Abstract
The Plücker matrix of the Lagrangian Grassmannian , is determined by the linear envelope of the Lagrangian Grassmannian. The linear envelope is the intersection of linear relations of Plücker of Lagrangian Grassmannian, defined here. The Plücker matrix is a direct sum of the incidence matrix of the configuration of subsets. These matrices determine the isotropy index and -atlas which are invariants associated with the symplectic vector space E.
Keywords:
Lagrangian Grassmannian; Linear Envelope; Contraction Map; Incidence Matrices; Radical Ideal; Seindeber’s lemma MSC:
14A99; 15B99; 37J11; 53D12; 94B35
1. Introduction
Given E a symplectic vector space of dimension , the set of all Lagrangian subspaces of E is called the Lagrangian Grassmannian of E. These spaces have a prominent role in symplectic geometry that, in the words of Dusa McDuff “Symplectic geometry: is the geometry of closed skew-symmetric form, thus symplectic geometry is essentially topological in nature ”, see [1]. In this article it is shown that the Lagrangian Grassmannian has a rich algebraic structure when we assign a coordinate system known as Plücker coordinates. For this we prove the existence of a matrix , whose kernel contains all the Lagrangian subspaces of E. This matrix is built with the minimal family of linear form in that nullify the Lagrangian Grassmannian under Plücker inclusion. another way of seeing is as a sum of matrices of the family where , is a -matrix, sparce, with -ones in each row and -ones in each column where and is an index that measures the degree of isotropy in E. In this paper, we have:
In Section 3 and Section 4 we construct a family of homogeneous polynomial equations whose solutions parameterize the elements of , we call this family of polynomials Plücker relations of Lagrangian Grassmannian. In Section 5 and Section 6 we show that the linear relations of Plücker of the Lagrangian-Grassmannain is the minimal family, up to linear combination, of homogeneous linear polynomials that nullify .
In Section 7 calculate the matrix associated with the linear envelope of , we call this the Plücker matrix of the Lagrangian Grassmannian. We can see that is the incidence matrix of a family of subsets of the set of indices and so is a direct sum of submatrices, each belonging to the set . Where is a sparce matrix of zeroes and ones with k-ones in each row and -ones in each column.
Section 8 the isotropy index is studied as an invariant of and that, among other things, allows us to compare Lagrangian Grassmannian.
De Concini and Lakshmibai [1981] [2] show that the Lagrangian Grassmannian is defined by quadratic relations. These relations are obtained by expressing as a linear section of , so , where is the projectivization of a vector space such that , where is -representation of highest weight , and where see [3] (pages 182–184), [2,4].
The advantage of our approach is that we have the equations that define as a projective variety and with this we obtain a totally explicit information of as a linear section of the Grassmannian (see Theorem 6) and in this way, we can give a connection with matrix theory and symplectic geometry which opens a computational horizon in these topics. In [5] we show that the homogeneous linear functionals also allow us to describe the k-Grassmannian-Isotropic , of a symplectic vector space E of dimension and we give the Plücker matrix of which is a generalization of . The following bibliography is relevant for this research in [6,7,8,9,10] we can see a few results about . See [9,11,12,13,14], where you can see some applications of .
2. Preliminaries
Using terminology and definitions as given in [15,16], let E be an vector space defined over an arbitrary field . A symplectic form is a bilinear map
that satisfies
and it is said to be skew-symmetric nondegenerate. is called a symplectic vector space, a symplectic vector space E is necessarily of even dimension and there is a basis of E such that
where is the Kroneker delta function. A subspace is said to be isotropic if for all . A subspace is said to be Lagrangian subspace if L is isotropic and . The collection of Lagrangian subspace of E, we call it Lagrangian Grassmannian of E or simply Lagrangian Grassmannian we denote it by . A subspace is a symplectic subspace of E if the symplectic form in E when restricted to W remains symplectic.
Indices
Let m be an integer we denote
to the set of the first m integers. Let m and ℓ be positive integers such that as usual in the literature denotes binomial coefficient. If then, . If is a positive integer and is a non-empty set, we define the sets
if then ; with this notation if we define . So we have
We say
if there is a permutation such that arrange the elements of in increasing order so we have .
if and are elements of then we say that
Let , suppose there are such that in this case and we write this pair as so we define the set
and if such that , then we say that and that , note that .
We define the sets
similarly we define
Lemma 1.
- (A)
- Let be an even number such that , then
- (B)
- Let be an odd and such that , then
Proof.
(A) Let even integer and let , then it is it is enough to prove the inclusion ⊆. If then it exists such that . If there are and such that and .
(B) If odd integer and let , analogously it is sufficient to show the ⊆.
Let then if , if then with .
If then it exists such that and with
with which the demonstration ends. □
Given a canonical basis , see [15], of symplectic vector space E, in this article, we redefine its elements as follows and we have such that
For , write
where means that the corresponding term is omitted.
Denote by the n-th exterior power of E, which is generated by . For , the coefficients are the Plücker coordinates of w, see also [17] (p. 42). Writing as , the vector
we call the Plücker vector of w.
3. Contraction Map
In this section, E is a symplectic vector space of dimension .
Definition 1.
The contraction map is defined as the linear transformation
where means that the corresponding term is omitted; see [18] (p. 283).
Lemma 2.
Let f the contraction map, and arbitrary element, in coordinates of Plücker, then the contraction map
where disappears from the equation if .
Proof.
Let arbitrary element then
where clearly disappears from the equation if
Let a symplectomorphism that sends the symplectic basis in the symplectic basis such that then let us consider the following linear transformations defined in generators such that and such that
Lemma 3.
Let the contraction map then the following diagram commutes
that is to say
4. Plücker Relations of Lagrangian Grassmannian
For an m-dimensional vector space E, denote by the set of vector subspaces of dimension ℓ of E. The Grassmannian is a algebraic variety of dimension and can be embedded in a projective space , where by Plücker embedding. The Plücker embedding is the injective mapping given on each by choosing a basis of W and then mapping the vector subspace to the tensor . Since choosing a different basis of W changes the tensor by a nonzero scalar, this tensor is a well-defined element in the projective space , where . If , then if and only if for each pair of tuples and , the Plücker coordinates of w satisfy the Plücker relations
where means that the corresponding term is omitted and where , , see [17] (Section 4) and [19]. Under the inclusion of Plücker the Lagrangian Grassmannian is given by
Lemma 4.
Proof.
Using the Definition 1 we clearly have given that for all .
Let then is a family of linearly independent vectors in more over by hypothesis then for all and so . □
The proof of the following lemma is a consequence of the Lemma 2 where the kernel of the contraction map f is characterized as follows
Lemma 5.
Let written in Plücker coordinates, then we have
where disappears from the equation if
For all we define a homogeneous linear polynomial
where
Remark 1.
Corollary 1.
is independent of the symplectic basis and
Proof.
From the Lemma 2, we have so then since is an isomorphism and then . □
Following [20] for definitions of algebraic set, we have below that , and are algebraic sets in
So we have to is an algebraic set of
see (14).
Theorem 1.
Definition 2.
To the set of homogeneous polynomials
where , , we call it relations of Pücker of Lagrangian Grassmannian. To the set of linear homogeneous polynomials
we call it linear relations of Plücker of the Lagrangian Grassmannian.
Example 1.
In the case we have the relations of Plücker of Lagrangian Grassmannian
Example 2.
The linear relations of Plücker of are
Example 3.
In the case it was shown in [21] (example 4) that linear relations consists of 28 homogeneous linear equations in 70 variables.
Ideal of
The ideal of in is defines by
Proposition 1.
If the field of definition of the symplectic vector, space E is algebraically closed then
so is a projective variety.
Proof.
By the Theorem 1 we have and by Hilbert’s Nullstellensatz theorem, see [20] (Theorem 1.3 A), the result is fulfilled. □
Let be a finite field with q elements, and denote by an algebraic closure of . For a vector space E over of finite dimension k, let be the corresponding vector space over the algebraically closed field . We will be considering algebraic varieties in the projective space . Recall that a projective variety is defined over the finite field if its vanishing ideal can be generated by polynomials with coefficients in . If E is a symplectic vector space, of dimension define over a finite field , then the rational points of are defined as the set
where , , and more over
see [22] (Prop. 2.14).
We define the ideal as
where y
Lemma 6.
The ideal is radical
Proof.
The ideal is zero-dimensional since the set of solutions to the homogeneous polynomial equations
given that (26) implies moreover so , and by Seindeber’s lemma, ver [23] (Proposition 3.7.15), is radical. □
Let a hyperplane of codimension t, we say that is a linear section of the Lagrangian Grassmannian and let
Lemma 7.
Suppose the basis field is perfect then the linear section of the Lagrangian Grassmannian satisfies
Proof.
Given the then the ideal is zero dimensional, , thus by Seindeber’s lemma we have that the ideal is radical. □
5. Factorable Morphisms
Let two integers, we define
with the notation (2) we define
Let
For be a basic vector and let generated by the vector and
In [16] (Lemma 1.4.38) it shows that there is a one-to-one correspondence between and , where is a symplectic vector space of dimension , generated by the symplectic basis , recall ^ means that the term was omitted.
As consequence we have and
so in Plücker coordinates we have
denotes the basis vector of the dual vector space and the basis vector of dual vector space . Now with this notation we define in generators an injective linear transformation
with and .
Definition 3.
We say that is factored if for some where and . We say h satisfies the factoring property if there are at least one coefficient and there are at least one element such that
Example 4.
The homogeneous linear polynomials given in the Example 2 are factored
Proposition 2.
Let such that and is factored. Then where .
Proof.
then and
□
We denote by , where ^ means that the corresponding term is omitted. We define
an element of in such that
Lemma 8.
Let where and see (28) then
Proof.
Let be from (33) we have and for f contraction map, we have
then
where and as stated before . Now iff , note that with this condition we have . Renaming , we have . Then
for all , that is where
for all □
Remark 2.
Note that
for all .
6. Linear Envelope of
In this section, E is a symplectic vector space of dimension .
Remark 3.
Sometimes in (37), it is necessary to distinguish the even case from the odd case so we write to n even number and for n odd number.
Lemma 9.
is a nontrivial vector subspace of
Proof.
The proof follows from (18) given that , so is a vector subspace of . □
Let to the set we call it a hyperplane containing .
Definition 4.
The Linear Envelope of is the smallest linear variety that contains in
The proof of the following corollary follows directly from the Definition 4.
Corollary 2.
Proposition 3.
Let and a coefficient different from zero of h then it exists such that .
Proof.
Suppose that for each you have to this means that then and so then which is a contradiction. Then there are such that . □
For each , we define
for each we define
Similarly
for each and we define
for each we define
Corollary 3.
Let E symplectic vector space of dimention
- (i)
- If even, and let thenwith such that and .
- (ii)
- If odd, and let thenwith such that and
Proof.
The proof follows directly from the Lemma 1 and Proposition 3. □
Lemma 10.
Let E symplectic vector space of dimension 4, and such that then for A a non-zero constant and .
Proof.
Clearly by Proposition 3 each it is of the form it is easy see that since it satisfies the Equation (24) more over consequently where .
□
Theorem 2.
Let E symplectic vector space of dimension , such that and is factored then .
Proof.
The proof is by induction on n. If it follows from Lemma 10.
We induction hypothesis is, let symplectic vector space of dimension with and such that then .
If such that and is factored, then by Lemma 2 where then by induction step so . □
Proposition 4.
Let such that and satisfies the factoring property from the Definition 3, then or , where and satisfies that for all coefficients moreover .
Proof.
The proof is by induction on n. If it follows from Lemma 10.
Induction Step): Let symplectic vector space of dimension with and such that then .
Let a non-zero coefficient that satisfies the factoring property, from the Definition 3, then there is but . Now let as in (30). So if then where . We define homogeneous linear polynomials and such that and so and . Note that , because as we mentioned before , from (33) we have that that is , since we obtain moreover with where then by Proposition 2 we have then . Given that and if satisfies the factoring property from the Definition 3 continuing recursively in the same way, the process ends in a finite number of steps in or , where and with for all coefficients and . □
Corollary 4.
Let E symplectic vector space of dimension
(a) If odd then
(b) If then such that and satisfies that for all coefficients and
Proof.
(a) If then by Corollary 3
with such that and satisfies the factoring property for all non-zero coefficients of h then by Proposition 4 we have
(b) If then by Corollary 3 we have where and with such that and . Clearly satisfies factoring property of the Definition 3 for all non-zero coefficients of , so by Proposition 4 we have and each coefficient of satisfies that and . □
For an arbitrary element where we say that
For an arbitrary subset then there are a partition of of the form
where
to and .
where
Lemma 11.
Let E symplectic vector space of dimention , non-empty set and such that then .
Proof.
Let and without loss of generality we can assume that for all . Now let We define
then for each we define
such that
Let a fixed element, and then
Moreover, implies so by Corollary 4 given that is odd number and we have so
then . □
Corollary 5.
If even then .
Proof.
By Corollary 4(b) and by the Lemma 11, we have . □
Theorem 3.
Let E be a symplectic vector space of dimension then
- (I)
- (II)
- .
Lemma 12.
Let a linear transformation such that then .
Proof.
we have so that is . □
Corollary 6.
Suppose the contraction map f is surjective and suppose is a surjective linear transformation that vanishes then there exists a unique isomorphism such that .
Proof.
By Lemma 12 we have , more over since both have the same dimension because f and G are surjective, then there exists a unique linear isomorphism h that makes the following diagram commute.
and so we have to . □
Corollary 7.
Suppose the contraction map f is surjective then
- (i)
- If H is a matrix of order and maximum rank that annuls , then , where P is an invertible matrix.
- (ii)
- Suppose that there exists R matrix such that . Then where P is an invertible matrix.
Proof.
The proof of (i) follows directly from the Lemma 6. For the (ii) suppose that is a rank matrix such that , then and . If the affirmation is followed by the previous clause of this lemma. Now suppose that then ; this implies that
which is a contradiction and therefore . □
7. The Plücker Matrix of the Lagrangian Grassmannian
Definition 5.
Let E symplectic vector space of dimension defined over an arbitrary field arbitrary. The matrix
of order associated to the linear equations system we call the Plücker matrix of the Lagrangian Grassmannian.
The main result of this section is
Theorem 4.
Let E symplectic vector space of dimension and let , then there exists a family
of -matrices, regular, sparse such that
- (A)
- If be an even integer and , thenwhere is a copy of , for each .
- (B)
- If be an odd integer thenwhere is a copy of for each and n-times.
7.1. Configuration of Subsets
Following [24] (p. 3) we call an n-set. Now let be m distinct subsets of the n-set X. We refer to this collection of subsets of an n-set as a configuration of subsets. We set if and we set if . The resulting -matrix , , of size m by n is the incidence matrix for the configurations of subsets of the n-set X. The s in row i of A display the elements in the subsets and the s in column j display the occurrences of the element among the subsets. If a matrix A has all its coefficients equal 0 or 1 is called a -matrix. Give a -matrix A we say that is regular if the number of 1’s is fixed in each column and has a fixed number of 1’s in each row. If A is not regular we say that is irregular see [10,25,26], for more information. A sparse matrix is a -matrix in which most of the elements are zero.
Configuration of Incidence
Let an n-set and be m subsets of the n-set S and the incidence matrix, for the configuration of subsets . The pair
we call configuration of incidence of S. If ,where , is other configuration of incidence then they are isomorphic if and only if there is a bijection
such that for all and note where and are -incidence matrices.
Let be an incidence configuration, with S an n-set and a set of cardinality 1 then using the Cartesian product we define the Cartesian incidence configuration as follows
where .
Lemma 13.
The Cartesian incidence configuration is isomorphic to and they have the same incidence matrix.
Proof.
Since then the projection mapping
is one–one and clearly ; thus, the configurations are isomorphic. Moreover, if and only if and so both configurations have the same incidence matrix. □
Remark 4.
Note that .
7.2. Properties
Let even integer, given the incidence configuration (50)
let then we have
Lemma 14.
For all we have .
Proof.
If and then
□
Lemma 15.
Let and two different elements of then
Proof.
Let . Then there are two different positive integers N and M such that , also , as a consequence we have to so
Suppose then there exist M, N distinct positive integers such that and and so it exists such that then we have and so . □
Corollary 8.
Let and be two different of then .
Proof.
Be then there are two positive integers N and M different such that , also , as a consequence we have to clearly then for Lemma 15 we have so we have to , analogously we have to . Suppose there is another then there exist y distinct positive integers such that , as then then . Analogously, , and so we have to so then so by (5) we have □
Corollary 9.
If then
Proof.
Suppose and then by Corollary 8 we have to ; however, this is not possible since by Lemma 14, but and so which implies that □
We call the intersection count of the
Lemma 16.
Proof.
Clearly if and only if , so the number subsets that contain is equal to
in consequence we have □
Let us denote by
the -incidence matrix from
Proposition 5.
Let even positive integer and let then
- (a)
- has -ones in each row;
- (b)
- has -ones in each column;
- (c)
- every two lines have at most one 1 in common;
- (d)
- is sparse.
Proof.
The case generates the matrix which trivially satisfies all the statements of this statement. So we assume that for the rest of the proof.
(a): the in row of display the elements in the subset so by Lemma 14 each row has exactly ones in each row.
(b): the in the column display the occurrences of the elements of among the subsets, which follows from Lemma 16.
(c): follows directly from Corollary 8.
(d): The density of ones in the matrix is given by
which approaches zero as m approaches infinity. □
Definition 6.
Let is an integer and we define the -atlas
of incidence matrices corresponding to the family
of incidence configurations.
7.3. Cartesian Configurations
For even, , and such that then we define as in (8). We define an Cartesian incidence configuration as in (49)
where
is an -set and the subsets are
for all .
Lemma 17.
For even, , and such that then the incidence matrix of (55) is an element of
Proof.
As if we make and , clearly and . A simple calculation shows that , then renumber the elements of so by the Lemma 13 the incidence configuration
is isomorphic to
so also for the Lemma 13 both have the same incidence matrix . We denote this matrix by
□
For odd and and , we define an Cartesian incidence configuration as in (49)
where
is a -set and its subsets are defined by
with .
For odd and , , consider such that we define , as in (8).
We define a Cartesian incidence configuration as in (49)
where
is -set where the family of subsets is given by
for all .
Proof.
For the proof of (a) If we do and define so on
so both have the same incidence matrix . Given that , we denote the incidence matrix (58) by
and part (a) has been proved.
For the proof of (b), clearly if we do and , clearly we have that . Now if we rename the elements of then
is isomorphic to the incidence configuration
then note that implies that . □
7.4. Function
For , we consider
Clearly
up to permutation of rows.
Lemma 19.
The function φ is injective.
Proof.
Let such that then this implies and by Corollary 9 we have □
Corollary 10.
Let integer even, then
- (a)
- (b)
- Let odd integer, then
- (c)
- for all
- (d)
Proof.
The proof follows directly from the Lemma 19 and Remark 5. □
7.5. Proof of the Theorem 4
(A) by Lemma 1 we have
since is injective we have a partition in the image, so
Associating the corresponding matrix, using the Corollary 10, we have that
Part (B) Proceeding as in part (A) of this proof, we have
we obtain
Example 5.
Let E be a symplectic vector space of dimension 12 and then his -atlas is given by the triplet of matrices
where
and
is a row matrix filled with zeros and with only two entries equal to 1.
The matrix , of size , associated to the homogeneous system can be given by a block diagonal matrix as follows
where there are 1 matrix , 60 submatrices , and 240 submatrices .
Example 6.
Let E be a symplectic vector space of dimension 14 and then his 4-atlas is given by
where , and .
8. Isotropy Index and -Atlas
Definition 7.
Let E symplectic vector space of dimension ; we call isotropy index of E.
Lemma 20.
Let f be the contraction map, then .
Proof.
Let and ρ Plücker embedding we denote , it as in (13), then by Lemma 2 and by Equation (68) the following diagram commutes i.e.,
so we have
which proves commutativity so .
□
Corollary 11.
is isomorphic to as vector spaces.
Proof.
From the Lemma 20 we have , and both have the same dimension so is an isomorphism of vector spaces. □
The following corollary follows directly from the Lemma 20.
Corollary 12.
Let E symplectic vector spaces of dimension then
Moreover, .
Theorem 5.
Let E symplectic vector space of dimension defined over an arbitrary field and the isotropy index.
Then, the following are equivalent:
(a) or
(b)
(c)
(d) and is a base of .
Proof.
(a) and (b) are equivalent by [27] (Theorem 6).
(b) and (c) are equivalent by Corollary 11 and by Corollary 12.
(c) and (d) are equivalent by Theorem 3. □
We say that the embedding rank of is the dimension of the linear envelope .
Lemma 21.
Embedding rank of is .
Proof.
We have that
□
Corollary 13.
Let E a symplectic vector space of dimension , let f the contraction map the isotropy index are equivalent
- (1)
- f is surjective
- (2)
- (3)
- (4)
- char or char
- (5)
- is maximum for everything .
Proof.
(1) is equivalent (2), (2) is equivalent (3) given that y .
(3) is equivalent (4) is followed from [27] (Theorem 6) and finally (3) is equivalent (5) is obvious. □
Example 7.
By Corollary 12 , the order of is and by Theorem 4, is a submatrix of and it is also easy to see that
and
Example 8.
Consider the matrices and in a field . By elementary matrix operations we have
This matrix we denote by .
As we saw in the Example 7, in it is of the form
and we denote by .
In [18] (Corollary 1.2) can find a more general case of Theorem 5 also see [28] for some examples where , and 7.
Let E be a symplectic vector space of dimension and let consider the family of matrices given in (53)
and we call the -atlas of .
Lemma 22.
Let n integer and let E and symplectic vector spaces of dimension and , respectively, then
Proof.
If both m and n are even integers or if both m and n odd integers then .
Suppose that n is even integer and is odd integer. If then so what it implies so .
If then . Now if then □
Corollary 14.
(a) If E and both are symplectic vector spaces of dimension so they share the same -atlas.
(b) Let E symplectic vector space of dimension and let symplectic vector space of dimension then both spaces share the same -atlas.
Proof.
The proof follows directly from the Lemma 22. □
Example 9.
Let and two symplectic vector spaces of dimension defined over a field and isotropy index . If char=0 or char then and they share
(a) the same isotropy index ;
(b) the same -atlas ;
(c) the same Plücker relations of the Lagrangian Grassmannian variety.
Example 10.
Consider the symplectic vector spaces and , two symplectic vector space non-symplectomorphisms, them
(a) They share the same isotropy index .
(b) They share the same Plücker relations of the Lagrangian Grassmannian variety.
However, they do not share the same 4-atlas so we have:
the 4-atlas of is see Example 5
but the 4-atlas of is see Example 8.
Hypersurfaces in
The linear sections of the Lagrangian-Grassmannian have applications in other fields of mathematics see [14]. Using the notation we define the following linear varieties.
Definition 8.
Let even integer then
- (a)
- (b)
Let odd integer then
- (c)
- (d)
Theorem 6.
Let E be a symplectic vector space of dimension then is intersection of linear sections of the Grassmannian variety and is included in a projective space of a direct sum of matrix kernels
- (A)
- If even integer and let , then
- (B)
- If odd integer and let , then
Funding
The APC was funded by research project 2024 del Colegio de Ciencia y Tecnología of the Universidad Autónoma de la Ciudad de México.
Data Availability Statement
Data are contained within the article.
Conflicts of Interest
The author does not declare a conflict of interest.
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