X-Git-Url: http://gitweb.michael.orlitzky.com/?a=blobdiff_plain;f=mjo%2Feja%2Feuclidean_jordan_algebra.py;h=da3f6001e2d3878f6c5311eb309f8b2a22676a00;hb=9b6acc401eb02e9565db6212698662c9844c4239;hp=74c0bf1cb11e38460f39aef4920fa70f60d02409;hpb=50b101f683406a9ec7bf5a313f8bfb1ac614b091;p=sage.d.git diff --git a/mjo/eja/euclidean_jordan_algebra.py b/mjo/eja/euclidean_jordan_algebra.py index 74c0bf1..da3f600 100644 --- a/mjo/eja/euclidean_jordan_algebra.py +++ b/mjo/eja/euclidean_jordan_algebra.py @@ -87,6 +87,33 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): return fmt.format(self.degree(), self.base_ring()) + def _a_regular_element(self): + """ + Guess a regular element. Needed to compute the basis for our + characteristic polynomial coefficients. + """ + gs = self.gens() + z = self.sum( (i+1)*gs[i] for i in range(len(gs)) ) + if not z.is_regular(): + raise ValueError("don't know a regular element") + return z + + + @cached_method + def _charpoly_basis_space(self): + """ + Return the vector space spanned by the basis used in our + characteristic polynomial coefficients. This is used not only to + compute those coefficients, but also any time we need to + evaluate the coefficients (like when we compute the trace or + determinant). + """ + z = self._a_regular_element() + V = z.vector().parent().ambient_vector_space() + V1 = V.span_of_basis( (z**k).vector() for k in range(self.rank()) ) + b = (V1.basis() + V1.complement().basis()) + return V.span_of_basis(b) + @cached_method def _charpoly_coeff(self, i): @@ -98,25 +125,38 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): store the trace/determinant (a_{r-1} and a_{0} respectively) separate from the entire characteristic polynomial. """ - (A_of_x, x) = self._charpoly_matrix() + (A_of_x, x, xr, detA) = self._charpoly_matrix_system() R = A_of_x.base_ring() - A_cols = A_of_x.columns() - A_cols[i] = (x**self.rank()).vector() - numerator = column_matrix(A_of_x.base_ring(), A_cols).det() - denominator = A_of_x.det() + if i >= self.rank(): + # Guaranteed by theory + return R.zero() + + # Danger: the in-place modification is done for performance + # reasons (reconstructing a matrix with huge polynomial + # entries is slow), but I don't know how cached_method works, + # so it's highly possible that we're modifying some global + # list variable by reference, here. In other words, you + # probably shouldn't call this method twice on the same + # algebra, at the same time, in two threads + Ai_orig = A_of_x.column(i) + A_of_x.set_column(i,xr) + numerator = A_of_x.det() + A_of_x.set_column(i,Ai_orig) # We're relying on the theory here to ensure that each a_i is # indeed back in R, and the added negative signs are to make # the whole charpoly expression sum to zero. - return R(-numerator/denominator) + return R(-numerator/detA) @cached_method - def _charpoly_matrix(self): + def _charpoly_matrix_system(self): """ Compute the matrix whose entries A_ij are polynomials in - X1,...,XN. This same matrix is used in more than one method and - it's not so fast to construct. + X1,...,XN, the vector ``x`` of variables X1,...,XN, the vector + corresponding to `x^r` and the determinent of the matrix A = + [A_ij]. In other words, all of the fixed (cachable) data needed + to compute the coefficients of the characteristic polynomial. """ r = self.rank() n = self.dimension() @@ -130,16 +170,49 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): idmat = identity_matrix(J.base_ring(), n) + W = self._charpoly_basis_space() + W = W.change_ring(R.fraction_field()) + + # Starting with the standard coordinates x = (X1,X2,...,Xn) + # and then converting the entries to W-coordinates allows us + # to pass in the standard coordinates to the charpoly and get + # back the right answer. Specifically, with x = (X1,X2,...,Xn), + # we have + # + # W.coordinates(x^2) eval'd at (standard z-coords) + # = + # W-coords of (z^2) + # = + # W-coords of (standard coords of x^2 eval'd at std-coords of z) + # + # We want the middle equivalent thing in our matrix, but use + # the first equivalent thing instead so that we can pass in + # standard coordinates. x = J(vector(R, R.gens())) - l1 = [column_matrix((x**k).vector()) for k in range(r)] + l1 = [column_matrix(W.coordinates((x**k).vector())) for k in range(r)] l2 = [idmat.column(k-1).column() for k in range(r+1, n+1)] A_of_x = block_matrix(R, 1, n, (l1 + l2)) - return (A_of_x, x) + xr = W.coordinates((x**r).vector()) + return (A_of_x, x, xr, A_of_x.det()) @cached_method def characteristic_polynomial(self): """ + + .. WARNING:: + + This implementation doesn't guarantee that the polynomial + denominator in the coefficients is not identically zero, so + theoretically it could crash. The way that this is handled + in e.g. Faraut and Koranyi is to use a basis that guarantees + the denominator is non-zero. But, doing so requires knowledge + of at least one regular element, and we don't even know how + to do that. The trade-off is that, if we use the standard basis, + the resulting polynomial will accept the "usual" coordinates. In + other words, we don't have to do a change of basis before e.g. + computing the trace or determinant. + EXAMPLES: The characteristic polynomial in the spin algebra is given in @@ -626,14 +699,13 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): True """ + if not self.is_invertible(): + raise ValueError("element not invertible") + if self.parent().is_associative(): elt = FiniteDimensionalAlgebraElement(self.parent(), self) - return elt.inverse() - - # TODO: we can do better once the call to is_invertible() - # doesn't crash on irregular elements. - #if not self.is_invertible(): - # raise ValueError('element is not invertible') + # elt is in the right coordinates, but has the wrong class. + return self.parent()(elt.inverse().vector()) # We do this a little different than the usual recursive # call to a finite-dimensional algebra element, because we @@ -1014,38 +1086,77 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): sage: J = random_eja() sage: x = J.random_element() sage: y = J.random_element() + sage: Lx = x.operator_matrix() + sage: Lxx = (x*x).operator_matrix() + sage: Qx = x.quadratic_representation() + sage: Qy = y.quadratic_representation() + sage: Qxy = x.quadratic_representation(y) + sage: Qex = J.one().quadratic_representation(x) + sage: n = ZZ.random_element(10) + sage: Qxn = (x^n).quadratic_representation() Property 1: - sage: actual = x.quadratic_representation(y) - sage: expected = ( (x+y).quadratic_representation() - ....: -x.quadratic_representation() - ....: -y.quadratic_representation() ) / 2 - sage: actual == expected + sage: 2*Qxy == (x+y).quadratic_representation() - Qx - Qy True Property 2: sage: alpha = QQ.random_element() - sage: actual = (alpha*x).quadratic_representation() - sage: expected = (alpha^2)*x.quadratic_representation() - sage: actual == expected + sage: (alpha*x).quadratic_representation() == (alpha^2)*Qx + True + + Property 3: + + sage: not x.is_invertible() or ( + ....: Qx*x.inverse().vector() == x.vector() ) + True + + sage: not x.is_invertible() or ( + ....: Qx.inverse() + ....: == + ....: x.inverse().quadratic_representation() ) + True + + sage: Qxy*(J.one().vector()) == (x*y).vector() + True + + Property 4: + + sage: not x.is_invertible() or ( + ....: x.quadratic_representation(x.inverse())*Qx + ....: == Qx*x.quadratic_representation(x.inverse()) ) + True + + sage: not x.is_invertible() or ( + ....: x.quadratic_representation(x.inverse())*Qx + ....: == + ....: 2*x.operator_matrix()*Qex - Qx ) + True + + sage: 2*x.operator_matrix()*Qex - Qx == Lxx True Property 5: - sage: Qy = y.quadratic_representation() - sage: actual = J(Qy*x.vector()).quadratic_representation() - sage: expected = Qy*x.quadratic_representation()*Qy - sage: actual == expected + sage: J(Qy*x.vector()).quadratic_representation() == Qy*Qx*Qy True Property 6: - sage: k = ZZ.random_element(1,10) - sage: actual = (x^k).quadratic_representation() - sage: expected = (x.quadratic_representation())^k - sage: actual == expected + sage: Qxn == (Qx)^n + True + + Property 7: + + sage: not x.is_invertible() or ( + ....: Qx*x.inverse().operator_matrix() == Lx ) + True + + Property 8: + + sage: not x.operator_commutes_with(y) or ( + ....: J(Qx*y.vector())^n == J(Qxn*(y^n).vector()) ) True """ @@ -1139,12 +1250,11 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): TESTS:: sage: set_random_seed() - sage: J = RealCartesianProductEJA(5) - sage: c = J.random_element().subalgebra_idempotent() - sage: c^2 == c - True - sage: J = JordanSpinEJA(5) - sage: c = J.random_element().subalgebra_idempotent() + sage: J = random_eja() + sage: x = J.random_element() + sage: while x.is_nilpotent(): + ....: x = J.random_element() + sage: c = x.subalgebra_idempotent() sage: c^2 == c True @@ -1233,6 +1343,47 @@ class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra): def trace_inner_product(self, other): """ Return the trace inner product of myself and ``other``. + + TESTS: + + The trace inner product is commutative:: + + sage: set_random_seed() + sage: J = random_eja() + sage: x = J.random_element(); y = J.random_element() + sage: x.trace_inner_product(y) == y.trace_inner_product(x) + True + + The trace inner product is bilinear:: + + sage: set_random_seed() + sage: J = random_eja() + sage: x = J.random_element() + sage: y = J.random_element() + sage: z = J.random_element() + sage: a = QQ.random_element(); + sage: actual = (a*(x+z)).trace_inner_product(y) + sage: expected = ( a*x.trace_inner_product(y) + + ....: a*z.trace_inner_product(y) ) + sage: actual == expected + True + sage: actual = x.trace_inner_product(a*(y+z)) + sage: expected = ( a*x.trace_inner_product(y) + + ....: a*x.trace_inner_product(z) ) + sage: actual == expected + True + + The trace inner product satisfies the compatibility + condition in the definition of a Euclidean Jordan algebra:: + + sage: set_random_seed() + sage: J = random_eja() + sage: x = J.random_element() + sage: y = J.random_element() + sage: z = J.random_element() + sage: (x*y).trace_inner_product(z) == y.trace_inner_product(x*z) + True + """ if not other in self.parent(): raise TypeError("'other' must live in the same algebra")