Miscellaneous matrix functions#

sage.matrix.matrix_misc.permanental_minor_polynomial(A, permanent_only=False, var='t', prec=None)[source]#

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() via A.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 – matrix

  • permanent_only – optional boolean. If True, 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

>>> from sage.all import *
>>> from sage.matrix.matrix_misc import permanental_minor_polynomial
>>> m = matrix([[Integer(1),Integer(1)],[Integer(1),Integer(2)]])
>>> permanental_minor_polynomial(m)
3*t^2 + 5*t + 1
>>> permanental_minor_polynomial(m, permanent_only=True)
3
>>> permanental_minor_polynomial(m, prec=Integer(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]

>>> from sage.all import *
>>> M = MatrixSpace(ZZ,Integer(4),Integer(4))
>>> A = M([Integer(1),Integer(0),Integer(1),Integer(0),Integer(1),Integer(0),Integer(1),Integer(0),Integer(1),Integer(0),Integer(10),Integer(10),Integer(1),Integer(0),Integer(1),Integer(1)])
>>> permanental_minor_polynomial(A)
84*t^3 + 114*t^2 + 28*t + 1
>>> [A.permanental_minor(i) for i in range(Integer(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

>>> from sage.all import *
>>> M = MatrixSpace(QQ,Integer(2),Integer(2))
>>> A = M([Integer(1)/Integer(5),Integer(2)/Integer(7),Integer(3)/Integer(2),Integer(4)/Integer(5)])
>>> 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

>>> from sage.all import *
>>> R = PolynomialRing(ZZ, names=('a',)); (a,) = R._first_ngens(1)
>>> A = MatrixSpace(R,Integer(2))([[a,Integer(1)], [a,a+Integer(1)]])
>>> 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

>>> from sage.all import *
>>> m = matrix(ZZ,Integer(4),[Integer(0),Integer(1),Integer(2),Integer(3),Integer(1),Integer(2),Integer(3),Integer(0),Integer(2),Integer(3),Integer(0),Integer(1),Integer(3),Integer(0),Integer(1),Integer(2)])
>>> 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 Python 1 << 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)[source]#

Return the product of p1 and p2, putting free variables in mask_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 of mask_free is \(1\))

  • prec – if prec is not None, truncate the product at precision prec

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}

>>> from sage.all import *
>>> from sage.matrix.matrix_misc import prm_mul
>>> t = polygen(ZZ, 't')
>>> p1 = {Integer(0): Integer(1), Integer(1): t, Integer(4): t}
>>> p2 = {Integer(0): Integer(1), Integer(1): t, Integer(2): t}
>>> prm_mul(p1, p2, Integer(1), None)
{0: 2*t + 1, 2: t^2 + t, 4: t^2 + t, 6: t^2}
sage.matrix.matrix_misc.row_iterator(A)[source]#