Generalising the scattered property of subspaces
Bence Csajb\'ok, Giuseppe Marino, Olga Polverino, Ferdinando Zullo

TL;DR
This paper extends the theory of scattered subspaces in finite vector spaces by establishing a new upper bound for their dimension when h>1, and explores their properties, duality, and applications.
Contribution
It generalizes the known bound for 1-scattered subspaces to h-scattered subspaces and provides constructions, duality relations, and equivalence analysis.
Findings
Established the upper bound rn/(h+1) for h-scattered subspaces.
Constructed examples achieving the new bound.
Analyzed intersection properties and duality of these subspaces.
Abstract
Let be an -dimensional -vector space. We call an -subspace of -scattered if meets the -dimensional -subspaces of in -subspaces of dimension at most . In 2000 Blokhuis and Lavrauw proved that when is -scattered. Subspaces attaining this bound have been investigated intensively because of their relations with projective two-weight codes and strongly regular graphs. MRD-codes with a maximum idealiser have also been linked to -dimensional -scattered subspaces and to -dimensional -scattered subspaces. In this paper we prove the upper bound for the dimension of -scattered subspaces, , and construct examples with this dimension. We study their intersection numbers with hyperplanes, introduce a duality relation among…
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Generalising the scattered property of subspaces
Bence Csajbók, Giuseppe Marino, Olga Polverino and Ferdinando Zullo
The research was supported by the Italian National Group for Algebraic and Geometric Structures and their Applications (GNSAGA
- INdAM). The first author was partially supported by the János Bolyai Research Scholarship of the Hungarian Academy of Sciences and by OTKA grants PD 132463 and K 124950. The last two authors were supported by the project “VALERE: VAnviteLli pEr la RicErca” of the University of Campania “Luigi Vanvitelli”.
Abstract
Let be an -dimensional -vector space. We call an -subspace of -scattered if meets the -dimensional -subspaces of in -subspaces of dimension at most . In 2000 Blokhuis and Lavrauw proved that when is -scattered. Subspaces attaining this bound have been investigated intensively because of their relations with projective two-weight codes and strongly regular graphs. MRD-codes with a maximum idealiser have also been linked to -dimensional -scattered subspaces and to -dimensional -scattered subspaces.
In this paper we prove the upper bound for the dimension of -scattered subspaces, , and construct examples with this dimension. We study their intersection numbers with hyperplanes, introduce a duality relation among them, and study the equivalence problem of the corresponding linear sets.
1 Introduction
Let denote an -dimensional -vector space. A -spread of is a set of -dimensional -subspaces such that each vector of is contained in exactly one element of . As shown by Segre in [26], a -spread of exists if and only if .
Let be an -dimensional -vector space and let be an -spread of , viewed as an -vector space. An -subspace of is called scattered w.r.t. if it meets every element of in an -subspace of dimension at most one, see [4]. If we consider as an -dimensional -vector space, then it is well-known that the one-dimensional -subspaces of , viewed as -dimensional -subspaces, form an -spread of . This spread is called the Desarguesian spread. In this paper scattered will always mean scattered w.r.t. the Desarguesian spread. For such subspaces Blokhuis and Lavrauw showed that their dimension can be bounded by . After a series of papers, it is now known that when then there always exist scattered subspaces of this dimension [1, 3, 4, 11].
In this paper we introduce and study the following special class of scattered subspaces.
Definition 1.1**.**
Let be an -dimensional -vector space. An -subspace of is called -scattered, , if and each -dimensional -subspace of meets in an -subspace of dimension at most . An -scattered subspace of highest possible dimension is called a maximum -scattered subspace.
With this definition, the -scattered subspaces are the scattered subspaces generating over . With the above definition would give the -dimensional -subspaces of defining subgeometries of . If and , then defines a scattered -linear set with respect to hyperplanes, introduced in [28, Definition 14]. A further generalisation of the concept of -scattered subspaces can be found in the recent paper [2].
In this paper we prove that for an -scattered subspace of , if does not define a subgeometry, then
[TABLE]
cf. Theorem 2.3. Clearly, -scattered subspaces reaching bound (1) are maximum -scattered. When then our examples prove that maximum -scattered subspaces have dimension , cf. Theorem 2.6. In Theorem 2.7 we show that -scattered subspaces of dimension meet hyperplanes of in -subspaces of dimension at least and at most . Then we introduce a duality relation between maximum -scattered subspaces of reaching bound (1) and maximum -scattered subspaces of reaching bound (1), which allows us to give some constructions also when is not a divisor of , cf. Theorem 3.6.
Proposition 2.1 shows us that -scattered subspaces are special classes of -scattered subspaces. In [28, Corollary 4.4] the -scattered subspaces of attaining bound (1), i.e. of dimension , have been shown to be equivalent to MRD-codes of with minimum rank distance and with left or right idealiser isomorphic to . In Section 4 we study the -linear set determined by an -scattered subspace . In contrast to the case of -scattered subspaces, it turns out that for any -scattered -subspaces and of with , the corresponding linear sets and are -equivalent if and only if and are -equivalent, cf. Theorem 4.5. For this result extends [28, Proposition 3.5] regarding the equivalence between MRD-codes and maximum -scattered subspaces attaining bound (1) into an equivalence between MRD-codes and the corresponding linear sets, see [28, Remarks 4, 5].
2 The maximum dimension of an -scattered subspace
We start this section by the following result.
Proposition 2.1**.**
For the -scattered subspaces are also -scattered for any . In particular they are all -scattered.
Proof.
Let be an -scattered subspace of . Suppose to the contrary that it is not -scattered for some . Therefore, there exists an -dimensional -subspace such that . As , there exist such that . Then
[TABLE]
a contradiction. ∎
In the proof of the main result of this section we will need the following lemma.
Lemma 2.2**.**
For any integer with in there exists an -scattered -subspace of dimension .
Proof.
Fix an -basis of , then the space can be seen as . Consider the -dimensional -subspace of . Let be any -dimensional -subspace of . The intersection of with a hyperplane of is
[TABLE]
which is clearly an -subspace of size at most . If then there was a hyperplane of containing , a contradiction, i.e. is an -scattered -subspace of . ∎
For , the following result was shown in [4].
Theorem 2.3**.**
Let be an -dimensional -vector space and an -scattered -subspace of . Then either
- •
, defines a subgeometry of and is -scattered, or
- •
*. *
Proof.
Let denote the dimension of over . Since , we have and in case of equality defines a subgeometry of which is clearly -scattered. From now on we may assume . First consider the case . Fix an -basis in and for denote the -th coordinate w.r.t. this basis by . Consider the following set of -linear maps from to :
[TABLE]
First we show that the non-zero maps of have rank at least . Indeed, if , then if and only if , i.e. , where is the hyperplane of . Since is -scattered, it follows that and hence the rank of is at least . Next we show that any two maps of are different. Suppose to the contrary , then is the zero map. If , then would be contained in the hyperplane , a contradiction since . Hence, .
Suppose to the contrary . The elements of form a -dimensional -subspace of and the non-zero maps of have rank at least . By Result 4.6 (Singleton-like bound) we get and hence , which contradicts .
From now on, we will assume , since the assertion has been proved in [4] for .
First we assume . Then by Lemma 2.2, in there exists an -scattered -subspace of dimension .
Let be an -linear transformation from to itself with . Clearly, . For each consider the -linear map
[TABLE]
Consider the following set of -linear maps
[TABLE]
Our aim is to show that these maps are pairwise distinct and hence . Suppose . It follows that is the zero map, i.e.
[TABLE]
For , put , let and let . We want to show that . If , then by (2)
[TABLE]
hence , which is not possible since is an -dimensional -subspace of and is -scattered. Hence . Assume . Let be the -linear map defined by the rule
[TABLE]
and consider the map . Note that is the restriction of on the -vector subspace of . It can be easily seen that
[TABLE]
[TABLE]
and by (2)
[TABLE]
Since , by Proposition 2.1 the -subspace is -scattered in and hence taking (3) and (4) into account we get , which yields
[TABLE]
By Proposition 2.1 the -subspace is also a -scattered subspace of , thus by (5)
[TABLE]
contradicting (6). It follows that , i.e. for each and hence . The trivial upper bound for the size of is the size of , thus
[TABLE]
which implies
[TABLE]
Now assume . By Proposition 2.1 is -scattered with . Since and , we can argue as before and derive , contradicting . ∎
The previous proof can be adapted also for the case without introducing the subspace , cf. [30].
The following result is a generalisation of [3, Theorem 3.1].
Theorem 2.4**.**
Let where and . If is an -scattered -subspace in , then the -subspace is -scattered in , with . Also, if is -scattered in and its dimension reaches bound (1), then is -scattered in and its dimension reaches bound (1).
Proof.
Clearly, it is enough to prove the assertion for .
If , the result easily follows from Proposition 2.1 and from [3, Theorem 3.1]; hence, we may assume .
By way of contradiction suppose that there exists an -dimensional -subspace of such that
[TABLE]
Clearly, cannot be contained in since is -scattered in . Let and . Then and by Proposition 2.1, the -subspace is -scattered in , thus . Denoting by , the Grassmann formula and (7) yield
[TABLE]
Consider the subspace of the quotient space . Then and contains the -subspace
[TABLE]
Since is also contained in the -subspace , then is -scattered in and hence by and by Proposition 2.1, is also -scattered in .
On the other hand,
[TABLE]
[TABLE]
and hence, by (8),
[TABLE]
a contradiction.
The last part follows from . ∎
Constructions of maximum -scattered -subspaces of exist for all values of , and , provided is even [1, 3, 4, 11]. For , see [2, Section 5]. Also, there are constructions of maximum -scattered -subspaces arising from MRD-codes (explained later in Section 4.1) for all values of , and , cf. [28, Corollary 4.4]. In particular, the so called Gabidulin codes produce Example 2.5. One can also prove directly that these are maximum -scattered subspaces by the same arguments as in the proof of Lemma 2.2.
Example 2.5**.**
In , if , then the -subspace
[TABLE]
is maximum -scattered of dimension .
Theorem 2.6**.**
If divides and , then in there exist maximum -scattered -subspaces of dimension .
Proof.
Put and consider , with an -subspace of with dimension . For each consider a maximum -scattered -subspace in of dimension which exists because of Example 2.5. By Theorem 2.4, is an -scattered -subspace of with dimension . ∎
In Theorem 2.6 we exhibit examples of maximum -scattered subspaces of whenever divides . In Section 3 we introduce a method to construct such subspaces also when does not divide . To do this, we will need an upper bound on the dimension of intersections of hyperplanes of with a maximum -scattered subspace of dimension . The proof of the following theorem is developed in Section 5.
Theorem 2.7**.**
If is a maximum -scattered -subspace of a vector space of dimension , then for any -dimensional -subspace of we have
[TABLE]
The above theorem is a generalisation of [4, Theorem 4.2] and the first part of its proof relies on the counting technique developed in [4, Theorem 4.2].
3 Delsarte dual of an -scattered subspace
Let be a -dimensional -subspace of a vector space , with . By [21, Theorems 1, 2] (see also [20, Theorem 1]), there is an embedding of in with for some -dimensional -subspace such that , where is a -dimensional -subspace of , and . Then the quotient space is isomorphic to and under this isomorphism is the image of the -subspace of .
Now, let be a non-degenerate reflexive sesquilinear form on with companion automorphism . Then can be extended to a non-degenerate reflexive sesquilinear form . Indeed if is an -basis of , since , for each we have
[TABLE]
where , and is an automorphism of such that . Let and be the orthogonal complement maps defined by and on the lattice of -subspaces of and of -subspaces of , respectively. For an -subspace of the -subspace of will be denoted by . In this case .
In this setting, we can prove the following preliminary result.
Proposition 3.1**.**
Let , , , , and be defined as above. If is a -dimensional -subspace of with and
[TABLE]
then is a -dimensional -subspace of the quotient space .
Proof.
As described above, turns out to be isomorphic to the -subspace of the quotient space . By ( ‣ 3.1), since each hyperplane of is of form where is a hyperplane of containing , it follows that
[TABLE]
To prove the assertion it is enough to prove
[TABLE]
Indeed, by way of contradiction, suppose that there exists a nonzero vector . Then the -hyperplane of contains the subspace and meets in the -dimensional -subspace , which contradicts ( ‣ 3). ∎
Definition 3.2**.**
Let be a -dimensional -subspace of , with and such that ( ‣ 3.1) is satisfied. Then the -dimensional -subspace of the quotient space (cf. Proposition 3.1) will be denoted by and we call it the Delsarte dual of (w.r.t. ).
The term Delsarte dual comes from the Delsarte dual operation acting on MRD-codes, as pointed out in Theorem 4.12.
Theorem 3.3**.**
Let be a maximum -scattered -subspace of a vector space of dimension , with . Then the -subspace of obtained by the procedure of Proposition 3.1 is maximum -scattered.
Proof.
Put . We first note that condition ( ‣ 3.1) is satisfied for since by Theorem 2.7 the hyperplanes of meet in -subspaces of dimension at most . Also, holds since .
Hence we can apply the procedure of Proposition 3.1 to obtain the -subspace of of dimension .
By way of contradiction, suppose that there exists an -dimensional -subspace of , say , such that
[TABLE]
Then , for some -dimensional -subspace of containing . For , by (9), it follows that
[TABLE]
Let be an -dimensional -subspace of contained in and let . Then, ,
[TABLE]
Since and , we get and , i.e.
[TABLE]
From (11) it follows that
[TABLE]
This implies that
[TABLE]
and hence is contained in a hyperplane of containing . Also, and, by (10), we get
[TABLE]
Then is a hyperplane of and, by recalling ,
[TABLE]
contradicting Theorem 2.7.
∎
In case of , Theorem 3.3 follows from [28] and from the theory of MRD codes. Our theorem generalises this result to each value of by using a geometric approach.
Corollary 3.4**.**
Starting from a maximum -scattered -subspace of of dimension , , the -subspace (cf. Definition 3.2) is a maximum -scattered -subspace of of dimension .
Corollary 3.5**.**
Starting from a maximum -scattered -subspace of , even, , (cf. Definition 3.2) is a maximum -scattered -subspace of whose dimension attains bound (1). ∎
Theorem 3.6**.**
If is even and is odd, then there exist maximum -scattered -subspaces of which cannot be obtained from the direct sum construction of Theorem 2.6.
Proof.
By [1, 3, 4, 11] it is always possible to construct maximum -scattered -subspaces of . Then the result follows from Corollary 3.5 and from the fact that in this case does not divide . ∎
Remark 3.7**.**
The Delsarte dual of an -subspace does not depend on the choice of the non-degenerate reflexive sesquilinear form on .
Indeed, fix an -basis of , since , we can see as and as . Let and be two non-degenerate reflexive sesquilinear forms on . Then, with respect to the basis , the forms and are defined by the following rules:
[TABLE]
where and is an automorphism of such that and , for . Now let and be their extensions over defined by the rules
[TABLE]
and let and be the orthogonal complement maps defined by and on the lattice of -subspaces of , respectively.
Again w.r.t. the basis , the -subspace described at the beginning of this section can be seen as a -dimensional subspace of . Then, for we have
[TABLE]
Straightforward computations show that the invertible semilinear map
[TABLE]
leaves invariant and maps to . Then maps to , i.e. maps the Delsarte dual of calculated w.r.t to the Delsarte dual of calculated w.r.t. . See also [25, Section 2] and [27, Section 6.2]. **
4 Linear sets defined by -scattered subspaces
Let be an -dimensional -vector space. A point set of is said to be an -linear set of of rank if it is defined by the non-zero vectors of a -dimensional -vector subspace of , i.e.
[TABLE]
One of the most natural questions about linear sets is their equivalence. Two linear sets and of are said to be -equivalent (or simply equivalent) if there is an element in such that . In the applications it is crucial to have methods to decide whether two linear sets are equivalent or not. This can be a difficult problem and some results in this direction can be found in [9, 8, 12]. For we have , where denotes the collineation of induced by . It follows that if and are -subspaces of belonging to the same orbit of , then and are equivalent. The above condition is only sufficient but not necessary to obtain equivalent linear sets. This follows also from the fact that -subspaces of with different dimensions can define the same linear set, for example -linear sets of of rank are all the same: they coincide with . Also, in [8, 12] for it was pointed out that there exist maximum -scattered -subspaces of on different orbits of defining -equivalent linear sets of . It is then natural to ask for which linear sets can we translate the question of -equivalence into the question of -equivalence of the defining subspaces. For further details on linear sets see [17, 18, 24].
In this section we study the equivalence issue of -linear sets defined by -scattered linear sets for .
Definition 4.1**.**
If is a (maximum) -scattered -subspace of , then the -linear set of is called (maximum) -scattered.
The -scattered -linear sets of rank were defined also in [28, Definition 14] and following the authors of [28], we will call these -linear sets maximum scattered with respect to hyperplanes. Also, we will call -scattered -linear sets (of any rank) scattered with respect to lines.
Proposition 4.2** ([5, pg. 3 Eq. (6) and Lemma 2.1]).**
Let be a two-dimensional vector space over .
If is an -subspace of with , then has dimension over . 2. 2.
Let and be two -subspaces of with of size . If , then .
Proposition 4.3**.**
If is a scattered -linear set with respect to lines of , then its rank is uniquely defined, i.e. for each -subspace of if , then .
Proof.
Let be an -subspace of such that and put . Since is a -scattered -subspace (cf. Proposition 2.1), . It follows that . Suppose that , then there exists at least one point such that . Let be a point different from , then has dimension at least but the linear set defined by is , thus it has size , contradicting part 1 of Proposition 4.2. ∎
Lemma 4.4**.**
*Let be a scattered -linear set with respect to lines in . If for some -subspace , then for some . *
Proof.
By Proposition 4.3, we have and hence, since is -scattered, also is -scattered. Let with , then for some we have . Put and note that . Our aim is to prove . Since and are -scattered, we have .
What is left, is to show for each that . To do this, consider the point and the line which meets in points. By part 1 of Proposition 4.2, the -subspace has dimension . Since , by part 2 of Proposition 4.2 we get
[TABLE]
Hence the assertion follows. ∎
Theorem 4.5**.**
Consider two -scattered linear sets and of with . They are -equivalent if and only if and are -equivalent.
Proof.
The if part is trivial. To prove the only if part assume that there exists such that , where is the collineation induced by . Since , by Proposition 2.1 and Lemma 4.4, there exists such that and hence and lie on the same orbit of . ∎
4.1 Scattered linear sets with respect to hyperplanes and MRD-codes
A rank distance (or RD) code of , , can be considered as a subset of , where and , with rank distance defined as . The minimum distance of is .
Result 4.6** ([13]).**
If is a rank distance code of , , with minimum distance , then
[TABLE]
Rank distance codes for which (12) holds with equality are called maximum rank distance (or MRD) codes.
From now on, we will only consider -linear MRD-codes of , i.e. those which can be identified with -subspaces of . Since is isomorphic to the ring of -polynomials over modulo , denoted by , with addition and composition as operations, we will consider as an -subspace of . Given two -linear MRD codes, and , they are equivalent if and only if there exist , permuting and such that
[TABLE]
where stands for the composition of maps and for . For a rank distance code given by a set of linearized polynomials, its left and right idealisers can be written as:
[TABLE]
[TABLE]
By [19, Section 2.7] and [28] the next result follows. We give a proof of the first part for the sake of completeness.
Result 4.7**.**
* is an -linear MRD-code of with minimum distance and with left-idealiser isomorphic to if and only if up to equivalence*
[TABLE]
for some and the -subspace
[TABLE]
is a maximum -scattered -subspace of .
Proof.
Let , where for each , and let denote the left-idealiser of . Since and are Singer cyclic subgroups of and any two such groups are conjugate (cf. [16, pg. 187]) it follows that there exists an invertible -polynomial such that . Then for each it holds that for each , which proves the first statement. For the second part see [28, Corollary 4.4]. ∎
Remark 4.8**.**
The adjoint of a -polynomial , with respect to the bilinear form (222Where denotes the trace function.), is given by
[TABLE]
If is a rank distance code given by -polynomials, then the adjoint code of is . The code is an MRD if and only if is an MRD and also , . Thus Result 4.7 can be translated also to codes with right-idealiser isomorphic to .
The next result follows from [28, Proposition 3.5].
Result 4.9**.**
Let and be two -linear MRD-codes of with minimum distance and with left-idealisers isomorphic to . Then and are -equivalent if and only if and are equivalent.
By Theorem 4.5, for we can extend Result 4.9 in the following way.
Theorem 4.10**.**
Let and be two -linear MRD-codes of with minimum distance , , and with left-idealisers isomorphic to . Then the linear sets and are -equivalent if and only if and are equivalent.
In the following we motivate why we used the term “Delsarte dual” in Definition 3.2. In particular, we prove that the duality of Section 3 corresponds to the Delsarte duality on MRD-codes when -scattered -subspaces of are considered.
First recall that in terms of linearized polynomials, the Delsarte dual of a rank distance code of introduced in [13] and in [14] can be interpreted as follows
[TABLE]
where for and .
Remark 4.11**.**
Let be an -linear MRD-code of with minimum distance and with left-idealiser isomorphic to . By Result 4.7 and by [10, Theorem 2.2], there exist distinct integers in such that, up to equivalence,
[TABLE]
where
[TABLE]
and .
Also, let . Then it is easy to see that the Delsarte dual of is
[TABLE]
where
[TABLE]
Theorem 4.12**.**
Let be an -linear MRD-code of with minimum distance and with left-idealiser isomorphic to . Then there exist such that, up to equivalence,
- •
,
- •
,
- •
the Delsarte dual of is the -subspace .
Proof.
By Remark 4.11, up to equivalence, , for some as in (13), and , for some as in (14). Note that, since is an MRD-code, the linearized polynomials have no common roots other than [math] since otherwise the code would not contain invertible maps, see e.g. [22, Lemma 2.1]. Our aim is to show that applying the duality introduced in Section 3 to we get the -subspace . By Result 4.7 we have that is a maximum -scattered -subspace of . If , i.e. has minimum distance greater than one, we can embed in in such a way that
[TABLE]
and hence the vector of is extended to the vector of as follows
[TABLE]
Let be the -subspace of of dimension represented by the equations
[TABLE]
and let . It can be seen that , otherwise the polynomials would have a common root. Also
[TABLE]
Let be the standard inner product, i.e. where and . Also, the restriction of over is \beta{\big{|}}_{W\times W}((x,x^{q},\dots,x^{q^{n-1}}),(y,y^{q},\dots,y^{q^{n-1}}))=\mathrm{Tr}_{q^{n}/q}(xy). Furthermore, with respect to the orthogonal complement operation defined by we have that
[TABLE]
Then the Delsarte dual of is the -subspace of the quotient space isomorphic to , where is the -subspace of of dimension represented by the following equations
[TABLE]
By identifying with , direct computations show that can be seen as the -subspace of dimension of , i.e. . ∎
5 Intersections of maximum -scattered subspaces with hyperplanes
This section is devoted to prove
Theorem 2.7 If is a maximum -scattered -subspace of a vector space of dimension , then for any -dimensional -subspace of we have
[TABLE]
As we already mentioned, the theorem above is a generalization of [4, Theorem 4.2], which is the case of our result. In that paper, the number of hyperplanes meeting a -scattered subspace of dimension in a subspace of dimension or has been determined as well. Subsequently to this paper, in [29] (see also [23] for the case), such values have been determined for every .
5.1 Preliminaries on Gaussian binomial coefficients
The Gaussian binomial coefficient is defined as the number of the -dimensional subspaces of the -dimensional vector space . Hence
[TABLE]
Recall the following properties of the Gaussian binomial coefficients.
[TABLE]
[TABLE]
[TABLE]
Definition 5.1**.**
The -Pochhammer symbol is defined as
[TABLE]
Theorem 5.2** (-binomial theorem [15, pg. 25, Exercise 1.3 (i)]).**
[TABLE]
[TABLE]
Corollary 5.3**.**
In (19) and (20) put and to obtain
[TABLE]
[TABLE]
respectively.
The -th elementary symmetric function of the variables is the sum of all distinct monomials which can be formed by multiplying together distinct variables.
Definition 5.4**.**
Denote by the -th elementary symmetric polynomial in variables evaluated in .
Lemma 5.5** ([6, Proposition 6.7 (b)]).**
[TABLE]
We will also need the following -binomial inverse formula of Carlitz.
Theorem 5.6** ([7, special case of Theorem 2, pg. 897 (4.2) and (4.3)]).**
Suppose that and are two sequences of complex numbers. If , then and vice versa.
5.2 Double counting
Put and let be an -dimensional -subspace of such that for each -dimensional -subspace , we have .
Let denote the number of -dimensional -subspaces meeting in an -subspace of dimension . It is easy to see that
[TABLE]
In , the integer coincides with the number of hyperplanes such that . Also, the number of hyperplanes is , which is the same as , thus
[TABLE]
For we can double count the set
[TABLE]
[TABLE]
By Proposition 2.1 this gives
[TABLE]
[TABLE]
or equivalently
Lemma 5.7**.**
[TABLE]
[TABLE]
∎
Our aim is to prove
[TABLE]
This would clearly yield , for , and hence Theorem 2.7.
5.3 Expressing
First for we will express
[TABLE]
Put (cf. (23)), and
[TABLE]
where the values of are known due to Lemma 5.7.
Recall
[TABLE]
Then it is easy to see that
[TABLE]
and hence, using also Lemma 5.5,
[TABLE]
or equivalently, by (17),
[TABLE]
Then Theorem 5.6 applied to the sequences and gives
[TABLE]
It is easy to see that
[TABLE]
and hence by Lemma 5.5
[TABLE]
By Lemma 5.7 we have
[TABLE]
[TABLE]
By (18) with
[TABLE]
thus
[TABLE]
[TABLE]
[TABLE]
[TABLE]
5.4 Proof of
Since -binomial coefficients out of range are defined as zero, cf. (15), it is enough to prove that the following expression is zero:
[TABLE]
It is clearly equivalent to prove , where
[TABLE]
[TABLE]
Proposition 5.8**.**
[TABLE]
Proof.
Clearly, it is enough to prove
[TABLE]
[TABLE]
where by (18)
[TABLE]
thus the triple sum can be reduced to
[TABLE]
By (16) and (17) this can be written as
[TABLE]
where again by (18)
[TABLE]
[TABLE]
[TABLE]
By (26) we have
[TABLE]
which by (21) equals . ∎
Proposition 5.9**.**
[TABLE]
Proof.
As before, can be written as
[TABLE]
Then the assertion follows from (22). ∎
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