X-Git-Url: http://gitweb.michael.orlitzky.com/?a=blobdiff_plain;f=mjo%2Feja%2Feuclidean_jordan_algebra.py;h=1281a029a8f5c4557d9d35ee50342caecdc51054;hb=a09a7e14df9b7dcae39fe558d42a6d74fb1c52b0;hp=bb460194970e3da92709082a81f78c86a6c88d5c;hpb=556690d808d614f2c271dc431dd909e353530594;p=sage.d.git diff --git a/mjo/eja/euclidean_jordan_algebra.py b/mjo/eja/euclidean_jordan_algebra.py index bb46019..1281a02 100644 --- a/mjo/eja/euclidean_jordan_algebra.py +++ b/mjo/eja/euclidean_jordan_algebra.py @@ -101,6 +101,21 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): Jordan algebras are always power-associative; see for example Faraut and Koranyi, Proposition II.1.2 (ii). + + .. WARNING: + + We have to override this because our superclass uses row vectors + instead of column vectors! We, on the other hand, assume column + vectors everywhere. + + EXAMPLES: + + sage: set_random_seed() + sage: J = eja_ln(5) + sage: x = J.random_element() + sage: x.matrix()*x.vector() == (x**2).vector() + True + """ A = self.parent() if n == 0: @@ -108,19 +123,91 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): elif n == 1: return self else: - return A.element_class(A, self.vector()*(self.matrix()**(n-1))) + return A.element_class(A, (self.matrix()**(n-1))*self.vector()) - def span_of_powers(self): + def characteristic_polynomial(self): + return self.matrix().characteristic_polynomial() + + + def is_nilpotent(self): """ - Return the vector space spanned by successive powers of - this element. + Return whether or not some power of this element is zero. + + The superclass method won't work unless we're in an + associative algebra, and we aren't. However, we generate + an assocoative subalgebra and we're nilpotent there if and + only if we're nilpotent here (probably). + + TESTS: + + The identity element is never nilpotent:: + + sage: set_random_seed() + sage: n = ZZ.random_element(2,10).abs() + sage: J = eja_rn(n) + sage: J.one().is_nilpotent() + False + sage: J = eja_ln(n) + sage: J.one().is_nilpotent() + False + + The additive identity is always nilpotent:: + + sage: set_random_seed() + sage: n = ZZ.random_element(2,10).abs() + sage: J = eja_rn(n) + sage: J.zero().is_nilpotent() + True + sage: J = eja_ln(n) + sage: J.zero().is_nilpotent() + True + """ - # The dimension of the subalgebra can't be greater than - # the big algebra, so just put everything into a list - # and let span() get rid of the excess. - V = self.vector().parent() - return V.span( (self**d).vector() for d in xrange(V.dimension()) ) + # The element we're going to call "is_nilpotent()" on. + # Either myself, interpreted as an element of a finite- + # dimensional algebra, or an element of an associative + # subalgebra. + elt = None + + if self.parent().is_associative(): + elt = FiniteDimensionalAlgebraElement(self.parent(), self) + else: + V = self.span_of_powers() + assoc_subalg = self.subalgebra_generated_by() + # Mis-design warning: the basis used for span_of_powers() + # and subalgebra_generated_by() must be the same, and in + # the same order! + elt = assoc_subalg(V.coordinates(self.vector())) + + # Recursive call, but should work since elt lives in an + # associative algebra. + return elt.is_nilpotent() + + + def is_regular(self): + """ + Return whether or not this is a regular element. + + EXAMPLES: + + The identity element always has degree one, but any element + linearly-independent from it is regular:: + + sage: J = eja_ln(5) + sage: J.one().is_regular() + False + sage: e0, e1, e2, e3, e4 = J.gens() # e0 is the identity + sage: for x in J.gens(): + ....: (J.one() + x).is_regular() + False + True + True + True + True + + """ + return self.degree() == self.parent().rank() def degree(self): @@ -153,6 +240,92 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): return self.span_of_powers().dimension() + def matrix(self): + """ + Return the matrix that represents left- (or right-) + multiplication by this element in the parent algebra. + + We have to override this because the superclass method + returns a matrix that acts on row vectors (that is, on + the right). + """ + fda_elt = FiniteDimensionalAlgebraElement(self.parent(), self) + return fda_elt.matrix().transpose() + + + def minimal_polynomial(self): + """ + EXAMPLES:: + + sage: set_random_seed() + sage: n = ZZ.random_element(1,10).abs() + sage: J = eja_rn(n) + sage: x = J.random_element() + sage: x.degree() == x.minimal_polynomial().degree() + True + + :: + + sage: set_random_seed() + sage: n = ZZ.random_element(1,10).abs() + sage: J = eja_ln(n) + sage: x = J.random_element() + sage: x.degree() == x.minimal_polynomial().degree() + True + + The minimal polynomial and the characteristic polynomial coincide + and are known (see Alizadeh, Example 11.11) for all elements of + the spin factor algebra that aren't scalar multiples of the + identity:: + + sage: set_random_seed() + sage: n = ZZ.random_element(2,10).abs() + sage: J = eja_ln(n) + sage: y = J.random_element() + sage: while y == y.coefficient(0)*J.one(): + ....: y = J.random_element() + sage: y0 = y.vector()[0] + sage: y_bar = y.vector()[1:] + sage: actual = y.minimal_polynomial() + sage: x = SR.symbol('x', domain='real') + sage: expected = x^2 - 2*y0*x + (y0^2 - norm(y_bar)^2) + sage: bool(actual == expected) + True + + """ + # The element we're going to call "minimal_polynomial()" on. + # Either myself, interpreted as an element of a finite- + # dimensional algebra, or an element of an associative + # subalgebra. + elt = None + + if self.parent().is_associative(): + elt = FiniteDimensionalAlgebraElement(self.parent(), self) + else: + V = self.span_of_powers() + assoc_subalg = self.subalgebra_generated_by() + # Mis-design warning: the basis used for span_of_powers() + # and subalgebra_generated_by() must be the same, and in + # the same order! + elt = assoc_subalg(V.coordinates(self.vector())) + + # Recursive call, but should work since elt lives in an + # associative algebra. + return elt.minimal_polynomial() + + + def span_of_powers(self): + """ + Return the vector space spanned by successive powers of + this element. + """ + # The dimension of the subalgebra can't be greater than + # the big algebra, so just put everything into a list + # and let span() get rid of the excess. + V = self.vector().parent() + return V.span( (self**d).vector() for d in xrange(V.dimension()) ) + + def subalgebra_generated_by(self): """ Return the associative subalgebra of the parent EJA generated @@ -171,6 +344,15 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): sage: x.subalgebra_generated_by().is_associative() True + Squaring in the subalgebra should be the same thing as + squaring in the superalgebra:: + + sage: J = eja_ln(5) + sage: x = J.random_element() + sage: u = x.subalgebra_generated_by().random_element() + sage: u.matrix()*u.vector() == (u**2).vector() + True + """ # First get the subspace spanned by the powers of myself... V = self.span_of_powers() @@ -187,6 +369,9 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): # b1 is what we get if we apply that matrix to b1. The # second row of the right multiplication matrix by b1 # is what we get when we apply that matrix to b2... + # + # IMPORTANT: this assumes that all vectors are COLUMN + # vectors, unlike our superclass (which uses row vectors). for b_left in V.basis(): eja_b_left = self.parent()(b_left) # Multiply in the original EJA, but then get the @@ -199,65 +384,72 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): # It's an algebra of polynomials in one element, and EJAs # are power-associative. - return FiniteDimensionalEuclideanJordanAlgebra(F, mats, assume_associative=True) + # + # TODO: choose generator names intelligently. + return FiniteDimensionalEuclideanJordanAlgebra(F, mats, assume_associative=True, names='f') - def minimal_polynomial(self): + def subalgebra_idempotent(self): """ - EXAMPLES:: - - sage: set_random_seed() - sage: n = ZZ.random_element(1,10).abs() - sage: J = eja_rn(n) - sage: x = J.random_element() - sage: x.degree() == x.minimal_polynomial().degree() - True + Find an idempotent in the associative subalgebra I generate + using Proposition 2.3.5 in Baes. - :: + TESTS:: sage: set_random_seed() - sage: n = ZZ.random_element(1,10).abs() - sage: J = eja_ln(n) - sage: x = J.random_element() - sage: x.degree() == x.minimal_polynomial().degree() + sage: J = eja_rn(5) + sage: c = J.random_element().subalgebra_idempotent() + sage: c^2 == c True - - The minimal polynomial and the characteristic polynomial coincide - and are known (see Alizadeh, Example 11.11) for all elements of - the spin factor algebra that aren't scalar multiples of the - identity:: - - sage: set_random_seed() - sage: n = ZZ.random_element(2,10).abs() - sage: J = eja_ln(n) - sage: y = J.random_element() - sage: while y == y.coefficient(0)*J.one(): - ....: y = J.random_element() - sage: y0 = y.vector()[0] - sage: y_bar = y.vector()[1:] - sage: actual = y.minimal_polynomial() - sage: x = SR.symbol('x', domain='real') - sage: expected = x^2 - 2*y0*x + (y0^2 - norm(y_bar)^2) - sage: bool(actual == expected) + sage: J = eja_ln(5) + sage: c = J.random_element().subalgebra_idempotent() + sage: c^2 == c True """ - if self.parent().is_associative(): - return self.matrix().minimal_polynomial() + if self.is_nilpotent(): + raise ValueError("this only works with non-nilpotent elements!") V = self.span_of_powers() - assoc_subalg = self.subalgebra_generated_by() + J = self.subalgebra_generated_by() # Mis-design warning: the basis used for span_of_powers() # and subalgebra_generated_by() must be the same, and in # the same order! - subalg_self = assoc_subalg(V.coordinates(self.vector())) - # Recursive call, but should work since the subalgebra is - # associative. - return subalg_self.minimal_polynomial() - + u = J(V.coordinates(self.vector())) + + # The image of the matrix of left-u^m-multiplication + # will be minimal for some natural number s... + s = 0 + minimal_dim = V.dimension() + for i in xrange(1, V.dimension()): + this_dim = (u**i).matrix().image().dimension() + if this_dim < minimal_dim: + minimal_dim = this_dim + s = i + + # Now minimal_matrix should correspond to the smallest + # non-zero subspace in Baes's (or really, Koecher's) + # proposition. + # + # However, we need to restrict the matrix to work on the + # subspace... or do we? Can't we just solve, knowing that + # A(c) = u^(s+1) should have a solution in the big space, + # too? + # + # Beware, solve_right() means that we're using COLUMN vectors. + # Our FiniteDimensionalAlgebraElement superclass uses rows. + u_next = u**(s+1) + A = u_next.matrix() + c_coordinates = A.solve_right(u_next.vector()) + + # Now c_coordinates is the idempotent we want, but it's in + # the coordinate system of the subalgebra. + # + # We need the basis for J, but as elements of the parent algebra. + # + basis = [self.parent(v) for v in V.basis()] + return self.parent().linear_combination(zip(c_coordinates, basis)) - def characteristic_polynomial(self): - return self.matrix().characteristic_polynomial() def eja_rn(dimension, field=QQ):