternating-Sign Matrices
The many faces of alternating-sign matrices James Propp∗ Department of Mathematics University of Wisconsin – Madison, Wisconsin, USA [email protected] June 1, 2018 Abstract I give a survey of different combinatorial forms of alternating-sign ma- trices, starting with the original form introduced by Mills, Robbins and Rumsey as well as corner-sum matrices, height-function matrices, three- colorings, monotone triangles, tetrahedral order ideals, square ice, gasket- and-basket tilings and full packings of loops. (This article has been pub- lished in a conference edition of the journal Discrete Mathematics and Theo- retical Computer Science, entitled “Discrete Models: Combinatorics, Com- putation, and Geometry,” edited by R. Cori, J. Mazoyer, M. Morvan, and R. Mosseri, and published in July 2001 in cooperation with le Maison de l’Informatique et des Math´ematiques Discr`etes, Paris, France: ISSN 1365- 8050, http://dmtcs.lori.fr.) 1 Introduction arXiv:math/0208125v1 [math.CO] 15 Aug 2002 An alternating-sign matrix of order n is an n-by-n array of 0’s, +1’s and −1’s with the property that in each row and each column, the non-zero entries alter- nate in sign, beginning and ending with a +1. For example, Figure 1 shows an ∗Supported by grants from the National Science Foundation and the National Security Agency. 1 alternating-sign matrix (ASM for short) of order 4. 0 +1 0 0 +1 −1 +1 0 0 0 0 +1 0 +1 0 0 Figure 1: An alternating-sign matrix of order 4. Figure 2 exhibits all seven of the ASMs of order 3. -
Matrix Lie Groups
Maths Seminar 2007 MATRIX LIE GROUPS Claudiu C Remsing Dept of Mathematics (Pure and Applied) Rhodes University Grahamstown 6140 26 September 2007 RhodesUniv CCR 0 Maths Seminar 2007 TALK OUTLINE 1. What is a matrix Lie group ? 2. Matrices revisited. 3. Examples of matrix Lie groups. 4. Matrix Lie algebras. 5. A glimpse at elementary Lie theory. 6. Life beyond elementary Lie theory. RhodesUniv CCR 1 Maths Seminar 2007 1. What is a matrix Lie group ? Matrix Lie groups are groups of invertible • matrices that have desirable geometric features. So matrix Lie groups are simultaneously algebraic and geometric objects. Matrix Lie groups naturally arise in • – geometry (classical, algebraic, differential) – complex analyis – differential equations – Fourier analysis – algebra (group theory, ring theory) – number theory – combinatorics. RhodesUniv CCR 2 Maths Seminar 2007 Matrix Lie groups are encountered in many • applications in – physics (geometric mechanics, quantum con- trol) – engineering (motion control, robotics) – computational chemistry (molecular mo- tion) – computer science (computer animation, computer vision, quantum computation). “It turns out that matrix [Lie] groups • pop up in virtually any investigation of objects with symmetries, such as molecules in chemistry, particles in physics, and projective spaces in geometry”. (K. Tapp, 2005) RhodesUniv CCR 3 Maths Seminar 2007 EXAMPLE 1 : The Euclidean group E (2). • E (2) = F : R2 R2 F is an isometry . → | n o The vector space R2 is equipped with the standard Euclidean structure (the “dot product”) x y = x y + x y (x, y R2), • 1 1 2 2 ∈ hence with the Euclidean distance d (x, y) = (y x) (y x) (x, y R2). -
Alternating Sign Matrices and Polynomiography
Alternating Sign Matrices and Polynomiography Bahman Kalantari Department of Computer Science Rutgers University, USA [email protected] Submitted: Apr 10, 2011; Accepted: Oct 15, 2011; Published: Oct 31, 2011 Mathematics Subject Classifications: 00A66, 15B35, 15B51, 30C15 Dedicated to Doron Zeilberger on the occasion of his sixtieth birthday Abstract To each permutation matrix we associate a complex permutation polynomial with roots at lattice points corresponding to the position of the ones. More generally, to an alternating sign matrix (ASM) we associate a complex alternating sign polynomial. On the one hand visualization of these polynomials through polynomiography, in a combinatorial fashion, provides for a rich source of algo- rithmic art-making, interdisciplinary teaching, and even leads to games. On the other hand, this combines a variety of concepts such as symmetry, counting and combinatorics, iteration functions and dynamical systems, giving rise to a source of research topics. More generally, we assign classes of polynomials to matrices in the Birkhoff and ASM polytopes. From the characterization of vertices of these polytopes, and by proving a symmetry-preserving property, we argue that polynomiography of ASMs form building blocks for approximate polynomiography for polynomials corresponding to any given member of these polytopes. To this end we offer an algorithm to express any member of the ASM polytope as a convex of combination of ASMs. In particular, we can give exact or approximate polynomiography for any Latin Square or Sudoku solution. We exhibit some images. Keywords: Alternating Sign Matrices, Polynomial Roots, Newton’s Method, Voronoi Diagram, Doubly Stochastic Matrices, Latin Squares, Linear Programming, Polynomiography 1 Introduction Polynomials are undoubtedly one of the most significant objects in all of mathematics and the sciences, particularly in combinatorics. -
LIE GROUPS and ALGEBRAS NOTES Contents 1. Definitions 2
LIE GROUPS AND ALGEBRAS NOTES STANISLAV ATANASOV Contents 1. Definitions 2 1.1. Root systems, Weyl groups and Weyl chambers3 1.2. Cartan matrices and Dynkin diagrams4 1.3. Weights 5 1.4. Lie group and Lie algebra correspondence5 2. Basic results about Lie algebras7 2.1. General 7 2.2. Root system 7 2.3. Classification of semisimple Lie algebras8 3. Highest weight modules9 3.1. Universal enveloping algebra9 3.2. Weights and maximal vectors9 4. Compact Lie groups 10 4.1. Peter-Weyl theorem 10 4.2. Maximal tori 11 4.3. Symmetric spaces 11 4.4. Compact Lie algebras 12 4.5. Weyl's theorem 12 5. Semisimple Lie groups 13 5.1. Semisimple Lie algebras 13 5.2. Parabolic subalgebras. 14 5.3. Semisimple Lie groups 14 6. Reductive Lie groups 16 6.1. Reductive Lie algebras 16 6.2. Definition of reductive Lie group 16 6.3. Decompositions 18 6.4. The structure of M = ZK (a0) 18 6.5. Parabolic Subgroups 19 7. Functional analysis on Lie groups 21 7.1. Decomposition of the Haar measure 21 7.2. Reductive groups and parabolic subgroups 21 7.3. Weyl integration formula 22 8. Linear algebraic groups and their representation theory 23 8.1. Linear algebraic groups 23 8.2. Reductive and semisimple groups 24 8.3. Parabolic and Borel subgroups 25 8.4. Decompositions 27 Date: October, 2018. These notes compile results from multiple sources, mostly [1,2]. All mistakes are mine. 1 2 STANISLAV ATANASOV 1. Definitions Let g be a Lie algebra over algebraically closed field F of characteristic 0. -
Unipotent Flows and Applications
Clay Mathematics Proceedings Volume 10, 2010 Unipotent Flows and Applications Alex Eskin 1. General introduction 1.1. Values of indefinite quadratic forms at integral points. The Op- penheim Conjecture. Let X Q(x1; : : : ; xn) = aijxixj 1≤i≤j≤n be a quadratic form in n variables. We always assume that Q is indefinite so that (so that there exists p with 1 ≤ p < n so that after a linear change of variables, Q can be expresses as: Xp Xn ∗ 2 − 2 Qp(y1; : : : ; yn) = yi yi i=1 i=p+1 We should think of the coefficients aij of Q as real numbers (not necessarily rational or integer). One can still ask what will happen if one substitutes integers for the xi. It is easy to see that if Q is a multiple of a form with rational coefficients, then the set of values Q(Zn) is a discrete subset of R. Much deeper is the following conjecture: Conjecture 1.1 (Oppenheim, 1929). Suppose Q is not proportional to a ra- tional form and n ≥ 5. Then Q(Zn) is dense in the real line. This conjecture was extended by Davenport to n ≥ 3. Theorem 1.2 (Margulis, 1986). The Oppenheim Conjecture is true as long as n ≥ 3. Thus, if n ≥ 3 and Q is not proportional to a rational form, then Q(Zn) is dense in R. This theorem is a triumph of ergodic theory. Before Margulis, the Oppenheim Conjecture was attacked by analytic number theory methods. (In particular it was known for n ≥ 21, and for diagonal forms with n ≥ 5). -
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. -
A Conjecture About Dodgson Condensation
Advances in Applied Mathematics 34 (2005) 654–658 www.elsevier.com/locate/yaama A conjecture about Dodgson condensation David P. Robbins ∗ Received 19 January 2004; accepted 2 July 2004 Available online 18 January 2005 Abstract We give a description of a non-archimedean approximate form of Dodgson’s condensation method for computing determinants and provide evidence for a conjecture which predicts a surprisingly high degree of accuracy for the results. 2005 Elsevier Inc. All rights reserved. In 1866 Charles Dodgson, usually known as Lewis Carroll, proposed a method, that he called condensation [2] for calculating determinants. Dodgson’s method is based on a determinantal identity. Suppose that n 2 is an integer and M is an n by n square matrix with entries in a ring and that R, S, T , and U are the upper left, upper right, lower left, and lower right n − 1byn − 1 square submatrices of M and that C is the central n − 2by n − 2 submatrix. Then it is known that det(C) det(M) = det(R) det(U) − det(S) det(T ). (1) Here the determinant of a 0 by 0 matrix is conventionally 1, so that when n = 2, Eq. (1) is the usual formula for a 2 by 2 determinant. David Bressoud gives an interesting discussion of the genesis of this identity in [1, p. 111]. Dodgson proposes to use this identity repeatedly to find the determinant of M by computing all of the connected minors (determinants of square submatrices formed from * Correspondence to David Bressoud. E-mail address: [email protected] (D. -
Contents 1 Root Systems
Stefan Dawydiak February 19, 2021 Marginalia about roots These notes are an attempt to maintain a overview collection of facts about and relationships between some situations in which root systems and root data appear. They also serve to track some common identifications and choices. The references include some helpful lecture notes with more examples. The author of these notes learned this material from courses taught by Zinovy Reichstein, Joel Kam- nitzer, James Arthur, and Florian Herzig, as well as many student talks, and lecture notes by Ivan Loseu. These notes are simply collected marginalia for those references. Any errors introduced, especially of viewpoint, are the author's own. The author of these notes would be grateful for their communication to [email protected]. Contents 1 Root systems 1 1.1 Root space decomposition . .2 1.2 Roots, coroots, and reflections . .3 1.2.1 Abstract root systems . .7 1.2.2 Coroots, fundamental weights and Cartan matrices . .7 1.2.3 Roots vs weights . .9 1.2.4 Roots at the group level . .9 1.3 The Weyl group . 10 1.3.1 Weyl Chambers . 11 1.3.2 The Weyl group as a subquotient for compact Lie groups . 13 1.3.3 The Weyl group as a subquotient for noncompact Lie groups . 13 2 Root data 16 2.1 Root data . 16 2.2 The Langlands dual group . 17 2.3 The flag variety . 18 2.3.1 Bruhat decomposition revisited . 18 2.3.2 Schubert cells . 19 3 Adelic groups 20 3.1 Weyl sets . 20 References 21 1 Root systems The following examples are taken mostly from [8] where they are stated without most of the calculations. -
Pattern Avoidance in Alternating Sign Matrices
PATTERN AVOIDANCE IN ALTERNATING SIGN MATRICES ROBERT JOHANSSON AND SVANTE LINUSSON Abstract. We generalize the definition of a pattern from permu- tations to alternating sign matrices. The number of alternating sign matrices avoiding 132 is proved to be counted by the large Schr¨oder numbers, 1, 2, 6, 22, 90, 394 . .. We give a bijection be- tween 132-avoiding alternating sign matrices and Schr¨oder-paths, which gives a refined enumeration. We also show that the 132, 123- avoiding alternating sign matrices are counted by every second Fi- bonacci number. 1. Introduction For some time, we have emphasized the use of permutation matrices when studying pattern avoidance. This and the fact that alternating sign matrices are generalizations of permutation matrices, led to the idea of studying pattern avoidance in alternating sign matrices. The main results, concerning 132-avoidance, are presented in Section 3. In Section 5, we enumerate sets of alternating sign matrices avoiding two patterns of length three at once. In Section 6 we state some open problems. Let us start with the basic definitions. Given a permutation π of length n we will represent it with the n×n permutation matrix with 1 in positions (i, π(i)), 1 ≤ i ≤ n and 0 in all other positions. We will also suppress the 0s. 1 1 3 1 2 1 Figure 1. The permutation matrix of 132. In this paper, we will think of permutations in terms of permutation matrices. Definition 1.1. (Patterns in permutations) A permutation π is said to contain a permutation τ as a pattern if the permutation matrix of τ is a submatrix of π. -
DECOMPOSITION of SYMPLECTIC MATRICES INTO PRODUCTS of SYMPLECTIC UNIPOTENT MATRICES of INDEX 2∗ 1. Introduction. Decomposition
Electronic Journal of Linear Algebra, ISSN 1081-3810 A publication of the International Linear Algebra Society Volume 35, pp. 497-502, November 2019. DECOMPOSITION OF SYMPLECTIC MATRICES INTO PRODUCTS OF SYMPLECTIC UNIPOTENT MATRICES OF INDEX 2∗ XIN HOUy , ZHENGYI XIAOz , YAJING HAOz , AND QI YUANz Abstract. In this article, it is proved that every symplectic matrix can be decomposed into a product of three symplectic 0 I unipotent matrices of index 2, i.e., every complex matrix A satisfying AT JA = J with J = n is a product of three −In 0 T 2 matrices Bi satisfying Bi JBi = J and (Bi − I) = 0 (i = 1; 2; 3). Key words. Symplectic matrices, Product of unipotent matrices, Symplectic Jordan Canonical Form. AMS subject classifications. 15A23, 20H20. 1. Introduction. Decomposition of elements in a matrix group into products of matrices with a special nature (such as unipotent matrices, involutions and so on) is a popular topic studied by many scholars. In the n × n matrix k ring Mn(F ) over a field F , a unipotent matrix of index k refers to a matrix A satisfying (A − In) = 0. Fong and Sourour in [4] proved that every matrix in the group SLn(C) (the special linear group over complex field C) is a product of three unipotent matrices (without limitation on the index). Wang and Wu in [6] gave a further result that every matrix in the group SLn(C) is a product of four unipotent matrices of index 2. In particular, decompositions of symplectic matrices have drawn considerable attention from numerous 0 I scholars (see [1, 3]). -
An Effective Lie–Kolchin Theorem for Quasi-Unipotent Matrices
Linear Algebra and its Applications 581 (2019) 304–323 Contents lists available at ScienceDirect Linear Algebra and its Applications www.elsevier.com/locate/laa An effective Lie–Kolchin Theorem for quasi-unipotent matrices Thomas Koberda a, Feng Luo b,∗, Hongbin Sun b a Department of Mathematics, University of Virginia, Charlottesville, VA 22904-4137, USA b Department of Mathematics, Rutgers University, Hill Center – Busch Campus, 110 Frelinghuysen Road, Piscataway, NJ 08854, USA a r t i c l e i n f oa b s t r a c t Article history: We establish an effective version of the classical Lie–Kolchin Received 27 April 2019 Theorem. Namely, let A, B ∈ GLm(C)be quasi-unipotent Accepted 17 July 2019 matrices such that the Jordan Canonical Form of B consists Available online 23 July 2019 of a single block, and suppose that for all k 0the Submitted by V.V. Sergeichuk matrix ABk is also quasi-unipotent. Then A and B have a A, B < C MSC: common eigenvector. In particular, GLm( )is a primary 20H20, 20F38 solvable subgroup. We give applications of this result to the secondary 20F16, 15A15 representation theory of mapping class groups of orientable surfaces. Keywords: © 2019 Elsevier Inc. All rights reserved. Lie–Kolchin theorem Unipotent matrices Solvable groups Mapping class groups * Corresponding author. E-mail addresses: [email protected] (T. Koberda), fl[email protected] (F. Luo), [email protected] (H. Sun). URLs: http://faculty.virginia.edu/Koberda/ (T. Koberda), http://sites.math.rutgers.edu/~fluo/ (F. Luo), http://sites.math.rutgers.edu/~hs735/ (H.