""" Euclidean Jordan Algebras. These are formally-real Jordan Algebras; specifically those where u^2 + v^2 = 0 implies that u = v = 0. They are used in optimization, and have some additional nice methods beyond what can be supported in a general Jordan Algebra. """ from sage.all import * def eja_minimal_polynomial(x): """ Return the minimal polynomial of ``x`` in its parent EJA """ return x._x.matrix().minimal_polynomial() def eja_rn(dimension, field=QQ): """ Return the Euclidean Jordan Algebra corresponding to the set `R^n` under the Hadamard product. EXAMPLES: This multiplication table can be verified by hand:: sage: J = eja_rn(3) sage: e0,e1,e2 = J.gens() sage: e0*e0 e0 sage: e0*e1 0 sage: e0*e2 0 sage: e1*e1 e1 sage: e1*e2 0 sage: e2*e2 e2 """ # The FiniteDimensionalAlgebra constructor takes a list of # matrices, the ith representing right multiplication by the ith # basis element in the vector space. So if e_1 = (1,0,0), then # right (Hadamard) multiplication of x by e_1 picks out the first # component of x; and likewise for the ith basis element e_i. Qs = [ matrix(field, dimension, dimension, lambda k,j: 1*(k == j == i)) for i in xrange(dimension) ] A = FiniteDimensionalAlgebra(QQ,Qs,assume_associative=True) return JordanAlgebra(A) def eja_ln(dimension, field=QQ): """ Return the Jordan algebra corresponding to the Lorenzt "ice cream" cone of the given ``dimension``. EXAMPLES: This multiplication table can be verified by hand:: sage: J = eja_ln(4) sage: e0,e1,e2,e3 = J.gens() sage: e0*e0 e0 sage: e0*e1 e1 sage: e0*e2 e2 sage: e0*e3 e3 sage: e1*e2 0 sage: e1*e3 0 sage: e2*e3 0 In one dimension, this is the reals under multiplication:: sage: J1 = eja_ln(1) sage: J2 = eja_rn(1) sage: J1 == J2 True """ Qs = [] id_matrix = identity_matrix(field,dimension) for i in xrange(dimension): ei = id_matrix.column(i) Qi = zero_matrix(field,dimension) Qi.set_row(0, ei) Qi.set_column(0, ei) Qi += diagonal_matrix(dimension, [ei[0]]*dimension) # The addition of the diagonal matrix adds an extra ei[0] in the # upper-left corner of the matrix. Qi[0,0] = Qi[0,0] * ~field(2) Qs.append(Qi) A = FiniteDimensionalAlgebra(QQ,Qs,assume_associative=True) return JordanAlgebra(A)