The Volume and Ehrhart Polynomial of the Alternating Sign Matrix Polytope
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Two Poset Polytopes
Discrete Comput Geom 1:9-23 (1986) G eometrv)i.~.reh, ~ ( :*mllmlati~ml © l~fi $1~ter-Vtrlq New Yorklu¢. t¢ Two Poset Polytopes Richard P. Stanley* Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139 Abstract. Two convex polytopes, called the order polytope d)(P) and chain polytope <~(P), are associated with a finite poset P. There is a close interplay between the combinatorial structure of P and the geometric structure of E~(P). For instance, the order polynomial fl(P, m) of P and Ehrhart poly- nomial i(~9(P),m) of O(P) are related by f~(P,m+l)=i(d)(P),m). A "transfer map" then allows us to transfer properties of O(P) to W(P). In particular, we transfer known inequalities involving linear extensions of P to some new inequalities. I. The Order Polytope Our aim is to investigate two convex polytopes associated with a finite partially ordered set (poset) P. The first of these, which we call the "order polytope" and denote by O(P), has been the subject of considerable scrutiny, both explicit and implicit, Much of what we say about the order polytope will be essentially a review of well-known results, albeit ones scattered throughout the literature, sometimes in a rather obscure form. The second polytope, called the "chain polytope" and denoted if(P), seems never to have been previously considered per se. It is a special case of the vertex-packing polytope of a graph (see Section 2) but has many special properties not in general valid or meaningful for graphs. -
Eigenvalues of Euclidean Distance Matrices and Rs-Majorization on R2
Archive of SID 46th Annual Iranian Mathematics Conference 25-28 August 2015 Yazd University 2 Talk Eigenvalues of Euclidean distance matrices and rs-majorization on R pp.: 1{4 Eigenvalues of Euclidean Distance Matrices and rs-majorization on R2 Asma Ilkhanizadeh Manesh∗ Department of Pure Mathematics, Vali-e-Asr University of Rafsanjan Alemeh Sheikh Hoseini Department of Pure Mathematics, Shahid Bahonar University of Kerman Abstract Let D1 and D2 be two Euclidean distance matrices (EDMs) with correspond- ing positive semidefinite matrices B1 and B2 respectively. Suppose that λ(A) = ((λ(A)) )n is the vector of eigenvalues of a matrix A such that (λ(A)) ... i i=1 1 ≥ ≥ (λ(A))n. In this paper, the relation between the eigenvalues of EDMs and those of the 2 corresponding positive semidefinite matrices respect to rs, on R will be investigated. ≺ Keywords: Euclidean distance matrices, Rs-majorization. Mathematics Subject Classification [2010]: 34B15, 76A10 1 Introduction An n n nonnegative and symmetric matrix D = (d2 ) with zero diagonal elements is × ij called a predistance matrix. A predistance matrix D is called Euclidean or a Euclidean distance matrix (EDM) if there exist a positive integer r and a set of n points p1, . , pn r 2 2 { } such that p1, . , pn R and d = pi pj (i, j = 1, . , n), where . denotes the ∈ ij k − k k k usual Euclidean norm. The smallest value of r that satisfies the above condition is called the embedding dimension. As is well known, a predistance matrix D is Euclidean if and 1 1 t only if the matrix B = − P DP with P = I ee , where I is the n n identity matrix, 2 n − n n × and e is the vector of all ones, is positive semidefinite matrix. -
Clustering by Left-Stochastic Matrix Factorization
Clustering by Left-Stochastic Matrix Factorization Raman Arora [email protected] Maya R. Gupta [email protected] Amol Kapila [email protected] Maryam Fazel [email protected] University of Washington, Seattle, WA 98103, USA Abstract 1.1. Related Work in Matrix Factorization Some clustering objective functions can be written as We propose clustering samples given their matrix factorization objectives. Let n feature vectors d×n pairwise similarities by factorizing the sim- be gathered into a feature-vector matrix X 2 R . T d×k ilarity matrix into the product of a clus- Consider the model X ≈ FG , where F 2 R can ter probability matrix and its transpose. be interpreted as a matrix with k cluster prototypes n×k We propose a rotation-based algorithm to as its columns, and G 2 R is all zeros except for compute this left-stochastic decomposition one (appropriately scaled) positive entry per row that (LSD). Theoretical results link the LSD clus- indicates the nearest cluster prototype. The k-means tering method to a soft kernel k-means clus- clustering objective follows this model with squared tering, give conditions for when the factor- error, and can be expressed as (Ding et al., 2005): ization and clustering are unique, and pro- T 2 arg min kX − FG kF ; (1) vide error bounds. Experimental results on F;GT G=I simulated and real similarity datasets show G≥0 that the proposed method reliably provides accurate clusterings. where k · kF is the Frobenius norm, and inequality G ≥ 0 is component-wise. This follows because the combined constraints G ≥ 0 and GT G = I force each row of G to have only one positive element. -
Double Posets and Real Invariant Varieties Dissertation
Fachbereich Mathematik und Informatik der Freien Universität Berlin Double posets and real invariant varieties Two interactions between combinatorics and geometry Dissertation eingereicht von Tobias Friedl Berlin 2017 Advisor and first reviewer: Prof. Dr. Raman Sanyal Second reviewer: Prof. Dr. Francisco Santos Third reviewer: Priv.-Doz. Dr. Christian Stump Date of the defense: May 19, 2017 Acknowledgements My deepest thanks go to my advisor Raman Sanyal. A PhD-student can only hope for an advisor who is as dedicated and enthusiastic about mathematics as you are. Thank you for getting your hands dirty and spending many hours in front of the blackboard teaching me how to do research. I spent the last years in the amazing and inspiring work group directed by Günter Ziegler at FU Berlin. Thank you for providing such a welcoming and challenging work environment. I really enjoyed the time with my friends and colleagues at the "villa", most of all Francesco Grande, Katharina Jochemko, Katy Beeler, Albert Haase, Lauri Loiskekoski, Philip Brinkmann, Nevena Palić and Jean-Philippe Labbé. Thanks also to Elke Pose, who provides valuable support and keeps bureaucracy at a low level. I want to thank my coauthor Cordian Riener for many fruitful mathematical discussions and for an interesting and enjoyable week of research in Helsinki at Aalto University. Moreover, I’d like to express my gratitude towards my coauthor and friend Tom Chappell. Thanks to the reviewers Paco Santos and Christian Stump for their helpful com- ments and to Christian additionally for many discussions regarding the combinatorics of reflection groups and posets throughout the last years. -
Similarity-Based Clustering by Left-Stochastic Matrix Factorization
JournalofMachineLearningResearch14(2013)1715-1746 Submitted 1/12; Revised 11/12; Published 7/13 Similarity-based Clustering by Left-Stochastic Matrix Factorization Raman Arora [email protected] Toyota Technological Institute 6045 S. Kenwood Ave Chicago, IL 60637, USA Maya R. Gupta [email protected] Google 1225 Charleston Rd Mountain View, CA 94301, USA Amol Kapila [email protected] Maryam Fazel [email protected] Department of Electrical Engineering University of Washington Seattle, WA 98195, USA Editor: Inderjit Dhillon Abstract For similarity-based clustering, we propose modeling the entries of a given similarity matrix as the inner products of the unknown cluster probabilities. To estimate the cluster probabilities from the given similarity matrix, we introduce a left-stochastic non-negative matrix factorization problem. A rotation-based algorithm is proposed for the matrix factorization. Conditions for unique matrix factorizations and clusterings are given, and an error bound is provided. The algorithm is partic- ularly efficient for the case of two clusters, which motivates a hierarchical variant for cases where the number of desired clusters is large. Experiments show that the proposed left-stochastic decom- position clustering model produces relatively high within-cluster similarity on most data sets and can match given class labels, and that the efficient hierarchical variant performs surprisingly well. Keywords: clustering, non-negative matrix factorization, rotation, indefinite kernel, similarity, completely positive 1. Introduction Clustering is important in a broad range of applications, from segmenting customers for more ef- fective advertising, to building codebooks for data compression. Many clustering methods can be interpreted in terms of a matrix factorization problem. -
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. -
Arxiv:1902.07301V3 [Math.CO] 13 Feb 2020 Which N H Itiuinwhere Distribution the and of Ojcuefo 8]Wihhsntbe Eovd H Oere Sole the Resolved
MINUSCULE DOPPELGANGERS,¨ THE COINCIDENTAL DOWN-DEGREE EXPECTATIONS PROPERTY, AND ROWMOTION SAM HOPKINS Abstract. We relate Reiner, Tenner, and Yong’s coincidental down-degree expec- tations (CDE) property of posets to the minuscule doppelg¨anger pairs studied by Hamaker, Patrias, Pechenik, and Williams. Via this relation, we put forward a series of conjectures which suggest that the minuscule doppelg¨anger pairs behave “as if” they had isomorphic comparability graphs, even though they do not. We further explore the idea of minuscule doppelg¨anger pairs pretending to have isomor- phic comparability graphs by considering the rowmotion operator on order ideals. We conjecture that the members of a minuscule doppelg¨anger pair behave the same way under rowmotion, as they would if they had isomorphic comparability graphs. Moreover, we conjecture that these pairs continue to behave the same way under the piecewise-linear and birational liftings of rowmotion introduced by Einstein and Propp. This conjecture motivates us to study the homomesies (in the sense of Propp and Roby) exhibited by birational rowmotion. We establish the birational analog of the antichain cardinality homomesy for the major examples of posets known or conjectured to have finite birational rowmotion order (namely: minuscule posets and root posets of coincidental type). 1. Introduction Let P be a finite poset. The down-degree of p ∈ P is the number of elements of P which p covers. Consider two probability distributions on P : the uniform distribution; and the distribution where p ∈ P occurs proportional to the number of maximal chains of P containing p. We say that P has the coincidental down-degree expectations (CDE) property if the expected value of the down-degree statistic is the same for these two distributions. -
Representations of Stochastic Matrices
Rotational (and Other) Representations of Stochastic Matrices Steve Alpern1 and V. S. Prasad2 1Department of Mathematics, London School of Economics, London WC2A 2AE, United Kingdom. email: [email protected] 2Department of Mathematics, University of Massachusetts Lowell, Lowell, MA. email: [email protected] May 27, 2005 Abstract Joel E. Cohen (1981) conjectured that any stochastic matrix P = pi;j could be represented by some circle rotation f in the following sense: Forf someg par- tition Si of the circle into sets consisting of …nite unions of arcs, we have (*) f g pi;j = (f (Si) Sj) = (Si), where denotes arc length. In this paper we show how cycle decomposition\ techniques originally used (Alpern, 1983) to establish Cohen’sconjecture can be extended to give a short simple proof of the Coding Theorem, that any mixing (that is, P N > 0 for some N) stochastic matrix P can be represented (in the sense of * but with Si merely measurable) by any aperiodic measure preserving bijection (automorphism) of a Lesbesgue proba- bility space. Representations by pointwise and setwise periodic automorphisms are also established. While this paper is largely expository, all the proofs, and some of the results, are new. Keywords: rotational representation, stochastic matrix, cycle decomposition MSC 2000 subject classi…cations. Primary: 60J10. Secondary: 15A51 1 Introduction An automorphism of a Lebesgue probability space (X; ; ) is a bimeasurable n bijection f : X X which preserves the measure : If S = Si is a non- ! f gi=1 trivial (all (Si) > 0) measurable partition of X; we can generate a stochastic n matrix P = pi;j by the de…nition f gi;j=1 (f (Si) Sj) pi;j = \ ; i; j = 1; : : : ; n: (1) (Si) Since the partition S is non-trivial, the matrix P has a positive invariant (stationary) distribution v = (v1; : : : ; vn) = ( (S1) ; : : : ; (Sn)) ; and hence (by de…nition) is recurrent. -
Coefficients of the Solid Angle and Ehrhart Quasi-Polynomials
COEFFICIENTS OF THE SOLID ANGLE AND EHRHART QUASI-POLYNOMIALS FABR´ICIO CALUZA MACHADO AND SINAI ROBINS Abstract. Macdonald studied a discrete volume measure for a rational poly- tope P , called solid angle sum, that gives a natural discrete volume for P . We give a local formula for the codimension two quasi-coefficient of the solid angle sum of P . We also show how to recover the classical Ehrhart quasi-polynomial from the solid angle sum and in particular we find a similar local formula for the codimension one and codimension two quasi-coefficients. These local formulas are naturally valid for all positive real dilates of P . An interesting open question is to determine necessary and sufficient con- ditions on a polytope P for which the discrete volume of P given by the solid d angle sum equals its continuous volume: AP (t) = vol(P )t . We prove that d a sufficient condition is that P tiles R by translations, together with the Hyperoctahedral group. Contents 1. Introduction1 2. Main results4 3. Preliminaries9 4. Lattice sums 12 5. Proofs of Theorem 5.2 and Corollary 5.3 15 6. Obtaining the Ehrhart quasi-coefficients ed−1(t) and ed−2(t) 21 7. Two examples in three dimensions 26 8. Concrete polytopes and further remarks 31 References 34 Appendix A. Obtaining the solid angle quasi-coefficients from the Ehrhart quasi-coefficients 35 Appendix B. Comments about local formulas and SI-interpolators 38 1. Introduction d arXiv:1912.08017v2 [math.CO] 26 Jun 2020 Given a polytope P ⊆ R , the number of integer points within P can be regarded as a discrete analog of the volume of the body. -
Ehrhart Theory for Lattice Polytopes
WASHINGTON UNIVERSITY Department of Mathematics Dissertation Examination Committee: John Shareshian, Chair Ramanath Cowsik Renato Feres Lev Gelb Mohan Kumar David Wright Ehrhart Theory for Lattice Polytopes by Benjamin James Braun A dissertation presented to the Graduate School of Arts and Sciences of Washington University in partial fulfillment for the degree of Doctor of Philosophy May 2007 Saint Louis, Missouri Acknowledgements To John Shareshian, for being an excellent advisor and valued mentor; I could not have done this without your guidance. To the faculty in the mathematics department from whom I’ve learned so much, especially Renato Feres, Gary Jensen, Mohan Kumar, John McCarthy, Rachel Roberts, and David Wright. To the other graduate students who have been great friends and colleagues, especially Kim, Jeff and Paul. To Matthias Beck and Sinai Robins for teaching me about Ehrhart theory and writing a great book; special thanks to Matt for introducing me to open problems re- garding Ehrhart polynomial roots and coefficients. To Mike Develin for collaborating and sharing his good ideas. To all of the members of the combinatorics community who I have had the pleasure of working with and learning from over the last few years, especially Paul Bendich, Tristram Bogart, Pat Byrnes, Jes´us De Loera, Tyrrell McAllister, Sam Payne, and Kevin Woods. To my parents and my brother, for always being there and supporting me. Most of all, to my wife, Laura, for your love and encouragement. None of this would mean anything if you were not in my life. ii Contents Acknowledgements ii List of Figures v 1 Introduction 2 1.1 Overture................................. -
The $ H^* $-Polynomial of the Order Polytope of the Zig-Zag Poset
THE h∗-POLYNOMIAL OF THE ORDER POLYTOPE OF THE ZIG-ZAG POSET JANE IVY COONS AND SETH SULLIVANT Abstract. We describe a family of shellings for the canonical tri- angulation of the order polytope of the zig-zag poset. This gives a new combinatorial interpretation for the coefficients in the numer- ator of the Ehrhart series of this order polytope in terms of the swap statistic on alternating permutations. 1. Introduction and Preliminaries The zig-zag poset Zn on ground set {z1,...,zn} is the poset with exactly the cover relations z1 < z2 > z3 < z4 > . That is, this partial order satisfies z2i−1 < z2i and z2i > z2i+1 for all i between n−1 1 and ⌊ 2 ⌋. The order polytope of Zn, denoted O(Zn) is the set n of all n-tuples (x1,...,xn) ∈ R that satisfy 0 ≤ xi ≤ 1 for all i and xi ≤ xj whenever zi < zj in Zn. In this paper, we introduce the “swap” permutation statistic on alternating permutations to give a new combinatorial interpretation of the numerator of the Ehrhart series of O(Zn). We began studying this problem in relation to combinatorial proper- ties of the Cavender-Farris-Neyman model with a molecular clock (or CFN-MC model) from mathematical phylogenetics [4]. We were inter- ested in the polytope associated to the toric variety obtained by ap- plying the discrete Fourier transform to the Cavender-Farris-Neyman arXiv:1901.07443v2 [math.CO] 1 Apr 2020 model with a molecular clock on a given rooted binary phylogenetic tree. We call this polytope the CFN-MC polytope. -
Triangulations of Integral Polytopes, Examples and Problems
数理解析研究所講究録 955 巻 1996 年 133-144 133 Triangulations of integral polytopes, examples and problems Jean-Michel KANTOR We are interested in polytopes in real space of arbitrary dimension, having vertices with integral co- ordinates: integral polytopes. The recent increase of interest for the study of these $\mathrm{p}\mathrm{o}\mathrm{l}\mathrm{y}$ topes and their triangulations has various motivations; let us mention the main ones: . the beautiful theory of toric varieties has built a bridge between algebraic geometry and the combina- torics of these integral polytopes [12]. Triangulations of cones and polytopes occur naturally for example in problems of existence of crepant resolution of singularities $[1,5]$ . The work of the school of $\mathrm{I}.\mathrm{M}$ . Gelfand on secondary polytopes gives a new insight on triangulations, with applications to algebraic geometry and group theory [13]. In statistical physics, random tilings lead to some interesting problems dealing with triangulations of order polytopes $[6,29]$ . With these motivations in mind, we introduce new tools: Generalizations of the Ehrhart polynomial (counting points “modulo congruence”), discrete length between integral points (and studying the geometry associated to it), arithmetic Euler-Poincar\'e formula which gives, in dimension 3, the Ehrhart polynomial in terms of the $\mathrm{f}$-vector of a minimal triangulation of the polytope (Theorem 7). Finally, let us mention the results in dimension 2 of the late Peter Greenberg, they led us to the study of “Arithmetical $\mathrm{P}\mathrm{L}$-topology” which, we believe with M. Gromov, D. Sullivan, and P. Vogel, has not yet revealed all its beauties.