The Partition Algebra and the Kronecker Product (Extended Abstract)
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Arxiv:1302.5859V2 [Math.RT] 13 May 2015 Upre Ynfflosi DMS-0902661
STABILITY PATTERNS IN REPRESENTATION THEORY STEVEN V SAM AND ANDREW SNOWDEN Abstract. We develop a comprehensive theory of the stable representation categories of several sequences of groups, including the classical and symmetric groups, and their relation to the unstable categories. An important component of this theory is an array of equivalences between the stable representation category and various other categories, each of which has its own flavor (representation theoretic, combinatorial, commutative algebraic, or categorical) and offers a distinct perspective on the stable category. We use this theory to produce a host of specific results, e.g., the construction of injective resolutions of simple objects, duality between the orthogonal and symplectic theories, a canonical derived auto-equivalence of the general linear theory, etc. Contents 1. Introduction 2 1.1. Overview 2 1.2. Descriptions of stable representation theory 4 1.3. Additional results, applications, and remarks 7 1.4. Relation to other work 10 1.5. Future directions 11 1.6. Organization 13 1.7. Notation and conventions 13 2. Preliminaries 14 2.1. Representations of categories 14 2.2. Polynomial representations of GL(∞) and category V 19 2.3. Twisted commutative algebras 22 2.4. Semi-group tca’s and diagram categories 24 2.5. Profinite vector spaces 25 arXiv:1302.5859v2 [math.RT] 13 May 2015 3. The general linear group 25 3.1. Representations of GL(∞) 25 3.2. The walled Brauer algebra and category 29 3.3. Modules over Sym(Ch1, 1i) 33 3.4. Rational Schur functors, universal property and specialization 36 4. The orthogonal and symplectic groups 39 4.1. -
Block Kronecker Products and the Vecb Operator* CORE View
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Block Kronecker Products and the vecb Operator* Ruud H. Koning Department of Economics University of Groningen P.O. Box 800 9700 AV, Groningen, The Netherlands Heinz Neudecker+ Department of Actuarial Sciences and Econometrics University of Amsterdam Jodenbreestraat 23 1011 NH, Amsterdam, The Netherlands and Tom Wansbeek Department of Economics University of Groningen P.O. Box 800 9700 AV, Groningen, The Netherlands Submitted hv Richard Rrualdi ABSTRACT This paper is concerned with two generalizations of the Kronecker product and two related generalizations of the vet operator. It is demonstrated that they pairwise match two different kinds of matrix partition, viz. the balanced and unbalanced ones. Relevant properties are supplied and proved. A related concept, the so-called tilde transform of a balanced block matrix, is also studied. The results are illustrated with various statistical applications of the five concepts studied. *Comments of an anonymous referee are gratefully acknowledged. ‘This work was started while the second author was at the Indian Statistiral Institute, New Delhi. LINEAR ALGEBRA AND ITS APPLICATIONS 149:165-184 (1991) 165 0 Elsevier Science Publishing Co., Inc., 1991 655 Avenue of the Americas, New York, NY 10010 0024-3795/91/$3.50 166 R. H. KONING, H. NEUDECKER, AND T. WANSBEEK INTRODUCTION Almost twenty years ago Singh [7] and Tracy and Singh [9] introduced a generalization of the Kronecker product A@B. They used varying notation for this new product, viz. A @ B and A &3B. Recently, Hyland and Collins [l] studied the-same product under rather restrictive order conditions. -
The Kronecker Product a Product of the Times
The Kronecker Product A Product of the Times Charles Van Loan Department of Computer Science Cornell University Presented at the SIAM Conference on Applied Linear Algebra, Monterey, Califirnia, October 26, 2009 The Kronecker Product B C is a block matrix whose ij-th block is b C. ⊗ ij E.g., b b b11C b12C 11 12 C = b b ⊗ 21 22 b21C b22C Also called the “Direct Product” or the “Tensor Product” Every bijckl Shows Up c11 c12 c13 b11 b12 c21 c22 c23 b21 b22 ⊗ c31 c32 c33 = b11c11 b11c12 b11c13 b12c11 b12c12 b12c13 b11c21 b11c22 b11c23 b12c21 b12c22 b12c23 b c b c b c b c b c b c 11 31 11 32 11 33 12 31 12 32 12 33 b c b c b c b c b c b c 21 11 21 12 21 13 22 11 22 12 22 13 b21c21 b21c22 b21c23 b22c21 b22c22 b22c23 b21c31 b21c32 b21c33 b22c31 b22c32 b22c33 Basic Algebraic Properties (B C)T = BT CT ⊗ ⊗ (B C) 1 = B 1 C 1 ⊗ − − ⊗ − (B C)(D F ) = BD CF ⊗ ⊗ ⊗ B (C D) =(B C) D ⊗ ⊗ ⊗ ⊗ C B = (Perfect Shuffle)T (B C)(Perfect Shuffle) ⊗ ⊗ R.J. Horn and C.R. Johnson(1991). Topics in Matrix Analysis, Cambridge University Press, NY. Reshaping KP Computations 2 Suppose B, C IRn n and x IRn . ∈ × ∈ The operation y =(B C)x is O(n4): ⊗ y = kron(B,C)*x The equivalent, reshaped operation Y = CXBT is O(n3): y = reshape(C*reshape(x,n,n)*B’,n,n) H.V. -
SCHUR-WEYL DUALITY Contents Introduction 1 1. Representation
SCHUR-WEYL DUALITY JAMES STEVENS Contents Introduction 1 1. Representation Theory of Finite Groups 2 1.1. Preliminaries 2 1.2. Group Algebra 4 1.3. Character Theory 5 2. Irreducible Representations of the Symmetric Group 8 2.1. Specht Modules 8 2.2. Dimension Formulas 11 2.3. The RSK-Correspondence 12 3. Schur-Weyl Duality 13 3.1. Representations of Lie Groups and Lie Algebras 13 3.2. Schur-Weyl Duality for GL(V ) 15 3.3. Schur Functors and Algebraic Representations 16 3.4. Other Cases of Schur-Weyl Duality 17 Appendix A. Semisimple Algebras and Double Centralizer Theorem 19 Acknowledgments 20 References 21 Introduction. In this paper, we build up to one of the remarkable results in representation theory called Schur-Weyl Duality. It connects the irreducible rep- resentations of the symmetric group to irreducible algebraic representations of the general linear group of a complex vector space. We do so in three sections: (1) In Section 1, we develop some of the general theory of representations of finite groups. In particular, we have a subsection on character theory. We will see that the simple notion of a character has tremendous consequences that would be very difficult to show otherwise. Also, we introduce the group algebra which will be vital in Section 2. (2) In Section 2, we narrow our focus down to irreducible representations of the symmetric group. We will show that the irreducible representations of Sn up to isomorphism are in bijection with partitions of n via a construc- tion through certain elements of the group algebra. -
Kronecker Products
Copyright ©2005 by the Society for Industrial and Applied Mathematics This electronic version is for personal use and may not be duplicated or distributed. Chapter 13 Kronecker Products 13.1 Definition and Examples Definition 13.1. Let A ∈ Rm×n, B ∈ Rp×q . Then the Kronecker product (or tensor product) of A and B is defined as the matrix a11B ··· a1nB ⊗ = . ∈ Rmp×nq A B . .. . (13.1) am1B ··· amnB Obviously, the same definition holds if A and B are complex-valued matrices. We restrict our attention in this chapter primarily to real-valued matrices, pointing out the extension to the complex case only where it is not obvious. Example 13.2. = 123 = 21 1. Let A 321and B 23. Then 214263 B 2B 3B 234669 A ⊗ B = = . 3B 2BB 634221 694623 Note that B ⊗ A = A ⊗ B. ∈ Rp×q ⊗ = B 0 2. For any B , I2 B 0 B . Replacing I2 by In yields a block diagonal matrix with n copies of B along the diagonal. 3. Let B be an arbitrary 2 × 2 matrix. Then b11 0 b12 0 0 b11 0 b12 B ⊗ I2 = . b21 0 b22 0 0 b21 0 b22 139 “ajlbook” — 2004/11/9 — 13:36 — page 139 — #147 From "Matrix Analysis for Scientists and Engineers" Alan J. Laub. Buy this book from SIAM at www.ec-securehost.com/SIAM/ot91.html Copyright ©2005 by the Society for Industrial and Applied Mathematics This electronic version is for personal use and may not be duplicated or distributed. 140 Chapter 13. Kronecker Products The extension to arbitrary B and In is obvious. -
A Constructive Arbitrary-Degree Kronecker Product Decomposition of Tensors
A CONSTRUCTIVE ARBITRARY-DEGREE KRONECKER PRODUCT DECOMPOSITION OF TENSORS KIM BATSELIER AND NGAI WONG∗ Abstract. We propose the tensor Kronecker product singular value decomposition (TKPSVD) that decomposes a real k-way tensor A into a linear combination of tensor Kronecker products PR (d) (1) with an arbitrary number of d factors A = j=1 σj Aj ⊗ · · · ⊗ Aj . We generalize the matrix (i) Kronecker product to tensors such that each factor Aj in the TKPSVD is a k-way tensor. The algorithm relies on reshaping and permuting the original tensor into a d-way tensor, after which a polyadic decomposition with orthogonal rank-1 terms is computed. We prove that for many (1) (d) different structured tensors, the Kronecker product factors Aj ;:::; Aj are guaranteed to inherit this structure. In addition, we introduce the new notion of general symmetric tensors, which includes many different structures such as symmetric, persymmetric, centrosymmetric, Toeplitz and Hankel tensors. Key words. Kronecker product, structured tensors, tensor decomposition, TTr1SVD, general- ized symmetric tensors, Toeplitz tensor, Hankel tensor AMS subject classifications. 15A69, 15B05, 15A18, 15A23, 15B57 1. Introduction. Consider the singular value decomposition (SVD) of the fol- lowing 16 × 9 matrix A~ 0 1:108 −0:267 −1:192 −0:267 −1:192 −1:281 −1:192 −1:281 1:102 1 B 0:417 −1:487 −0:004 −1:487 −0:004 −1:418 −0:004 −1:418 −0:228C B C B−0:127 1:100 −1:461 1:100 −1:461 0:729 −1:461 0:729 0:940 C B C B−0:748 −0:243 0:387 −0:243 0:387 −1:241 0:387 −1:241 −1:853C B C B 0:417 -
Introduction to Kronecker Products
Math 515 Fall, 2010 Introduction to Kronecker Products If A is an m × n matrix and B is a p × q matrix, then the Kronecker product of A and B is the mp × nq matrix 2 3 a11B a12B ··· a1nB 6 7 6 a21B a22B ··· a2nB 7 A ⊗ B = 6 . 7 6 . 7 4 . 5 am1B am2B ··· amnB Note that if A and B are large matrices, then the Kronecker product A⊗B will be huge. MATLAB has a built-in function kron that can be used as K = kron(A, B); However, you will quickly run out of memory if you try this for matrices that are 50 × 50 or larger. Fortunately we can exploit the block structure of Kronecker products to do many compu- tations involving A ⊗ B without actually forming the Kronecker product. Instead we need only do computations with A and B individually. To begin, we state some very simple properties of Kronecker products, which should not be difficult to verify: • (A ⊗ B)T = AT ⊗ BT • If A and B are square and nonsingular, then (A ⊗ B)−1 = A−1 ⊗ B−1 • (A ⊗ B)(C ⊗ D) = AC ⊗ BD The next property we want to consider involves the matrix-vector multiplication y = (A ⊗ B)x; where A 2 Rm×n and B 2 Rp×q. Thus A ⊗ B 2 Rmp×nq, x 2 Rnq, and y 2 Rmp. Our goal is to exploit the block structure of the Kronecker product matrix to compute y without explicitly forming (A ⊗ B). To see how this can be done, first partition the vectors x and y as 2 3 2 3 x1 y1 6 x 7 6 y 7 6 2 7 q 6 2 7 p x = 6 . -
Boolean Product Polynomials, Schur Positivity, and Chern Plethysm
BOOLEAN PRODUCT POLYNOMIALS, SCHUR POSITIVITY, AND CHERN PLETHYSM SARA C. BILLEY, BRENDON RHOADES, AND VASU TEWARI Abstract. Let k ≤ n be positive integers, and let Xn = (x1; : : : ; xn) be a list of n variables. P The Boolean product polynomial Bn;k(Xn) is the product of the linear forms i2S xi where S ranges over all k-element subsets of f1; 2; : : : ; ng. We prove that Boolean product polynomials are Schur positive. We do this via a new method of proving Schur positivity using vector bundles and a symmetric function operation we call Chern plethysm. This gives a geometric method for producing a vast array of Schur positive polynomials whose Schur positivity lacks (at present) a combinatorial or representation theoretic proof. We relate the polynomials Bn;k(Xn) for certain k to other combinatorial objects including derangements, positroids, alternating sign matrices, and reverse flagged fillings of a partition shape. We also relate Bn;n−1(Xn) to a bigraded action of the symmetric group Sn on a divergence free quotient of superspace. 1. Introduction The symmetric group Sn of permutations of [n] := f1; 2; : : : ; ng acts on the polynomial ring C[Xn] := C[x1; : : : ; xn] by variable permutation. Elements of the invariant subring Sn (1.1) C[Xn] := fF (Xn) 2 C[Xn]: w:F (Xn) = F (Xn) for all w 2 Sn g are called symmetric polynomials. Symmetric polynomials are typically defined using sums of products of the variables x1; : : : ; xn. Examples include the power sum, the elementary symmetric polynomial, and the homogeneous symmetric polynomial which are (respectively) (1.2) k k X X pk(Xn) = x1 + ··· + xn; ek(Xn) = xi1 ··· xik ; hk(Xn) = xi1 ··· xik : 1≤i1<···<ik≤n 1≤i1≤···≤ik≤n Given a partition λ = (λ1 ≥ · · · ≥ λk > 0) with k ≤ n parts, we have the monomial symmetric polynomial X (1.3) m (X ) = xλ1 ··· xλk ; λ n i1 ik i1; : : : ; ik distinct as well as the Schur polynomial sλ(Xn) whose definition is recalled in Section 2. -
Kronecker Products and Matrix Calculus in System Theory
772 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS, VOL. CAS-25, NO. 9, ISEFTEMBER 1978 Kronecker Products and Matrix Calculus in System Theory JOHN W. BREWER I Absfrucr-The paper begins with a review of the algebras related to and are based on the developments of Section IV. Also, a Kronecker products. These algebras have several applications in system novel and interesting operator notation will ble introduced theory inclluding the analysis of stochastic steady state. The calculus of matrk valued functions of matrices is reviewed in the second part of the in Section VI. paper. This cakuhs is then used to develop an interesting new method for Concluding comments are placed in Section VII. the identification of parameters of linear time-invariant system models. II. NOTATION I. INTRODUCTION Matrices will be denoted by upper case boldface (e.g., HE ART of differentiation has many practical ap- A) and column matrices (vectors) will be denoted by T plications in system analysis. Differentiation is used lower case boldface (e.g., x). The kth row of a matrix such. to obtain necessaryand sufficient conditions for optimiza- as A will be denoted A,. and the kth colmnn will be tion in a.nalytical studies or it is used to obtain gradients denoted A.,. The ik element of A will be denoted ujk. and Hessians in numerical optimization studies. Sensitiv- The n x n unit matrix is denoted I,,. The q-dimensional ity analysis is yet another application area for. differentia- vector which is “I” in the kth and zero elsewhereis called tion formulas. In turn, sensitivity analysis is applied to the the unit vector and is denoted design of feedback and adaptive feedback control sys- tems. -
Plethysm and Lattice Point Counting
Plethysm and lattice point counting Thomas Kahle OvG Universit¨at Magdeburg joint work with M. Micha lek Some representation theory • Finite-dimensional, rational, irreducible representations of GL(n) are indexed by Young diagrams with at most n rows. • The correspondence is via the Schur functor: λ $ Sλ. • Sλ applied to the trivial representation gives an irreducible representation. Example and Convention • Only one row (λ = (d)): SλW = SdW (degree d forms on W ) • Only one column (λ = (1;:::; 1)): Sλ(W ) = Vd W Schur functor plethysm The general plethysm A Schur functor Sµ can be applied to any representation, for instance an irreducible SνW µ ν L λ ⊕cλ • S (S W ) decomposes into λ(S ) . • General plethysm: Determine cλ as a function of (λ, µ, ν). ! impossible? More modest goals { Symmetric powers • Decompose Sd(SkW ) into irreducibles. ! still hard. • Low degree: Fix d, seek function of (k; λ). Proposition (Thrall, 1942) One has GL(W )-module decompositions 2 M ^ M S2(SkW ) = SλW; (SkW ) = SµW; where • λ runs over tableaux with 2k boxes in two rows of even length. • µ runs over tableaux with 2k boxes in two rows of odd length. • Note: Divisibility conditions on the appearing tableaux. • Similar formulas for S3(Sk) obtained by Agaoka, Chen, Duncan, Foulkes, Garsia, Howe, Plunkett, Remmel, Thrall, . • A few things are known about S4(Sk) (Duncan, Foulkes, Howe). • Observation: Tableaux counting formulas get unwieldy quickly. Theorem (KM) λ d k Fix d. For any k 2 N, and λ ` dk, the multiplicity of S in S (S ) is d−1 X Dα X (−1)( 2 ) sgn(π)Q (k; λ ) ; d! α π α`d π2Sd−1 where • Qα are counting functions of parametric lattice polytopes • Dα is the number of permutations of cycle type α. -
Topics in Group Representation Theory
Topics in Group Representation Theory Peter Webb October 2, 2017 Contents 0.1 Representations of Sr ............................1 0.2 Polynomial representations of GLn(k)...................1 0.3 Representations of GL(Fpn ).........................2 0.4 Functorial methods . .2 1 Dual spaces and bilinear forms 3 2 Representations of Sr 4 2.1 Tableaux and tabloids . .4 2.2 Permutation modules . .5 2.2.1 Exercise . .7 2.3 p-regular partitions . .7 2.4 Young modules . .9 3 The Schur algebra 11 3.1 Tensor space . 11 3.2 Homomorphisms between permutation modules . 14 3.3 The structure of endomorphism rings . 15 3.4 The radical . 17 3.5 Projective covers, Nakayama's lemma and lifting of idempotents . 20 3.6 Projective modules for finite dimensional algebras . 24 3.7 Cartan invariants . 26 3.8 Projective and simple modules for SF (n; r)................ 28 3.9 Duality and the Schur algebra . 30 4 Polynomial representations of GLn(F ) 33 4.0.1 Multi-indices . 39 4.1 Weights and Characters . 40 5 Connections between the Schur algebra and the symmetric group: the Schur functor 47 5.1 The general theory of the functor f : B -mod ! eBe -mod . 51 5.2 Applications . 51 i CONTENTS ii 6 Representations of the category of vector spaces 54 6.1 Simple representations of the matrix monoid . 55 6.2 Simple representations of categories and the category algebra . 57 6.3 Parametrization of simple and projective representations . 60 6.4 Projective functors . 63 7 Bibliography 68 Landmark Theorems 0.1 Representations of Sr λ Theorem 0.1.1. -
Schur Functors in This Lecture We Will Examine the Schur Polynomials and Young Tableaux to Obtain Irreducible Representations of Sl(N +1, C)
MATH 223A NOTES 2011 LIE ALGEBRAS 91 23. Schur functors In this lecture we will examine the Schur polynomials and Young tableaux to obtain irreducible representations of sl(n +1, C). The construction is based on Fulton’s book “Young Tableaux” which is highly recommended for beginning students because it is a beautifully written book with minimal prerequisites. This will be only in the case of L = sl(n +1,F). Other cases and the proof of the Weyl character formula will be next. First, we construct the irreducible representations V (λi) corresponding to the funda- mental dominant weights λi = 1 + + i. Then we use Young tableaux to find the modules V (λ) inside tensor products··· of these fundamental representations. 23.1. Fundamental representations. Theorem 23.1.1. The fundamental representation V (λj) is equal to the ith exterior power of V = F n+1: V (λ )= jV j ∧ L = sl(n +1,F) acts by the isomorphism sl(n +1,F) ∼= sl(V ). Definition 23.1.2. Recall that the jth exterior power of a vector space V is the quotient of the jth tensor power by all elements of the form w1 wj where the wi are not j ⊗···⊗ distinct. The image of w1 wj in V is denoted w1 wj. When the characteristic of F is not equal to 2, this⊗··· is equivalent⊗ ∧ to saying that∧ the··· wedge∧ is skew symmetric: w w = sgn(σ)w w σ(1) ∧···∧ σ(j) 1 ∧···∧ j If v , ,v is a basis for V then 1 ··· n+1 v v v i1 ∧ i2 ∧···∧ ij for 1 i <i < <i n + 1 is a basis for jV .