Miscellaneous matrix functions#
- sage.matrix.matrix_misc.permanental_minor_polynomial(A, permanent_only=False, var='t', prec=None)#
Return the polynomial of the sums of permanental minors of
A
.INPUT:
\(A\) – a matrix
\(permanent_only\) – if True, return only the permanent of \(A\)
\(var\) – name of the polynomial variable
\(prec\) – if prec is not None, truncate the polynomial at precision \(prec\)
The polynomial of the sums of permanental minors is
\[\sum_{i=0}^{min(nrows, ncols)} p_i(A) x^i\]where \(p_i(A)\) is the \(i\)-th permanental minor of \(A\) (that can also be obtained through the method
permanental_minor()
viaA.permanental_minor(i)
).The algorithm implemented by that function has been developed by P. Butera and M. Pernici, see [BP2015]. Its complexity is \(O(2^n m^2 n)\) where \(m\) and \(n\) are the number of rows and columns of \(A\). Moreover, if \(A\) is a banded matrix with width \(w\), that is \(A_{ij}=0\) for \(|i - j| > w\) and \(w < n/2\), then the complexity of the algorithm is \(O(4^w (w+1) n^2)\).
INPUT:
A
– matrixpermanent_only
– optional boolean. IfTrue
, only the permanent is computed (might be faster).var
– a variable name
EXAMPLES:
sage: from sage.matrix.matrix_misc import permanental_minor_polynomial sage: m = matrix([[1,1],[1,2]]) sage: permanental_minor_polynomial(m) 3*t^2 + 5*t + 1 sage: permanental_minor_polynomial(m, permanent_only=True) 3 sage: permanental_minor_polynomial(m, prec=2) 5*t + 1
sage: M = MatrixSpace(ZZ,4,4) sage: A = M([1,0,1,0,1,0,1,0,1,0,10,10,1,0,1,1]) sage: permanental_minor_polynomial(A) 84*t^3 + 114*t^2 + 28*t + 1 sage: [A.permanental_minor(i) for i in range(5)] [1, 28, 114, 84, 0]
An example over \(\QQ\):
sage: M = MatrixSpace(QQ,2,2) sage: A = M([1/5,2/7,3/2,4/5]) sage: permanental_minor_polynomial(A, True) 103/175
An example with polynomial coefficients:
sage: R.<a> = PolynomialRing(ZZ) sage: A = MatrixSpace(R,2)([[a,1], [a,a+1]]) sage: permanental_minor_polynomial(A, True) a^2 + 2*a
A usage of the
var
argument:sage: m = matrix(ZZ,4,[0,1,2,3,1,2,3,0,2,3,0,1,3,0,1,2]) sage: permanental_minor_polynomial(m, var='x') 164*x^4 + 384*x^3 + 172*x^2 + 24*x + 1
ALGORITHM:
The permanent \(perm(A)\) of a \(n \times n\) matrix \(A\) is the coefficient of the \(x_1 x_2 \ldots x_n\) monomial in
\[\prod_{i=1}^n \left( \sum_{j=1}^n A_{ij} x_j \right)\]Evaluating this product one can neglect \(x_i^2\), that is \(x_i\) can be considered to be nilpotent of order \(2\).
To formalize this procedure, consider the algebra \(R = K[\eta_1, \eta_2, \ldots, \eta_n]\) where the \(\eta_i\) are commuting, nilpotent of order \(2\) (i.e. \(\eta_i^2 = 0\)). Formally it is the quotient ring of the polynomial ring in \(\eta_1, \eta_2, \ldots, \eta_n\) quotiented by the ideal generated by the \(\eta_i^2\).
We will mostly consider the ring \(R[t]\) of polynomials over \(R\). We denote a generic element of \(R[t]\) by \(p(\eta_1, \ldots, \eta_n)\) or \(p(\eta_{i_1}, \ldots, \eta_{i_k})\) if we want to emphasize that some monomials in the \(\eta_i\) are missing.
Introduce an “integration” operation \(\langle p \rangle\) over \(R\) and \(R[t]\) consisting in the sum of the coefficients of the non-vanishing monomials in \(\eta_i\) (i.e. the result of setting all variables \(\eta_i\) to \(1\)). Let us emphasize that this is not a morphism of algebras as \(\langle \eta_1 \rangle^2 = 1\) while \(\langle \eta_1^2 \rangle = 0\)!
Let us consider an example of computation. Let \(p_1 = 1 + t \eta_1 + t \eta_2\) and \(p_2 = 1 + t \eta_1 + t \eta_3\). Then
\[p_1 p_2 = 1 + 2t \eta_1 + t (\eta_2 + \eta_3) + t^2 (\eta_1 \eta_2 + \eta_1 \eta_3 + \eta_2 \eta_3)\]and
\[\langle p_1 p_2 \rangle = 1 + 4t + 3t^2\]In this formalism, the permanent is just
\[perm(A) = \langle \prod_{i=1}^n \sum_{j=1}^n A_{ij} \eta_j \rangle\]A useful property of \(\langle . \rangle\) which makes this algorithm efficient for band matrices is the following: let \(p_1(\eta_1, \ldots, \eta_n)\) and \(p_2(\eta_j, \ldots, \eta_n)\) be polynomials in \(R[t]\) where \(j \ge 1\). Then one has
\[\langle p_1(\eta_1, \ldots, \eta_n) p_2 \rangle = \langle p_1(1, \ldots, 1, \eta_j, \ldots, \eta_n) p_2 \rangle\]where \(\eta_1,..,\eta_{j-1}\) are replaced by \(1\) in \(p_1\). Informally, we can “integrate” these variables before performing the product. More generally, if a monomial \(\eta_i\) is missing in one of the terms of a product of two terms, then it can be integrated in the other term.
Now let us consider an \(m \times n\) matrix with \(m \leq n\). The sum of permanental `k`-minors of `A` is
\[perm(A, k) = \sum_{r,c} perm(A_{r,c})\]where the sum is over the \(k\)-subsets \(r\) of rows and \(k\)-subsets \(c\) of columns and \(A_{r,c}\) is the submatrix obtained from \(A\) by keeping only the rows \(r\) and columns \(c\). Of course \(perm(A, \min(m,n)) = perm(A)\) and note that \(perm(A,1)\) is just the sum of all entries of the matrix.
The generating function of these sums of permanental minors is
\[g(t) = \left\langle \prod_{i=1}^m \left(1 + t \sum_{j=1}^n A_{ij} \eta_j\right) \right\rangle\]In fact the \(t^k\) coefficient of \(g(t)\) corresponds to choosing \(k\) rows of \(A\); \(\eta_i\) is associated to the i-th column; nilpotency avoids having twice the same column in a product of \(A\)’s.
For more details, see the article [BP2015].
From a technical point of view, the product in \(K[\eta_1, \ldots, \eta_n][t]\) is implemented as a subroutine in
prm_mul()
. The indices of the rows and columns actually start at \(0\), so the variables are \(\eta_0, \ldots, \eta_{n-1}\). Polynomials are represented in dictionary form: to a variable \(\eta_i\) is associated the key \(2^i\) (or in Python1 << i
). The keys associated to products are obtained by considering the development in base \(2\): to the monomial \(\eta_{i_1} \ldots \eta_{i_k}\) is associated the key \(2^{i_1} + \ldots + 2^{i_k}\). So the product \(\eta_1 \eta_2\) corresponds to the key \(6 = (110)_2\) while \(\eta_0 \eta_3\) has key \(9 = (1001)_2\). In particular all operations on monomials are implemented via bitwise operations on the keys.
- sage.matrix.matrix_misc.prm_mul(p1, p2, mask_free, prec)#
Return the product of
p1
andp2
, putting free variables inmask_free
to \(1\).This function is mainly use as a subroutine of
permanental_minor_polynomial()
.INPUT:
\(p1,p2\) – polynomials as dictionaries
mask_free
– an integer mask that give the list of free variables (the \(i\)-th variable is free if the \(i\)-th bit ofmask_free
is \(1\))prec
– ifprec
is notNone
, truncate the product at precisionprec
EXAMPLES:
sage: from sage.matrix.matrix_misc import prm_mul sage: t = polygen(ZZ, 't') sage: p1 = {0: 1, 1: t, 4: t} sage: p2 = {0: 1, 1: t, 2: t} sage: prm_mul(p1, p2, 1, None) {0: 2*t + 1, 2: t^2 + t, 4: t^2 + t, 6: t^2}
- sage.matrix.matrix_misc.row_iterator(A)#