Tesler Matrices. Drew Armstrong. Bruce Saganfest Gainesville, March University of Miami armstrong

Similar documents
Hyperplane Arrangements & Diagonal Harmonics

arxiv: v2 [math.co] 21 Mar 2017

arxiv: v1 [math.co] 26 Oct 2016

Noncrossing Parking Functions

Rational Parking Functions

Generalized Tesler matrices, virtual Hilbert series, and Macdonald polynomial operators

RCC. Drew Armstrong. FPSAC 2017, Queen Mary, London. University of Miami armstrong

The (q, t)-catalan Numbers and the Space of Diagonal Harmonics. James Haglund. University of Pennsylvania

Rational Catalan Combinatorics

Catalan numbers, parking functions, and invariant theory

The volume of the caracol polytope

Rational Catalan Combinatorics: Intro

Ehrhart Positivity. Federico Castillo. December 15, University of California, Davis. Joint work with Fu Liu. Ehrhart Positivity

Torus Knots and q, t-catalan Numbers

LARGE SCHRÖDER PATHS BY TYPES AND SYMMETRIC FUNCTIONS

A Survey of Parking Functions

An injection from standard fillings to parking functions

Combinatorics of non-associative binary operations

Macdonald polynomials and Hilbert schemes. Mark Haiman U.C. Berkeley LECTURE

Rational Catalan combinatorics

Partitions, rooks, and symmetric functions in noncommuting variables

EVALUATION OF A FAMILY OF BINOMIAL DETERMINANTS

Combinatorics of colored factorizations, flow polytopes and of matrices over finite fields. Alejandro Henry Morales

arxiv: v2 [math.co] 21 Apr 2015

A Proof of the q, t-square Conjecture

CSL361 Problem set 4: Basic linear algebra

A proof of the Square Paths Conjecture

Some Catalan Musings p. 1. Some Catalan Musings. Richard P. Stanley

A proof of the Square Paths Conjecture

Affine permutations and rational slope parking functions

ENUMERATION OF TREES AND ONE AMAZING REPRESENTATION OF THE SYMMETRIC GROUP. Igor Pak Harvard University

Some Catalan Musings p. 1. Some Catalan Musings. Richard P. Stanley

Patterns in Standard Young Tableaux

Combinatorics of Tesler matrices in the theory of parking functions and diagonal harmonics

Knot invariants, Hilbert schemes and Macdonald polynomials (joint with A. Neguț, J. Rasmussen)

Background on Chevalley Groups Constructed from a Root System

arxiv: v2 [math.co] 10 Sep 2010

q,t-fuß Catalan numbers for finite reflection groups

ACI-matrices all of whose completions have the same rank

CAYLEY COMPOSITIONS, PARTITIONS, POLYTOPES, AND GEOMETRIC BIJECTIONS

Catalan functions and k-schur positivity

Adjoint Representations of the Symmetric Group

Combinatorics of Tesler matrices in the theory of parking functions and diagonal harmonics

Combinatorics for algebraic geometers

Fall 2014 Commutative Algebra Final Exam (Solution)

Auslander s Theorem for permutation actions on noncommutative algebras

Extreme diagonally and antidiagonally symmetric alternating sign matrices of odd order

Tutte Polynomials of Bracelets. Norman Biggs

arxiv:math/ v1 [math.co] 13 Jul 2005

Self-dual interval orders and row-fishburn matrices

Knots-quivers correspondence, lattice paths, and rational knots

arxiv: v1 [math.rt] 5 Aug 2016

Primitive ideals and automorphisms of quantum matrices

Higher Spin Alternating Sign Matrices

An explicit formula for ndinv, a new statistic for two-shuffle parking functions

PARKING FUNCTIONS. Richard P. Stanley Department of Mathematics M.I.T Cambridge, MA

This manuscript has been accepted for publication in Linear Algebra and Its Applications. The manuscript will undergo copyediting, typesetting, and

Linear Algebra and Matrix Inversion

Citation Osaka Journal of Mathematics. 43(2)

The Catalan matroid.

COMMUTATIVE ALGEBRA OF n POINTS IN THE PLANE

FREE PROBABILITY THEORY

Tropicalizations of Positive Parts of Cluster Algebras The conjectures of Fock and Goncharov David Speyer

MATRICES. a m,1 a m,n A =

ON IDEALS OF TRIANGULAR MATRIX RINGS

Counting chains in noncrossing partition lattices

Problems in Linear Algebra and Representation Theory

Chapter 5: Integer Compositions and Partitions and Set Partitions

1 Linear transformations; the basics

Linear Algebra M1 - FIB. Contents: 5. Matrices, systems of linear equations and determinants 6. Vector space 7. Linear maps 8.

The (m, n)-rational q, t-catalan polynomials for m = 3 and their q, t-symmetry

Refined Cauchy/Littlewood identities and partition functions of the six-vertex model

ELE/MCE 503 Linear Algebra Facts Fall 2018

MATH PRACTICE EXAM 1 SOLUTIONS

On the singular elements of a semisimple Lie algebra and the generalized Amitsur-Levitski Theorem

Outline. Some Reflection Group Numerology. Root Systems and Reflection Groups. Example: Symmetries of a triangle. Paul Renteln

MATH 167: APPLIED LINEAR ALGEBRA Least-Squares

Chapter 5: Integer Compositions and Partitions and Set Partitions

YOUNG TABLEAUX AND THE REPRESENTATIONS OF THE SYMMETRIC GROUP

Combinatorics of the Cell Decomposition of Affine Springer Fibers

A Recursive Relation for Weighted Motzkin Sequences

Diagonal Invariants of the symmetric group and products of linear forms

Results and conjectures on the number of standard strong marked tableaux

L(C G (x) 0 ) c g (x). Proof. Recall C G (x) = {g G xgx 1 = g} and c g (x) = {X g Ad xx = X}. In general, it is obvious that

JOSEPH ALFANO* Department of Mathematics, Assumption s y i P (x; y) = 0 for all r; s 0 (with r + s > 0). Computer explorations by

Special types of matrices

Izergin-Korepin determinant reloaded

A Schröder Generalization of Haglund s Statistic on Catalan Paths

Enumeration of Concave Integer Partitions

Homomesy of Alignments in Perfect Matchings

In honor of Manfred Schocker ( ). The authors would also like to acknowledge the contributions that he made to this paper.

Constructing Critical Indecomposable Codes

Fix(g). Orb(x) i=1. O i G. i=1. O i. i=1 x O i. = n G

Involutions of the Symmetric Group and Congruence B-Orbits (Extended Abstract)

Coxeter-Knuth Classes and a Signed Little Bijection

NOTES ON FOCK SPACE PETER TINGLEY

Notes on the Matrix-Tree theorem and Cayley s tree enumerator

4. Killing form, root space inner product, and commutation relations * version 1.5 *

Unit 2, Section 3: Linear Combinations, Spanning, and Linear Independence Linear Combinations, Spanning, and Linear Independence

Homework 5 M 373K Mark Lindberg and Travis Schedler

Transcription:

Tesler Matrices Drew Armstrong University of Miami www.math.miami.edu/ armstrong Bruce Saganfest Gainesville, March 2014

Plan 1. Tesler Matrices (with A. Garsia, J. Haglund, B. Rhoades and B. Sagan) 2. Tesler Polytopes (with A. Morales and K. Mészáros)

Plan 1. Tesler Matrices (with A. Garsia, J. Haglund, B. Rhoades and B. Sagan) 2. Tesler Polytopes (with A. Morales and K. Mészáros)

What is a Tesler Matrix?

What is a Tesler Matrix? Definition We say that A = (a i,j ) Mat n (Z) is a Tesler matrix if: A is upper triangular. For all 1 k n we have (a k,k + a k,k+1 + + a k,n ) (a 1,k + a 2,k + a k 1,k ) = 1. Picture: k k 0 0 0 + + + + 0 0 0 0 0 0 0 0 0 0 0 0 = 1 for all k.

What is a Tesler Matrix? Definition We say that A = (a i,j ) Mat n (Z) is a Tesler matrix if: A is upper triangular. For all 1 k n we have (a k,k + a k,k+1 + + a k,n ) (a 1,k + a 2,k + a k 1,k ) = 1. Picture: k k 0 0 0 + + + + 0 0 0 0 0 0 0 0 0 0 0 0 = 1 for all k.

What is a Tesler Matrix? Definition We say that A = (a i,j ) Mat n (Z) is a Tesler matrix if: A is upper triangular. For all 1 k n we have (a k,k + a k,k+1 + + a k,n ) (a 1,k + a 2,k + a k 1,k ) = 1. Picture: k k 0 0 0 + + + + 0 0 0 0 0 0 0 0 0 0 0 0 = 1 for all k.

What is a Tesler Matrix? Definition We say that A = (a i,j ) Mat n (Z) is a Tesler matrix if: A is upper triangular. For all 1 k n we have (a k,k + a k,k+1 + + a k,n ) (a 1,k + a 2,k + a k 1,k ) = 1. Picture: k k 0 0 0 + + + + 0 0 0 0 0 0 0 0 0 0 0 0 = 1 for all k.

What is a Tesler Matrix? Example 0 0 1 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 2 0 0 0 0 4 = 1

What is a Tesler Matrix? Example 0 0 1 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 2 0 0 0 0 4 = 1

What is a Tesler Matrix? Example 0 0 1 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 2 0 0 0 0 4 = 1

What is a Tesler Matrix? Example 0 0 1 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 2 0 0 0 0 4 = 1

What is a Tesler Matrix? Example 0 0 1 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 2 0 0 0 0 4 = 1

What is a Tesler Matrix? Recursion Let Tesler(n) be the set of n n Tesler matrices. There is a natural map Tesler(n) Tesler(n 1) defined by adding a k,n to a k,k then deleting the n-th row and column: 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 2 0 1 0 0 0 0 1 1 0 0 0 2 0 0 0 0 4 This can be used to efficiently generate Tesler(n).

What is a Tesler Matrix? Recursion Let Tesler(n) be the set of n n Tesler matrices. There is a natural map Tesler(n) Tesler(n 1) defined by adding a k,n to a k,k then deleting the n-th row and column: 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 2 0 1 0 0 0 0 1 1 0 0 0 2 0 0 0 0 4 This can be used to efficiently generate Tesler(n).

What is a Tesler Matrix? Recursion Let Tesler(n) be the set of n n Tesler matrices. There is a natural map Tesler(n) Tesler(n 1) defined by adding a k,n to a k,k then deleting the n-th row and column: 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 2 0 1 0 0 0 0 1 1 0 0 0 2 0 0 0 0 4 This can be used to efficiently generate Tesler(n).

What is a Tesler Matrix? Recursion Let Tesler(n) be the set of n n Tesler matrices. There is a natural map Tesler(n) Tesler(n 1) defined by adding a k,n to a k,k then deleting the n-th row and column: 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 2 0 1 0 0 0 0 1 1 0 0 0 2 0 0 0 0 4 This can be used to efficiently generate Tesler(n).

What is a Tesler Matrix? Example Tesler(3) 1 0 0 1 0 1 0 0 1 1 0 2 1 0 0 0 1 2 0 0 1 0 1 3 0 1 0 2 0 1 0 1 0 0 2 3 0 1 0 1 1 2 Tesler(2) ( 1 0 1 ) ( 0 1 2 )

What is a Tesler Matrix? History The sequence Tes(n) := #Tesler(n) is A008608 in the OEIS: 1, 2, 7, 40, 357, 4820, 96030, 2766572, 113300265,... Entered by Glenn Tesler (1996), then forgotten for 15 years. Rediscovered by Jim Haglund (2011), who named them Tesler matrices Now everybody cares. Why?

What is a Tesler Matrix? History The sequence Tes(n) := #Tesler(n) is A008608 in the OEIS: 1, 2, 7, 40, 357, 4820, 96030, 2766572, 113300265,... Entered by Glenn Tesler (1996), then forgotten for 15 years. Rediscovered by Jim Haglund (2011), who named them Tesler matrices Now everybody cares. Why?

What is a Tesler Matrix? History The sequence Tes(n) := #Tesler(n) is A008608 in the OEIS: 1, 2, 7, 40, 357, 4820, 96030, 2766572, 113300265,... Entered by Glenn Tesler (1996), then forgotten for 15 years. Rediscovered by Jim Haglund (2011), who named them Tesler matrices Now everybody cares. Why?

What is a Tesler Matrix? History The sequence Tes(n) := #Tesler(n) is A008608 in the OEIS: 1, 2, 7, 40, 357, 4820, 96030, 2766572, 113300265,... Entered by Glenn Tesler (1996), then forgotten for 15 years. Rediscovered by Jim Haglund (2011), who named them Tesler matrices Now everybody cares. Why?

Why Do We Care? Coinvariants The symmetric group S n acts on S := C[x 1,..., x n ]. (Newton) The subalgebra of symmetric polynomials is isomorphic to a polynomial algebra S Sn C[p 1,..., p n ], where p k = n i=1 x i k are the power sum polynomials. (Chevalley) The coinvariant algebra R := S/(p 1,..., p n ) is isomorphic to the regular representation (R CS n ) and its Hilbert series is given by the q-factorial: Hilb(n; q) := i dim(r i ) q i = [n] q!

Why Do We Care? Coinvariants The symmetric group S n acts on S := C[x 1,..., x n ]. (Newton) The subalgebra of symmetric polynomials is isomorphic to a polynomial algebra S Sn C[p 1,..., p n ], where p k = n i=1 x i k are the power sum polynomials. (Chevalley) The coinvariant algebra R := S/(p 1,..., p n ) is isomorphic to the regular representation (R CS n ) and its Hilbert series is given by the q-factorial: Hilb(n; q) := i dim(r i ) q i = [n] q!

Why Do We Care? Coinvariants The symmetric group S n acts on S := C[x 1,..., x n ]. (Newton) The subalgebra of symmetric polynomials is isomorphic to a polynomial algebra S Sn C[p 1,..., p n ], where p k = n i=1 x i k are the power sum polynomials. (Chevalley) The coinvariant algebra R := S/(p 1,..., p n ) is isomorphic to the regular representation (R CS n ) and its Hilbert series is given by the q-factorial: Hilb(n; q) := i dim(r i ) q i = [n] q!

Why Do We Care? Coinvariants The symmetric group S n acts diagonally on the double algebra DS := C[x 1,..., x n, y 1,..., y n ]. (Weyl) The subalgebra of diagonal invariants is generated by the polarized power sums n p k,l = xi k yi l for k + l > 0, i=1 but these are not algebraically independent. (Haiman) The algebra of diagonal coinvariants DR := DS/(p k,l : k + l > 0) has dimension (n + 1) n 1. The proof is hard :(

Why Do We Care? Coinvariants The symmetric group S n acts diagonally on the double algebra DS := C[x 1,..., x n, y 1,..., y n ]. (Weyl) The subalgebra of diagonal invariants is generated by the polarized power sums n p k,l = xi k yi l for k + l > 0, i=1 but these are not algebraically independent. (Haiman) The algebra of diagonal coinvariants DR := DS/(p k,l : k + l > 0) has dimension (n + 1) n 1. The proof is hard :(

Why Do We Care? Coinvariants The symmetric group S n acts diagonally on the double algebra DS := C[x 1,..., x n, y 1,..., y n ]. (Weyl) The subalgebra of diagonal invariants is generated by the polarized power sums n p k,l = xi k yi l for k + l > 0, i=1 but these are not algebraically independent. (Haiman) The algebra of diagonal coinvariants DR := DS/(p k,l : k + l > 0) has dimension (n + 1) n 1. The proof is hard :(

Why Do We Care? Coinvariants The diagonal Hilbert series Hilb(n; q, t) := i,j dim(dr i,j ) q i t j satisfies: Hilb(n; q, t) = Hilb(n; t, q) Hilb(n; q, 0) = Hilb(n; q) = [n] q! Hilb(n; 1, 1) = (n + 1) n 1 Hilb(n; q, q 1 ) = ([n + 1] q ) n 1 /q (n 2) But after many years it is still a big mystery :(

Why Do We Care? Coinvariants The diagonal Hilbert series Hilb(n; q, t) := i,j dim(dr i,j ) q i t j satisfies: Hilb(n; q, t) = Hilb(n; t, q) Hilb(n; q, 0) = Hilb(n; q) = [n] q! Hilb(n; 1, 1) = (n + 1) n 1 Hilb(n; q, q 1 ) = ([n + 1] q ) n 1 /q (n 2) But after many years it is still a big mystery :(

Why Do We Care? Coinvariants The diagonal Hilbert series Hilb(n; q, t) := i,j dim(dr i,j ) q i t j satisfies: Hilb(n; q, t) = Hilb(n; t, q) Hilb(n; q, 0) = Hilb(n; q) = [n] q! Hilb(n; 1, 1) = (n + 1) n 1 Hilb(n; q, q 1 ) = ([n + 1] q ) n 1 /q (n 2) But after many years it is still a big mystery :(

Why Do We Care? Coinvariants The diagonal Hilbert series Hilb(n; q, t) := i,j dim(dr i,j ) q i t j satisfies: Hilb(n; q, t) = Hilb(n; t, q) Hilb(n; q, 0) = Hilb(n; q) = [n] q! Hilb(n; 1, 1) = (n + 1) n 1 Hilb(n; q, q 1 ) = ([n + 1] q ) n 1 /q (n 2) But after many years it is still a big mystery :(

Why Do We Care? Coinvariants The diagonal Hilbert series Hilb(n; q, t) := i,j dim(dr i,j ) q i t j satisfies: Hilb(n; q, t) = Hilb(n; t, q) Hilb(n; q, 0) = Hilb(n; q) = [n] q! Hilb(n; 1, 1) = (n + 1) n 1 Hilb(n; q, q 1 ) = ([n + 1] q ) n 1 /q (n 2) But after many years it is still a big mystery :(

Why Do We Care? Coinvariants The diagonal Hilbert series Hilb(n; q, t) := i,j dim(dr i,j ) q i t j satisfies: Hilb(n; q, t) = Hilb(n; t, q) Hilb(n; q, 0) = Hilb(n; q) = [n] q! Hilb(n; 1, 1) = (n + 1) n 1 Hilb(n; q, q 1 ) = ([n + 1] q ) n 1 /q (n 2) But after many years it is still a big mystery :(

Here s Why We Care. Haglund s Theorem (2011) Given a Tesler matrix A Tesler(n), let Pos(A) be the set of positive entries of A and let pos(a) := #Pos(A). We define the weight by weight(a) := ( M) pos(a) n [a i,j ] q,t Z[q, t], a i,j Pos(A) where M = (1 q)(1 t) and [k] q,t = (q k t k )/(q t). 0 0 1 0 0 0 1 0 0 0 weight 0 0 0 1 1 0 0 0 0 2 = ( (1 q)(1 t))1 [1] 4 q,t[2] q,t [4] q,t 0 0 0 0 4

Here s Why We Care. Haglund s Theorem (2011) Given a Tesler matrix A Tesler(n), let Pos(A) be the set of positive entries of A and let pos(a) := #Pos(A). We define the weight by weight(a) := ( M) pos(a) n [a i,j ] q,t Z[q, t], a i,j Pos(A) where M = (1 q)(1 t) and [k] q,t = (q k t k )/(q t). 0 0 1 0 0 0 1 0 0 0 weight 0 0 0 1 1 0 0 0 0 2 = ( (1 q)(1 t))1 [1] 4 q,t[2] q,t [4] q,t 0 0 0 0 4

Here s Why We Care. Haglund s Theorem (2011) Given a Tesler matrix A Tesler(n), let Pos(A) be the set of positive entries of A and let pos(a) := #Pos(A). We define the weight by weight(a) := ( M) pos(a) n [a i,j ] q,t Z[q, t], a i,j Pos(A) where M = (1 q)(1 t) and [k] q,t = (q k t k )/(q t). 0 0 1 0 0 0 1 0 0 0 weight 0 0 0 1 1 0 0 0 0 2 = ( (1 q)(1 t))1 [1] 4 q,t[2] q,t [4] q,t 0 0 0 0 4

Here s Why We Care. Haglund s Theorem (2011) Given a Tesler matrix A Tesler(n), let Pos(A) be the set of positive entries of A and let pos(a) := #Pos(A). We define the weight by weight(a) := ( M) pos(a) n [a i,j ] q,t Z[q, t], a i,j Pos(A) where M = (1 q)(1 t) and [k] q,t = (q k t k )/(q t). 0 0 1 0 0 0 1 0 0 0 weight 0 0 0 1 1 0 0 0 0 2 = (1 q)(1 t)(q + t)2 (q 2 + t 2 ) 0 0 0 0 4

Here s Why We Care. Haglund s Theorem (2011) If we define Tes(n; q, t) := weight(a), A Tesler(n) then it is true that Tes(n; q, t) = Hilb(n; q, t). In particular, we have Tes(n; q, t) N[q, t], which is not obvious.

Here s Why We Care. Haglund s Theorem (2011) If we define Tes(n; q, t) := weight(a), A Tesler(n) then it is true that Tes(n; q, t) = Hilb(n; q, t). In particular, we have Tes(n; q, t) N[q, t], which is not obvious.

Here s Why We Care. Haglund s Theorem (2011) If we define Tes(n; q, t) := weight(a), A Tesler(n) then it is true that Tes(n; q, t) = Hilb(n; q, t). In particular, we have Tes(n; q, t) N[q, t], which is not obvious.

Here s Why We Care. Example (n = 3) 1 1 1 1 1 2 1 2 1 1 1 1 2 1 2 3 1 1 2 1 1 3 1 q+t q+t M(q+t) (q+t)(q 2 +qt+t 2 ) q+t q 2 +qt+t 2 1 1 1 1 1 1 1 1 2 1 1 1 1 2 2 1 1 1 1 1 1 Tes(3; q, t) = 1 2 2 1 2 3 1 2 1 1

Here s Why We Care. A New Hope Maybe Haglund s Theorem can be used to prove the famous Shuffle Conjecture of HHLRU (2005). To do this we must relate Tesler matrices to parking functions.

Here s Why We Care. A New Hope Maybe Haglund s Theorem can be used to prove the famous Shuffle Conjecture of HHLRU (2005). To do this we must relate Tesler matrices to parking functions.

Refinement of Haglund s Theorem Definition Given a Tesler matrix A Tesler(n) consider the lowest lattice path above the positive entries. Let Touch(A) {1, 2,..., n 1} be the set of positions where this path touches the diagonal. Examples:

Refinement of Haglund s Theorem Definition Given a Tesler matrix A Tesler(n) consider the lowest lattice path above the positive entries. Let Touch(A) {1, 2,..., n 1} be the set of positions where this path touches the diagonal. Examples: Touch 0 0 1 0 0 1 0 0 0 0 1 1 0 2 4 = { }

Refinement of Haglund s Theorem Definition Given a Tesler matrix A Tesler(n) consider the lowest lattice path above the positive entries. Let Touch(A) {1, 2,..., n 1} be the set of positions where this path touches the diagonal. Examples: Touch 0 1 0 0 0 2 0 0 0 1 0 0 0 1 2 = {2, 3}

Refinement of Haglund s Theorem Definition We say A Tesler(n) is connected if Touch(A) = { }. Let CTesler(n) := {A Tesler(n) : A is connected } and define CTes(n; q, t) := weight(a). A CTesler(n)

Refinement of Haglund s Theorem Example Here are the first few values of CTes(n; q, t): n = 1 n = 2 n = 3 n = 4 4 5 3 1 [ ] 2 1 9 9 4 1 [ ] 1 1 3 1 9 9 4 1 1 2 1 4 9 4 1 1 5 4 1 3 1 1

Refinement of Haglund s Theorem Theorem It is sufficient to study connected Tesler matrices: Tes(n; q, t) = {s 1< <s k } [n 1] k 1 CTes(s i+1 s i ; q, t). i=1

Refinement of Haglund s Theorem Example Tes(3; q, t) = CTes(1; q, t) 3 + 2 CTes(1; q, t)tes(2; q, t) + CTes(3; q, t) 1 2 2 1 2 3 1 2 1 = [ 1 ] 3 [ ] [ ] 1 + 2 1 1 1 + 2 1 1 2 1 3 1

Refinement of Haglund s Theorem Conjectures and Problems CTes(n; q, t) N[q, t] for all n N. CTes(n; 1, 1) = # connected parking functions CPF(n) in the sense of Novelli and Thibon (2007). This is A122708 in OEIS: 1, 2, 11, 92, 1014, 13795, 223061, 4180785, 89191196,... Find statistics qstat, tstat : CPF(n) N such that CTes(n; q, t) = Prove the Shuffle Conjecture. cpf CPF(n) q qstat(cpf ) t tstat(cpf ).

Refinement of Haglund s Theorem Conjectures and Problems CTes(n; q, t) N[q, t] for all n N. CTes(n; 1, 1) = # connected parking functions CPF(n) in the sense of Novelli and Thibon (2007). This is A122708 in OEIS: 1, 2, 11, 92, 1014, 13795, 223061, 4180785, 89191196,... Find statistics qstat, tstat : CPF(n) N such that CTes(n; q, t) = Prove the Shuffle Conjecture. cpf CPF(n) q qstat(cpf ) t tstat(cpf ).

Refinement of Haglund s Theorem Conjectures and Problems CTes(n; q, t) N[q, t] for all n N. CTes(n; 1, 1) = # connected parking functions CPF(n) in the sense of Novelli and Thibon (2007). This is A122708 in OEIS: 1, 2, 11, 92, 1014, 13795, 223061, 4180785, 89191196,... Find statistics qstat, tstat : CPF(n) N such that CTes(n; q, t) = Prove the Shuffle Conjecture. cpf CPF(n) q qstat(cpf ) t tstat(cpf ).

Refinement of Haglund s Theorem Conjectures and Problems CTes(n; q, t) N[q, t] for all n N. CTes(n; 1, 1) = # connected parking functions CPF(n) in the sense of Novelli and Thibon (2007). This is A122708 in OEIS: 1, 2, 11, 92, 1014, 13795, 223061, 4180785, 89191196,... Find statistics qstat, tstat : CPF(n) N such that CTes(n; q, t) = Prove the Shuffle Conjecture. cpf CPF(n) q qstat(cpf ) t tstat(cpf ).

Pause

Generalized Tesler Matrices Definition Given any positive integer vector r = (r 1,..., r n ) N n we say that A = (a i,j ) Mat n (Z) is an r-tesler matrix if: A is upper triangular. For all 1 k n we have (a k,k + a k,k+1 + + a k,n ) (a 1,k + a 2,k + a k 1,k ) = r k. Picture: The k-th hook sum equals r k k 0 k 0 0 + + + + 0 0 0 0 0 0 0 0 0 0 0 0 = r k for all k.

Generalized Tesler Matrices Definition Given any positive integer vector r = (r 1,..., r n ) N n we say that A = (a i,j ) Mat n (Z) is an r-tesler matrix if: A is upper triangular. For all 1 k n we have (a k,k + a k,k+1 + + a k,n ) (a 1,k + a 2,k + a k 1,k ) = r k. Picture: The k-th hook sum equals r k k 0 k 0 0 + + + + 0 0 0 0 0 0 0 0 0 0 0 0 = r k for all k.

Generalized Tesler Matrices Definition Given any positive integer vector r = (r 1,..., r n ) N n we say that A = (a i,j ) Mat n (Z) is an r-tesler matrix if: A is upper triangular. For all 1 k n we have (a k,k + a k,k+1 + + a k,n ) (a 1,k + a 2,k + a k 1,k ) = r k. Picture: The k-th hook sum equals r k k 0 k 0 0 + + + + 0 0 0 0 0 0 0 0 0 0 0 0 = r k for all k.

Generalized Tesler Matrices Definition Given any positive integer vector r = (r 1,..., r n ) N n we say that A = (a i,j ) Mat n (Z) is an r-tesler matrix if: A is upper triangular. For all 1 k n we have (a k,k + a k,k+1 + + a k,n ) (a 1,k + a 2,k + a k 1,k ) = r k. Picture: The k-th hook sum equals r k k 0 k 0 0 + + + + 0 0 0 0 0 0 0 0 0 0 0 0 = r k for all k.

Generalized Tesler Matrices Definition Given r N n, let Tesler(r) be the set of r-tesler matrices and define Tes(r; q, t) := weight(a) as before. A Tesler(r) = ( M) pos(a) n [a i,j ] q,t, A Tesler(r) a i,j Pos(A)

Generalized Tesler Matrices Conjecture (Haglund) If r N n is increasing (1 r 1 r 2 r n ) then we have Tes(r; q, t) N[q, t]. Question For which other r N n is Tes(r; q, t) N[q, t]?

Generalized Tesler Matrices Conjecture (Haglund) If r N n is increasing (1 r 1 r 2 r n ) then we have Tes(r; q, t) N[q, t]. Question For which other r N n is Tes(r; q, t) N[q, t]?

Generalized Tesler Matrices Theorem (Armstrong Sagan Haglund) Recall from Haglund s and Haiman s Theorems that we have Tes(n; 1, 1) = Hilb(n; 1, 1) = (n + 1) n 1. For general r = (r 1,..., r n ) N n we have Tes(r; 1, 1) = r 1 (r 1 + nr 2 )(r 1 + r 2 + (n 1)r 3 ) (r 1 + + r n 1 + 2r n ). Conjectured by me; proved by Bruce.

Generalized Tesler Matrices Theorem (Armstrong Sagan Haglund) Recall from Haglund s and Haiman s Theorems that we have Tes(n; 1, 1) = Hilb(n; 1, 1) = (n + 1) n 1. For general r = (r 1,..., r n ) N n we have Tes(r; 1, 1) = r 1 (r 1 + nr 2 )(r 1 + r 2 + (n 1)r 3 ) (r 1 + + r n 1 + 2r n ). Conjectured by me; proved by Bruce.

Generalized Tesler Matrices Theorem (Armstrong Sagan Haglund) Then Jim defined r-parking functions PF(r) such that Tes(r; 1, 1) = #PF(r). Special Case: m-parking functions Tes((1, m, m,..., m); 1, 1) = (mn + 1) n 1.

Generalized Parking Functions Conjecture For all r N n we have q ( i i r n i+1) n Tes(r; q, q 1 ) = [r 1 ] q n+1[r 1 + nr 2 ] q [r 1 + r 2 + (n 1)r 3 ] q [r 1 + + r n 1 + 2r n ] q. Problem Find an algebraic interpretation of Tes(r; q, t).

Generalized Parking Functions Conjecture For all r N n we have q ( i i r n i+1) n Tes(r; q, q 1 ) = [r 1 ] q n+1[r 1 + nr 2 ] q [r 1 + r 2 + (n 1)r 3 ] q [r 1 + + r n 1 + 2r n ] q. Problem Find an algebraic interpretation of Tes(r; q, t).

Pause

Tesler Polytopes (New!)

Tesler Polytopes Definition We say that A = (a i,j ) Mat n (R) is a null-hook matrix if A is upper triangular. For all 1 k n we have (a k,k + a k,k+1 + + a k,n ) (a 1,k + a 2,k + a k 1,k ) = 0. Picture: The k-th hook sum equals 0 k 0 k 0 0 + + + + 0 0 0 0 0 0 0 0 0 0 0 0 = 0 for all k.

Tesler Polytopes Definition We say that A = (a i,j ) Mat n (R) is a null-hook matrix if A is upper triangular. For all 1 k n we have (a k,k + a k,k+1 + + a k,n ) (a 1,k + a 2,k + a k 1,k ) = 0. Picture: The k-th hook sum equals 0 k 0 k 0 0 + + + + 0 0 0 0 0 0 0 0 0 0 0 0 = 0 for all k.

Tesler Polytopes Definition We say that A = (a i,j ) Mat n (R) is a null-hook matrix if A is upper triangular. For all 1 k n we have (a k,k + a k,k+1 + + a k,n ) (a 1,k + a 2,k + a k 1,k ) = 0. Picture: The k-th hook sum equals 0 k 0 k 0 0 + + + + 0 0 0 0 0 0 0 0 0 0 0 0 = 0 for all k.

Tesler Polytopes Observations Let Hook 0 Mat n (R) be the set of null-hook matrices. These form an R-subspace of Mat n (R) of dimension ( n 2) : Hook 0 R (n 2) with basis given by the following null-transposition matrices: i j T (i, j) := i j 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 for all 1 i < j n.

Tesler Polytopes Observations Let ZHook 0 denote the lattice of integer points in Hook 0 : ZHook 0 := Z{T (i, j) : 1 i < j n} Hook 0. And given any r N n, let ZHook r be the lattice of integer points in the affine space of r-hook matrices : r 1 r 2 ZHook r Hook r := Hook 0 +.... rn Given any two r-tesler matrices A, B Tesler(r) observe that we have A B Hook 0, and hence Tesler(r) ZHook r.

Tesler Polytopes Observations Let ZHook 0 denote the lattice of integer points in Hook 0 : ZHook 0 := Z{T (i, j) : 1 i < j n} Hook 0. And given any r N n, let ZHook r be the lattice of integer points in the affine space of r-hook matrices : r 1 r 2 ZHook r Hook r := Hook 0 +.... rn Given any two r-tesler matrices A, B Tesler(r) observe that we have A B Hook 0, and hence Tesler(r) ZHook r.

Tesler Polytopes Observations Let ZHook 0 denote the lattice of integer points in Hook 0 : ZHook 0 := Z{T (i, j) : 1 i < j n} Hook 0. And given any r N n, let ZHook r be the lattice of integer points in the affine space of r-hook matrices : r 1 r 2 ZHook r Hook r := Hook 0 +.... rn Given any two r-tesler matrices A, B Tesler(r) observe that we have A B Hook 0, and hence Tesler(r) ZHook r.

Tesler Polytopes Observations In fact, the r-tesler matrices are precisely the integer points in the polytope of positive r-hook matrices : Tesler(r) Hook r (R 0 ) (n 2)

Tesler Polytopes Suggestion Study this polytope. Call it the r-tesler polytope : Tes (r) := Hook r (R 0 ) (n 2). One example has been studied before. If r = (1, 2, 3,..., n) then CRY n := Tes (r) is called the Chan-Robbins-Yuen polytope. Its volume was conjectured by Chan-Robbins-Yuen and proved by Zeilberger (1998) to be a product of Catalan numbers: Vol(CRY n ) = n 2 i=1 1 i + 1 ( ) 2i. i

Tesler Polytopes Suggestion Study this polytope. Call it the r-tesler polytope : Tes (r) := Hook r (R 0 ) (n 2). One example has been studied before. If r = (1, 2, 3,..., n) then CRY n := Tes (r) is called the Chan-Robbins-Yuen polytope. Its volume was conjectured by Chan-Robbins-Yuen and proved by Zeilberger (1998) to be a product of Catalan numbers: Vol(CRY n ) = n 2 i=1 1 i + 1 ( ) 2i. i

Tesler Polytopes More generally, the r-tesler polytope is an example of a flow polytope. These have been studied by Postnikov-Stanley, Baldoni-Vergne, Mèszàros and Morales. They have beautiful properties related to the Kostant partition function for type A root systems.

Tesler Polytopes Conjecture (Morales) If r = (1, 1,..., 1) N n we say that Tes (n) := Tes (r) is the standard Tesler polytope. Its volume seems to be ( n ) Vol( Tes (n)) = 2 (n 2) 2! 1!2! n!.

Tesler Polytopes Final Suggestions Can one learn more about diagonal Hilbert series by studing Tesler polytopes? Is there a way to incorporate q and t into the Ehrhart series? It seems that the vertices of Tes (r) are the monomial r-tesler matrices: MTesler(r) := {A Tesler(r) : pos(a) n = 0}. Paul Levande gave a natural bijection MTesler(r) S n. More genrally, given A Tesler(r) it seems that pos(a) n is the dimension of the face of Tes (r) containing A. That should be important.

Tesler Polytopes Final Suggestions Can one learn more about diagonal Hilbert series by studing Tesler polytopes? Is there a way to incorporate q and t into the Ehrhart series? It seems that the vertices of Tes (r) are the monomial r-tesler matrices: MTesler(r) := {A Tesler(r) : pos(a) n = 0}. Paul Levande gave a natural bijection MTesler(r) S n. More genrally, given A Tesler(r) it seems that pos(a) n is the dimension of the face of Tes (r) containing A. That should be important.

Tesler Polytopes Final Suggestions Can one learn more about diagonal Hilbert series by studing Tesler polytopes? Is there a way to incorporate q and t into the Ehrhart series? It seems that the vertices of Tes (r) are the monomial r-tesler matrices: MTesler(r) := {A Tesler(r) : pos(a) n = 0}. Paul Levande gave a natural bijection MTesler(r) S n. More genrally, given A Tesler(r) it seems that pos(a) n is the dimension of the face of Tes (r) containing A. That should be important.

Tesler Polytopes Final Suggestions Can one learn more about diagonal Hilbert series by studing Tesler polytopes? Is there a way to incorporate q and t into the Ehrhart series? It seems that the vertices of Tes (r) are the monomial r-tesler matrices: MTesler(r) := {A Tesler(r) : pos(a) n = 0}. Paul Levande gave a natural bijection MTesler(r) S n. More genrally, given A Tesler(r) it seems that pos(a) n is the dimension of the face of Tes (r) containing A. That should be important.

Happy Birthday Bruce!