+ n = self.dimension()
+ var_names = [ "X" + str(z) for z in range(1,n+1) ]
+ d = 0
+ ideal_dim = len(var_names)
+ def L_x_i_j(i,j):
+ # From a result in my book, these are the entries of the
+ # basis representation of L_x.
+ return sum( vars[d+k]*self.monomial(k).operator().matrix()[i,j]
+ for k in range(n) )
+
+ while ideal_dim == len(var_names):
+ coeff_names = [ "a" + str(z) for z in range(d) ]
+ R = PolynomialRing(self.base_ring(), coeff_names + var_names)
+ vars = R.gens()
+ L_x = matrix(R, n, n, L_x_i_j)
+ x_powers = [ vars[k]*(L_x**k)*self.one().to_vector()
+ for k in range(d) ]
+ eqs = [ sum(x_powers[k][j] for k in range(d)) for j in range(n) ]
+ ideal_dim = R.ideal(eqs).dimension()
+ d += 1
+
+ # Subtract one because we increment one too many times, and
+ # subtract another one because "d" is one greater than the
+ # answer anyway; when d=3, we go up to x^2.
+ return d-2
+
+ def _rank_computation2(self):
+ r"""
+ Instead of using the dimension of an ideal, find the rank of a
+ matrix containing polynomials.
+ """
+ n = self.dimension()
+ var_names = [ "X" + str(z) for z in range(1,n+1) ]
+ R = PolynomialRing(self.base_ring(), var_names)
+ vars = R.gens()
+
+ def L_x_i_j(i,j):
+ # From a result in my book, these are the entries of the
+ # basis representation of L_x.
+ return sum( vars[k]*self.monomial(k).operator().matrix()[i,j]
+ for k in range(n) )