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A objects theorems Combinatorics Splines Algebra Approximation Arithmetic Residues Z

Polytopes, partition functions and box–splines

Claudio Procesi.

PISA 2010

1 Introduction2 Combinatorics3 Splines4 Algebra5 Approximation theory6 Arithmetic7 Residues

BASIC INPUT OF THE THEORY

A real, sometimes integer n ×m matrix A.We always think of A as a LIST of vectors in V = Rn, its columns:

A := (a1, . . . , am)

ConstrainWe assume that 0 is NOT in the convex hull of its columns.

From A we make several constructions, algebraic, combinatorial,analytic etc..

BASIC REFERENCE

The book:C. De Boor, K. Höllig, S. Riemenschneider,

Box splines

Applied Mathematical Sciences 98 (1993).

Forthcoming book

Topics in hyperplane arrangements, polytopes andbox–splines

De Concini C., Procesi C.http://www.mat.uniroma1.it/ procesi/dida.html

SEVERAL NAMES OF CONTRIBUTORS

From numerical analysisA.A. Akopyan; Ben-Artzi, Asher; C.K. Chui, C. De Boor, W.Dahmen, H. Diamond, N. Dyn, K. Höllig, C. Micchelli, Jia, RongQing, A. Ron, A.A. Saakyan

From algebraic geometryOrlik–Solomon on cohomology, Baldoni, Brion, Szenes, Vergne andof Jeffrey–Kirwan, on partition functions.In fact a lot of work originated from the seminal paper ofKhovanskiı, Pukhlikov, interpret the counting formulas asRiemann–Roch formulas for toric varieties

From enumerative combinatoricsA.I. Barvinok, Matthias Beck, Sinai Robins, Richard Stanley

CONVEX POLYTOPES FIRST OUTPUT

FROM THE MATRIX A WE PRODUCE:First a system of linear equations:

m∑i=1

aixi = b, or Ax = b, A := (a1, . . . , am) (1)

The columns ai , b are vectors with n coordinates(aj,i , bj , j = 1, . . . , n).

Variable polytopes

As in Linear Programming Theory we deduce and want to studytheVARIABLE POLYTOPES:

ΠA(b) := {x |Ax = b, xi ≥ 0, ∀i}

Π1A(b) := {x |Ax = b, 1 ≥ xi ≥ 0, ∀i}

which are convex and bounded for every b.

Variable polytopes

The fact that

ΠA(b) := {x |Ax = b, xi ≥ 0, ∀i}

Π1A(b) := {x |Ax = b, 1 ≥ xi ≥ 0, ∀i}

are convex polyhedra for every b is by definition.The property of being bounded is trivial for Π1

A(b) while for ΠA(b)depends on the fact that there is a linear function φ with φ(ai ) > 0for all i so that φ(ai ) > c > 0.∑

ixiai = b =⇒

∑ixiφ(ai ) = φ(b) =⇒

∑ixi < φ(b)c−1.

Geometric picture

The polytopes Π1A(b) are sections of a hypercube,

for instance the simple case of a cube A a list of 3 numbers:

Geometric pictures

The object of study

Basic functionsSet TA(x), BA(x) to be the volume of ΠA(x),Π1

A(x).If A, b have integer coordinates

Arithmetic case, A, x with integer coefficientsIn this case set PA(x) to be the number of solutions of the systemin which the coordinates xi are non negative integers.

In other words

PA(x) is the number of integral points in the variable polytopeΠA(x).

Up to a multiplicative normalization constant:TA(x) is the Multivariate–splineBA(x) the Box–splinePA(x) is called the partition function

We are interested inComputing the three functions TA(x), BA(x), PA(x) and describetheir qualitative properties.Applications of these functions to arithmetic, numerical analysis,Lie theory, equivariant cohomology, equivariant K–theory,symplectic geometry and index theory.

Variable polytopes

TheVARIABLE POLYTOPE:

ΠA(b) := {x |Ax = b, xi ≥ 0, ∀i}

Is the set of (x1, . . . , xm) ∈ Rm, xi ≥ 0, |∑m

i=1 xiai = b.So it is empty unless b belongs to the cone

C(A) := {m∑

i=1xiai , xi ≥ 0}

of positive combinations of the ai .

The hypothesis that 0 is NOT in the convex hull of the ai impliesthat C(A) is pointed, that is there is a linear function φ strictlypositive on all non zero points of C(A),

IMPORTANT EXAMPLE

A is the list of POSITIVE ROOTS of a root system,e.g. B2:

∣∣∣∣∣−1 1 1 01 0 1 1

∣∣∣∣∣

OO ??��

__???????

We also identify vectors withlinear forms as:

−x + y , x , x + y , y

IMPORTANT EXAMPLE

A is the list of POSITIVE ROOTS of a root system,the associated cone C(A) has three big cells

. . . . . . . . .

. . . . . . . .

. . . . . . .

. . . . . .

OO >>}}}}}}}

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}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}

AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA. . . .

EXAMPLE, the C 1 function TA, case ZP

In the literature of numerical analysis the Box spline associated tothe root system B2 is called the Zwart–Powell or ZP element:

∣∣∣∣∣1 1 0 −11 0 1 1

∣∣∣∣∣//

OO ??��

__???????

EXAMPLE, the C 1 function TA, case B2 or ZP

2TA is 0 outside the cone and on the three cells:

(x+y)2

2(x+y)2

2 − x2

triangle penthagon

0 triangle y2

yyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyy

EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

EXAMPLE, the C 1 function TA, case B2 or ZP

2TA is 0 outside the cone and on the three cells:

(x+y)2

2(x+y)2

2 − x2

triangle penthagon

0 triangle y2

yyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyy

EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

The box–spline for type B2, ZP element

9−6y+y2 ��

��

12+3x+x2

2+y−xy− y2

92+3x+

(x−y)2

2−3y 5

2+x−x2−y

��

92+3x−x2

−3y−xy+y2

4−4x+x2

2+xy− y2

8−4x+x2

2 −4y

+xy+y2

7−2x−x2

−4y+xy+y2

?????????????????????????????????

?????????????????????????????????????????????????

1+2x+x2

−1+x2

2+2y+xy− y2

��

− 12+x−x2+y

− 32+x+3y−y2 − 3

2+x−x2+3y−y2

��

− 12−x+3y−y2

− 12−x+x2

2+3y−xy− y2

4−4x+x2????????????????

(x+y)2

????????????????????????????????? y2

2+xy+ y2

��

− 12+x

− (x−y)2

12−x+

(x−y)2

��

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WHAT DO WE SEE

The computation of the box–spline has some geometric,combinatorial and algebraic flavor. It appears as a piecewisepolynomial function on a compact polyhedron.

From simple data we get soon a complicatedpicture!

THE PARTITION FUNCTION

When A, b have integer elements it is natural to think of anexpression like:b = t1a1 + · · ·+ tmam with ti not negative integers as a:

partition of b with the vectors ai ,in t1 + t2 + · · ·+ tm parts, hence the name partition function forthe number PA(b), thought of as a function of the vector b.

SIMPLE EXAMPLE m = 2, n = 1, A = {2, 3}

Parts are 2 and 3In how many ways can you write a number b as:

b = 2x + 3y , x , y ∈ N ?

THE PARTITION FUNCTION

When A, b have integer elements it is natural to think of anexpression like:b = t1a1 + · · ·+ tmam with ti not negative integers as a:

partition of b with the vectors ai ,in t1 + t2 + · · ·+ tm parts, hence the name partition function forthe number PA(b), thought of as a function of the vector b.

SIMPLE EXAMPLE m = 2, n = 1, A = {2, 3}

Parts are 2 and 3In how many ways can you write a number b as:

b = 2x + 3y , x , y ∈ N ?

ANSWER (Quasi polynomial!)

It depends on the class of n modulo 6.

n ∼= 0 n6 + 1

n ∼= 1 n6 −

n ∼= 2 n6 + 2

n ∼= 3 n6 + 1

n ∼= 4 n6 + 1

n ∼= 5 n6 + 1

What is a quasi–polynomial?

Two equivalent definitions

FIRSTA function on a lattice Λ which is a polynomial on each cosetof some sublattice M of finite index

SECONDThe restriction to a lattice Λ of a function which is a sum ofproducts of a polynomial with an exponential function,periodic on some sublattice M of finite index

OTHER EXAMPLE, THE HAT FUNCTION

∣∣∣∣∣1 0 10 1 1

∣∣∣∣∣ the corresponding partition function PA is piecewise

polynomial with top degree coinciding with TA

0 x + 1

yyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyy

IMPORTANT EXAMPLE Lie theory

In Lie theory the Kostant partition function counts inhow many ways can you decompose a weight as a sum of positiveroots.This is used in many computations.

THE OBJECTS OF STUDY

Main objects associated to A

First we may think of the elements ai as a list of linear equations,each defining a hyperplane (in the dual space U = V ∗), the pointsin which it vanishes. This is a

central hyperplane arrangement

One way to study this arrangement is to study the algebra ofpolynomials S[V ] localized at

∏mi=1 ai so we study the algebra

RA := S[V ][m∏

i=1a−1

The algebra RA consists of those rational functions which have atthe denominator a product of powers of the linear forms ai . It isclearly a module under the Weyl algebra of differential operatorswith polynomial coefficients and we analyze in depth the modulestructure.

When the coordinates of the elements ai = (ai ,1, . . . , ai ,s) areintegers we think of ai as a character

∏sj=1 x

ai,jj .

This defines a subgroup of the torus (C∗)s the points in which thecharacter is 1 or where 1−

∏sj=1 x

ai,jj vanishes.

This is called a central toric arrangement.One way to study this arrangement is to study the algebra ofLurent polynomials S[x±1

1 , . . . , x±1s ] localized at∏m

i=1(1−∏s

j=1 xai,jj ) so we study the algebra

S[x±11 , . . . , x±1

s ][m∏

i=1(1−

s∏j=1

xai,jj )−1].

We study also his as a module over the algebra of differentialoperators with coefficients Laurent polynomials.

THE THEOREMS

TheoremsThere are several general formulas to compute the previousfunctions which are obtained by a mixture of techniques.A main geometric notion that plays a role is that of BIG CELL

What are the big cells?

ASSUME THAT A SPANS V .

The singular points are the points in the cone C(A) lying insome cone C(Y ) for any sublist Y of A which does NOT spanthe ambient space.The other points are called regularA big cell is a connected component of the set or regularpoints.

What are the big cells?

In other words, take all the hyperplanes H spanned by sublists of Aand then the cones C(H ∩ A). The union of all these cones formsthe set of singular vectors.It is easy to see that the big cells are convex.

CELLS ARE CONVEX POLYHEDRA

Take all bases b extracted from A, for each basis consider the coneC(b) generated by b. Its boundary is made of singular points.A standard fact of polyhedra is that

C(A) = ∪bC(b)

So if a point p ∈ C(A) is regular it lies either in the interior◦C (b)

of outside each C(b). It follows that the big cell c in which p lies isthe intersection of all the

◦C (b) containing it.

c = ∩b | p∈

◦C(b)

◦C (b).

A 3-dimensional example

The positive roots of type A3 are

α1, α2, α3, α1 + α2, α2 + α3, α1 + α2 + α3.

Visualize the cone and the big cells.We do everything on a transversal section, where the cone lookslike a bounded convex polytope and then project.We want to decompose the cone C(A) into big cells and see itssingular and regular points.

PROJECTING A POLYHEDRON TO FORM A CONE

EXAMPLE Type A3 in section (big cells):

====================================

��

α1 + α2 α2 + α3

α1 + α2 + α3

kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk α3

SSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSS

We have 7 big cells.

Onen question, Type An :

Which and how many are the big cells?

Assume A spans Rn.

Let m(A) denote the minimum number of columns that one canremove from A so that the remaining columns do not span Rn.

The basic function TA(x) is a spline1 TA(x) has support on the cone C(A)

2 TA(x) is of class m(A)− 23 TA(x) coincides with a homogeneous polynomial of degree

m − n on each big cell.

Assume A spans Rn.

Let m(A) denote the minimum number of columns that one canremove from A so that the remaining columns do not span Rn.

The basic function TA(x) is a spline1 TA(x) has support on the cone C(A)

2 TA(x) is of class m(A)− 23 TA(x) coincides with a homogeneous polynomial of degree

m − n on each big cell.

SUMMARIZING

In order to compute TA(x) we need to1 Determine the decomposition of C(A) into cells2 Compute on each big cell the homogeneous polynomial of

degree m − n coinciding with TA(x) .

SUMMARIZING

GENERAL FORMULA FOR TA

One can find explicit polynomials pb,A(x), indexed by acombinatorial object called unbroken bases andcharacterized by certain explicit differential equations so that:Given a point x in the closure of a big cell c we have

Jeffry-Kirwan residue formula

TA(x) =∑

b | c⊂C(b)

| det(b)|−1pb,A(−x).

From TA one computes BA

For a given subset S of A define aS :=∑

a∈S a

the basic formula is:

BA(x) =∑S⊂A

(−1)|S|TA(x − aS).

So TA is the fundamental object.

Notice that the local pieces of BA are no more homogeneouspolynomials.

From TA one computes BA

For a given subset S of A define aS :=∑

a∈S a

the basic formula is:

BA(x) =∑S⊂A

(−1)|S|TA(x − aS).

So TA is the fundamental object.

Notice that the local pieces of BA are no more homogeneouspolynomials.

THE FORMULA FOR THE PARTITION FUNCTION

There is a parallel theory, as a result we can compute a set ofpolynomials qb,φ(−x) indexed by pairs, a character φ of finiteorder and a unbroken basis in Aφ = {a ∈ A |φ(ea) = 1}.The analogue of the Jeffrey–Kirwan formula is:

TheoremGiven a point x in the closure of a big cell c we have

Residue formula for partition function

PA(x) =∑

φ∈P(A)

eφ∑

b∈NBAφ | c⊂C(b)

qb,φ(−x)

PA(x) =∑

φ∈P(A)

eφ∑

qb,φ(−x)

PA(x) =∑

φ∈P(A)

eφ∑

qb,φ(−x)

From volumes to partition functions

One can deduce the partition function from some combinatoricsand multivariate splines (with parameters):

TheoremFor the points x in the interior of big cells c we have

PA(x) =∑

φ∈P(A)

QφTAφ,φ(x)

Qφ =∏

a/∈Aφ

11− e−a

∏a∈Aφ

a − 〈φ | a〉1− e−a+〈φ | a〉

From volumes to partition functions

One can deduce the partition function from some combinatoricsand multivariate splines (with parameters):

TheoremFor the points x in the interior of big cells c we have

PA(x) =∑

φ∈P(A)

QφTAφ,φ(x)

Qφ =∏

a/∈Aφ

11− e−a

∏a∈Aφ

a − 〈φ | a〉1− e−a+〈φ | a〉

FIRST STEP

SOME COMBINATORICS

IN THE COMBINATORIAL PART

one can explain the ideas relating unbroken bases with cells and allbases with the structure of the polyhedron where the box–splinehas its support.Let us illustrate these ideas through examples.

Unbroken bases

From the theory of matroids to cells.

Let c := ai1 , . . . , aik ∈ A, i1 < i2 · · · < ik , be a sublist of linearlyindependent elements.

DefinitionWe say that ai breaks c if there is an index 1 ≤ e ≤ k suchthat:

i ≤ ie .ai is linearly dependent on aie , . . . , aik .

Unbroken bases

From the theory of matroids to cells.

Let c := ai1 , . . . , aik ∈ A, i1 < i2 · · · < ik , be a sublist of linearlyindependent elements.

DefinitionWe say that ai breaks c if there is an index 1 ≤ e ≤ k suchthat:

i ≤ ie .ai is linearly dependent on aie , . . . , aik .

EXAMPLE of the elements breaking a basis

ExampleTake as A the list of positive roots for type A3.

A = {α1, α2, α3, α1 + α2, α2 + α3, α1 + α2 + α3}.

Let us draw in Red one particular basis and in Green the elementswhich break it.We have 16 bases10 broken and 6 unbroken

FIRST THE 10 broken

EXAMPLE of the elements breaking a basis

ExampleTake as A the list of positive roots for type A3.

A = {α1, α2, α3, α1 + α2, α2 + α3, α1 + α2 + α3}.

Let us draw in Red one particular basis and in Green the elementswhich break it.We have 16 bases10 broken and 6 unbroken

FIRST THE 10 broken

α1, α2, α3, α1 + α2, α2 + α3, α1 + α2 + α3

We have 6 unbroken, all contain necessarily α1:

α1, α2, α3, α1 + α2, α2 + α3, α1 + α2 + α3.

Let us visualize the simplices generated by the 6 unbroken:α2

111111111111111111111111111

1111111111111

α1 α3

99999999999999

��

α2 + α3

pppppppppppppppppppppppp

��

α1 + α2 + α3

kkkkkkkkkkkkkkk

α1 + α2

��

LLLLLLLLLLLLLLLLLLLLLL

α1 α3

α1 + α2 + α3

SSSSSSSSSSSSSSS

kkkkkkkkkkkkkkk

α1 α3

α1 + α2

��α2 + α3

mmmmmmmmmmmmmmmmmmmmmmmmmmmm

Decomposition into big cells and unbroken basesLet us visualize the decomposition into big cells, obtainedoverlapping the cones generated by unbroken bases.

A remarkable fact

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The overlapping theorem

You have visually seen a theorem we proved in general:

by overlapping the cones generated by the unbroken bases oneobtains the entire decomposition into big cells!!

HYPERPLANE ARRANGEMENTS

A list of vectors ai can be thought of as alist of linear equationsdefining aset of linear hyperplanes.

These intersect in a complicated pattern giving rise to a secondinteresting combinatorial geometric object, the set of all theirintersections called:

HYPERPLANE ARRANGEMENT

PROJECTIVE PICTURE OF THE ARRANGEMENT A3

Same example but as linear functions, or hyperplane arrangement:

We have drawn in theprojective plane of 4−tuples ofreal numbers with sum 0, the6 lines

xi − xj = 0, 1 ≤ i < j ≤ 4

the 7 intersection points are also subspaces of the arrangement!

PROJECTIVE PICTURE OF THE ARRANGEMENT A3

Same example but as linear functions, or hyperplane arrangement:

We have drawn in theprojective plane of 4−tuples ofreal numbers with sum 0, the6 lines

xi − xj = 0, 1 ≤ i < j ≤ 4

the 7 intersection points are also subspaces of the arrangement!

HYPERPLANE ARRANGEMENTS

Linear equationsThe list of vectors gives rise to a list of linear forms and so to ahyperplane arrangement

Integral vectorsIn the case of vectors in a lattice it is better to think of thesevectors λ = (a1, . . . , as), ai ∈ Z as characters x 7→ xλ =

∏si=1 x

on a torus and we we have the toric arrangement formedby the connected components of the intersections of the subgroupsxλ = 1.

Periodic arrangementUsing logarithms one can interpret the toric arrangement as aperiodic hyperplane arrangement.

PROBLEM

Describe the previous pictures for root systems.For type An the unbroken bases are known and can be indexed bycertain binary graphs or by permutations of n elements.The decomposition into cells is unknown.

THE BOX

The box spline BA(x) is supported in the compact polytope:

THE BOX B(A)

The Box B(A)

that is the compact convex polytope

B(A) := {m∑

i=1tiai}, 0 ≤ ti ≤ 1, ∀i .

THE BOX

The box spline BA(x) is supported in the compact polytope:

THE BOX B(A)

The Box B(A)

that is the compact convex polytope

B(A) := {m∑

i=1tiai}, 0 ≤ ti ≤ 1, ∀i .

The box B(A) has a nice combinatorial structure, proved byShephard, it can be paved by a set of parallelepipeds indexed by:all the bases which one can extract from A!

ExampleIn the next example

∣∣∣∣∣1 0 1 −1 2 10 1 1 1 1 2

∣∣∣∣∣we have 15 bases and 15 parallelograms.

The box B(A) has a nice combinatorial structure, proved byShephard, it can be paved by a set of parallelepipeds indexed by:all the bases which one can extract from A!

ExampleIn the next example

∣∣∣∣∣1 0 1 −1 2 10 1 1 1 1 2

∣∣∣∣∣we have 15 bases and 15 parallelograms.

EXAMPLE paving the box

∣∣∣∣∣1 0 1 −1 2 10 1 1 1 1 2

∣∣∣∣∣

Step–wise paving of the box

∣∣∣∣∣1 0 1 −1 2 10 1 1 1 1 2

∣∣∣∣∣START WITH

∣∣∣∣∣1 00 1

∣∣∣∣∣

∣∣∣∣∣1 0 10 1 1

∣∣∣∣∣

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∣∣∣∣∣1 0 1 −10 1 1 1

∣∣∣∣∣

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∣∣∣∣∣1 0 1 −1 20 1 1 1 1

∣∣∣∣∣

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SECOND

SECOND STEP

THE SPLINES

SPLINES

The volume VA(x) =√detAAtTA(x) of the variable polytope

ΠA(x) equals, up to the multiplicative constant√detAAt to the

multivariate splinethat is the function TA(x) characterized by the formula:∫

Rnf (x)TA(x)dx =

f (m∑

i=1tiai )dt,

where f (x) is any continuous function with compact support.

SPLINES

Rnf (x)TA(x)dx =

f (m∑

i=1tiai )dt,

SPLINES

Rnf (x)TA(x)dx =

f (m∑

i=1tiai )dt,

THIS IS FUBINI’S THEOREM

Decompose the map A : Rm → Rm,A(t1, . . . , tm) =∑

i tiai ascomposition BC where B is an orthogonal projection and Cinvertible. Set u := C(t).∫

f (BC(t))dt = | det(C)|−1∫

C(Rm+)

f (Bu)du

∫C(Rm

+)f (Bu)du =

∫B−1x∩C(Rm

+)1dm]f (x)dx

B−1x ∩ C(Rm+) = C(ΠX (x)) so

∫B−1x∩C(Rm

+) 1dm is the volume ofC(ΠX (x)) which is cVX (x) for some constant c.

WARNING

We have given a weak definition for TA(x)

In generalTA(x) is a tempered distribution, supported on the cone C(A)!

Only when A has maximal rank TA(x) is a function.

WARNING

BOX SPLINES

While the function TA(x) is the basic object, the more interestingobject for numerical analysis is the

box splinethat is the function BA(x) characterized by the formula:∫

Rnf (x)BA(x)dx =

∫[0,1]m

f (m∑

i=1tiai )dt,

where f (x) is any continuous function.

The bm−spline of Schoenberg A = {1, 1, . . . , 1},m + 1−times

bm(x) is of class Cm−1, it is supported in the interval [0,m + 1],and given by the formula:

bm(x) :=k∑

i=0(−1)i

(m + 1

)(x − i)m

m!, ∀x ∈ [k, k + 1].

The b5−spline of Schoenberg

two box splines of class C 0, h = 2 and C 1, h = 3

∣∣∣∣∣1 0 10 1 1

∣∣∣∣∣ Hat functionor Courant element

∣∣∣∣∣1 0 1 −10 1 1 1

∣∣∣∣∣Zwart-Powell element

EXAMPLE OF A two dimensional box spline

Non continuous, A =

∣∣∣∣∣1 2 1 1 10 0 0 0 2

∣∣∣∣∣ (h = 1)

3 reasons WHY the BOX SPLINE ?

BA(x)dx = 1

recursive definition

B[A,v ](x) =

0BA(x − tv)dt

in the case of integral vectors, we have

PARTITION OF UNITYThe translates BA(x − λ), λ runs over the integral vectors form a

partition of 1.

BA(x)dx = 1

B[A,v ](x) =

0BA(x − tv)dt

partition of 1.

BA(x)dx = 1

B[A,v ](x) =

0BA(x − tv)dt

partition of 1.

TRIVIAL EXAMPLE

A = {1, 1} we have for the box spline

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Now let us add to it its translates!

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EXAMPLE OF THE HAT FUNCTION

∣∣∣∣∣1 0 10 1 1

∣∣∣∣∣From the function TA

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EXAMPLE OF THE HAT FUNCTIONwe get the box spline hat function summing over the 6translates of TA

Recall the box–spline for type B2

9−6y+y2 ��

��

12+3x+x2

2+y−xy− y2

92+3x+

(x−y)2

2−3y 5

2+x−x2−y

��

92+3x−x2

−3y−xy+y2

4−4x+x2

2+xy− y2

8−4x+x2

2 −4y

+xy+y2

7−2x−x2

−4y+xy+y2

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1+2x+x2

−1+x2

2+2y+xy− y2

��

− 12+x−x2+y

− 32+x+3y−y2 − 3

2+x−x2+3y−y2

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− 12−x+3y−y2

− 12−x+x2

2+3y−xy− y2

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(x+y)2

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2+xy+ y2

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− 12+x

− (x−y)2

12−x+

(x−y)2

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APPROXIMATION THEORY

The box spline, when the ai are integral vectors, can be effectivelyused in the finite element method to approximatefunctions.We define the CARDINAL SPLINE SPACE

SX := {∑i∈Zs

BA(x − i)ai}

The function ai on Zs is called a mesh function.Define D(A) to be the space of polynomials contained in SX .For a function f weThe semi–discrete convolution

f 7→ BX ∗′ f :=∑i∈Zs

BA(x − i)f (i)

The theory of Dahmen–Micchelli

Consists of several Theorems.The basic is that

semidiscrete convolution maps D(A) into D(A).The dimension of D(A) equals the number of bases which onecan extract from A.D(A) contains all polynomials of degree < m(A) (m(A) is theminimum length of a cocircuit).D(A) is the space of solutions of the differential equationsDY f = 0 as

1 Y runs over the cocircuits of A.2 DY :=

∏a∈Y Da

3 Da is the directional derivative relative to a.

THIRD STEP

FROM ANALYSIS TO ALGEBRA

Here comes the algebra

How to compute TA? or the partition function PA?We use the Laplace transform which will change the analyticproblem to one in

algebra

LAPLACE TRANSFORM from Rs = V to U = V ∗.

Lf (u) :=

∫Ve−〈u | v〉f (v)dv .

In coordinates u = (y1, . . . , ys), v = (x1, . . . , xs) we have

Lf (y1, . . . , ys) :=

e−∑s

i=1 yi xi f (x1, . . . , xs)dx1 . . . dxs .

basic propertiesp ∈ U,w ∈ V , write p, Dw for the linear function 〈p | v〉 and thedirectional derivative on V

L(Dw f )(u) = wLf (u), L(pf )(u) = −DpLf (u),

L(epf )(u) = Lf (u − p), L(f (v + w))(u) = ewLf (u).

Basic transformation rules

Consider any a ∈ V as a LINEAR FUNCTION ON U we have:

An easy computation gives the Laplace transforms:

LBA =∏a∈A

1− e−a

aand LTA =

∏a∈A

Basic transformation rules

Consider any a ∈ V as a LINEAR FUNCTION ON U we have:

An easy computation gives the Laplace transforms:

LBA =∏a∈A

1− e−a

aand LTA =

∏a∈A

The Laplace transform of the multivariatespline∫

Rne−∑

i yi xiTA(x)dx =

e−∑m

i=1 ti ai (y)dt1 . . . dtm,

e−ti ai (y)dti =m∏

1ai (y)

MORE PARTIAL FRACTIONS

We need to rewrite LTA =∏

a∈A1a for this we need to develop a

theory of partial fractions in several variables, in this case for thealgebra

S[V ][∏a∈A

a−1]

WE DO THIS BY NON COMMUTATIVE ALGEBRA!!!!

BASIC NON COMMUTATIVE ALGEBRAS

Algebraic Fourier transform

Weyl algebrasSet W (V ),W (U) be the two Weyl algebras of differentialoperators with polynomial coefficients on V and U.

Fourier transformThere is an algebraic Fourier isomorphism between them, so anyW (V ) module M becomes a W (U) module M

We want to interpret some of these formal properties in thelanguage of Algebra. Recall we set U := V ∗ the dual space.

Weyl algebrasSet W (V ),W (U) be the two Weyl algebras of differentialoperators with polynomial coefficients on V and U.

In coordinates x1, . . . , xs of V we identify the basis of V with∂

∂x1, . . . ,

Weyl algebrasW (V ) is the algebra generated by V ⊕ U with commutationrelations

[v , u] = 〈u | v〉, v ∈ V , u ∈ U.

W (U) is the algebra generated by U ⊕ V with commutationrelations

[v , u] = −〈u | v〉, v ∈ V , u ∈ U.

Algebraic Fourier transform

Fourier transformThere is an algebraic Fourier isomorphism A 7→ A between them,

ˆ: v 7→ v , u 7→ −u

so any W (V ) module M becomes a W (U) module M

The formulas we have for the Laplace transform mean that it issemi–linear with respect to :

L(Af ) = AL(f ).

We shall apply this to tempered distributions, the dual of Schwarzspace of rapidly decreasing C∞ functions.

D−modules in Fourier duality:

Two modules Fourier isomorphic

1. The D−module DA := W (V )TA generated, in the space oftempered distributions, by TA under the action of the algebraW (V ) of differential operators on V with polynomial coefficients.

2. The algebra RA := S[V ][∏

a∈A a−1] obtained from thepolynomials on U by inverting the element dA :=

∏a∈A a

RA = W (U)d−1A

1. The D−module DA := W (V )TA generated, in the space oftempered distributions, by TA under the action of the algebraW (V ) of differential operators on V with polynomial coefficients.

2. The algebra RA := S[V ][∏

a∈A a−1] obtained from thepolynomials on U by inverting the element dA :=

∏a∈A a

RA = W (U)d−1A

RA and it is the coordinate ring of the open set AAcomplement of the union of the hyperplanes of Uof equations a = 0, a ∈ A.

It is a cyclic module under W (U) generated by d−1A .

The module RA

LocalizationIt is well known that once we invert an element in a polynomialalgebra we get a holonomic module over the algebra of differentialoperators. RA is holonomic!

In particular RA has a finitecomposition series and it is cyclic

We want to describe a composition series of RA.

The module RA

The building blocks

The irreducible module NW

For each subspace W of U we have an irreducible module NWgenerated by the δ function of W .

As W runs over the subspaces of the hyperplanearrangement given by the equations ai = 0, ai ∈ Athe NW run over all the composition factors of RA (with multiplicities).

Take coordinates x1, . . . , xn

W = {x1 = x2 = . . . = xk = 0}

NW is generated by an element δW satisfying:

xiδW = 0, i ≤ k, ∂

∂xiδ, i > k.

NW is free of rank 1 generated by δW over:

C[x1, x2, . . . , xk ,∂

∂xk+1, . . . ,

∂xn].

Take coordinates x1, . . . , xn

W = {x1 = x2 = . . . = xk = 0}

NW is generated by an element δW satisfying:

xiδW = 0, i ≤ k, ∂

∂xiδ, i > k.

NW is free of rank 1 generated by δW over:

C[x1, x2, . . . , xk ,∂

∂xk+1, . . . ,

∂xn].

The filtration of RA by polar order

DefinitionRA is filtered by the W (U)−submodules:

filtration degree ≤ kRA,k the span of all the fractions f

∏a∈A a−ha , ha ≥ 0

for which the set of vectors a, with ha > 0, spans a space ofdimension ≤ k.

We have RA,s = RA.

For all k we have that RA,k/RA,k−1 is semisimple.

The isotypic components of RA,k/RA,k−1 are of type NW asW runs over the subspaces of the arrangement ofcodimension k.The space

RA,s/RA,s−1 is a free module over

S[U] = C[∂

∂x1, . . . ,

∂xs]

The linear span of all the fractions∏m

i=1 a−hii , hi ≥ 0 so that the

ai with hi > 0 span is a complement of RA,s−1 in RA,s and it is afree module over

S[U] = C[∂

∂x1, . . . ,

∂xs]

It is important to choose a basis.

The basis theorem

Theorem

A basis for RA,s/RA,s−1 over S[U] is given by

the classes of the elements∏

a∈b a−1 as b runsover the set of unbroken bases.

The expansion of d−1A =∏a∈A a−1

Denote by NB the unbroken bases extracted from A.

We have a more precise THEOREM.

1∏a∈A a =

∑b∈NB

pb∏a∈b

a−1, pb ∈ S[U] = C[∂

∂x1, . . . ,

∂xs]

EXAMPLE Courant element

ExampleUSE COORDINATES x , y SET

A = [x + y , x , y ] = [x , y ]

∣∣∣∣∣1 0 11 1 0

∣∣∣∣∣1

(x + y) x y =1

x (x + y)2 +1

y (x + y)2 =

− ∂

∂y( 1x (x + y)

)− ∂

∂x( 1y (x + y)

EXAMPLE ZP element

Example

A = [x + y , x , y ,−x + y ] = [x , y ]

∣∣∣∣∣1 0 1 −11 1 0 1

∣∣∣∣∣1

(x + y) x y (−x + y)=

1(x + y)3x +

4(x + y)3(−x + y)

− 1(x + y)3y =

1/2[∂2

∂2y( 1

(x + y)x)+(

∂x +∂

∂y )2( 1(x + y)(−x + y)

)− ∂2

∂2x( 1

(x + y) y)]

We now need the basic inversion

Let A = {a1, . . . , an} be a basis,d := | det(a1, . . . , as)|χC(A) the characteristic function of the positive quadrant C(A)generated by A.

Basic example of inversion

L(d−1χC(A)) =n∏

i=1a−1

We are ready to invert!

We want to invert

d−1A =

∑b∈NB

pb,A(∂

∂x1, . . . ,

∂xs)∏a∈b

From the basic example and the properties!

We get ∑b∈NB

pb,A(−x1, . . . ,−xs)d−1b χC(b)

We are ready to invert!

We want to invert

d−1A =

∑b∈NB

pb,A(∂

∂x1, . . . ,

∂xs)∏a∈b

From the basic example and the properties!

We get ∑b∈NB

pb,A(−x1, . . . ,−xs)d−1b χC(b)

EXAMPLE ZP element

Inverting

1/2[∂2

∂2y( 1x (x + y)

∂x +∂

∂y )2( 1(x + y)(−x + y)

)− ∂2

∂2x( 1y (x + y)

we get

1/2[y2χC((1,0),(1,1)) +(x + y)2

2 χC((1,1),(−1,1) − x2χC((0,1),(1,1))]

We need a basic definition of combinatorial natureDefinitionWe say that a sublist Y ⊂ A is a cocircuit, if the elements inA− Y do not span V .

The basic differential operatorsFor such Y set DY :=

∏a∈Y Da, a differential operator with

constant coefficients.

(Da is directional derivative, first order operator)

We need a basic definition of combinatorial natureDefinitionWe say that a sublist Y ⊂ A is a cocircuit, if the elements inA− Y do not span V .

The basic differential operatorsFor such Y set DY :=

∏a∈Y Da, a differential operator with

constant coefficients.

(Da is directional derivative, first order operator)

For a given unbroken circuit basis b, consider the elementDb :=

∏a/∈b Da.

Characterization by differential equationsThe polynomials pb,A are characterized by the differential equations

DY p = 0, ∀Y , a cocircuit in A

Dbpc,A(x1, . . . , xs) =

{1 if b = c0 if b 6= c

∏a/∈b Da.

Dbpc,A(x1, . . . , xs) =

{1 if b = c0 if b 6= c

∏a/∈b Da.

Dbpc,A(x1, . . . , xs) =

{1 if b = c0 if b 6= c

The space D(A)

A remarkable space of polynomials

D(A) := {p |DY p = 0, ∀Y , a cocircuit in A}

The theorem of Dhamen MicchellidimD(A) equals the total number of bases extracted from A

We have a more precise theorem

The polynomials pb,A form a basis for the topdegree part (m − n) of D(A).

The graded dimension of D(A) is given by

HA(q) =∑

b∈B(A)

qm−n(b).

n(b) is the number of elements of A breaking b.

HA(q) =∑

b∈B(A)

qm−n(b).

HA(q) =∑

b∈B(A)

qm−n(b).

HA(q) =∑

b∈B(A)

qm−n(b).

For A3 we get

The graded dimension is:

6q3 + 6q2 + 3q + 1

Remark that for all polynomials in three variables it is:

. . .+ 10q3 + 6q2 + 3q + 1

Interpretation

The theorem of Dahmen–Micchelli can be formulated both interms of commutative as well as non–commutative algebra.

APPROXIMATION THEORY

APPROXIMATION POWER

THE STRANG-FIX CONDITIONS

The interest of the space of polynomials D(A) comes inapproximation theory from the problem of studying theapproximation of a function f (x) by thefinite element method :

discrete convolution

f (x) 7→∑i∈Zn

BA(x − i)f (i)

Or at order n ∈ N:

f (x) 7→∑i∈Zn

BA(nx − i/n)f (i/n)

More generally we want to find weights ci and approximate fby f (x) 7→

∑i∈Zn BA(nx − i/n)ci and determine a constant k ∈ N

so that (on some bounded region):

|f (x)−∑i∈Zs

BA(nx − i/n)ci | ≤ Cn−k

The maximum k is the

approximation power of BA.

THE CARDINAL SPLINE SPACE

Given a spline M(x) on Rs , with compact support one may defineTHE CARDINAL SPLINE SPACE to be the spaceSM of all (infinite) linear combinations:

SM := {∑i∈Zs

M(x − i)ci}.

The approximation power of M(x) is related to two questions:For which polynomials f (x) we have that

∑i∈Zs M(x − i)f (i) is a

polynomial?2. Which polynomials lie in the cardinal spline space?

THE REMARKABLE SPACE D(A)

TheoremA polynomial f (x) is in the cardinal spline space if and only if

DY f = 0, ∀Y ⊂ A | the span of A \ Y is not V

In other words DY f = 0, ∀Y ⊂ A which are cocircuits.

Strang–Fix conditions

The Strang–Fix conditions is a general statement:

The approximation power of a function M isthe maximum r such that the space of allpolynomials of degree ≤ r is contained in thecardinal space SM.

The case of BA

For the cardinal spline space in the case BA(x) with A integral wehave:1. D(A) is characterized as the space of polynomials f (x) whichreproduce, i.e.map to polynomials under thediscrete convolution.2. D(A) is also characterized as the space of polynomials lying inthe cardinal spline space.

Strang-Fix conditionsThe power of approximation by discrete convolution is measured bythe maximum degree of the space of polynomials which reproduceunder discrete convolution.

SUPERFUNCTIONS

Consider the following algorithm applied to a function g :

gh :=∑i∈Λ

F (x/h − i)g(hi)

There are functions F in the cardinal spline space such that thistransformation is the identity on polynomials of degree < m(A),these are the super–functions. For such functions the previousalgorithm satisfies the requirements of the Strang–Fixapproximation

TheoremWe have, under the explicit algorithm previously constructed that,for any domain G:

||fh − f ||L∞(G) = O(hm(A)).

For every multi–index α ∈ Ns with |α| ≤ m(A)− 1, we have:

||∂αfh − ∂αf ||L∞(G) = ||∂α(fh − f )||L∞(G) = O(hm(A)−|α|).

THE DISCRETE CASE

PARTITION FUNCTIONS

THE DISCRETE CASE

PARTITION FUNCTION

Also in general for vectors we have

The partition function is a quasi polynomial on each big cellin fact on the larger neighborhood c− B(A) on the big cell c.

B(A) :=∑m

i=1 tiai , 0 ≤ ti ≤ 1 is the support of the box spline.

PARTIAL FRACTIONS

The classical method of study of the partition function associatedto a list of numbers a1, . . . , ai , . . . , am is to expand in partialfractions the generating function:

m∏i=1

11− xai

∞∑k=0

xkai =∑

bPA(b)xb

This is done by using the decomposition 1− xn =∏n−1

k=0(1− ζkn x)

where ζn = e 2πin .

PARTIAL FRACTIONS

One then develops in partial fractions as a sum of terms each withpole only in a root of 1, each term can be then developed in asimpler way and one obtains a possible formula.This is also achieved as a residue computation.

IN HIGHER DIMENSION?

One can still develop a much more sophisticated theory of partialfractions and residues!!!

THE DISCRETE CASE

We think of the partition function

PA(b) = #{t1, . . . , tm ∈ N |m∑

i=1tiai = b}

as a distribution

∑λ∈Λ

PA(λ)δλ.withLaplacetransform ∑

λ∈Λ

PA(λ)e−λ =∏a∈A

1(1− e−a)

THE DISCRETE CASE

PA(b) = #{t1, . . . , tm ∈ N |m∑

i=1tiai = b}

as a distribution

∑λ∈Λ

λ∈Λ

1(1− e−a)

THE DISCRETE CASE

PA(b) = #{t1, . . . , tm ∈ N |m∑

i=1tiai = b}

as a distribution

∑λ∈Λ

λ∈Λ

1(1− e−a)

One can see that the leading part of the formula for the partitionfunction is given by the multivariate spline TA and also that thereare formulas using differential operators to pass from the functionsTA to the partition functions.

OTHER FOURIER ISOMORPHIC ALGEBRAS

The periodic Weyl algebras W (U) and W#(Λ)

W (U) = C[e±t1 , . . . , e±tn ,∂

∂t1, . . . ,

∂tn]

algebra of difference operators

W#(Λ) = C[x1, . . . , xn,∇±x1 , . . . ,∇±xn ]

∇xi (xj) =

{xj if i 6= jxi − 1

1. The W#−module D#A := W#(Λ)PA generated, in the space of

tempered distributions, by the partition distribution PA.

2. The W (U) module SA = C[Λ][∏

a∈A(1− e−a)−1] is the algebraobtained from the character ring C[Λ] by invertinguA :=

∏a∈A(1− e−a).

The toric arrangement

T the torus of character group Λ

SA = C[Λ][u−1A ] is the coordinate ring of the open set TA ⊂ T

complement of the union of the subgroups of T of equationsea = 1, a ∈ A.

T the torus of character group Λ

SA = C[Λ][u−1A ] is the coordinate ring of the open set TA ⊂ T

complement of the union of the subgroups of T of equationsea = 1, a ∈ A.

The toric arrangement is the finite set consisting of all theconnected components of the subvarieties obtained by intersectingthe subgroups of T of equations ea = 1, a ∈ A.

EXAMPLE s = 1, T = C∗, A = {5, 3}The arrangement consists of the connected components of thevariety x5 = 1 or x3 = 1, i.e. of the five, fifth roots of 1 and thethree third roots of 1.

Points of the arrangement

The elements of the toric arrangement are ordered by reverseinclusion, particular importance is given to thepoints of the arrangement, P(A)which are the zero-dimensional, i.e. points, elements of thearrangement.A very special case is when P(A) reduces to the point 1, this is theunimodular case.

Each point p ∈ P(A) determines a sublist:

Ap := {a ∈ A | ea(p) = 1}.

EXAMPLE ZP

∣∣∣∣∣1 1 0 −11 0 1 1

∣∣∣∣∣The subgroups are

xy = 1, x = 1, y = 1, x−1y = 1.

We have two points in P(A)

(1, 1), (−1,−1).

A(1,1) = A, A(−1,−1) =

∣∣∣∣∣1 −11 1

∣∣∣∣∣ .

THE FILTRATION

We have as for hyperplanes a filtration by polar orders on SA.

Each graded piece is semisimple.The isopypic components appearing in grade k correspond to theconnected components of the toric arrangement of codimension k

For the top part SA,n/SA,n−1 we have a sum over thepoints of the arrangement P(A).

The isotypic component associated to a point eφ decomposes asdirect sum of irreducibles indexed by the unbroken bases in

Aφ := {a ∈ A | e〈a |φ〉 = 1}.

THE FILTRATION

Aφ := {a ∈ A | e〈a |φ〉 = 1}.

THE FILTRATION

Aφ := {a ∈ A | e〈a |φ〉 = 1}.

THE FILTRATION

Aφ := {a ∈ A | e〈a |φ〉 = 1}.

The previous formula shows in particular, that the partitionfunction is on each cell a

Local structure of PA

LINEAR COMBINATION OFPOLYNOMIALS TIMEPERIODIC EXPONENTIALS.

Such a function is called aQUASI POLINOMIAL

Basic equations

As for the case of the multivariate spline:The quasi polynomials appearing in the formula for PA satisfy

special difference equations

DIFFERENCE OPERATORS

For a ∈ Λ and f a function on Λ we define the the

difference operator:

∇af (x) = f (x)− f (x − a), ∇a = 1− τa.

ExampleAs special functions we have the characters, eigenvectors ofdifference operators.

DIFFERENCE EQUATIONS

Parallel to the study of D(A), one can study thesystem of difference equations

∇Y f = 0, where ∇Y :=∏

v∈Y ∇v

as Y ∈ E(A) runs over the cocircuits.

Let us denote the space of solutions by:

∇(A) := {f : Λ→ C, | ∇Y (f ) = 0, ∀Y ∈ E(A)}.

DIFFERENCE EQUATIONS

Parallel to the study of D(A), one can study thesystem of difference equations

∇Y f = 0, where ∇Y :=∏

v∈Y ∇v

as Y ∈ E(A) runs over the cocircuits.

Let us denote the space of solutions by:

∇(A) := {f : Λ→ C, | ∇Y (f ) = 0, ∀Y ∈ E(A)}.

SECOND THEOREM OF DAHMEN–MICCHELLI

Weighted dimension

The dimension of ∇(A) is the Volume of the box B(A)!δ(A) we have:

δ(A) :=∑

b∈B(A)

| det(b)|.

This formula has a strict connection with the paving of the box.

ExampleLet us take

∣∣∣∣∣0 1 1 −11 0 1 1

∣∣∣∣∣

See thatδ(A) = 1+1+1+1+1+2 = 7is the number of points inwhich the box B(A), shiftedgenerically a little, intersectsthe lattice!

FROM DIFFERENCE TO DIFFERENTIALEQUATIONS

logarithm isomorphismThere is a formal machinery which allows us to interpret, locallyaround a point, difference equations as restriction to the lattice ofdifferential equations, we call it the

logarithm isomorphism

We have this for any moduleover the periodic Weyl algebraC[ ∂

∂xi, exi ] as soon as for

algebraic reasons (nilpotency)we can deduce from the actionof exi also an action of xi .

ALGEBRAIC GEOMETRY

WONDERFUL MODELS

RESIDUES

There is an approach to compute the partition function based onresidues

Start from the case of numbers. Fix positive numbersh := (h1, . . . , hm).

Given an integer n,the number of ways n =

∑i kihi is the coefficient of x−1 in

Ph(x) =∏

x−n−1

1− xhi;

Thus it is a residue.We can use the residue theorem by passing to the other poleswhich are roots on one.

multidimensional residues

In order to define multidimensional residues we need divisors withnormal crossings or a function which in some coordinates xi has apole on the hyperplanes xi = 0, i = 1, . . . , s.The residue is the coefficient of

∏i x−1

DefinitionGiven a subset A ⊂ X the list A := X ∩ 〈A〉 will be called thecompletion of A. In particular A is called complete if A = A.

The space of vectors φ ∈ U such that 〈a|φ〉 = 0 for every a ∈ Awill be denoted by A⊥. Notice that clearly A equals to the list ofvectors a ∈ X which vanish on A⊥.From this we see that we get a bijection between the completesubsets of X and subspaces of the arrangement defined by X .

A central notion in what follows is given by

DefinitionGiven a complete set A ⊂ X , a decomposition is adecomposition A = A1 ∪ A2 in non empty sets, such that:

〈A〉 = 〈A1〉 ⊕ 〈A2〉.

Clearly the two sets A1,A2 are necessarily complete.

We shall say that :a complete set A is irreducible if it does not have a non trivialdecomposition.

TheoremEvery set A can be decomposed as A = A1 ∪ A2 ∪ · · · ∪ Ak withthe Ai irreducible and:

〈A〉 = 〈A1〉 ⊕ 〈A2〉 ⊕ · · · ⊕ 〈Ak〉.This decomposition is unique up to order.

A = A1 ∪ A2 ∪ · · · ∪ Ak is called the decomposition intoirreducibles of A.

Example

An interesting example is that of the configuration space of s-plesof point in a line (or the root system As−1). In this caseX = {zi − zj |1 ≤ i < j ≤ s}.In this case, irreducible sets are in bijection with subsets of{1, . . . , s} with least 2 elements. If S is such a subset thecorresponding irreducible is IS = {zj − zj |{i , j} ⊂ S}.Given a complete set C , the irreducible decomposition of Ccorresponds to a family of disjoint subsets S1, . . . ,Sk of {1, . . . , s}each with at least 2 elements.

DefinitionA family S of irreducibles Ai is called nested if, given elementsAi1 , . . . ,Aih ∈ S mutually incomparable we have thatC := A1 ∪ A2 ∪ · · · ∪ Ai is complete and C := A1 ∪ A2 ∪ · · · ∪ Ai isits decomposition into irreducibles.

Consider the hyperplane arrangement HX and the open set

AX = U/(∪H∈HXH)

complement of the union of the given hyperplanes.Let us denote by I the family of irreducible subsets in X .We construct a minimal smooth variety ZX containing AX as anopen set with complement a normal crossings divisor, plus a propermap π : ZX → U extending the identity of AX .

For any irreducible subset A ∈ I take the vector space V /A⊥ andthe projective space P(V /A⊥).Notice that, since A⊥ ∩ AX = ∅ we have a natural projectionπA : AX → P(V /A⊥). If we denote by j : AX → U the inclusionwe get a map

The model

i := j × (×a∈I πA) : AX → U × (×a∈I P(U/A⊥))

DefinitionThe model ZX is the closure of i(AX ) in U × (×a∈IP(U/A⊥)).

There is a very efficient approach to computations byresidue at points at infinity in the wonderful compactificationof the associated hyperplane arrangement.Divisors at infinity correspond to irreducible subsets.Points at infinity correspond to maximal nested sets. Around eachsuch point one can consider a s−dimensional torus and its class inhomologyA basis of the homology or of the corresponding residuescorresponds to the tori around special points indexed by unbrokenbases.

The non linear coordinates

Given a MNS S choose a basis b := b1, . . . , bs from X so that ifAi is the minimal element of S containing bi we have all the Aidistinct.Construct new coordinates zA, A ∈ S using the monomialexpressions:

bA :=∏

B∈S, A⊆BzB. (2)

The residue at the point 0 for these coordinates is denoted by resb.There is a similar theory for toric arrangements. Now we need tobuild such data for each point of the arrangement getting residuesresb,φ.

GENERAL FORMULA FOR TX

One can find explicit polynomials pb,A(x), indexed by the points atinfinity associated to the maximal nested set generated byunbroken bases so that:Given a point x in the closure of a big cell c we have

Jeffry-Kirwan residue formula

TA(x) =∑

b | c⊂C(b)

| det(b)|−1pb,A(−x).

PA(x) =∑

φ∈P(A)

eφ∑

qb,φ(−x)

PA(x) =∑

φ∈P(A)

eφ∑

qb,φ(−x)

The residue

The polynomials building the multivariate spline and the partitionfunctions areresidues:

Spline

pb,X (−y) = det(b)resb( e〈y |x〉∏

a∈X 〈x | a〉). (3)

Partition function

qb,Xφ(−y) = det(b)resb,φ( e〈y |z〉∏

a∈X (1− e−a(z)−〈φ | a〉)

). (4)

SUMMARIZING

Given a list of vectors A.

We have the two functions TA,BA.TA is supported on the cone C(A) and coincides on each bigcell with a homogeneous polynomial in the space D(A)defined by the differential equations DY f = 0, Y ∈ E(A).The space D(A) has as dimension the number d(A) of basesextracted from A.There are explicit formulas to compute TA on each cell.BA is deduced from TA.

When A is in a lattice we have the partition function PA.

PA is supported on the intersection of the lattice with thecone C(A) and coincides on each big cell with a quasipolynomial in the space ∇(A) defined by the differenceequations ∇Y f = 0, Y ∈ E(A).The space ∇(A) has as dimension δ(A) the weighted numberof bases extracted from A.There are explicit formulas to compute PA on each cell.

We have shown that there are interesting constructions incommutative and non commutative algebra associated to the studyof these functions.

A particularly interesting case is when we take for A the list ofpositive roots of a root system, or multiples of this list. In this caseone has applications to Clebsh–Gordan coefficients.

THE END

· AobjectstheoremsCombinatoricsSplinesAlgebraApproximationArithmeticResiduesZ...

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