Computing the Haar state on
Abstract.
This paper shows that to compute the Haar state on , it suffices to compute the Haar states of a special type of monomials which we define as standard monomials. Then, we provide an algorithm to explicitly compute the Haar states of standard monomials on with reasonable computational cost. The numerical results on will be used in the future study of the -deformed Weingarten function.
Keywords — Quantum groups; quantum special linear group; Haar state.
1. Introduction
The Haar measure on a compact topological group is a well-studied object. In particular, when the group is , the group of unitary matrices, there is an elegant formula for the integral of matrix coefficients with respect to the Haar measure. This formula is given by so-called Weingarten functions, introduced by Collins in 2003 [3]. The current paper will study a -deformation of the Haar measure on the Drinfeld–Jimbo [4] [5] quantum groups which is dual to [7].
In the context of , the most relevant algebraic structure is that it is a co-semisimple Hopf algebra. From Sweedler [14], any co-semisimple Hopf algebra has a unique “Haar state” up to normalization. In the context here, co-semisimplicity plays the role of compactness: the Lie algebra of a compact Lie group is always a semisimple Lie algebra. becomes a ∗–Hopf algebra when setting where is the antipode. In this case, we call the ∗–Hopf algebra . In particular, when , the space of coordinate functions on is a co-semisimple Hopf algebra, and its Haar state is simply the integral of a function with respect to Haar measure on . Thus, Haar states serves as the integral functional on these deformed ’s. Since the Haar state on is defined by extending the Haar state on by the ∗ operation, we will focus on the Haar state of only in this paper.
In the -deformed case, there are no explicit formulas in terms of parameter for the Haar state except when (Klimyk and Schmüdgen [6]). The difficulty when arises from the form of the –determinant. When , the -determinant is of the form , where are the generators of . Because the -determinant only has two terms, once the Haar state of is computed, then so is the Haar state of . However, this simplification does not work in general because the -determinant generally has terms. For other related works on , see Nagy [10], Vaksman and Soibelman [15] [8].
In this paper, the generator of is denoted as for .
Definition 1.
The counting matrix of a monomial , denoted as , is a matrix with entries where equals the number of appearance of generator in .
Definition 2.
The row sum and column sum of a matrix , denoted as and , are vectors in :
Here, we denote as a vector whose entries all equal to .
Definition 3.
Let be a matrix with non-negative integer entries. Then is a -doubly stochastic matrix [13] if there is a positive integer such that .
Definition 4.
Let be the permutation group on letters. Monomials in form , where and and , are called standard monomials. is called the order and each is called a segment.
The current paper will prove the following theorem on :
Theorem 1.
The following are true on :
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Let be a monomial. Then implies that there exist such that is a -doubly stochastic matrix.
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Every monomial with non-zero Haar state value can be written as a linear combination of standard monomials.
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Let , be the set of standard monomials of order . Then, we can write and as linear combinations of ’s and the coefficient of each is a linear combination of ’s.
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Let be the inverse number of . Then :
where and
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When changing the order of generators in a monomial, the newly generated monomials cannot contain more generator and and cannot contain less generator and , comparing to the monomial being reordered.
For simplicity, the generators of on are denoted as:
Then, standard monomials of order are in the form:
Definition 5.
Segments , , and are high-complexity segments.
Definition 6.
Segments , , and are low-complexity segments.
Remark.
Low complexity segments commute with each other but high complexity segments do not commute with any other segments.
The current paper will provide an algorithm for explicitly computing the Haar states of monomials on with . Explicit expressions in terms of parameter are provided for a special type of monomials, and explicit expressions for general monomials can be computed given enough computational resources.
Using this –deformed Haar measure, we hope to pursue –deformed Weingarten functions in future work. Examples of –deformed Weingarten functions are provided in Appendix C. For all of these examples, it can be seen directly that when , the usual Haar measure is recovered.
2. Haar state on
By Noumi et al. [11], monomials on form a basis. As a quotient group of , monomials on form a basis as well. To define the Haar state on , it suffices to define the Haar state of each monomial.
2.1. Characterization of monomial such that
Not every monomial has a non-zero Haar state value. In this section, we will give a criterion to determine whether the Haar state of a monomial is zero or not.
Let be the diagonal subgroup of . Recall that the coordinate Hopf algebra is the commutative algebra of all Laurent polynomials in indeterminates with comultiplication and counit . The surjective homomorphism is given by . Since we have for all , tells us .
The right and left action of on , denoted as and , is defined as:
Given vector , we write . If is a monomial, we have:
The next theorem is a generalization of Klimyk and Schmudgen’s observation [6]. It gives the necessary condition such that for :
Theorem 1 a): Let be a monomial. Then implies that there exist such that is a -doubly stochastic matrix.
Proof: Consider . There are two ways to compute this object:
Thus, . Since , we get . This means that we can find integer such that . Thus, .
Apply the same argument to , we get . Thus, we can find such that . But we must have since
2.2. The linear subspace spanned by monomials with non-zero Haar states
Let be the linear subspace spanned by monomials with non-zero Haar states. In this section, we give a criterion to pick a basis for . We write as the set of -doubly stochastic matrices and as the set of monomials on whose counting matrices belong to .
First, we introduce a total order ’’ on . For every , we associate a vector
and we compare such vectors in lexicographic order. We say matrices if . With this total order, we have the following observation:
If where are two monomials and we switch the order of so that:
where and , then we have: and .
Based on the observation, we get the following lemma:
Lemma 1: For each , we fix monomial such that . If is a monomial with counting matrix , then we can decompose as:
(1) |
Proof: Since and have the same counting matrix, we can permute the generators in to the same order as in . We denote this process as a chain:
where each is a reordering of and we get by switching the order of two adjacent generators in . From to , we may get a new term . As discussed before, and . We can permute these newly generated ’s to their corresponding ’s, and we may get new terms in this process as well. However, each time we repeat this permuting process to a monomial , the counting matrix of the newly generated monomial is always smaller than . Since the counting matrix of the newly generated monomial is always descending, we can finish this permuting process in finite steps. In other words, we will get a chain on which every transposition does not generate new monomials. Then, every monomial appearing in the summation will be in the desired form, and we get Equation (1).
Lemma 1 provides a criterion for picking a basis for . Let . Then, we can write
Theorem 1 b): Every monomial with non-zero Haar state value can be written as a linear combination of standard monomials.
Proof: By the Birkhoff-Von Neumann Theorem [2] [16], every can be decomposed into , where ’s are matrix in and ’s are non-negative integers whose sum is . Notice that each matrix can be identified with a permutation on letters. We denote the corresponding permutation as as well. Then, the counting matrix of the monomial is . This implies that for every , we can choose in form . Combining Lemma 1, the statement in Main Theorem b) is clear.
Notice that the set of all standard monomials contains a basis of , but the set itself is not a basis of . The reason is that the different standard monomials could have the same counting matrix. In this case, standard monomials with the same counting matrix are linearly dependent(see Appendix A, Equation (22) and (23)). To find a basis of , for each , we have to preserve only one standard monomial corresponding to and filter out ’unnecessary’ standard monomials.
2.3. Comultiplication of standard monomials
Once we solve the Haar state of standard monomials of each order, we can find the Haar state for other monomials according to their linear combination. We will use the defining relation to solve the Haar state of every standard basis. We start with the investigation of the comultiplication of a monomial.
Lemma 2: Let be a monomial and we write:
with an index set and monomials. Then we have the following equations:
Proof: Recall that and is a morphism of algebra. If , then
For each the -th generator is in the same row as the -th generator in , and for each the -th generator is in the same column as the -th generator in . The column index of the -th generator in is the same as the row index of the -th generator in . Thus, the row sum of equals the row sum of ; the column sum of equals the column sum of , and the column sum of equals the row sum of .
With Lemma 2, we have the following result:
Lemma 3: If then (or ) if and only if ( or ). Moreover, if and only if .
Proof: Use Theorem 1 a) and Lemma 2.
Theorem 1 c): Let , be the set of standard monomials of order . Then, we can write and as linear combinations of ’s and the coefficient of each is a linear combination of ’s.
Proof: If is a standard basis, Lemma 3 implies that
(2) |
Then, by Lemma 1, we can decompose each and as:
(3) |
(4) |
Substitute Equation (3) and Equation (4) into Equation (2), we get:
(5) |
Remark.
Here, is a basis of standard monomials of order .
In Equation (2), we call the relation component and call the comparing component. We will say (or ) contains if (or ).
Since we can identify with , we get . Notice that we can decompose as a linear combination of standard monomials of order . Thus, by comparing the coefficient of the same standard monomial on both sides of , we can find a linear relation consisting of the Haar states of standard monomials of order (See section 2.5 for more detail). We call such a linear relation linear relation of order . We call a linear system consisting of linear relations of order a system of order .
2.4. System of order 1
In this section, we will prove Theorem 1 d). The standard basis for is in the form of where is a permutation on letters. We have:
By Lemma 3, after apply to , we get:
(6) |
On the other hand, recall that
(7) |
where is the inverse number of .
Thus, using and comparing the coefficients of each standard basis, we get for every :
(8) |
In general, is not a standard monomial. However, if we choose such that , then every generator in commutes with each other and . Moreover, . Thus, from Equation (8) we get:
(9) |
Therefore, using Equation (9) and Equation (7) we get:
(10) |
which gives
(11) |
Then by Equation (8), notice that the inverse number for the corresponding to is just , we get for every :
(12) |
Now, let be the number of permutations on letters with inversions. Then, the denominator of Equation (12) can be rewritten as:
By Andrews [1], the generating function of is
So the denominator of Equation (12) can be rewritten as
and we get:
2.5. Liner relations of higher order
In this section, let be a set of linearly independent standard monomials of order . Recall Equation (5):
We can do the same thing to and get:
(13) |
By comparing the coefficients of standard bases in and in , we get:
(14) |
for every . We will call Equation (14) the linear relation derived from equation basis and comparing basis . Each index corresponds to linear relations, so there are linear relations. Since there are unknowns, it is possible to construct more than one system of order . Notice that these linear relations all have the zero right-hand side. One way to get a linear relation with the non-zero right-hand side is by decomposing into a sum of standard monomials. Although we can construct more than one system of order , not every system is invertible. We will give a more robust approach to compute the Haar state of later.
In the order 1 case, finding Equation (5) and (13) is an easy task. However, the situation is much more complicate in higher order case. To understand the difficulty to find the two equations in higher order case, we introduce the order restriction for each summand appearing in the comultiplication of a monomial:
Let be a monomial and the comultiplication of be
Then:
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the -th generator of the left component is in the -th row
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the -th generator of the right component is in the -th column
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The column index of the -th generator in equals to the row index of the -th generator in .
The order restriction is a direct consequence of the fact that the comultiplication is an algebra homomorphism. Since each index ranges from to , every possible combination of that satisfies the order restriction will appear in the summation of . In higher order case, this means that Equation (2) includes not only summand whose left and right components are standard monomials but also summand whose left and right components are reordering of standard monomials satisfying the order restriction. As an example in , if is the left component of one of the tensor products in then all reordering of the left component satisfying property i) of the order restriction are:
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Thus, the comultipilcation of a standard monomial of higher order contains not only standard monomials but also variations of standard monomials satisfying the order restriction. This is the major difference between the case of order 1 and higher order cases. To find a linear relation derived from equation basis and comparing basis in higher order case, we have to:
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find all left (or right) component appearing in that contains and compute the corresponding coefficient in Equation (4);
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find the decomposition of the right (or left) component in corresponding to the left (or right) component in i) and sum all such decomposition together to get a linear relation;
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decompose every summand containing in Equation (13) to find .
All 3 steps involve decomposing non-standard monomials into a linear combination of standard monomials and such decomposition is not easy in general. However, there is a simple criterion to determine whether a standard monomial appears in the decomposition of a non-standard monomial or not.
Theorem 1 e):When changing the order of generators in a monomial, the newly generated monomials cannot contain more generator and and cannot contain less generator and comparing to the monomial being reordered.
Proof.
When a new monomial is generated, we replace a pair of () by a pair of to get the new monomial. Notice that none of and can be or and none of and can be or . Thus, and can never be replaced by other generators and and can never be used as the generator to replace other generators. This finishes the proof. ∎
Remark.
The decomposition of a monomial does not contain those standard monomials whose number of generator and (or and ) exceeds (or less than) that of monomial .
Notice that every standard monomial in contains at least one of , , , and . Thus, Theorem 1 e) will play an important role in the computation of the Haar state on later.
3. A monomial basis for in
As mentioned in section 2.2, the set of standard monomials is not a basis for linear subspace . In this section, we will provide a criterion to pick a monomial basis for from the set of standard monomials in and define a monomials basis for based on the criterion.
Proposition 2.
Let be a -doubly stochastic matrix. If there exist such that , then is uniquely decomposed into a linear combination of matrices in .
Proof.
Index the six matrices in as:
Without loss of generality, assume that . Then, matrix and cannot appear in the decomposition of . The only matrix in whose -entry is not zero is . Hence, the coefficient of in the decomposition of is . Similarly, the only matrix in whose -entry is not zero is , so the coefficient of in the decomposition of is ; the only matrix in whose -entry is not zero is , so the coefficient of in the decomposition of is ; the only matrix in whose -entry is not zero is , so the coefficient of in the decomposition of is . Hence, is decomposed into:
The arguments for other cases are identical to ∎
Denote
Then, every -doubly stochastic matrix can be written as:
where and is a -doubly stochastic matrix with at least one entry equals to . By Proposition 2, the decomposition of is unique. To define a monomial basis consisting of standard monomials, we need to specify the decomposition of matrix . There are two possible choices for : and . They satisfy Equation (22) in Appendix A. In this paper, we choose the standard monomial corresponding to matrix as:
Thus, if the unique standard monomial corresponding to is:
the standard monomial corresponding to is:
Notice that in the monomial corresponding to , at least one of , , and has to be zero since has a zero entry. For the same reason, at least one of , , and has to be zero. This implies that for every -doubly stochastic matrix , the corresponding standard monomial contains at most two of the three segments , , . Hence, we define the monomial basis consisting of standard monomials as:
4. Explicit formulas for special standard monomials on
In this section, we will construct a linear systems of order called the Source Matrix of order based on the relation and the explicit solution to the Source Matrix is given. Then, we derive the explicit recursive relation between the Haar state of standard monomials in the form of . We start with the motivation of the construction of the Source matrix.
4.1. The Source Matrix of order
Recall the 3 difficulties we introduced in section 2.5. To reduce the computation in step i), we prefer to pick a comparing basis such that the number of ’s in Equation (2) whose decomposition contain is as small as possible. To reduce the computation in step ii), we prefer to pick a equation basis such that in Equation (2) the decomposition of the ’s corresponding to the ’s in step i) is as simple as possible. To reduce the computation in step iii), we prefer to pick a comparing basis such that the number of terms in the expansion of whose decomposition contain is as small as possible.
According to Theorem 1 e), the decomposition of a monomial does not contain those standard monomials whose number of generator and exceed that of the original monomial. Thus, we should pick those standard monomials containing as many generator and as possible to be the comparing basis so that only limited number of ’s in Equation (2) contains . Theorem 1 e) also tells us that the decomposition of a monomial contains only those standard monomials whose number of generator and equals to or exceeds that of the original monomial. Thus, we should pick those equation basis such that in Equation (2) the ’s corresponding to those ’s which contains contain as many generator and as possible so that the decomposition of ’s contain only a limited number of standard monomials.
Based on the analysis, we pick standard monomial as the equation basis and consider the linear relation derived from comparing basis , , , , , and , respectively. Notice that these comparing basis contains at least generator and at least generator . We exclude the comparing basis since the corresponding linear relation is an identity. According to the order restriction, we list all the terms in whose contains one of our chosen comparing basis. Notice that these ’s are variations of our chosen comparing basis under the order restriction.
Variations of :
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Variations of :
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Variations of :
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Variations of :
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Variations of :
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Variations of :
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The right components corresponding to our chosen comparing basis contain at least generator and at least generator . Thus, the unknowns in these linear relations are the Haar state of , , , , , , and . Now we get linear relations containing unknowns. To get a solvable linear system, we add the quantum determinant relation:
The linear system of order consisting of the 7 equations is called the Source Matrix of order . Besides the quantum determinant relation, the right-hand-sides of all other linear relations are zero. Thus, the linear system is recursive. The Haar state of is solved from the Source Matrix of order and then used as the only non-zero right-hand-side term in the Source Matrix of order . The general solution to the Source Matrix of order is:
For the entries of the Source matrix of order , see Table 1 on the next page.
LHS | ||||||||
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0 | ||||||||
0 | 0 | 0 | 0 | |||||
0 | 0 | 0 | ||||||
0 | 0 | 0 | 0 | |||||
0 | 0 | 0 | 0 | 0 | 0 | |||
0 | 0 | 0 | 0 | 0 | 0 |
Through direct computation, we can verify that the Source Matrix of order and fit in the general form. In following subsections we provide the steps to compute the contribution of tensor products in the forms of:
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to each linear relation in the Source Matrix. The contributions of other tensor products to the Source Matrix are computed in a similar way.
4.1.1. Decomposition of left components
We start with left components in the form of . Besides considering the coefficient of , the decomposition of the above Left components may contains other standard monomials with generator and generator . Since there is only generator ’s in these left components, we may ignore the new monomials generated by switching with other generators.
Notice that every term appearing in the decomposition contains exactly generator and generator besides . Thus, we have:
and
and
Together, we have:
Then, we consider left components in the form of . Again, we may ignore the new monomials generated from switching generator with other generators. Thus, we get:
For , we have:
Then, using the result of the decomposition of , we get:
Finally we consider left components in the form of . Similar to previous two cases, we may ignore the new monomials generated from switching generator with other generators. Thus, we get:
For , we have:
Then, using the result of the decomposition of , we get:
4.1.2. Decomposition of right components
When the left component is , the corresponding right component is . We have:
Recall the decomposition of in subsection 4.1.1. By summing over index , the contribution of to the linear relation corresponding to comparing basis is:
The contribution to the linear relation corresponding to comparing basis
is:
Similarly, the contribution to the linear relations corresponding to comparing basis and are the same:
The contribution to the linear relations corresponding to comparing basis
is:
When the left component is , the corresponding right component is . We have:
Recall the decomposition of in subsection 4.1.1. We write . By summing over index and , the contribution of to the linear relation corresponding to comparing basis is:
The contribution to the linear relation corresponding to comparing basis
is:
The contribution to the linear relations corresponding to comparing basis is:
The contribution to the linear relations corresponding to comparing basis is:
The contribution to the linear relations corresponding to comparing basis is:
When the left component is , the corresponding right component is . We have:
Recall the decomposition of in subsection 4.1.1. By summing over index and , the contribution of to the linear relation corresponding to comparing basis is:
The contribution to the linear relation corresponding to comparing basis
is:
The contribution to the linear relations corresponding to comparing basis
is:
The contribution to the linear relations corresponding to comparing basis
is:
The contribution to the linear relations corresponding to comparing basis
is:
4.1.3. Contribution to linear relations corresponding to different comparing basis
The contribution of the 3 types of tensor products determines the linear relation corresponding to comparing basis . The coefficient of is:
When computing the coefficient of , we have to consider the coefficient of in . By Theorem 1 e), terms in whose decomposition contains has to be in the form of . Thus, the coefficient of in is . Then, the coefficient of is:
The contribution of the 3 types of tensor products to the coefficient of in the linear relation corresponding to comparing basis is:
The contribution of the 3 types of tensor products to the coefficient of in the linear relation corresponding to comparing basis is:
The contribution of the 3 types of tensor products to the coefficient of in the linear relation corresponding to comparing basis is:
The contribution of the 3 types of tensor products to the coefficient of in the linear relation corresponding to comparing basis is:
Notice that the sign of the coefficient of is always the opposite as that of in the decomposition of all 3 possible forms of right components. The contribution of the 3 types of tensor products to the coefficient of in the linear relation corresponding to comparing basis is:
The contribution of the 3 types of tensor products to the coefficient of in the linear relation corresponding to comparing basis is:
The contribution of the 3 types of tensor products to the coefficient of in the linear relation corresponding to comparing basis is:
The contribution of the 3 types of tensor products to the coefficient of in the linear relation corresponding to comparing basis is:
4.2. The recursive relation for the Haar state of standard monomials in the form of
In the following, we will derive the recursive relation of . The case is solved in the Source Matrix. We will start with the general case and then the case .
4.2.1. Recursive relation of
We will use equation basis and comparing basis to derive the recursive relation of the Haar state of . In the comultiplication of , the left components containing are
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When the left component is in the form , the coefficient of in the decomposition of is and the corresponding relation components are:
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When the left component is in the form , the coefficient of in the decomposition of is and the corresponding relation components are:
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When the left component is in the form , the coefficient of in the decomposition of is and the corresponding relation components are:
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For case 1), we have:
The contribution of case 1) to the linear relation is:
For case 2), we have:
The contribution of case 2) to the linear relation is:
For case 3), we have:
The contribution of case 3) to the linear relation is:
For case 4), we have:
The contribution of case 4) to the linear relation is:
For case 5), we have:
The contribution of case 5) to the linear relation is:
For case 6), we have:
The contribution of case 6) to the linear relation is:
For case 7), we have:
The contribution of case 7) to the linear relation is:
For case 8), we have:
The contribution of case 8) to the linear relation is:
The term appears in case 2), 5), and 8). Summing the contributions from these cases, the coefficient of is:
The term appears in case 2), 4), 5), 7), and the right-hand side of Equation (14). Summing the contributions from these cases, the coefficient of is:
The term appears in case 1), 3), 4), 6), and 7). Notice that if we combine the contribution of case 1) and case 3), we get:
which corresponds to in the summation of case 3). Thus, we can treat in the same way as the general case , which appears in case 3), 4), 6), and 7). Summing the contributions from these cases, the coefficient of for is:
Notice that if we put in the above coefficient, we get:
The term appears in case 3) and 4). Summing the contributions from these cases, the coefficient of is:
The expressions of the coefficients of and for are consistent. Thus, the expression of is:
(15) |
where
4.2.2. Special case
We use the linear relation derived from and equation basis . The situation in this subsection is similar to the previous subsection but we do not have case 3) and 6) anymore. The linear relation in this case only involves , , and . Here, is the same as in the previous subsection. If we substitute into the contribution of case 3) and 6), we find that the contributions corresponding the two cases are automatically zero. Thus, recursive relation Equation (15) is consistent with the case in subsection 4.2.2.
4.2.3. Special case
We use the linear relation derived from and equation basis . The situation in this subsection is similar to the previous subsection but we do not have case 5) and 8) anymore. Thus, the coefficient of is and the coefficient of is:
On the other hand, if we substitute in to the coefficient of , we get and coefficient of , we get:
Thus, recursive relation Equation (15) is consistent with the case in subsection 4.2.3.
4.3. The recursive relation for the Haar state of standard monomials in the form of
We will use equation basis and comparing basis to derive the recursive relation for . The computation involved in finding this recursive relation is similar to that of finding the recursive relation of and the derived recursive relation of is the same as Equation (15).
5. General algorithm to compute the Haar states of standard monomials on
In this section, we assume that the Haar states of all standard monomials of order are known and we want to compute the Haar states of all standard monomials of order . For the simplicity of our argument, we will show that our proposed algorithm is able to compute the Haar state of all standard monomials, not just the monomials basis we picked.
5.1. Zigzag recursive pattern for standard monomials in the form
and with
We start with the Haar state of , . In this case, we compute the Haar state of monomials in form and . We use an induction on the value . We know the Haar state for case from the solution of the source matrix of order . For , the Haar state of is know as well. To compute the Haar state of , we use the linear relation derived from equation basis and comparing basis . Assume we have solved all the Haar state for . Then, we can compute the Haar state of by the linear relation derived from equation basis and comparing basis . Next, we compute the Haar state of of by the linear relation derived from equation basis and comparing basis . During the process, the only monomial with unknown Haar state appearing in the linear relation is the monomial which we are pursuing. The order that we used to compute these Haar states are depicted in Appendix B. Since we solve the Haar state of and in a “zigzag” pattern in the figure, we call this recursive relation the Zigzag recursive relation.
We can compute the Haar states of monomials in form and in the same order. When we derive linear relations, we use equation basis to substitute and to substitute and use comparing basis to substitute .
5.2. Standard monomials ending with and standard monomials ending with , , and .
5.2.1. Standard monomials ending with or
First, notice that if we choose as the equation basis and use as the comparing basis, the derived linear relation only includes the Haar state of , , , and . Combining the results from previous subsections, we can find the Haar state of .
Similarly, if we choose as the equation basis and use as the comparing basis, the derived linear relation only includes the Haar state of , , , and . Combining the results from previous subsections, we can find the Haar state of .
Next, we consider equation bases with comparing basis and . The linear relation derived by comparing basis includes , , , and . Thus, we can solve the Haar state of from this linear relation. Similarly, The linear relation derived by comparing basis includes , , , and . Thus, we can solve the Haar state of from this linear relation.
5.2.2. Standard monomials ending with or
First, we consider standard monomials ending with . They are , , ,
, and . Here, the Haar states of and are still unknown. To compute the Haar states of the two monomial, we construct a linear system consisting of:
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the quantum determinant condition: ;
-
2)
the linear relation derived from equation basis and comparing basis .
Similarly, we need to compute the Haar state of monomials and . We construct a linear system consisting of:
-
1)
the quantum determinant condition: ;
-
2)
the linear relation derived from equation basis and comparing basis .
5.2.3. Matrix of , , and
To compute the Haar state of these three monomials, we construct a linear system consisting of:
-
1)
the quantum determinant condition: ;
-
2)
the linear relation derived from equation basis and comparing basis ;
-
3)
the linear relation derived from equation basis and comparing basis .
Entries of the system matrix are listed below:
Quantum Determinant | 1 | ||
0 | |||
Using Gauss elimination, we have:
The determinant of the matrix is:
Since , the determinant is always positive for . Thus, the matrix is invertible.
5.2.4. Standard monomials with two segments of , , or
Consider equation basis with comparing basis . The derived linear relation includes , ,
, , and . Thus, we can solve the Haar state of from this linear relation.
Similarly, consider equation basis with comparing basis . The derived linear relation includes , ,
, , and . Thus, we can solve the Haar state of from this linear relation.
Then, using the equality , we can solve the Haar state of . Replacing by in the above equation, we can find the Haar state of . Finally, using the equality , we can solve the Haar state of .
At this point, we have computed all the Haar states of standard monomials ending with or ending with , , and . Also, we computed the Haar state of standard monomials and with . In the next subsection, we assume that the Haar state of standard monomials ending with or ending with , , and are known. We will also assume that the Haar state of standard monomials and with are known. Based on this assumption, we will compute the Haar state of standard monomials ending with or ending with , , and . We will also compute the Haar state of standard monomials and with .
5.3. Standard monomials ending with and standard monomials ending with, , and
We can apply an inductive approach to compute the pursuing Haar states.
5.3.1. Monomials in form and
with and
First, notice that by our assumption, we already know the Haar states of
and with and since these monomials end with .
To compute the Haar state of , we use equation basis with comparing basis . Using the Theorem 1 e), we know that the derived linear relation only contains standard monomials in the form
since no generator can appear in the newly generated monomials. Thus, the only monomial with unknown Haar state appearing in the linear relation is and we can compute its Haar state. This finish the case for . Similarly, we can compute the Haar state of using the linear relation derived from equation basis with comparing basis .
Now assume we know the Haar state of for all . To compute the Haar state of case , we use equation basis
and comparing basis . Here, we have to compute the case first, then the case , case , until the case . This is an analog to the zigzag recursive relation. To solve , we use equation basis and comparing basis and use the same strategy as for monomials .
5.3.2. Monomials with one high-complexity segment ending with , , and
The monomials we are considering are in the form:
and
and
We start with monomials ending with . From subsection 4.2, we already know the Haar state of by the zigzag recursive relation. To compute the Haar state of and , we build a linear system consisting of:
-
1)
;
-
2)
the linear relation derived from equation basis and comparing basis .
Similarly, the Haar state of is known from subsection 4.2. To compute the Haar state of and , we build a linear system consisting of:
-
1)
;
-
2)
the linear relation derived from equation basis and comparing basis .
Then, we compute the Haar state of ,
, and . By our assumption, we have solved the Haar state of ,
, ,
, and for . Thus, to compute the Haar state of , , we use equation .
To compute the case , notice that by our assumption the Haar states of and are known by our assumption. To construct a linear system of and
, we use linear relation derived from:
-
1)
Equation .
-
2)
Equation basis and comparing basis .
To construct a linear system of and , we use linear relation derived from
-
1)
Equation .
-
2)
Equation basis and comparing basis .
5.3.3. Monomials with two high-complexity segments ending with
Finally, we compute the Haar states of monomials with two high-complexity segments ending with . We start with monomials in form
and . Notice that and can be written as a linear combination of and other monomials with at most one high-complexity segment. Thus,
and can be written as a linear combination of and other monomials with at most one high-complexity segment. Thus, we can compute the Haar state of and using the Haar states we known. To compute the Haar state of , we use equation basis and comparing basis . To compute the Haar state of , we use equation basis and comparing basis . At last, to compute the Haar state of , we use the equation . To compute the Haar state of , we use the equation . To compute the Haar state of , we use the equation
.
At this point, we have solved the Haar state of all monomials with at most two high-complexity segments ending with and monomials with at most one high-complexity segment ending with , , and .
Starting from the next sub-section, we assume that the Haar states of monomials with at most high-complexity segments ending with and monomials with at most high-complexity segments ending with , , and are known. Now, we compute the Haar states of monomials with high-complexity segments ending with and monomials with high-complexity segments ending with , , and .
5.3.4. Monomials with high-complexity segments ending with , and
We start with monomials ending with . By subsection 4.4.2, we know the Haar state of standard monomials with . Thus, we know the Haar state of . Then, we construct a linear system of and consisting of:
-
1)
;
-
2)
linear relation derived from equation basis and comparing basis .
Next, we compute the Haar state of monomials in the form
and . We have solve the case . Now, assume that we have solved all . When , we have the following equation:
By the Theorem 1 e), can be written as a linear combination of and other standard monomials with at most high-complexity segments ending with . Thus, the Haar state of is known. Similarly, the Haar states of , , and
are known. So the above equation is a linear relation between and
. For the other linear relation between the two monomials, we use the linear relation derived from equation basis
and comparing basis .
Finally, we compute the Haar states of monomials in the form
. From the previous paragraph, we have solve the case . Assume that we know the Haar state of all . to compute the case , we use the quantum determinant condition
in which the only monomial with unknown Haar state is
.
The Haar state of monomials with high-complexity segments ending with
can be computed by an approach similar to the case of
with every segment replaced by and every segment replaced by .
5.3.5. Monomials in form ,
, and
with
From the previous sub-section, we know the Haar states of
for all . Thus, when , we only need to focus on and . To construct a linear system containing the two monomials, we use linear relation derived from
-
1)
.
-
2)
Equation basis and comparing basis .
When , we need to solve the Haar state of , , and at the same time. To construct a linear system containing the three monomials, we use linear relation derived from
-
1)
.
-
2)
Equation basis and comparing basis .
-
3)
Equation basis and comparing
basis .
5.3.6. Monomials in form with
To start, we compute the Haar states of monomials in form
and
, with and at the same time. We already solve the case . Without loss of generality, we assume that the Haar states of all are known. To solve the case , firstly, notice that we have the following equation:
Here, can be written as a linear combination of and other monomials with less number of high-complexity segments. Thus, besides the Haar states of and
, the Haar state of other monomials re known. So, in case , we only need one more linear relation to construct a system of the two monomials. We will use the linear relation derived from equation basis and comparing basis .
Then, we compute the Haar state of monomials in the form
with . We already compute the case of . After solving the case of , we can compute the case by the equation for all .
5.3.7. Monomials with high-complexity segments ending with
We start with monomials in form with and . Monomials in this form contain at least one segment and one segment. We can write the monomial as a linear combination of and other monomials with less number of high-complexity segments. To compute the Haar state of , we can apply Equation (22) and Equation (23) in Appendix A to rewrite it as a linear combination of monomials with known Haar states.
Next, we compute the Haar state of monomials in form and . To compute , we use equation basis and comparing basis
. To compute , we use equation basis and comparing basis .
Finally, we consider monomials in the form and with . To start, we compute the Haar state of using the equation . If we have computed the Haar state for all , to compute the case , we use the following equation . We can compute the Haar state of
in the same way. When , we use the equation to compute the Haar state of .
At this point, we have solved the Haar state of all monomials with at most high-complexity segments ending with and monomials with at most high-complexity segments ending with , , and . Using an induction argument, we can compute the Haar state of all monomials with at most high-complexity segments ending with and monomials with at most high-complexity segments ending with , , and . Thus, this subsection shows that we can use induction on the value of from until and compute the Haar states of all monomials ending with , , or .
5.4. Monomials with at most one low-complexity segment
5.4.1. Monomials ending with or
Here, we only show the procedure to compute monomials ending with . The case of is solved similarly.
We start with monomials in form and with . We have to solve the Haar states of the two monomials at the same time. The first linear relation comes from equation . The second linear relation is derived from equation basis
and comparing basis .
Next, we consider monomials in the form and
with , . We solve the case in the last paragraph. If we have known the Haar states of all , we can solve the case by linear relations derived from
-
1)
.
-
2)
Equation basis and comparing
basis .
Finally, we compute the Haar states of monomials in the form with , , and . The case is solved in the last paragraph. Assume that we have solved the Haar state for all . To solve the case , we use equation where and .
At this point, we have solved the Haar states of all monomials ending with .
5.4.2. Monomials without low-complexity segment
Now, we are able to solve the Haar states of all monomials with at least one low-complexity segment since the number of generators and cannot decrease. We start with monomial in form . When , we use equation basis and comparing basis . When , we use equation basis and comparing basis . Finally, we compute monomials in form . To compute the case , we use equation with . Now, we assume that the Haar state of monomials in the form with are known. To compute with , we use equation .
At this point, we have computed the Haar states of all monomials of order .
Acknowledgement
This author is advised by Professor Jeffrey Kuan from Texas A&M University and Professor Micheal Brannan from University of Waterloo. This work is partially funded by National Science Foundation (NSF grant DMS-2000331).
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Appendix A Useful equations
When we switch the order of high complexity segments
with segment:
(16) |
with segment:
(17) |
with segment:
(18) |
The key observation is that when we switch the order of a high-complexity segment with a low-complexity segment, the newly generated monomials contain at most one high-complexity segment.
When we switch the order of two high complexity segments:
(19) |
(20) |
(21) |
In Equation (19), the newly generated monomials contain at most one high-complexity segment. In Equation (LABEL:apeq:5) and Equation (21), the newly generated monomials contain at most one high-complexity segment except the monomial .
Standard monomials , and have the same counting matrix:
We have the following equation:
(22) |
(23) |
Appendix B Illustration of the Zigzag Recursive Relation
Appendix C Example of -Deformed Weingarten Function
We know that when , becomes and the Haar state on becomes the Haar measure on . This implies that
where ’s are generators of and ’s are coordinate function on . The Haar state on the quantum sphere serves as an example of -deformed Weingarten function on (for detail, see Noumi et al. [11], Reshetikhin et al. [12], Mikkelsen et al. [9]).
One major difference between the Haar state and the integral is that the order of generators affects the Haar state. However, the order of the coordinate functions does not affect the integral. In the following examples on , we show that the order of generators in the Haar state does not affect the limit at .
Example 1:
The Haar states of monomials in other orders can be computed by the relation where is the homomorphism on such that . When , all Haar state values goes to which is consistent with
Example 2:
When , all Haar state values goes to which is consistent with
Example 3:
When , all Haar state values goes to which is consistent with