Hall-Littlewood Functions and Kostka-Foulkes Polynomials in Representation Theory
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Representation Theory
Representation Theory Andrew Kobin Fall 2014 Contents Contents Contents 1 Introduction 1 1.1 Group Theory Review . .1 1.2 Partitions . .2 2 Group Representations 4 2.1 Representations . .4 2.2 G-homomorphisms . .9 2.3 Schur's Lemma . 12 2.4 The Commutant and Endomorphism Algebras . 13 2.5 Characters . 17 2.6 Tensor Products . 24 2.7 Restricted and Induced Characters . 25 3 Representations of Sn 28 3.1 Young Subgroups of Sn .............................. 28 3.2 Specht Modules . 33 3.3 The Decomposition of M µ ............................ 44 3.4 The Hook Length Formula . 48 3.5 Application: The RSK Algorithm . 49 4 Symmetric Functions 52 4.1 Generating Functions . 52 4.2 Symmetric Functions . 53 4.3 Schur Functions . 57 4.4 Symmetric Functions and Character Representations . 60 i 1 Introduction 1 Introduction The following are notes from a course in representation theory taught by Dr. Frank Moore at Wake Forest University in the fall of 2014. The main topics covered are: group repre- sentations, characters, the representation theory of Sn, Young tableaux and tabloids, Specht modules, the RSK algorithm and some further applications to combinatorics. 1.1 Group Theory Review Definition. A group is a nonempty set G with a binary operation \·00 : G×G ! G satisfying (1) (Associativity) a(bc) = (ab)c for all a; b; c 2 G. (2) (Identity) There exists an identity element e 2 G such that for every a 2 G, ae = ea = a. (3) (Inverses) For every a 2 G there is some b 2 G such that ab = ba = e. -
Random Plane Partitions and Corner Distributions Volume 4, Issue 4 (2021), P
ALGEBRAIC COMBINATORICS Damir Yeliussizov Random plane partitions and corner distributions Volume 4, issue 4 (2021), p. 599-617. <http://alco.centre-mersenne.org/item/ALCO_2021__4_4_599_0> © The journal and the authors, 2021. Some rights reserved. This article is licensed under the CREATIVE COMMONS ATTRIBUTION 4.0 INTERNATIONAL LICENSE. http://creativecommons.org/licenses/by/4.0/ Access to articles published by the journal Algebraic Combinatorics on the website http://alco.centre-mersenne.org/ implies agreement with the Terms of Use (http://alco.centre-mersenne.org/legal/). Algebraic Combinatorics is member of the Centre Mersenne for Open Scientific Publishing www.centre-mersenne.org Algebraic Combinatorics Volume 4, issue 4 (2021), p. 599–617 https://doi.org/10.5802/alco.171 Random plane partitions and corner distributions Damir Yeliussizov Abstract We explore some probabilistic applications arising in connections with K-theoretic symmetric functions. For instance, we determine certain corner distributions of random lozenge tilings and plane partitions. We also introduce some distributions that are naturally related to the corner growth model. Our main tools are dual symmetric Grothendieck polynomials and normalized Schur functions. 1. Introduction Combinatorics arising in connection with K-theoretic Schubert calculus is quite rich. Accompanied by certain families of symmetric functions, it usually presents some in- homogeneous deformations of objects beyond classical Schur (or Schubert) case. While the subject is intensively studied from combinatorial, algebraic and geometric aspects, see [20,7,8, 34, 19, 13, 35] and many references therein, much less is known about probabilistic connections (unlike interactions between probability and representation theory). Some work in this direction was done in [32, 22] and related problems were addressed in [38].(1) In this paper, we give several probabilistic applications obtained with tools from combinatorial K-theory. -
On the Complexity of Computing Kostka Numbers and Littlewood-Richardson Coefficients
Formal Power Series and Algebraic Combinatorics S´eries Formelles et Combinatoire Alg´ebrique San Diego, California 2006 . On the complexity of computing Kostka numbers and Littlewood-Richardson coefficients Hariharan Narayanan Abstract. Kostka numbers and Littlewood-Richardson coefficients appear in combina- torics and representation theory. Interest in their computation stems from the fact that they are present in quantum mechanical computations since Wigner ([17]). In recent times, there have been a number of algorithms proposed to perform this task ([1], [13], [14], [2], [3]). The issue of their computational complexity was explicitly asked by E. Rassart ([13]). We prove that the computation of either quantity is #P -complete. This, implies that unless P = NP , which is widely disbelieved, there do not exist efficient algorithms that compute these numbers. Resum´ e.´ Les nombres de Kostka et les coefficients de Littlewood-Richardson apparaissent en combinatoire et en th´eorie de la repr´esentation. Il est int´eressant de les calculer car ils ap- paraissent dans certains calculs en m´ecanique quantique depuis Wigner ([17]). R´ecemment, plusieurs algorithmes ont ´et´epropos´espour les calculer ([1], [13], [14], [2], [3]). Le probl`eme de la complexit´ede ce calcul a ´et´epos´epar E. Rassart ([13]). Nous d´emontrons que le calcul des nombres de Kotska et des coefficients de Littlewood-Richardson est #P-complet. Cela implique que, `amoins que P=NP, il n’existe pas d’algorithme efficace pour calculer ces nombres. 1. Introduction Let N = {1, 2,...} be the set of positive integers and Z≥0 = N ∪ {0}. Let λ =(λ1,...,λs) ∈ Ns Zt Zu , λ1 ≥ λ2 ≥ ··· ≥ λs ≥ 1, µ = (µ1,...,µt) ∈ ≥0, ν = (ν1,...,νu) ∈ ≥0 and v α = (α1,...,αv) ∈ N , α1 ≥···≥ αv ≥ 1. -
On the Number of Partition Weights with Kostka Multiplicity One
On the number of partition weights with Kostka multiplicity one Zachary Gates∗ Brian Goldmany Department of Mathematics Mathematics Department University of Virginia College of William and Mary Charlottesville, VA Williamsburg, VA [email protected] [email protected] C. Ryan Vinrootz Mathematics Department College of William and Mary Williamsburg, VA [email protected] Submitted: Jul 24, 2012; Accepted: Dec 19, 2012; Published: Dec 31, 2012 Mathematics Subject Classification: 05A19 Abstract Given a positive integer n, and partitions λ and µ of n, let Kλµ denote the Kostka number, which is the number of semistandard Young tableaux of shape λ and weight µ. Let J(λ) denote the number of µ such that Kλµ = 1. By applying a result of Berenshtein and Zelevinskii, we obtain a formula for J(λ) in terms of restricted partition functions, which is recursive in the number of distinct part sizes of λ. We use this to classify all partitions λ such that J(λ) = 1 and all λ such that J(λ) = 2. We then consider signed tableaux, where a semistandard signed tableau of shape λ has entries from the ordered set f0 < 1¯ < 1 < 2¯ < 2 < · · · g, and such that i and ¯i contribute equally to the weight. For a weight (w0; µ) with µ a partition, the signed Kostka number K± is defined as the number of semistandard signed tableaux λ,(w0,µ) ± of shape λ and weight (w0; µ), and J (λ) is then defined to be the number of weights ± (w0; µ) such that K = 1. Using different methods than in the unsigned case, λ,(w0,µ) we find that the only nonzero value which J ±(λ) can take is 1, and we find all sequences of partitions with this property. -
Spin Kostka Polynomials
J Algebr Comb (2013) 37:117–138 DOI 10.1007/s10801-012-0362-4 Spin Kostka polynomials Jinkui Wan · Weiqiang Wang Received: 23 February 2011 / Accepted: 13 March 2012 / Published online: 24 March 2012 © Springer Science+Business Media, LLC 2012 Abstract We introduce a spin analogue of Kostka polynomials and show that these polynomials enjoy favorable properties parallel to the Kostka polynomials. Further connections of spin Kostka polynomials with representation theory are established. Keywords Kostka polynomials · Symmetric groups · Schur Q-functions · Hall–Littlewood functions · q-weight multiplicity · Hecke–Clifford algebra 1 Introduction 1.1 The Kostka numbers and Kostka(–Foulkes) polynomials are ubiquitous in algebraic combinatorics, geometry, and representation theory. A most interesting property of Kostka polynomials is that they have non-negative integer coefficients (due to Las- coux and Schützenberger [11]), and this has been derived by Garsia and Procesi [5] from Springer theory of Weyl group representations [18]. Kostka polynomials also coincide with Lusztig’s q-weight multiplicity in finite-dimensional irreducible rep- resentations of the general linear Lie algebra [9, 12]. R. Brylinski [1] introduced a Brylinski–Kostant filtration on weight spaces of finite-dimensional irreducible repre- sentations and proved that Lusztig’s q-weight multiplicities (and hence Kostka poly- nomials) are precisely the polynomials associated to such a filtration. For more on J. Wan () Department of Mathematics, Beijing Institute of Technology, Beijing, 100081, PR China e-mail: [email protected] W. Wang Department of Mathematics, University of Virginia, Charlottesville, VA 22904, USA e-mail: [email protected] 118 J Algebr Comb (2013) 37:117–138 Kostka polynomials, we refer to Macdonald [13] or the survey paper of Désarménien, Leclerc and Thibon [3]. -
On the Number of Partition Weights with Kostka Multiplicity One
On the number of partition weights with Kostka multiplicity one Zachary Gates, Brian Goldman, and C. Ryan Vinroot Abstract Given a positive integer n, and partitions λ and µ of n, let Kλµ denote the Kostka num- ber, which is the number of semistandard Young tableaux of shape λ and weight µ. Let J(λ) denote the number of µ such that Kλµ = 1. By applying a result of Berenshtein and Zelevinskii, we obtain a formula for J(λ) in terms of restricted partition functions, which is recursive in the number of distinct part sizes of λ. We use this to classify all partitions λ such that J(λ) = 1 and all λ such that J(λ) = 2. We then consider the signed Kostka number, corresponding to the number of signed tableau of a specified shape and weight, and define J ±(λ) analogously. We find that the only nonzero value which this can take is 1, and we find all sequences of partitions with this property. We conclude with an application to the character theory of finite unitary groups. 2010 Mathematics Subject Classification: 05A19 1 Introduction Given partitions λ and µ of a positive integer n, the Kostka number Kλµ is the number of semistandard Young tableaux of shape λ and weight µ. Motivated by the importance of Kostka numbers and their generalizations in the representation theory of Lie algebras, Berenshtein and Zelevinskii [1] addressed the question of precisely when it is that Kλµ = 1. It may be seen, for example, that we always have Kλλ = 1. The results of this paper were motivated by the question: For which partitions λ is it true that Kλµ = 1 if and only if µ = λ? The answer to this question, given in Corollary 5.1, turns out to be quite nice, which is that this holds exactly for those partitions λ for which the conjugate partition λ0 has distinct parts. -
Logarithmic Concavity of Schur and Related Polynomials
LOGARITHMIC CONCAVITY OF SCHUR AND RELATED POLYNOMIALS JUNE HUH, JACOB P. MATHERNE, KAROLA MESZ´ AROS,´ AND AVERY ST. DIZIER ABSTRACT. We show that normalized Schur polynomials are strongly log-concave. As a conse- quence, we obtain Okounkov’s log-concavity conjecture for Littlewood–Richardson coefficients in the special case of Kostka numbers. 1. INTRODUCTION Schur polynomials are the characters of finite-dimensional irreducible polynomial represen- tations of the general linear group GLmpCq. Combinatorially, the Schur polynomial of a partition λ in m variables is the generating function µpTq µpTq µ1pTq µmpTq sλpx1; : : : ; xmq “ x ; x “ x1 ¨ ¨ ¨ xm ; T ¸ where the sum is over all Young tableaux T of shape λ with entries from rms, and µipTq “ the number of i’s among the entries of T; for i “ 1; : : : ; m. Collecting Young tableaux of the same weight together, we get µ sλpx1; : : : ; xmq “ Kλµ x ; µ ¸ where Kλµ is the Kostka number counting Young tableaux of given shape λ and weight µ [Kos82]. Correspondingly, the Schur module Vpλq, an irreducible representation of the general linear group with highest weight λ, has the weight space decomposition Vpλq “ Vpλqµ with dim Vpλqµ “ Kλµ: µ arXiv:1906.09633v3 [math.CO] 25 Sep 2019 à Schur polynomials were first studied by Cauchy [Cau15], who defined them as ratios of alter- nants. The connection to the representation theory of GLmpCq was found by Schur [Sch01]. For a gentle introduction to these remarkable polynomials, and for all undefined terms, we refer to [Ful97]. June Huh received support from NSF Grant DMS-1638352 and the Ellentuck Fund. -
Boolean Witt Vectors and an Integral Edrei–Thoma Theorem
Boolean Witt vectors and an integral Edrei–Thoma theorem The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation Borger, James, and Darij Grinberg. “Boolean Witt Vectors and an Integral Edrei–Thoma Theorem.” Selecta Mathematica 22, no. 2 (November 4, 2015): 595–629. As Published http://dx.doi.org/10.1007/s00029-015-0198-6 Publisher Springer International Publishing Version Author's final manuscript Citable link http://hdl.handle.net/1721.1/106897 Terms of Use Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. Sel. Math. New Ser. (2016) 22:595–629 Selecta Mathematica DOI 10.1007/s00029-015-0198-6 New Series Boolean Witt vectors and an integral Edrei–Thoma theorem James Borger1 · Darij Grinberg2 Published online: 4 November 2015 © Springer Basel 2015 Abstract A subtraction-free definition of the big Witt vector construction was recently given by the first author. This allows one to define the big Witt vectors of any semiring. Here we give an explicit combinatorial description of the big Witt vectors of the Boolean semiring. We do the same for two variants of the big Witt vector construction: the Schur Witt vectors and the p-typical Witt vectors. We use this to give an explicit description of the Schur Witt vectors of the natural numbers, which can be viewed as the classification of totally positive power series with integral coefficients, first obtained by Davydov. -
Groups, Geometries and Representation Theory
Groups, Geometries and Representation Theory David A. Craven Spring Term, 2013 Contents 1 Combinatorics of partitions 1 1.1 Previous knowledge about symmetric groups . .1 1.2 Partitions . .2 1.3 James's abacus . .3 1.4 Tableaux . .7 1.5 The Robinson{Schensted correspondence . 11 1.6 Tabloids and polytabloids . 13 1.7 Two orderings on partitions . 15 1.8 An ordering on tabloids . 16 2 Constructing the representations of the symmetric group 19 2.1 M λ and Sλ .................................... 19 2.2 The branching rule . 22 2.3 The characteristic p case . 23 2.4 Young's rule . 28 3 The Murnaghan{Nakayama rule 32 3.1 Skew tableaux . 32 3.2 The Littlewood{Richardson rule . 33 3.3 The Murnaghan{Nakayama rule . 37 i Chapter 1 Combinatorics of partitions 1.1 Previous knowledge about symmetric groups A few facts about the symmetric group first that we need, that should be known from earlier courses. We will use cycle notation for the elements of the symmetric group Sn, i.e., (1; 2; 5)(3; 4). We multiply left to right, so that (1; 2)(1; 3) = (1; 2; 3). Writing a permutation σ 2 Sn as a product of disjoint cycles, the cycle type of σ, writ- a1 a2 an ten 1 2 : : : n , is the string where ai is the number of cycles of length i in σ. Hence 1 1 2 (1; 2; 3)(4; 5; 6)(7; 8)(9) 2 S9 has cycle type 1 2 3 . Recall that conjugation acts by acting on the cycle itself by the conjugating element, so that (1; 2; 3)(4; 5), acted on by (2; 5; 3), becomes (1; 5; 2)(4; 3). -
Combinatorics, Symmetric Functions, and Hilbert Schemes
COMBINATORICS, SYMMETRIC FUNCTIONS, AND HILBERT SCHEMES MARK HAIMAN Abstract. We survey the proof of a series of conjectures in combinatorics using new results on the geometry of Hilbert schemes. The combinatorial results include the positivity conjecture for Macdonald’s symmetric functions, and the “n!” and “(n + 1)n−1” conjectures relating Macdonald polynomials to the characters of doubly-graded Sn modules. To make the treatment self-contained, we include background material from combinatorics, symmetric function theory, representation theory and geometry. At the end we discuss future directions, new conjectures and related work of Ginzburg, Kumar and Thomsen, Gordon, and Haglund and Loehr. Contents 1. Introduction 2 2. Background from combinatorics 4 2.1. Partitions, tableaux and jeu-de-taquin 4 2.2. Catalan numbers and q-analogs 6 2.3. Tree enumeration and q-analogs 8 2.4. q-Lagrange inversion 9 3. Background from symmetric function theory 11 3.1. Generalities 11 3.2. The Frobenius map 12 3.3. Plethystic or λ-ring notation 13 3.4. Hall-Littlewood polynomials 15 3.5. Macdonald polynomials 25 4. The n! and (n + 1)n−1 conjectures 35 4.1. The n! conjecture 35 4.2. The (n + 1)n−1 conjecture 38 5. Hilbert scheme interpretation 40 5.1. The Hilbert scheme and isospectral Hilbert scheme 40 5.2. Main theorem on the isospectral Hilbert scheme 43 5.3. Theorem of Bridgeland, King and Reid 44 5.4. How the conjectures follow 45 6. Discussion of proofs of the main theorems 51 6.1. Theorem on the isospectral Hilbert scheme 51 6.2. -
Schur Polynomials Do Not Have Small Formulas If the Determinant Doesn't
Schur Polynomials Do Not Have Small Formulas If the Determinant Doesn’t Prasad Chaugule Department of Computer Science & Engineering, IIT Bombay, India [email protected] Mrinal Kumar Department of Computer Science & Engineering, IIT Bombay, India [email protected] Nutan Limaye Department of Computer Science & Engineering, IIT Bombay, India [email protected] Chandra Kanta Mohapatra Department of Computer Science & Engineering, IIT Bombay, India [email protected] Adrian She Department of Computer Science, University of Toronto, Canada [email protected] Srikanth Srinivasan Department of Mathematics, IIT Bombay, India [email protected] Abstract Schur Polynomials are families of symmetric polynomials that have been classically studied in Combinatorics and Algebra alike. They play a central role in the study of Symmetric functions, in Representation theory [39], in Schubert calculus [26] as well as in Enumerative combinatorics [14, 38, 39]. In recent years, they have also shown up in various incarnations in Computer Science, e.g, Quantum computation [17, 31] and Geometric complexity theory [21]. However, unlike some other families of symmetric polynomials like the Elementary Symmet- ric polynomials, the Power Symmetric polynomials and the Complete Homogeneous Symmetric polynomials, the computational complexity of syntactically computing Schur polynomials has not been studied much. In particular, it is not known whether Schur polynomials can be computed efficiently by algebraic formulas. In this work, we address this question, and show that unless every polynomial with a small algebraic branching program (ABP) has a small algebraic formula, there are Schur polynomials that cannot be computed by algebraic formula of polynomial size. -
Sampling Young Tableaux and Contingency Tables
Sampling Young Tableaux and Contingency Tables Samuel King Advised by Daniel Stefankoviˇcandˇ Sevak Mkrtchyan Based on work previously completed with Mandar Juvekar 1 Contingency Tables and Markov Chains The problem motivating this work is that of sampling contingency tables. A contingency table A is a matrix with nonnegative integer entries whose rows and columns sum to some specified values. In other words, given vectors m n r = (ri)i=1 and c = (cj)j=1 of positive integers, a contingency table with row sums r and column sums c is some A 2 Matm×n(N) such that n m X X ri = Ai;t and cj = At;j (1) t=1 t=1 for each i 2 [m] and j 2 [n] (we use the notation [k] = f1; : : : ; kg). Notice that P P it must be that ri = cj for such a contingency table to possibly exist. We are motivated by the problem of sampling uniformly from Ω(r; c), the space of all contingency tables with row sums r and column sums c. Throughout the majority of this discussion, the primary method of sam- pling will be using Markov chains. The definitions and main results deal- ing with Markov chains are primarily acquired from Mark Jerrum's textbook Counting, Sampling and Integrating: Algorithms and Complexity [9]. A (finite- space) Markov chain is a sequence of random variables X0;:::;Xt;::: with values in a finite state space Ω such that Pr(Xt = a j X0 = a0;X1 = a1;:::;Xt−1 = at−1) = Pr(Xt = a j Xt−1 = at−1) (2) for all t 2 N and a0; : : : ; at 2 Ω.