Hook Lengths and Contents. Richard P. Stanley M.I.T.

Size: px
Start display at page:

Download "Hook Lengths and Contents. Richard P. Stanley M.I.T."

Transcription

1 Hook Lengths and Contents p. Hook Lengths and Contents Richard P. Stanley M.I.T.

2 Hook Lengths and Contents p. Standard Young tableau standard Young tableau (SYT) of shape λ = (4, 4, 3, 1): < <

3 f λ : number of SYT of shape λ Hook Lengths and Contents p.

4 Hook Lengths and Contents p. f λ : number of SYT of shape λ f 3,2 = 5:

5 Hook Lengths and Contents p. Hook length formula For u λ, let h u be the hook length at u, i.e., the number of squares directly below or to the right of u (counting u once)

6 Hook Lengths and Contents p. Hook length formula For u λ, let h u be the hook length at u, i.e., the number of squares directly below or to the right of u (counting u once)

7 Hook Lengths and Contents p. Theorem (Frame-Robinson-Thrall). Let λ n. Then n! f λ = u λ h. u

8 Hook Lengths and Contents p. Theorem (Frame-Robinson-Thrall). Let λ n. Then n! f λ = u λ h. u f (4,4,3,1) = 12! = 2970

9 Hook Lengths and Contents p. Nekrasov-Okounkov identity RSK algorithm (or representation theory) f 2 λ = n! λ n

10 Hook Lengths and Contents p. Nekrasov-Okounkov identity RSK algorithm (or representation theory) f 2 λ = n! λ n Nekrasov-Okounkov (2006), G. Han (2008): n 0 ( λ n f 2 λ ) x n (t + h 2 u ) u λ n! 2 = i 1 (1 x i ) t 1

11 Hook Lengths and Contents p. A corollary e k : kth elementary SF e k (x 1, x 2,... ) = x i1 x i2 x ik 1 i 1 <i 2 < <i k

12 Hook Lengths and Contents p. A corollary e k : kth elementary SF e k (x 1, x 2,... ) = x i1 x i2 x ik 1 i 1 <i 2 < <i k Corollary. Let g k (n) = 1 n! Then g k (n) Q[n]. f 2 λ e k(h 2 u : u λ). λ n

13 Hook Lengths and Contents p. Conjecture of Conjecture (Han). Let j P. Then Q[n]. 1 n! λ n f 2 λ u λ h 2j u

14 Hook Lengths and Contents p. Conjecture of Conjecture (Han). Let j P. Then Q[n]. 1 n! λ n f 2 λ u λ True for j = 1 by above. h 2j u

15 Hook Lengths and Contents p. Conjecture of Conjecture (Han). Let j P. Then Q[n]. 1 n! λ n f 2 λ u λ True for j = 1 by above. h 2j u Stronger conjecture. For any symmetric function F, f 2 λ F (h2 u : u λ) Q[n]. 1 n! λ n

16 Hook Lengths and Contents p. Examples Let d k (n)= 1 n! λ n f 2 λ u λ h 2k u. d 1 (n) = 1 n(3n 1) 2 d 2 (n) = 1 24 n(n 1)(27n2 67n + 74) d 3 (n) = 1 n(n 1)(n 2) 48 (27n 3 174n n 552).

17 Hook Lengths and Contents p. 1 Open variants 1 n! λ n f 2 λ u λ h u =? 1 n! λ n f λ u λ h u =? 1 n! λ n f λ u λ h 2 u =?

18 Hook Lengths and Contents p. 1 Contents For u λ let c(u)= i j, the content of square u = (i, j).

19 Hook Lengths and Contents p. 1 Contents For u λ let c(u)= i j, the content of square u = (i, j)

20 Hook Lengths and Contents p. 1 Semistandard tableaux semistandard Young tableau (SSYT) of shape λ = (4, 4, 3, 1): < < x T = x 1 x 3 2 x3 4 x 5 x 2 6 x2 7

21 Hook Lengths and Contents p. 1 Schur functions s λ = T x T, summed over all SSYT of shape λ

22 Hook Lengths and Contents p. 1 Schur functions s λ = T x T, summed over all SSYT of shape λ s 3 = i j k s 1,1,1 = i<j<k x i x j x k x i x j x k = e 3.

23 Hook Lengths and Contents p. 1 Hook-content formula f(1 t )= f(1, 1,..., 1) (t 1 s)

24 Hook Lengths and Contents p. 1 Hook-content formula f(1 t )= f(1, 1,..., 1) (t 1 s) s λ (1 t ) = #{SSYT of shape λ, max t}.

25 Hook Lengths and Contents p. 1 Hook-content formula f(1 t )= f(1, 1,..., 1) (t 1 s) s λ (1 t ) = #{SSYT of shape λ, max t}. Theorem. s λ (1 t ) = u λ t + c u h u.

26 Hook Lengths and Contents p. 1 A curiosity Let κ(w) denote the number of cycles of w S n. Then 1 n! u,v S n q κ(uvu 1v 1) = λ n (q + c u ). u λ

27 Hook Lengths and Contents p. 1 A curiosity Let κ(w) denote the number of cycles of w S n. Then 1 n! u,v S n q κ(uvu 1v 1) = Restatement: n! λ n e k (c u : u λ) λ n (q + c u ). u λ = #{(u, v) S n S n : κ(uvu 1 v 1 ) = n k}

28 Hook Lengths and Contents p. 1 Another curiosity w S n q κ(w2) = λ n f λ (q + c u ) u λ

29 Hook Lengths and Contents p. 1 Han s conjecture for contents Theorem. For any symmetric function F, 1 n! f 2 λ F (c u : u λ) Q[n]. λ n Idea of proof. By linearity, suffices to take F = e µ.

30 Hook Lengths and Contents p. 1 Power sum symmetric functions x = (x 1, x 2,... ) p m = p m (x) = i xm i p λ = p λ1 p λ2

31 Hook Lengths and Contents p. 1 Power sum symmetric functions x = (x 1, x 2,... ) p m = p m (x) = i xm i p λ = p λ1 p λ2 Let l(λ) be the length (number of parts) of λ p m (1 t ) = t p λ (1 t ) = t l(λ)

32 Hook Lengths and Contents p. 1 Some notation x (1),..., x (k) : disjoint sets of variables

33 Hook Lengths and Contents p. 1 Some notation x (1),..., x (k) : disjoint sets of variables ρ(w): cycle type of w S n, e.g., w = (1, 3, 4)(2)(5) p ρ(w) = p (3,1,1) = p 3 p 2 1

34 Hook Lengths and Contents p. 1 Some notation x (1),..., x (k) : disjoint sets of variables ρ(w): cycle type of w S n, e.g., w = (1, 3, 4)(2)(5) p ρ(w) = p (3,1,1) = p 3 p 2 1 H λ = u λ h u

35 Hook Lengths and Contents p. 2 The fundamental tool Theorem. λ n H k 2 λ s λ (x (1) ) s λ (x (k) ) = 1 n! w 1 w k =1 in S n p ρ(w1)(x (1) ) p ρ(wk )(x (k) )

36 Hook Lengths and Contents p. 2 The fundamental tool Theorem. λ n H k 2 λ s λ (x (1) ) s λ (x (k) ) = 1 n! w 1 w k =1 in S n p ρ(w1)(x (1) ) p ρ(wk )(x (k) ) Proof. Follows from representation theory: the relation between multiplying conjugacy class sums in Z(CG) (the center of the group algebra of the finite group G) and the irreducible characters of G.

37 Hook Lengths and Contents p. 2 Set x (i) = 1 t i, so s λ(x (i) ) t i +c u h u p ρ(wi )(x (i) ) t κ(w i) i. Get λ n H 2 λ 1 n! (t 1 + c u ) (t k + c u ) = u λ w 1 w k =1 in S n κ(w t 1 ) 1 t κ(w k) k.

38 Hook Lengths and Contents p. 2 Completion of proof Take coefficient of t n µ 1 1 t n µ k 1 n! λ n k : f 2 λ e µ(c u : u λ) = #{(w 1,..., w k ) S k n : w 1 w k = 1, c(w i ) = n µ i }.

39 Hook Lengths and Contents p. 2 Completion of proof Take coefficient of t n µ 1 1 t n µ k 1 n! λ n k : f 2 λ e µ(c u : u λ) = #{(w 1,..., w k ) S k n : w 1 w k = 1, c(w i ) = n µ i }. Elementary combinatorial argument shows this is a polynomial in n.

40 Hook Lengths and Contents p. 2 Shifted parts Let λ = (λ 1,..., λ n ) n. partitions of 3: 300, 210, 111

41 Hook Lengths and Contents p. 2 Shifted parts Let λ = (λ 1,..., λ n ) n. partitions of 3: 300, 210, 111 shifted parts of λ: λ i + n i, 1 i n

42 Hook Lengths and Contents p. 2 Shifted parts Let λ = (λ 1,..., λ n ) n. partitions of 3: 300, 210, 111 shifted parts of λ: λ i + n i, 1 i n shifted parts of (3, 1, 1, 0, 0): 7, 4, 3, 1, 0

43 Hook Lengths and Contents p. 2 Contents and shifted parts Let F (x; y) be symmetric in x and y variables separately, e.g., p 1 (x)p 2 (y) = (x 1 + x 2 + )(y y2 2 + ).

44 Hook Lengths and Contents p. 2 Contents and shifted parts Let F (x; y) be symmetric in x and y variables separately, e.g., p 1 (x)p 2 (y) = (x 1 + x 2 + )(y y2 2 + ). Theorem. Let r(n)= 1 f 2 λ n! F (c u, u λ; λ i + n i, 1 i n λ n Then r(n) Z (n fixed), and r(n) Q[n].

45 Hook Lengths and Contents p. 2 Similar results are false Note. Let P (n)= 1 n! f 2 λ ( λ 2 i ). λ n i

46 Hook Lengths and Contents p. 2 Similar results are false Note. Let P (n)= 1 n! f 2 λ ( λ 2 i ). λ n i Then P (3) = 16 3 Z P (n) Q[n]

47 Hook Lengths and Contents p. 2 ϕ Λ Q = {symmetric functions over Q} Define a linear transformation ϕ : Λ Q Q[t] by ϕ(s λ ) = n (t + λ i + n i) i=1 H λ.

48 Hook Lengths and Contents p. 2 Key lemma for shifted parts Lemma. Let µ n, l = l(µ). Then ϕ(p µ ) = ( 1) n l m i=0 ( m i ) t(t + 1) (t + i 1), where m= m 1 (µ), the number of parts of µ equal to 1.

49 Hook Lengths and Contents p. 2 Proof of main result Theorem. For any symmetric function F, f 2 λ F (h2 u : u λ) Q[n]. 1 n! λ n

50 Hook Lengths and Contents p. 2 Proof of main result Theorem. For any symmetric function F, f 2 λ F (h2 u : u λ) Q[n]. 1 n! λ n Proof based on the multiset identity {h u : u λ} {λ i λ j i + j : 1 i < j n} = {n + c u : u λ} {1 n 1, 2 n 2,..., n 1}.

51 Hook Lengths and Contents p. 2 Example. λ = (3, 1, 0, 0), λ (1, 2, 3, 4) = (2, 1, 3, 4): {4, 2, 1, 1} {3, 5, 6, 2, 3, 1} = {3, 4, 5, 6} {1, 1, 1, 2, 2, 3}

52 Hook Lengths and Contents p. 3 {h u : u λ} {λ i λ j i + j : 1 i < j n} = {n + c u : u λ} {1 n 1, 2 n 2,..., n 1}.

53 Hook Lengths and Contents p. 3 {h u : u λ} {λ i λ j i + j : 1 i < j n} = {n + c u : u λ} {1 n 1, 2 n 2,..., n 1}. Note. λ i λ j i + j = (λ i + n i) (λ j + n j) Not symmetric in λ i + n i and λ j + n j, but (λ i λ j i + j) 2 is symmetric.

54 Hook Lengths and Contents p. 3 Further results Recall: Nekrasov-Okounkov (2006), G. Han (2008): n 0 ( λ n f 2 λ ) x n (t + h 2 u ) u λ n! 2 = i 1 (1 x i ) t 1

55 Hook Lengths and Contents p. 3 Content analogue Content Nekrasov-Okounkov identity n 0 ( λ n f 2 λ ) x n (t + c 2 u ) u λ n! 2 = (1 x) t. Proof: simple consequence of dual Cauchy identity" and the hook-content formula.

56 Hook Lengths and Contents p. 3 Fujii et al. variant 1 n! λ n f 2 λ u λ (c 2 u i2 ) k 1 i=0 = (2k)! (k + 1)! 2(n) k+1. where (n) k+1 = n(n 1) (n k).

57 Hook Lengths and Contents p. 3 Okada s conjecture 1 n! λ n f 2 λ u λ k (h 2 u i2 ) i=1 = 1 2(k + 1) 2 ( )( ) 2k 2k + 2 (n) k+1 k k + 1

58 Hook Lengths and Contents p. 3 Proof of Okada s conjecture Proof (Greta Panova).

59 Hook Lengths and Contents p. 3 Proof of Okada s conjecture Proof (Greta Panova). Both sides are polynomials in n.

60 Hook Lengths and Contents p. 3 Proof of Okada s conjecture Proof (Greta Panova). Both sides are polynomials in n. The two sides agree for 1 n k + 2.

61 Hook Lengths and Contents p. 3 Proof of Okada s conjecture Proof (Greta Panova). Both sides are polynomials in n. The two sides agree for 1 n k + 2. Key step: both sides have degree k + 1.

62 Hook Lengths and Contents p. 3 Central factorial numbers x n = k T (n, k)x(x 1 2 )(x 2 2 ) (x k 2 )

63 Hook Lengths and Contents p. 3 Central factorial numbers x n = k T (n, k)x(x 1 2 )(x 2 2 ) (x k 2 ) n\k

64 Hook Lengths and Contents p. 3 Okada-Panova redux Corollary. = k+1 j=1 1 n! λ n f 2 λ u λ h 2k u T (k + 1, j) 1 2j 2 ( )( 2(j 1) 2j j 1 j ) (n) j.

arxiv:math/0606467v2 [math.co] 5 Jul 2006

arxiv:math/0606467v2 [math.co] 5 Jul 2006 A Conjectured Combinatorial Interpretation of the Normalized Irreducible Character Values of the Symmetric Group arxiv:math/0606467v [math.co] 5 Jul 006 Richard P. Stanley Department of Mathematics, Massachusetts

More information

Prime power degree representations of the symmetric and alternating groups

Prime power degree representations of the symmetric and alternating groups Prime power degree representations of the symmetric and alternating groups Antal Balog, Christine Bessenrodt, Jørn B. Olsson, Ken Ono Appearing in the Journal of the London Mathematical Society 1 Introduction

More information

BABY VERMA MODULES FOR RATIONAL CHEREDNIK ALGEBRAS

BABY VERMA MODULES FOR RATIONAL CHEREDNIK ALGEBRAS BABY VERMA MODULES FOR RATIONAL CHEREDNIK ALGEBRAS SETH SHELLEY-ABRAHAMSON Abstract. These are notes for a talk in the MIT-Northeastern Spring 2015 Geometric Representation Theory Seminar. The main source

More information

ON GENERALIZED RELATIVE COMMUTATIVITY DEGREE OF A FINITE GROUP. A. K. Das and R. K. Nath

ON GENERALIZED RELATIVE COMMUTATIVITY DEGREE OF A FINITE GROUP. A. K. Das and R. K. Nath International Electronic Journal of Algebra Volume 7 (2010) 140-151 ON GENERALIZED RELATIVE COMMUTATIVITY DEGREE OF A FINITE GROUP A. K. Das and R. K. Nath Received: 12 October 2009; Revised: 15 December

More information

1 A duality between descents and connectivity.

1 A duality between descents and connectivity. The Descent Set and Connectivity Set of a Permutation 1 Richard P. Stanley 2 Department of Mathematics, Massachusetts Institute of Technology Cambridge, MA 02139, USA rstan@math.mit.edu version of 16 August

More information

1 Lecture: Integration of rational functions by decomposition

1 Lecture: Integration of rational functions by decomposition Lecture: Integration of rational functions by decomposition into partial fractions Recognize and integrate basic rational functions, except when the denominator is a power of an irreducible quadratic.

More information

15. Symmetric polynomials

15. Symmetric polynomials 15. Symmetric polynomials 15.1 The theorem 15.2 First examples 15.3 A variant: discriminants 1. The theorem Let S n be the group of permutations of {1,, n}, also called the symmetric group on n things.

More information

Domino Tableaux, Schützenberger Involution, and the Symmetric Group Action

Domino Tableaux, Schützenberger Involution, and the Symmetric Group Action Domino Tableaux, Schützenberger Involution, and the Symmetric Group Action Arkady Berenstein and Anatol N. Kirillov CRM-2510 September 1997 Department of Mathematics, Cornell University, Ithaca, NY 14853,

More information

Mathematics Course 111: Algebra I Part IV: Vector Spaces

Mathematics Course 111: Algebra I Part IV: Vector Spaces Mathematics Course 111: Algebra I Part IV: Vector Spaces D. R. Wilkins Academic Year 1996-7 9 Vector Spaces A vector space over some field K is an algebraic structure consisting of a set V on which are

More information

Prime numbers and prime polynomials. Paul Pollack Dartmouth College

Prime numbers and prime polynomials. Paul Pollack Dartmouth College Prime numbers and prime polynomials Paul Pollack Dartmouth College May 1, 2008 Analogies everywhere! Analogies in elementary number theory (continued fractions, quadratic reciprocity, Fermat s last theorem)

More information

Lecture Notes on Polynomials

Lecture Notes on Polynomials Lecture Notes on Polynomials Arne Jensen Department of Mathematical Sciences Aalborg University c 008 Introduction These lecture notes give a very short introduction to polynomials with real and complex

More information

Section 6.1 - Inner Products and Norms

Section 6.1 - Inner Products and Norms Section 6.1 - Inner Products and Norms Definition. Let V be a vector space over F {R, C}. An inner product on V is a function that assigns, to every ordered pair of vectors x and y in V, a scalar in F,

More information

Math 115A HW4 Solutions University of California, Los Angeles. 5 2i 6 + 4i. (5 2i)7i (6 + 4i)( 3 + i) = 35i + 14 ( 22 6i) = 36 + 41i.

Math 115A HW4 Solutions University of California, Los Angeles. 5 2i 6 + 4i. (5 2i)7i (6 + 4i)( 3 + i) = 35i + 14 ( 22 6i) = 36 + 41i. Math 5A HW4 Solutions September 5, 202 University of California, Los Angeles Problem 4..3b Calculate the determinant, 5 2i 6 + 4i 3 + i 7i Solution: The textbook s instructions give us, (5 2i)7i (6 + 4i)(

More information

arxiv:math/0202219v1 [math.co] 21 Feb 2002

arxiv:math/0202219v1 [math.co] 21 Feb 2002 RESTRICTED PERMUTATIONS BY PATTERNS OF TYPE (2, 1) arxiv:math/0202219v1 [math.co] 21 Feb 2002 TOUFIK MANSOUR LaBRI (UMR 5800), Université Bordeaux 1, 351 cours de la Libération, 33405 Talence Cedex, France

More information

Introduction to Finite Fields (cont.)

Introduction to Finite Fields (cont.) Chapter 6 Introduction to Finite Fields (cont.) 6.1 Recall Theorem. Z m is a field m is a prime number. Theorem (Subfield Isomorphic to Z p ). Every finite field has the order of a power of a prime number

More information

Algebra 1 Course Title

Algebra 1 Course Title Algebra 1 Course Title Course- wide 1. What patterns and methods are being used? Course- wide 1. Students will be adept at solving and graphing linear and quadratic equations 2. Students will be adept

More information

Integer Sequences and Matrices Over Finite Fields

Integer Sequences and Matrices Over Finite Fields Integer Sequences and Matrices Over Finite Fields arxiv:math/0606056v [mathco] 2 Jun 2006 Kent E Morrison Department of Mathematics California Polytechnic State University San Luis Obispo, CA 93407 kmorriso@calpolyedu

More information

Tensor invariants of SL(n), wave graphs and L-tris arxiv:math/9802119v1 [math.rt] 27 Feb 1998

Tensor invariants of SL(n), wave graphs and L-tris arxiv:math/9802119v1 [math.rt] 27 Feb 1998 Tensor invariants of SL(n), wave graphs and L-tris arxiv:math/9802119v1 [math.rt] 27 Feb 1998 Aleksandrs Mihailovs Department of Mathematics University of Pennsylvania Philadelphia, PA 19104-6395 mihailov@math.upenn.edu

More information

Stern Sequences for a Family of Multidimensional Continued Fractions: TRIP-Stern Sequences

Stern Sequences for a Family of Multidimensional Continued Fractions: TRIP-Stern Sequences Stern Sequences for a Family of Multidimensional Continued Fractions: TRIP-Stern Sequences arxiv:1509.05239v1 [math.co] 17 Sep 2015 I. Amburg K. Dasaratha L. Flapan T. Garrity C. Lee C. Mihaila N. Neumann-Chun

More information

FUNCTIONAL ANALYSIS LECTURE NOTES: QUOTIENT SPACES

FUNCTIONAL ANALYSIS LECTURE NOTES: QUOTIENT SPACES FUNCTIONAL ANALYSIS LECTURE NOTES: QUOTIENT SPACES CHRISTOPHER HEIL 1. Cosets and the Quotient Space Any vector space is an abelian group under the operation of vector addition. So, if you are have studied

More information

Lecture 13 - Basic Number Theory.

Lecture 13 - Basic Number Theory. Lecture 13 - Basic Number Theory. Boaz Barak March 22, 2010 Divisibility and primes Unless mentioned otherwise throughout this lecture all numbers are non-negative integers. We say that A divides B, denoted

More information

THE AVERAGE DEGREE OF AN IRREDUCIBLE CHARACTER OF A FINITE GROUP

THE AVERAGE DEGREE OF AN IRREDUCIBLE CHARACTER OF A FINITE GROUP THE AVERAGE DEGREE OF AN IRREDUCIBLE CHARACTER OF A FINITE GROUP by I. M. Isaacs Mathematics Department University of Wisconsin 480 Lincoln Dr. Madison, WI 53706 USA E-Mail: isaacs@math.wisc.edu Maria

More information

COMMUTATIVITY DEGREES OF WREATH PRODUCTS OF FINITE ABELIAN GROUPS

COMMUTATIVITY DEGREES OF WREATH PRODUCTS OF FINITE ABELIAN GROUPS COMMUTATIVITY DEGREES OF WREATH PRODUCTS OF FINITE ABELIAN GROUPS IGOR V. EROVENKO AND B. SURY ABSTRACT. We compute commutativity degrees of wreath products A B of finite abelian groups A and B. When B

More information

Minimally Infeasible Set Partitioning Problems with Balanced Constraints

Minimally Infeasible Set Partitioning Problems with Balanced Constraints Minimally Infeasible Set Partitioning Problems with alanced Constraints Michele Conforti, Marco Di Summa, Giacomo Zambelli January, 2005 Revised February, 2006 Abstract We study properties of systems of

More information

Recall that two vectors in are perpendicular or orthogonal provided that their dot

Recall that two vectors in are perpendicular or orthogonal provided that their dot Orthogonal Complements and Projections Recall that two vectors in are perpendicular or orthogonal provided that their dot product vanishes That is, if and only if Example 1 The vectors in are orthogonal

More information

Refined enumerations of alternating sign matrices. Ilse Fischer. Universität Wien

Refined enumerations of alternating sign matrices. Ilse Fischer. Universität Wien Refined enumerations of alternating sign matrices Ilse Fischer Universität Wien 1 Central question Which enumeration problems have a solution in terms of a closed formula that (for instance) only involves

More information

ON GALOIS REALIZATIONS OF THE 2-COVERABLE SYMMETRIC AND ALTERNATING GROUPS

ON GALOIS REALIZATIONS OF THE 2-COVERABLE SYMMETRIC AND ALTERNATING GROUPS ON GALOIS REALIZATIONS OF THE 2-COVERABLE SYMMETRIC AND ALTERNATING GROUPS DANIEL RABAYEV AND JACK SONN Abstract. Let f(x) be a monic polynomial in Z[x] with no rational roots but with roots in Q p for

More information

SHARP BOUNDS FOR THE SUM OF THE SQUARES OF THE DEGREES OF A GRAPH

SHARP BOUNDS FOR THE SUM OF THE SQUARES OF THE DEGREES OF A GRAPH 31 Kragujevac J. Math. 25 (2003) 31 49. SHARP BOUNDS FOR THE SUM OF THE SQUARES OF THE DEGREES OF A GRAPH Kinkar Ch. Das Department of Mathematics, Indian Institute of Technology, Kharagpur 721302, W.B.,

More information

Au = = = 3u. Aw = = = 2w. so the action of A on u and w is very easy to picture: it simply amounts to a stretching by 3 and 2, respectively.

Au = = = 3u. Aw = = = 2w. so the action of A on u and w is very easy to picture: it simply amounts to a stretching by 3 and 2, respectively. Chapter 7 Eigenvalues and Eigenvectors In this last chapter of our exploration of Linear Algebra we will revisit eigenvalues and eigenvectors of matrices, concepts that were already introduced in Geometry

More information

Chapter 4, Arithmetic in F [x] Polynomial arithmetic and the division algorithm.

Chapter 4, Arithmetic in F [x] Polynomial arithmetic and the division algorithm. Chapter 4, Arithmetic in F [x] Polynomial arithmetic and the division algorithm. We begin by defining the ring of polynomials with coefficients in a ring R. After some preliminary results, we specialize

More information

Continuity of the Perron Root

Continuity of the Perron Root Linear and Multilinear Algebra http://dx.doi.org/10.1080/03081087.2014.934233 ArXiv: 1407.7564 (http://arxiv.org/abs/1407.7564) Continuity of the Perron Root Carl D. Meyer Department of Mathematics, North

More information

On the representability of the bi-uniform matroid

On the representability of the bi-uniform matroid On the representability of the bi-uniform matroid Simeon Ball, Carles Padró, Zsuzsa Weiner and Chaoping Xing August 3, 2012 Abstract Every bi-uniform matroid is representable over all sufficiently large

More information

1 = (a 0 + b 0 α) 2 + + (a m 1 + b m 1 α) 2. for certain elements a 0,..., a m 1, b 0,..., b m 1 of F. Multiplying out, we obtain

1 = (a 0 + b 0 α) 2 + + (a m 1 + b m 1 α) 2. for certain elements a 0,..., a m 1, b 0,..., b m 1 of F. Multiplying out, we obtain Notes on real-closed fields These notes develop the algebraic background needed to understand the model theory of real-closed fields. To understand these notes, a standard graduate course in algebra is

More information

Prime Numbers and Irreducible Polynomials

Prime Numbers and Irreducible Polynomials Prime Numbers and Irreducible Polynomials M. Ram Murty The similarity between prime numbers and irreducible polynomials has been a dominant theme in the development of number theory and algebraic geometry.

More information

MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS

MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS Systems of Equations and Matrices Representation of a linear system The general system of m equations in n unknowns can be written a x + a 2 x 2 + + a n x n b a

More information

1 Introduction. Linear Programming. Questions. A general optimization problem is of the form: choose x to. max f(x) subject to x S. where.

1 Introduction. Linear Programming. Questions. A general optimization problem is of the form: choose x to. max f(x) subject to x S. where. Introduction Linear Programming Neil Laws TT 00 A general optimization problem is of the form: choose x to maximise f(x) subject to x S where x = (x,..., x n ) T, f : R n R is the objective function, S

More information

MOP 2007 Black Group Integer Polynomials Yufei Zhao. Integer Polynomials. June 29, 2007 Yufei Zhao yufeiz@mit.edu

MOP 2007 Black Group Integer Polynomials Yufei Zhao. Integer Polynomials. June 29, 2007 Yufei Zhao yufeiz@mit.edu Integer Polynomials June 9, 007 Yufei Zhao yufeiz@mit.edu We will use Z[x] to denote the ring of polynomials with integer coefficients. We begin by summarizing some of the common approaches used in dealing

More information

Factorization in Polynomial Rings

Factorization in Polynomial Rings Factorization in Polynomial Rings These notes are a summary of some of the important points on divisibility in polynomial rings from 17 and 18 of Gallian s Contemporary Abstract Algebra. Most of the important

More information

An example of a computable

An example of a computable An example of a computable absolutely normal number Verónica Becher Santiago Figueira Abstract The first example of an absolutely normal number was given by Sierpinski in 96, twenty years before the concept

More information

PROBLEM SET 6: POLYNOMIALS

PROBLEM SET 6: POLYNOMIALS PROBLEM SET 6: POLYNOMIALS 1. introduction In this problem set we will consider polynomials with coefficients in K, where K is the real numbers R, the complex numbers C, the rational numbers Q or any other

More information

How To Prove The Dirichlet Unit Theorem

How To Prove The Dirichlet Unit Theorem Chapter 6 The Dirichlet Unit Theorem As usual, we will be working in the ring B of algebraic integers of a number field L. Two factorizations of an element of B are regarded as essentially the same if

More information

The Characteristic Polynomial

The Characteristic Polynomial Physics 116A Winter 2011 The Characteristic Polynomial 1 Coefficients of the characteristic polynomial Consider the eigenvalue problem for an n n matrix A, A v = λ v, v 0 (1) The solution to this problem

More information

Lecture 4: Partitioned Matrices and Determinants

Lecture 4: Partitioned Matrices and Determinants Lecture 4: Partitioned Matrices and Determinants 1 Elementary row operations Recall the elementary operations on the rows of a matrix, equivalent to premultiplying by an elementary matrix E: (1) multiplying

More information

COMMUTATIVITY DEGREES OF WREATH PRODUCTS OF FINITE ABELIAN GROUPS

COMMUTATIVITY DEGREES OF WREATH PRODUCTS OF FINITE ABELIAN GROUPS Bull Austral Math Soc 77 (2008), 31 36 doi: 101017/S0004972708000038 COMMUTATIVITY DEGREES OF WREATH PRODUCTS OF FINITE ABELIAN GROUPS IGOR V EROVENKO and B SURY (Received 12 April 2007) Abstract We compute

More information

The Prime Numbers. Definition. A prime number is a positive integer with exactly two positive divisors.

The Prime Numbers. Definition. A prime number is a positive integer with exactly two positive divisors. The Prime Numbers Before starting our study of primes, we record the following important lemma. Recall that integers a, b are said to be relatively prime if gcd(a, b) = 1. Lemma (Euclid s Lemma). If gcd(a,

More information

Basic Probability Concepts

Basic Probability Concepts page 1 Chapter 1 Basic Probability Concepts 1.1 Sample and Event Spaces 1.1.1 Sample Space A probabilistic (or statistical) experiment has the following characteristics: (a) the set of all possible outcomes

More information

Module MA3411: Abstract Algebra Galois Theory Appendix Michaelmas Term 2013

Module MA3411: Abstract Algebra Galois Theory Appendix Michaelmas Term 2013 Module MA3411: Abstract Algebra Galois Theory Appendix Michaelmas Term 2013 D. R. Wilkins Copyright c David R. Wilkins 1997 2013 Contents A Cyclotomic Polynomials 79 A.1 Minimum Polynomials of Roots of

More information

MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS. + + x 2. x n. a 11 a 12 a 1n b 1 a 21 a 22 a 2n b 2 a 31 a 32 a 3n b 3. a m1 a m2 a mn b m

MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS. + + x 2. x n. a 11 a 12 a 1n b 1 a 21 a 22 a 2n b 2 a 31 a 32 a 3n b 3. a m1 a m2 a mn b m MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS 1. SYSTEMS OF EQUATIONS AND MATRICES 1.1. Representation of a linear system. The general system of m equations in n unknowns can be written a 11 x 1 + a 12 x 2 +

More information

LINEAR ALGEBRA W W L CHEN

LINEAR ALGEBRA W W L CHEN LINEAR ALGEBRA W W L CHEN c W W L Chen, 1997, 2008 This chapter is available free to all individuals, on understanding that it is not to be used for financial gain, and may be downloaded and/or photocopied,

More information

Chapter 3. Distribution Problems. 3.1 The idea of a distribution. 3.1.1 The twenty-fold way

Chapter 3. Distribution Problems. 3.1 The idea of a distribution. 3.1.1 The twenty-fold way Chapter 3 Distribution Problems 3.1 The idea of a distribution Many of the problems we solved in Chapter 1 may be thought of as problems of distributing objects (such as pieces of fruit or ping-pong balls)

More information

BANACH AND HILBERT SPACE REVIEW

BANACH AND HILBERT SPACE REVIEW BANACH AND HILBET SPACE EVIEW CHISTOPHE HEIL These notes will briefly review some basic concepts related to the theory of Banach and Hilbert spaces. We are not trying to give a complete development, but

More information

Arithmetic complexity in algebraic extensions

Arithmetic complexity in algebraic extensions Arithmetic complexity in algebraic extensions Pavel Hrubeš Amir Yehudayoff Abstract Given a polynomial f with coefficients from a field F, is it easier to compute f over an extension R of F than over F?

More information

GROUPS WITH TWO EXTREME CHARACTER DEGREES AND THEIR NORMAL SUBGROUPS

GROUPS WITH TWO EXTREME CHARACTER DEGREES AND THEIR NORMAL SUBGROUPS GROUPS WITH TWO EXTREME CHARACTER DEGREES AND THEIR NORMAL SUBGROUPS GUSTAVO A. FERNÁNDEZ-ALCOBER AND ALEXANDER MORETÓ Abstract. We study the finite groups G for which the set cd(g) of irreducible complex

More information

CS 103X: Discrete Structures Homework Assignment 3 Solutions

CS 103X: Discrete Structures Homework Assignment 3 Solutions CS 103X: Discrete Structures Homework Assignment 3 s Exercise 1 (20 points). On well-ordering and induction: (a) Prove the induction principle from the well-ordering principle. (b) Prove the well-ordering

More information

The Ideal Class Group

The Ideal Class Group Chapter 5 The Ideal Class Group We will use Minkowski theory, which belongs to the general area of geometry of numbers, to gain insight into the ideal class group of a number field. We have already mentioned

More information

a 1 x + a 0 =0. (3) ax 2 + bx + c =0. (4)

a 1 x + a 0 =0. (3) ax 2 + bx + c =0. (4) ROOTS OF POLYNOMIAL EQUATIONS In this unit we discuss polynomial equations. A polynomial in x of degree n, where n 0 is an integer, is an expression of the form P n (x) =a n x n + a n 1 x n 1 + + a 1 x

More information

The cyclotomic polynomials

The cyclotomic polynomials The cyclotomic polynomials Notes by G.J.O. Jameson 1. The definition and general results We use the notation e(t) = e 2πit. Note that e(n) = 1 for integers n, e(s + t) = e(s)e(t) for all s, t. e( 1 ) =

More information

A characterization of trace zero symmetric nonnegative 5x5 matrices

A characterization of trace zero symmetric nonnegative 5x5 matrices A characterization of trace zero symmetric nonnegative 5x5 matrices Oren Spector June 1, 009 Abstract The problem of determining necessary and sufficient conditions for a set of real numbers to be the

More information

Die ganzen zahlen hat Gott gemacht

Die ganzen zahlen hat Gott gemacht Die ganzen zahlen hat Gott gemacht Polynomials with integer values B.Sury A quote attributed to the famous mathematician L.Kronecker is Die Ganzen Zahlen hat Gott gemacht, alles andere ist Menschenwerk.

More information

8.1 Examples, definitions, and basic properties

8.1 Examples, definitions, and basic properties 8 De Rham cohomology Last updated: May 21, 211. 8.1 Examples, definitions, and basic properties A k-form ω Ω k (M) is closed if dω =. It is exact if there is a (k 1)-form σ Ω k 1 (M) such that dσ = ω.

More information

Algebraic and Transcendental Numbers

Algebraic and Transcendental Numbers Pondicherry University July 2000 Algebraic and Transcendental Numbers Stéphane Fischler This text is meant to be an introduction to algebraic and transcendental numbers. For a detailed (though elementary)

More information

DETERMINANTS IN THE KRONECKER PRODUCT OF MATRICES: THE INCIDENCE MATRIX OF A COMPLETE GRAPH

DETERMINANTS IN THE KRONECKER PRODUCT OF MATRICES: THE INCIDENCE MATRIX OF A COMPLETE GRAPH DETERMINANTS IN THE KRONECKER PRODUCT OF MATRICES: THE INCIDENCE MATRIX OF A COMPLETE GRAPH CHRISTOPHER RH HANUSA AND THOMAS ZASLAVSKY Abstract We investigate the least common multiple of all subdeterminants,

More information

WHERE DOES THE 10% CONDITION COME FROM?

WHERE DOES THE 10% CONDITION COME FROM? 1 WHERE DOES THE 10% CONDITION COME FROM? The text has mentioned The 10% Condition (at least) twice so far: p. 407 Bernoulli trials must be independent. If that assumption is violated, it is still okay

More information

9.2 Summation Notation

9.2 Summation Notation 9. Summation Notation 66 9. Summation Notation In the previous section, we introduced sequences and now we shall present notation and theorems concerning the sum of terms of a sequence. We begin with a

More information

Chapter 7: Products and quotients

Chapter 7: Products and quotients Chapter 7: Products and quotients Matthew Macauley Department of Mathematical Sciences Clemson University http://www.math.clemson.edu/~macaule/ Math 42, Spring 24 M. Macauley (Clemson) Chapter 7: Products

More information

How To Understand The Theory Of Hyperreals

How To Understand The Theory Of Hyperreals Ultraproducts and Applications I Brent Cody Virginia Commonwealth University September 2, 2013 Outline Background of the Hyperreals Filters and Ultrafilters Construction of the Hyperreals The Transfer

More information

TOPICS IN ALGEBRAIC COMBINATORICS

TOPICS IN ALGEBRAIC COMBINATORICS TOPICS IN ALGEBRAIC COMBINATORICS Richard P. Stanley Version of 1 February 2013 4 CONTENTS Preface 3 Notation 6 Chapter 1 Walks in graphs 9 Chapter 2 Cubes and the Radon transform 21 Chapter 3 Random walks

More information

it is easy to see that α = a

it is easy to see that α = a 21. Polynomial rings Let us now turn out attention to determining the prime elements of a polynomial ring, where the coefficient ring is a field. We already know that such a polynomial ring is a UF. Therefore

More information

SECTION 10-2 Mathematical Induction

SECTION 10-2 Mathematical Induction 73 0 Sequences and Series 6. Approximate e 0. using the first five terms of the series. Compare this approximation with your calculator evaluation of e 0.. 6. Approximate e 0.5 using the first five terms

More information

1 VECTOR SPACES AND SUBSPACES

1 VECTOR SPACES AND SUBSPACES 1 VECTOR SPACES AND SUBSPACES What is a vector? Many are familiar with the concept of a vector as: Something which has magnitude and direction. an ordered pair or triple. a description for quantities such

More information

arxiv:math/0503204v1 [math.gr] 10 Mar 2005

arxiv:math/0503204v1 [math.gr] 10 Mar 2005 arxiv:math/0503204v1 [math.gr] 10 Mar 2005 Symmetric Groups and Expanders Martin Kassabov Abstract We construct an explicit generating sets F n and F n of the alternating and the symmetric groups, which

More information

FACTORING POLYNOMIALS IN THE RING OF FORMAL POWER SERIES OVER Z

FACTORING POLYNOMIALS IN THE RING OF FORMAL POWER SERIES OVER Z FACTORING POLYNOMIALS IN THE RING OF FORMAL POWER SERIES OVER Z DANIEL BIRMAJER, JUAN B GIL, AND MICHAEL WEINER Abstract We consider polynomials with integer coefficients and discuss their factorization

More information

COMBINATORIAL PROPERTIES OF THE HIGMAN-SIMS GRAPH. 1. Introduction

COMBINATORIAL PROPERTIES OF THE HIGMAN-SIMS GRAPH. 1. Introduction COMBINATORIAL PROPERTIES OF THE HIGMAN-SIMS GRAPH ZACHARY ABEL 1. Introduction In this survey we discuss properties of the Higman-Sims graph, which has 100 vertices, 1100 edges, and is 22 regular. In fact

More information

α = u v. In other words, Orthogonal Projection

α = u v. In other words, Orthogonal Projection Orthogonal Projection Given any nonzero vector v, it is possible to decompose an arbitrary vector u into a component that points in the direction of v and one that points in a direction orthogonal to v

More information

Every Positive Integer is the Sum of Four Squares! (and other exciting problems)

Every Positive Integer is the Sum of Four Squares! (and other exciting problems) Every Positive Integer is the Sum of Four Squares! (and other exciting problems) Sophex University of Texas at Austin October 18th, 00 Matilde N. Lalín 1. Lagrange s Theorem Theorem 1 Every positive integer

More information

Continued Fractions. Darren C. Collins

Continued Fractions. Darren C. Collins Continued Fractions Darren C Collins Abstract In this paper, we discuss continued fractions First, we discuss the definition and notation Second, we discuss the development of the subject throughout history

More information

On the largest prime factor of x 2 1

On the largest prime factor of x 2 1 On the largest prime factor of x 2 1 Florian Luca and Filip Najman Abstract In this paper, we find all integers x such that x 2 1 has only prime factors smaller than 100. This gives some interesting numerical

More information

Continued Fractions and the Euclidean Algorithm

Continued Fractions and the Euclidean Algorithm Continued Fractions and the Euclidean Algorithm Lecture notes prepared for MATH 326, Spring 997 Department of Mathematics and Statistics University at Albany William F Hammond Table of Contents Introduction

More information

FACTORING SPARSE POLYNOMIALS

FACTORING SPARSE POLYNOMIALS FACTORING SPARSE POLYNOMIALS Theorem 1 (Schinzel): Let r be a positive integer, and fix non-zero integers a 0,..., a r. Let F (x 1,..., x r ) = a r x r + + a 1 x 1 + a 0. Then there exist finite sets S

More information

Notes on Determinant

Notes on Determinant ENGG2012B Advanced Engineering Mathematics Notes on Determinant Lecturer: Kenneth Shum Lecture 9-18/02/2013 The determinant of a system of linear equations determines whether the solution is unique, without

More information

Solving Linear Systems, Continued and The Inverse of a Matrix

Solving Linear Systems, Continued and The Inverse of a Matrix , Continued and The of a Matrix Calculus III Summer 2013, Session II Monday, July 15, 2013 Agenda 1. The rank of a matrix 2. The inverse of a square matrix Gaussian Gaussian solves a linear system by reducing

More information

Mathematics for Computer Science/Software Engineering. Notes for the course MSM1F3 Dr. R. A. Wilson

Mathematics for Computer Science/Software Engineering. Notes for the course MSM1F3 Dr. R. A. Wilson Mathematics for Computer Science/Software Engineering Notes for the course MSM1F3 Dr. R. A. Wilson October 1996 Chapter 1 Logic Lecture no. 1. We introduce the concept of a proposition, which is a statement

More information

Factorization Algorithms for Polynomials over Finite Fields

Factorization Algorithms for Polynomials over Finite Fields Degree Project Factorization Algorithms for Polynomials over Finite Fields Sajid Hanif, Muhammad Imran 2011-05-03 Subject: Mathematics Level: Master Course code: 4MA11E Abstract Integer factorization is

More information

The Variance of Sample Variance for a Finite Population

The Variance of Sample Variance for a Finite Population The Variance of Sample Variance for a Finite Population Eungchun Cho November 11, 004 Key Words: variance of variance, variance estimator, sampling variance, randomization variance, moments Abstract The

More information

Systems of Linear Equations

Systems of Linear Equations Systems of Linear Equations Beifang Chen Systems of linear equations Linear systems A linear equation in variables x, x,, x n is an equation of the form a x + a x + + a n x n = b, where a, a,, a n and

More information

I. GROUPS: BASIC DEFINITIONS AND EXAMPLES

I. GROUPS: BASIC DEFINITIONS AND EXAMPLES I GROUPS: BASIC DEFINITIONS AND EXAMPLES Definition 1: An operation on a set G is a function : G G G Definition 2: A group is a set G which is equipped with an operation and a special element e G, called

More information

MATH10212 Linear Algebra. Systems of Linear Equations. Definition. An n-dimensional vector is a row or a column of n numbers (or letters): a 1.

MATH10212 Linear Algebra. Systems of Linear Equations. Definition. An n-dimensional vector is a row or a column of n numbers (or letters): a 1. MATH10212 Linear Algebra Textbook: D. Poole, Linear Algebra: A Modern Introduction. Thompson, 2006. ISBN 0-534-40596-7. Systems of Linear Equations Definition. An n-dimensional vector is a row or a column

More information

GROUP ALGEBRAS. ANDREI YAFAEV

GROUP ALGEBRAS. ANDREI YAFAEV GROUP ALGEBRAS. ANDREI YAFAEV We will associate a certain algebra to a finite group and prove that it is semisimple. Then we will apply Wedderburn s theory to its study. Definition 0.1. Let G be a finite

More information

Ideal Class Group and Units

Ideal Class Group and Units Chapter 4 Ideal Class Group and Units We are now interested in understanding two aspects of ring of integers of number fields: how principal they are (that is, what is the proportion of principal ideals

More information

17. Inner product spaces Definition 17.1. Let V be a real vector space. An inner product on V is a function

17. Inner product spaces Definition 17.1. Let V be a real vector space. An inner product on V is a function 17. Inner product spaces Definition 17.1. Let V be a real vector space. An inner product on V is a function, : V V R, which is symmetric, that is u, v = v, u. bilinear, that is linear (in both factors):

More information

Orthogonal Diagonalization of Symmetric Matrices

Orthogonal Diagonalization of Symmetric Matrices MATH10212 Linear Algebra Brief lecture notes 57 Gram Schmidt Process enables us to find an orthogonal basis of a subspace. Let u 1,..., u k be a basis of a subspace V of R n. We begin the process of finding

More information

4: EIGENVALUES, EIGENVECTORS, DIAGONALIZATION

4: EIGENVALUES, EIGENVECTORS, DIAGONALIZATION 4: EIGENVALUES, EIGENVECTORS, DIAGONALIZATION STEVEN HEILMAN Contents 1. Review 1 2. Diagonal Matrices 1 3. Eigenvectors and Eigenvalues 2 4. Characteristic Polynomial 4 5. Diagonalizability 6 6. Appendix:

More information

Groups with the same orders and large character degrees as PGL(2, 9) 1. Introduction and preliminary results

Groups with the same orders and large character degrees as PGL(2, 9) 1. Introduction and preliminary results Quasigroups and Related Systems 21 (2013), 239 243 Groups with the same orders and large character degrees as PGL(2, 9) Behrooz Khosravi Abstract. An interesting class of problems in character theory arises

More information

1 Norms and Vector Spaces

1 Norms and Vector Spaces 008.10.07.01 1 Norms and Vector Spaces Suppose we have a complex vector space V. A norm is a function f : V R which satisfies (i) f(x) 0 for all x V (ii) f(x + y) f(x) + f(y) for all x,y V (iii) f(λx)

More information

Finding and counting given length cycles

Finding and counting given length cycles Finding and counting given length cycles Noga Alon Raphael Yuster Uri Zwick Abstract We present an assortment of methods for finding and counting simple cycles of a given length in directed and undirected

More information

Degree Bounds for Gröbner Bases in Algebras of Solvable Type

Degree Bounds for Gröbner Bases in Algebras of Solvable Type Degree Bounds for Gröbner Bases in Algebras of Solvable Type Matthias Aschenbrenner University of California, Los Angeles (joint with Anton Leykin) Algebras of Solvable Type... introduced by Kandri-Rody

More information

Applied Linear Algebra I Review page 1

Applied Linear Algebra I Review page 1 Applied Linear Algebra Review 1 I. Determinants A. Definition of a determinant 1. Using sum a. Permutations i. Sign of a permutation ii. Cycle 2. Uniqueness of the determinant function in terms of properties

More information

Partial Fractions. Combining fractions over a common denominator is a familiar operation from algebra:

Partial Fractions. Combining fractions over a common denominator is a familiar operation from algebra: Partial Fractions Combining fractions over a common denominator is a familiar operation from algebra: From the standpoint of integration, the left side of Equation 1 would be much easier to work with than

More information

MATH 289 PROBLEM SET 4: NUMBER THEORY

MATH 289 PROBLEM SET 4: NUMBER THEORY MATH 289 PROBLEM SET 4: NUMBER THEORY 1. The greatest common divisor If d and n are integers, then we say that d divides n if and only if there exists an integer q such that n = qd. Notice that if d divides

More information

POLYNOMIAL RINGS AND UNIQUE FACTORIZATION DOMAINS

POLYNOMIAL RINGS AND UNIQUE FACTORIZATION DOMAINS POLYNOMIAL RINGS AND UNIQUE FACTORIZATION DOMAINS RUSS WOODROOFE 1. Unique Factorization Domains Throughout the following, we think of R as sitting inside R[x] as the constant polynomials (of degree 0).

More information