X-Git-Url: http://gitweb.michael.orlitzky.com/?a=blobdiff_plain;f=mjo%2Feja%2Feja_algebra.py;h=6048363d64402b91326ea599b109c238574f7cf8;hb=c3b925473fca353cc16b13ab24de6664821ac305;hp=9e808ae1cb1c6ec41ca59136c8a6787fd589e61c;hpb=82b8e49f8f8fae889a64c40139abcbaef71101af;p=sage.d.git diff --git a/mjo/eja/eja_algebra.py b/mjo/eja/eja_algebra.py index 9e808ae..6048363 100644 --- a/mjo/eja/eja_algebra.py +++ b/mjo/eja/eja_algebra.py @@ -20,7 +20,9 @@ from itertools import repeat from sage.algebras.quatalg.quaternion_algebra import QuaternionAlgebra from sage.categories.magmatic_algebras import MagmaticAlgebras -from sage.combinat.free_module import CombinatorialFreeModule +from sage.categories.sets_cat import cartesian_product +from sage.combinat.free_module import (CombinatorialFreeModule, + CombinatorialFreeModule_CartesianProduct) from sage.matrix.constructor import matrix from sage.matrix.matrix_space import MatrixSpace from sage.misc.cachefunc import cached_method @@ -31,7 +33,7 @@ from sage.rings.all import (ZZ, QQ, AA, QQbar, RR, RLF, CLF, QuadraticField) from mjo.eja.eja_element import FiniteDimensionalEJAElement from mjo.eja.eja_operator import FiniteDimensionalEJAOperator -from mjo.eja.eja_utils import _mat2vec +from mjo.eja.eja_utils import _all2list, _mat2vec class FiniteDimensionalEJA(CombinatorialFreeModule): r""" @@ -39,17 +41,25 @@ class FiniteDimensionalEJA(CombinatorialFreeModule): INPUT: - - basis -- a tuple of basis elements in their matrix form. - - - jordan_product -- function of two elements (in matrix form) - that returns their jordan product in this algebra; this will - be applied to ``basis`` to compute a multiplication table for - the algebra. - - - inner_product -- function of two elements (in matrix form) that - returns their inner product. This will be applied to ``basis`` to - compute an inner-product table (basically a matrix) for this algebra. - + - basis -- a tuple of basis elements in "matrix form," which + must be the same form as the arguments to ``jordan_product`` + and ``inner_product``. In reality, "matrix form" can be either + vectors, matrices, or a Cartesian product (ordered tuple) + of vectors or matrices. All of these would ideally be vector + spaces in sage with no special-casing needed; but in reality + we turn vectors into column-matrices and Cartesian products + `(a,b)` into column matrices `(a,b)^{T}` after converting + `a` and `b` themselves. + + - jordan_product -- function of two ``basis`` elements (in + matrix form) that returns their jordan product, also in matrix + form; this will be applied to ``basis`` to compute a + multiplication table for the algebra. + + - inner_product -- function of two ``basis`` elements (in matrix + form) that returns their inner product. This will be applied + to ``basis`` to compute an inner-product table (basically a + matrix) for this algebra. """ Element = FiniteDimensionalEJAElement @@ -60,10 +70,23 @@ class FiniteDimensionalEJA(CombinatorialFreeModule): field=AA, orthonormalize=True, associative=False, + cartesian_product=False, check_field=True, check_axioms=True, prefix='e'): + # Keep track of whether or not the matrix basis consists of + # tuples, since we need special cases for them damned near + # everywhere. This is INDEPENDENT of whether or not the + # algebra is a cartesian product, since a subalgebra of a + # cartesian product will have a basis of tuples, but will not + # in general itself be a cartesian product algebra. + self._matrix_basis_is_cartesian = False + n = len(basis) + if n > 0: + if hasattr(basis[0], 'cartesian_factors'): + self._matrix_basis_is_cartesian = True + if check_field: if not field.is_subring(RR): # Note: this does return true for the real algebraic @@ -73,7 +96,18 @@ class FiniteDimensionalEJA(CombinatorialFreeModule): # If the basis given to us wasn't over the field that it's # supposed to be over, fix that. Or, you know, crash. - basis = tuple( b.change_ring(field) for b in basis ) + if not cartesian_product: + # The field for a cartesian product algebra comes from one + # of its factors and is the same for all factors, so + # there's no need to "reapply" it on product algebras. + if self._matrix_basis_is_cartesian: + # OK since if n == 0, the basis does not consist of tuples. + P = basis[0].parent() + basis = tuple( P(tuple(b_i.change_ring(field) for b_i in b)) + for b in basis ) + else: + basis = tuple( b.change_ring(field) for b in basis ) + if check_axioms: # Check commutativity of the Jordan and inner-products. @@ -96,15 +130,17 @@ class FiniteDimensionalEJA(CombinatorialFreeModule): if associative: # Element subalgebras can take advantage of this. category = category.Associative() + if cartesian_product: + category = category.CartesianProducts() # Call the superclass constructor so that we can use its from_vector() # method to build our multiplication table. - n = len(basis) - super().__init__(field, - range(n), - prefix=prefix, - category=category, - bracket=False) + CombinatorialFreeModule.__init__(self, + field, + range(n), + prefix=prefix, + category=category, + bracket=False) # Now comes all of the hard work. We'll be constructing an # ambient vector space V that our (vectorized) basis lives in, @@ -115,8 +151,7 @@ class FiniteDimensionalEJA(CombinatorialFreeModule): degree = 0 if n > 0: - # Works on both column and square matrices... - degree = len(basis[0].list()) + degree = len(_all2list(basis[0])) # Build an ambient space that fits our matrix basis when # written out as "long vectors." @@ -130,10 +165,10 @@ class FiniteDimensionalEJA(CombinatorialFreeModule): # Save a copy of the un-orthonormalized basis for later. # Convert it to ambient V (vector) coordinates while we're # at it, because we'd have to do it later anyway. - deortho_vector_basis = tuple( V(b.list()) for b in basis ) + deortho_vector_basis = tuple( V(_all2list(b)) for b in basis ) from mjo.eja.eja_utils import gram_schmidt - basis = gram_schmidt(basis, inner_product) + basis = tuple(gram_schmidt(basis, inner_product)) # Save the (possibly orthonormalized) matrix basis for # later... @@ -142,7 +177,7 @@ class FiniteDimensionalEJA(CombinatorialFreeModule): # Now create the vector space for the algebra, which will have # its own set of non-ambient coordinates (in terms of the # supplied basis). - vector_basis = tuple( V(b.list()) for b in basis ) + vector_basis = tuple( V(_all2list(b)) for b in basis ) W = V.span_of_basis( vector_basis, check=check_axioms) if orthonormalize: @@ -174,7 +209,7 @@ class FiniteDimensionalEJA(CombinatorialFreeModule): # The jordan product returns a matrixy answer, so we # have to convert it to the algebra coordinates. elt = jordan_product(q_i, q_j) - elt = W.coordinate_vector(V(elt.list())) + elt = W.coordinate_vector(V(_all2list(elt))) self._multiplication_table[i][j] = self.from_vector(elt) if not orthonormalize: @@ -222,6 +257,35 @@ class FiniteDimensionalEJA(CombinatorialFreeModule): def product_on_basis(self, i, j): + r""" + Returns the Jordan product of the `i` and `j`th basis elements. + + This completely defines the Jordan product on the algebra, and + is used direclty by our superclass machinery to implement + :meth:`product`. + + SETUP:: + + sage: from mjo.eja.eja_algebra import random_eja + + TESTS:: + + sage: set_random_seed() + sage: J = random_eja() + sage: n = J.dimension() + sage: ei = J.zero() + sage: ej = J.zero() + sage: ei_ej = J.zero()*J.zero() + sage: if n > 0: + ....: i = ZZ.random_element(n) + ....: j = ZZ.random_element(n) + ....: ei = J.gens()[i] + ....: ej = J.gens()[j] + ....: ei_ej = J.product_on_basis(i,j) + sage: ei*ej == ei_ej + True + + """ # We only stored the lower-triangular portion of the # multiplication table. if j <= i: @@ -279,22 +343,32 @@ class FiniteDimensionalEJA(CombinatorialFreeModule): sage: y = J.random_element() sage: (n == 1) or (x.inner_product(y) == (x*y).trace()/2) True + """ B = self._inner_product_matrix return (B*x.to_vector()).inner_product(y.to_vector()) - def _is_commutative(self): + def is_associative(self): r""" - Whether or not this algebra's multiplication table is commutative. + Return whether or not this algebra's Jordan product is associative. + + SETUP:: + + sage: from mjo.eja.eja_algebra import ComplexHermitianEJA + + EXAMPLES:: + + sage: J = ComplexHermitianEJA(3, field=QQ, orthonormalize=False) + sage: J.is_associative() + False + sage: x = sum(J.gens()) + sage: A = x.subalgebra_generated_by(orthonormalize=False) + sage: A.is_associative() + True - This method should of course always return ``True``, unless - this algebra was constructed with ``check_axioms=False`` and - passed an invalid multiplication table. """ - return all( self.product_on_basis(i,j) == self.product_on_basis(i,j) - for i in range(self.dimension()) - for j in range(self.dimension()) ) + return "Associative" in self.category().axioms() def _is_jordanian(self): r""" @@ -303,13 +377,13 @@ class FiniteDimensionalEJA(CombinatorialFreeModule): We only check one arrangement of `x` and `y`, so for a ``True`` result to be truly true, you should also check - :meth:`_is_commutative`. This method should of course always + :meth:`is_commutative`. This method should of course always return ``True``, unless this algebra was constructed with ``check_axioms=False`` and passed an invalid multiplication table. """ - return all( (self.monomial(i)**2)*(self.monomial(i)*self.monomial(j)) + return all( (self.gens()[i]**2)*(self.gens()[i]*self.gens()[j]) == - (self.monomial(i))*((self.monomial(i)**2)*self.monomial(j)) + (self.gens()[i])*((self.gens()[i]**2)*self.gens()[j]) for i in range(self.dimension()) for j in range(self.dimension()) ) @@ -331,9 +405,9 @@ class FiniteDimensionalEJA(CombinatorialFreeModule): for i in range(self.dimension()): for j in range(self.dimension()): for k in range(self.dimension()): - x = self.monomial(i) - y = self.monomial(j) - z = self.monomial(k) + x = self.gens()[i] + y = self.gens()[j] + z = self.gens()[k] diff = (x*y).inner_product(z) - x.inner_product(y*z) if self.base_ring().is_exact(): @@ -377,6 +451,15 @@ class FiniteDimensionalEJA(CombinatorialFreeModule): ... ValueError: not an element of this algebra + Tuples work as well, provided that the matrix basis for the + algebra consists of them:: + + sage: J1 = HadamardEJA(3) + sage: J2 = RealSymmetricEJA(2) + sage: J = cartesian_product([J1,J2]) + sage: J( (J1.matrix_basis()[1], J2.matrix_basis()[2]) ) + e(0, 1) + e(1, 2) + TESTS: Ensure that we can convert any element of the two non-matrix @@ -393,13 +476,23 @@ class FiniteDimensionalEJA(CombinatorialFreeModule): sage: J(x.to_vector().column()) == x True + We cannot coerce elements between algebras just because their + matrix representations are compatible:: + + sage: J1 = HadamardEJA(3) + sage: J2 = JordanSpinEJA(3) + sage: J2(J1.one()) + Traceback (most recent call last): + ... + ValueError: not an element of this algebra + sage: J1(J2.zero()) + Traceback (most recent call last): + ... + ValueError: not an element of this algebra + """ msg = "not an element of this algebra" - if elt == 0: - # The superclass implementation of random_element() - # needs to be able to coerce "0" into the algebra. - return self.zero() - elif elt in self.base_ring(): + if elt in self.base_ring(): # Ensure that no base ring -> algebra coercion is performed # by this method. There's some stupidity in sage that would # otherwise propagate to this method; for example, sage thinks @@ -407,9 +500,11 @@ class FiniteDimensionalEJA(CombinatorialFreeModule): raise ValueError(msg) try: + # Try to convert a vector into a column-matrix... elt = elt.column() except (AttributeError, TypeError): - # Try to convert a vector into a column-matrix + # and ignore failure, because we weren't really expecting + # a vector as an argument anyway. pass if elt not in self.matrix_space(): @@ -422,14 +517,20 @@ class FiniteDimensionalEJA(CombinatorialFreeModule): # closure whereas the base ring of the 3-by-3 identity matrix # could be QQ instead of QQbar. # + # And, we also have to handle Cartesian product bases (when + # the matrix basis consists of tuples) here. The "good news" + # is that we're already converting everything to long vectors, + # and that strategy works for tuples as well. + # # We pass check=False because the matrix basis is "guaranteed" # to be linearly independent... right? Ha ha. - V = VectorSpace(self.base_ring(), elt.nrows()*elt.ncols()) - W = V.span_of_basis( (_mat2vec(s) for s in self.matrix_basis()), + elt = _all2list(elt) + V = VectorSpace(self.base_ring(), len(elt)) + W = V.span_of_basis( (V(_all2list(s)) for s in self.matrix_basis()), check=False) try: - coords = W.coordinate_vector(_mat2vec(elt)) + coords = W.coordinate_vector(V(elt)) except ArithmeticError: # vector is not in free module raise ValueError(msg) @@ -656,8 +757,8 @@ class FiniteDimensionalEJA(CombinatorialFreeModule): # And to each subsequent row, prepend an entry that belongs to # the left-side "header column." - M += [ [self.monomial(i)] + [ self.product_on_basis(i,j) - for j in range(n) ] + M += [ [self.gens()[i]] + [ self.product_on_basis(i,j) + for j in range(n) ] for i in range(n) ] return table(M, header_row=True, header_column=True, frame=True) @@ -687,7 +788,7 @@ class FiniteDimensionalEJA(CombinatorialFreeModule): Why implement this for non-matrix algebras? Avoiding special cases for the :class:`BilinearFormEJA` pays with simplicity in its own right. But mainly, we would like to be able to assume - that elements of a :class:`DirectSumEJA` can be displayed + that elements of a :class:`CartesianProductEJA` can be displayed nicely, without having to have special classes for direct sums one of whose components was a matrix algebra. @@ -727,17 +828,54 @@ class FiniteDimensionalEJA(CombinatorialFreeModule): we think of them as matrices (including column vectors of the appropriate size). - Generally this will be an `n`-by-`1` column-vector space, + "By default" this will be an `n`-by-`1` column-matrix space, except when the algebra is trivial. There it's `n`-by-`n` (where `n` is zero), to ensure that two elements of the matrix - space (empty matrices) can be multiplied. + space (empty matrices) can be multiplied. For algebras of + matrices, this returns the space in which their + real embeddings live. + + SETUP:: + + sage: from mjo.eja.eja_algebra import (ComplexHermitianEJA, + ....: JordanSpinEJA, + ....: QuaternionHermitianEJA, + ....: TrivialEJA) + + EXAMPLES: + + By default, the matrix representation is just a column-matrix + equivalent to the vector representation:: + + sage: J = JordanSpinEJA(3) + sage: J.matrix_space() + Full MatrixSpace of 3 by 1 dense matrices over Algebraic + Real Field + + The matrix representation in the trivial algebra is + zero-by-zero instead of the usual `n`-by-one:: + + sage: J = TrivialEJA() + sage: J.matrix_space() + Full MatrixSpace of 0 by 0 dense matrices over Algebraic + Real Field + + The matrix space for complex/quaternion Hermitian matrix EJA + is the space in which their real-embeddings live, not the + original complex/quaternion matrix space:: + + sage: J = ComplexHermitianEJA(2,field=QQ,orthonormalize=False) + sage: J.matrix_space() + Full MatrixSpace of 4 by 4 dense matrices over Rational Field + sage: J = QuaternionHermitianEJA(1,field=QQ,orthonormalize=False) + sage: J.matrix_space() + Full MatrixSpace of 4 by 4 dense matrices over Rational Field - Matrix algebras override this with something more useful. """ if self.is_trivial(): return MatrixSpace(self.base_ring(), 0) else: - return self._matrix_basis[0].matrix_space() + return self.matrix_basis()[0].parent() @cached_method @@ -750,23 +888,57 @@ class FiniteDimensionalEJA(CombinatorialFreeModule): sage: from mjo.eja.eja_algebra import (HadamardEJA, ....: random_eja) - EXAMPLES:: + EXAMPLES: + + We can compute unit element in the Hadamard EJA:: sage: J = HadamardEJA(5) sage: J.one() e0 + e1 + e2 + e3 + e4 + The unit element in the Hadamard EJA is inherited in the + subalgebras generated by its elements:: + + sage: J = HadamardEJA(5) + sage: J.one() + e0 + e1 + e2 + e3 + e4 + sage: x = sum(J.gens()) + sage: A = x.subalgebra_generated_by(orthonormalize=False) + sage: A.one() + f0 + sage: A.one().superalgebra_element() + e0 + e1 + e2 + e3 + e4 + TESTS: - The identity element acts like the identity:: + The identity element acts like the identity, regardless of + whether or not we orthonormalize:: sage: set_random_seed() sage: J = random_eja() sage: x = J.random_element() sage: J.one()*x == x and x*J.one() == x True + sage: A = x.subalgebra_generated_by() + sage: y = A.random_element() + sage: A.one()*y == y and y*A.one() == y + True - The matrix of the unit element's operator is the identity:: + :: + + sage: set_random_seed() + sage: J = random_eja(field=QQ, orthonormalize=False) + sage: x = J.random_element() + sage: J.one()*x == x and x*J.one() == x + True + sage: A = x.subalgebra_generated_by(orthonormalize=False) + sage: y = A.random_element() + sage: A.one()*y == y and y*A.one() == y + True + + The matrix of the unit element's operator is the identity, + regardless of the base field and whether or not we + orthonormalize:: sage: set_random_seed() sage: J = random_eja() @@ -774,6 +946,27 @@ class FiniteDimensionalEJA(CombinatorialFreeModule): sage: expected = matrix.identity(J.base_ring(), J.dimension()) sage: actual == expected True + sage: x = J.random_element() + sage: A = x.subalgebra_generated_by() + sage: actual = A.one().operator().matrix() + sage: expected = matrix.identity(A.base_ring(), A.dimension()) + sage: actual == expected + True + + :: + + sage: set_random_seed() + sage: J = random_eja(field=QQ, orthonormalize=False) + sage: actual = J.one().operator().matrix() + sage: expected = matrix.identity(J.base_ring(), J.dimension()) + sage: actual == expected + True + sage: x = J.random_element() + sage: A = x.subalgebra_generated_by(orthonormalize=False) + sage: actual = A.one().operator().matrix() + sage: expected = matrix.identity(A.base_ring(), A.dimension()) + sage: actual == expected + True Ensure that the cached unit element (often precomputed by hand) agrees with the computed one:: @@ -785,6 +978,15 @@ class FiniteDimensionalEJA(CombinatorialFreeModule): sage: J.one() == cached True + :: + + sage: set_random_seed() + sage: J = random_eja(field=QQ, orthonormalize=False) + sage: cached = J.one() + sage: J.one.clear_cache() + sage: J.one() == cached + True + """ # We can brute-force compute the matrices of the operators # that correspond to the basis elements of this algebra. @@ -929,14 +1131,12 @@ class FiniteDimensionalEJA(CombinatorialFreeModule): if not c.is_idempotent(): raise ValueError("element is not idempotent: %s" % c) - from mjo.eja.eja_subalgebra import FiniteDimensionalEJASubalgebra - # Default these to what they should be if they turn out to be # trivial, because eigenspaces_left() won't return eigenvalues # corresponding to trivial spaces (e.g. it returns only the # eigenspace corresponding to lambda=1 if you take the # decomposition relative to the identity element). - trivial = FiniteDimensionalEJASubalgebra(self, ()) + trivial = self.subalgebra(()) J0 = trivial # eigenvalue zero J5 = VectorSpace(self.base_ring(), 0) # eigenvalue one-half J1 = trivial # eigenvalue one @@ -946,9 +1146,7 @@ class FiniteDimensionalEJA(CombinatorialFreeModule): J5 = eigspace else: gens = tuple( self.from_vector(b) for b in eigspace.basis() ) - subalg = FiniteDimensionalEJASubalgebra(self, - gens, - check_axioms=False) + subalg = self.subalgebra(gens, check_axioms=False) if eigval == 0: J0 = subalg elif eigval == 1: @@ -1037,6 +1235,21 @@ class FiniteDimensionalEJA(CombinatorialFreeModule): r""" The `r` polynomial coefficients of the "characteristic polynomial of" function. + + SETUP:: + + sage: from mjo.eja.eja_algebra import random_eja + + TESTS: + + The theory shows that these are all homogeneous polynomials of + a known degree:: + + sage: set_random_seed() + sage: J = random_eja() + sage: all(p.is_homogeneous() for p in J._charpoly_coefficients()) + True + """ n = self.dimension() R = self.coordinate_polynomial_ring() @@ -1046,7 +1259,7 @@ class FiniteDimensionalEJA(CombinatorialFreeModule): 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] + return sum( vars[k]*self.gens()[k].operator().matrix()[i,j] for k in range(n) ) L_x = matrix(F, n, n, L_x_i_j) @@ -1072,10 +1285,17 @@ class FiniteDimensionalEJA(CombinatorialFreeModule): # The theory says that only the first "r" coefficients are # nonzero, and they actually live in the original polynomial - # ring and not the fraction field. We negate them because - # in the actual characteristic polynomial, they get moved - # to the other side where x^r lives. - return -A_rref.solve_right(E*b).change_ring(R)[:r] + # ring and not the fraction field. We negate them because in + # the actual characteristic polynomial, they get moved to the + # other side where x^r lives. We don't bother to trim A_rref + # down to a square matrix and solve the resulting system, + # because the upper-left r-by-r portion of A_rref is + # guaranteed to be the identity matrix, so e.g. + # + # A_rref.solve_right(Y) + # + # would just be returning Y. + return (-E*b)[:r].change_ring(R) @cached_method def rank(self): @@ -1136,7 +1356,7 @@ class FiniteDimensionalEJA(CombinatorialFreeModule): sage: set_random_seed() # long time sage: J = random_eja() # long time - sage: caches = J.rank() # long time + sage: cached = J.rank() # long time sage: J.rank.clear_cache() # long time sage: J.rank() == cached # long time True @@ -1145,6 +1365,14 @@ class FiniteDimensionalEJA(CombinatorialFreeModule): return len(self._charpoly_coefficients()) + def subalgebra(self, basis, **kwargs): + r""" + Create a subalgebra of this algebra from the given basis. + """ + from mjo.eja.eja_subalgebra import FiniteDimensionalEJASubalgebra + return FiniteDimensionalEJASubalgebra(self, basis, **kwargs) + + def vector_space(self): """ Return the vector space that underlies this algebra. @@ -1163,7 +1391,6 @@ class FiniteDimensionalEJA(CombinatorialFreeModule): return self.zero().to_vector().parent().ambient_vector_space() - Element = FiniteDimensionalEJAElement class RationalBasisEJA(FiniteDimensionalEJA): r""" @@ -1201,6 +1428,7 @@ class RationalBasisEJA(FiniteDimensionalEJA): if not all( all(b_i in QQ for b_i in b.list()) for b in basis ): raise TypeError("basis not rational") + self._rational_algebra = None if field is not QQ: # There's no point in constructing the extra algebra if this # one is already rational. @@ -1261,7 +1489,14 @@ class RationalBasisEJA(FiniteDimensionalEJA): a = ( a_i.change_ring(self.base_ring()) for a_i in self._rational_algebra._charpoly_coefficients() ) - # Now convert the coordinate variables back to the + if self._deortho_matrix is None: + # This can happen if our base ring was, say, AA and we + # chose not to (or didn't need to) orthonormalize. It's + # still faster to do the computations over QQ even if + # the numbers in the boxes stay the same. + return tuple(a) + + # Otherwise, convert the coordinate variables back to the # deorthonormalized ones. R = self.coordinate_polynomial_ring() from sage.modules.free_module_element import vector @@ -1595,6 +1830,38 @@ class RealSymmetricEJA(ConcreteEJA, RealMatrixEJA): class ComplexMatrixEJA(MatrixEJA): + # A manual dictionary-cache for the complex_extension() method, + # since apparently @classmethods can't also be @cached_methods. + _complex_extension = {} + + @classmethod + def complex_extension(cls,field): + r""" + The complex field that we embed/unembed, as an extension + of the given ``field``. + """ + if field in cls._complex_extension: + return cls._complex_extension[field] + + # Sage doesn't know how to adjoin the complex "i" (the root of + # x^2 + 1) to a field in a general way. Here, we just enumerate + # all of the cases that I have cared to support so far. + if field is AA: + # Sage doesn't know how to embed AA into QQbar, i.e. how + # to adjoin sqrt(-1) to AA. + F = QQbar + elif not field.is_exact(): + # RDF or RR + F = field.complex_field() + else: + # Works for QQ and... maybe some other fields. + R = PolynomialRing(field, 'z') + z = R.gen() + F = field.extension(z**2 + 1, 'I', embedding=CLF(-1).sqrt()) + + cls._complex_extension[field] = F + return F + @staticmethod def dimension_over_reals(): return 2 @@ -1649,9 +1916,10 @@ class ComplexMatrixEJA(MatrixEJA): blocks = [] for z in M.list(): - a = z.list()[0] # real part, I guess - b = z.list()[1] # imag part, I guess - blocks.append(matrix(field, 2, [[a,b],[-b,a]])) + a = z.real() + b = z.imag() + blocks.append(matrix(field, 2, [ [ a, b], + [-b, a] ])) return matrix.block(field, n, blocks) @@ -1690,26 +1958,7 @@ class ComplexMatrixEJA(MatrixEJA): super(ComplexMatrixEJA,cls).real_unembed(M) n = ZZ(M.nrows()) d = cls.dimension_over_reals() - - # If "M" was normalized, its base ring might have roots - # adjoined and they can stick around after unembedding. - field = M.base_ring() - R = PolynomialRing(field, 'z') - z = R.gen() - - # Sage doesn't know how to adjoin the complex "i" (the root of - # x^2 + 1) to a field in a general way. Here, we just enumerate - # all of the cases that I have cared to support so far. - if field is AA: - # Sage doesn't know how to embed AA into QQbar, i.e. how - # to adjoin sqrt(-1) to AA. - F = QQbar - elif not field.is_exact(): - # RDF or RR - F = field.complex_field() - else: - # Works for QQ and... maybe some other fields. - F = field.extension(z**2 + 1, 'I', embedding=CLF(-1).sqrt()) + F = cls.complex_extension(M.base_ring()) i = F.gen() # Go top-left to bottom-right (reading order), converting every @@ -1805,7 +2054,6 @@ class ComplexHermitianEJA(ConcreteEJA, ComplexMatrixEJA): sage: set_random_seed() sage: n = ZZ.random_element(1,5) - sage: field = QuadraticField(2, 'sqrt2') sage: B = ComplexHermitianEJA._denormalized_basis(n) sage: all( M.is_symmetric() for M in B) True @@ -1823,18 +2071,27 @@ class ComplexHermitianEJA(ConcreteEJA, ComplexMatrixEJA): # * The diagonal will (as a result) be real. # S = [] + Eij = matrix.zero(F,n) for i in range(n): for j in range(i+1): - Eij = matrix(F, n, lambda k,l: k==i and l==j) + # "build" E_ij + Eij[i,j] = 1 if i == j: Sij = cls.real_embed(Eij) S.append(Sij) else: # The second one has a minus because it's conjugated. - Sij_real = cls.real_embed(Eij + Eij.transpose()) + Eij[j,i] = 1 # Eij = Eij + Eij.transpose() + Sij_real = cls.real_embed(Eij) S.append(Sij_real) - Sij_imag = cls.real_embed(I*Eij - I*Eij.transpose()) + # Eij = I*Eij - I*Eij.transpose() + Eij[i,j] = I + Eij[j,i] = -I + Sij_imag = cls.real_embed(Eij) S.append(Sij_imag) + Eij[j,i] = 0 + # "erase" E_ij + Eij[i,j] = 0 # Since we embedded these, we can drop back to the "field" that we # started with instead of the complex extension "F". @@ -1870,6 +2127,25 @@ class ComplexHermitianEJA(ConcreteEJA, ComplexMatrixEJA): return cls(n, **kwargs) class QuaternionMatrixEJA(MatrixEJA): + + # A manual dictionary-cache for the quaternion_extension() method, + # since apparently @classmethods can't also be @cached_methods. + _quaternion_extension = {} + + @classmethod + def quaternion_extension(cls,field): + r""" + The quaternion field that we embed/unembed, as an extension + of the given ``field``. + """ + if field in cls._quaternion_extension: + return cls._quaternion_extension[field] + + Q = QuaternionAlgebra(field,-1,-1) + + cls._quaternion_extension[field] = Q + return Q + @staticmethod def dimension_over_reals(): return 4 @@ -1974,8 +2250,7 @@ class QuaternionMatrixEJA(MatrixEJA): # Use the base ring of the matrix to ensure that its entries can be # multiplied by elements of the quaternion algebra. - field = M.base_ring() - Q = QuaternionAlgebra(field,-1,-1) + Q = cls.quaternion_extension(M.base_ring()) i,j,k = Q.gens() # Go top-left to bottom-right (reading order), converting every @@ -2090,23 +2365,39 @@ class QuaternionHermitianEJA(ConcreteEJA, QuaternionMatrixEJA): # * The diagonal will (as a result) be real. # S = [] + Eij = matrix.zero(Q,n) for i in range(n): for j in range(i+1): - Eij = matrix(Q, n, lambda k,l: k==i and l==j) + # "build" E_ij + Eij[i,j] = 1 if i == j: Sij = cls.real_embed(Eij) S.append(Sij) else: # The second, third, and fourth ones have a minus # because they're conjugated. - Sij_real = cls.real_embed(Eij + Eij.transpose()) + # Eij = Eij + Eij.transpose() + Eij[j,i] = 1 + Sij_real = cls.real_embed(Eij) S.append(Sij_real) - Sij_I = cls.real_embed(I*Eij - I*Eij.transpose()) + # Eij = I*(Eij - Eij.transpose()) + Eij[i,j] = I + Eij[j,i] = -I + Sij_I = cls.real_embed(Eij) S.append(Sij_I) - Sij_J = cls.real_embed(J*Eij - J*Eij.transpose()) + # Eij = J*(Eij - Eij.transpose()) + Eij[i,j] = J + Eij[j,i] = -J + Sij_J = cls.real_embed(Eij) S.append(Sij_J) - Sij_K = cls.real_embed(K*Eij - K*Eij.transpose()) + # Eij = K*(Eij - Eij.transpose()) + Eij[i,j] = K + Eij[j,i] = -K + Sij_K = cls.real_embed(Eij) S.append(Sij_K) + Eij[j,i] = 0 + # "erase" E_ij + Eij[i,j] = 0 # Since we embedded these, we can drop back to the "field" that we # started with instead of the quaternion algebra "Q". @@ -2208,7 +2499,11 @@ class HadamardEJA(ConcreteEJA): if "check_axioms" not in kwargs: kwargs["check_axioms"] = False column_basis = tuple( b.column() for b in FreeModule(ZZ, n).basis() ) - super().__init__(column_basis, jordan_product, inner_product, **kwargs) + super().__init__(column_basis, + jordan_product, + inner_product, + associative=True, + **kwargs) self.rank.set_cache(n) if n == 0: @@ -2524,244 +2819,433 @@ class TrivialEJA(ConcreteEJA): # inappropriate for us. return cls(**kwargs) -# class DirectSumEJA(ConcreteEJA): -# r""" -# The external (orthogonal) direct sum of two other Euclidean Jordan -# algebras. Essentially the Cartesian product of its two factors. -# Every Euclidean Jordan algebra decomposes into an orthogonal -# direct sum of simple Euclidean Jordan algebras, so no generality -# is lost by providing only this construction. - -# SETUP:: - -# sage: from mjo.eja.eja_algebra import (random_eja, -# ....: HadamardEJA, -# ....: RealSymmetricEJA, -# ....: DirectSumEJA) - -# EXAMPLES:: - -# sage: J1 = HadamardEJA(2) -# sage: J2 = RealSymmetricEJA(3) -# sage: J = DirectSumEJA(J1,J2) -# sage: J.dimension() -# 8 -# sage: J.rank() -# 5 - -# TESTS: - -# The external direct sum construction is only valid when the two factors -# have the same base ring; an error is raised otherwise:: - -# sage: set_random_seed() -# sage: J1 = random_eja(field=AA) -# sage: J2 = random_eja(field=QQ,orthonormalize=False) -# sage: J = DirectSumEJA(J1,J2) -# Traceback (most recent call last): -# ... -# ValueError: algebras must share the same base field - -# """ -# def __init__(self, J1, J2, **kwargs): -# if J1.base_ring() != J2.base_ring(): -# raise ValueError("algebras must share the same base field") -# field = J1.base_ring() - -# self._factors = (J1, J2) -# n1 = J1.dimension() -# n2 = J2.dimension() -# n = n1+n2 -# V = VectorSpace(field, n) -# mult_table = [ [ V.zero() for j in range(i+1) ] -# for i in range(n) ] -# for i in range(n1): -# for j in range(i+1): -# p = (J1.monomial(i)*J1.monomial(j)).to_vector() -# mult_table[i][j] = V(p.list() + [field.zero()]*n2) - -# for i in range(n2): -# for j in range(i+1): -# p = (J2.monomial(i)*J2.monomial(j)).to_vector() -# mult_table[n1+i][n1+j] = V([field.zero()]*n1 + p.list()) - -# # TODO: build the IP table here from the two constituent IP -# # matrices (it'll be block diagonal, I think). -# ip_table = [ [ field.zero() for j in range(i+1) ] -# for i in range(n) ] -# super(DirectSumEJA, self).__init__(field, -# mult_table, -# ip_table, -# check_axioms=False, -# **kwargs) -# self.rank.set_cache(J1.rank() + J2.rank()) - - -# def factors(self): -# r""" -# Return the pair of this algebra's factors. - -# SETUP:: - -# sage: from mjo.eja.eja_algebra import (HadamardEJA, -# ....: JordanSpinEJA, -# ....: DirectSumEJA) - -# EXAMPLES:: - -# sage: J1 = HadamardEJA(2, field=QQ) -# sage: J2 = JordanSpinEJA(3, field=QQ) -# sage: J = DirectSumEJA(J1,J2) -# sage: J.factors() -# (Euclidean Jordan algebra of dimension 2 over Rational Field, -# Euclidean Jordan algebra of dimension 3 over Rational Field) - -# """ -# return self._factors - -# def projections(self): -# r""" -# Return a pair of projections onto this algebra's factors. - -# SETUP:: - -# sage: from mjo.eja.eja_algebra import (JordanSpinEJA, -# ....: ComplexHermitianEJA, -# ....: DirectSumEJA) - -# EXAMPLES:: - -# sage: J1 = JordanSpinEJA(2) -# sage: J2 = ComplexHermitianEJA(2) -# sage: J = DirectSumEJA(J1,J2) -# sage: (pi_left, pi_right) = J.projections() -# sage: J.one().to_vector() -# (1, 0, 1, 0, 0, 1) -# sage: pi_left(J.one()).to_vector() -# (1, 0) -# sage: pi_right(J.one()).to_vector() -# (1, 0, 0, 1) - -# """ -# (J1,J2) = self.factors() -# m = J1.dimension() -# n = J2.dimension() -# V_basis = self.vector_space().basis() -# # Need to specify the dimensions explicitly so that we don't -# # wind up with a zero-by-zero matrix when we want e.g. a -# # zero-by-two matrix (important for composing things). -# P1 = matrix(self.base_ring(), m, m+n, V_basis[:m]) -# P2 = matrix(self.base_ring(), n, m+n, V_basis[m:]) -# pi_left = FiniteDimensionalEJAOperator(self,J1,P1) -# pi_right = FiniteDimensionalEJAOperator(self,J2,P2) -# return (pi_left, pi_right) - -# def inclusions(self): -# r""" -# Return the pair of inclusion maps from our factors into us. - -# SETUP:: - -# sage: from mjo.eja.eja_algebra import (random_eja, -# ....: JordanSpinEJA, -# ....: RealSymmetricEJA, -# ....: DirectSumEJA) - -# EXAMPLES:: - -# sage: J1 = JordanSpinEJA(3) -# sage: J2 = RealSymmetricEJA(2) -# sage: J = DirectSumEJA(J1,J2) -# sage: (iota_left, iota_right) = J.inclusions() -# sage: iota_left(J1.zero()) == J.zero() -# True -# sage: iota_right(J2.zero()) == J.zero() -# True -# sage: J1.one().to_vector() -# (1, 0, 0) -# sage: iota_left(J1.one()).to_vector() -# (1, 0, 0, 0, 0, 0) -# sage: J2.one().to_vector() -# (1, 0, 1) -# sage: iota_right(J2.one()).to_vector() -# (0, 0, 0, 1, 0, 1) -# sage: J.one().to_vector() -# (1, 0, 0, 1, 0, 1) - -# TESTS: - -# Composing a projection with the corresponding inclusion should -# produce the identity map, and mismatching them should produce -# the zero map:: - -# sage: set_random_seed() -# sage: J1 = random_eja() -# sage: J2 = random_eja() -# sage: J = DirectSumEJA(J1,J2) -# sage: (iota_left, iota_right) = J.inclusions() -# sage: (pi_left, pi_right) = J.projections() -# sage: pi_left*iota_left == J1.one().operator() -# True -# sage: pi_right*iota_right == J2.one().operator() -# True -# sage: (pi_left*iota_right).is_zero() -# True -# sage: (pi_right*iota_left).is_zero() -# True - -# """ -# (J1,J2) = self.factors() -# m = J1.dimension() -# n = J2.dimension() -# V_basis = self.vector_space().basis() -# # Need to specify the dimensions explicitly so that we don't -# # wind up with a zero-by-zero matrix when we want e.g. a -# # two-by-zero matrix (important for composing things). -# I1 = matrix.column(self.base_ring(), m, m+n, V_basis[:m]) -# I2 = matrix.column(self.base_ring(), n, m+n, V_basis[m:]) -# iota_left = FiniteDimensionalEJAOperator(J1,self,I1) -# iota_right = FiniteDimensionalEJAOperator(J2,self,I2) -# return (iota_left, iota_right) - -# def inner_product(self, x, y): -# r""" -# The standard Cartesian inner-product. - -# We project ``x`` and ``y`` onto our factors, and add up the -# inner-products from the subalgebras. - -# SETUP:: - - -# sage: from mjo.eja.eja_algebra import (HadamardEJA, -# ....: QuaternionHermitianEJA, -# ....: DirectSumEJA) - -# EXAMPLE:: - -# sage: J1 = HadamardEJA(3,field=QQ) -# sage: J2 = QuaternionHermitianEJA(2,field=QQ,orthonormalize=False) -# sage: J = DirectSumEJA(J1,J2) -# sage: x1 = J1.one() -# sage: x2 = x1 -# sage: y1 = J2.one() -# sage: y2 = y1 -# sage: x1.inner_product(x2) -# 3 -# sage: y1.inner_product(y2) -# 2 -# sage: J.one().inner_product(J.one()) -# 5 - -# """ -# (pi_left, pi_right) = self.projections() -# x1 = pi_left(x) -# x2 = pi_right(x) -# y1 = pi_left(y) -# y2 = pi_right(y) - -# return (x1.inner_product(y1) + x2.inner_product(y2)) + +class CartesianProductEJA(CombinatorialFreeModule_CartesianProduct, + FiniteDimensionalEJA): + r""" + The external (orthogonal) direct sum of two or more Euclidean + Jordan algebras. Every Euclidean Jordan algebra decomposes into an + orthogonal direct sum of simple Euclidean Jordan algebras which is + then isometric to a Cartesian product, so no generality is lost by + providing only this construction. + + SETUP:: + + sage: from mjo.eja.eja_algebra import (random_eja, + ....: CartesianProductEJA, + ....: HadamardEJA, + ....: JordanSpinEJA, + ....: RealSymmetricEJA) + + EXAMPLES: + + The Jordan product is inherited from our factors and implemented by + our CombinatorialFreeModule Cartesian product superclass:: + + sage: set_random_seed() + sage: J1 = HadamardEJA(2) + sage: J2 = RealSymmetricEJA(2) + sage: J = cartesian_product([J1,J2]) + sage: x,y = J.random_elements(2) + sage: x*y in J + True + + The ability to retrieve the original factors is implemented by our + CombinatorialFreeModule Cartesian product superclass:: + + sage: J1 = HadamardEJA(2, field=QQ) + sage: J2 = JordanSpinEJA(3, field=QQ) + sage: J = cartesian_product([J1,J2]) + sage: J.cartesian_factors() + (Euclidean Jordan algebra of dimension 2 over Rational Field, + Euclidean Jordan algebra of dimension 3 over Rational Field) + + You can provide more than two factors:: + + sage: J1 = HadamardEJA(2) + sage: J2 = JordanSpinEJA(3) + sage: J3 = RealSymmetricEJA(3) + sage: cartesian_product([J1,J2,J3]) + Euclidean Jordan algebra of dimension 2 over Algebraic Real + Field (+) Euclidean Jordan algebra of dimension 3 over Algebraic + Real Field (+) Euclidean Jordan algebra of dimension 6 over + Algebraic Real Field + + Rank is additive on a Cartesian product:: + + sage: J1 = HadamardEJA(1) + sage: J2 = RealSymmetricEJA(2) + sage: J = cartesian_product([J1,J2]) + sage: J1.rank.clear_cache() + sage: J2.rank.clear_cache() + sage: J.rank.clear_cache() + sage: J.rank() + 3 + sage: J.rank() == J1.rank() + J2.rank() + True + + The same rank computation works over the rationals, with whatever + basis you like:: + + sage: J1 = HadamardEJA(1, field=QQ, orthonormalize=False) + sage: J2 = RealSymmetricEJA(2, field=QQ, orthonormalize=False) + sage: J = cartesian_product([J1,J2]) + sage: J1.rank.clear_cache() + sage: J2.rank.clear_cache() + sage: J.rank.clear_cache() + sage: J.rank() + 3 + sage: J.rank() == J1.rank() + J2.rank() + True + + The product algebra will be associative if and only if all of its + components are associative:: + + sage: J1 = HadamardEJA(2) + sage: J1.is_associative() + True + sage: J2 = HadamardEJA(3) + sage: J2.is_associative() + True + sage: J3 = RealSymmetricEJA(3) + sage: J3.is_associative() + False + sage: CP1 = cartesian_product([J1,J2]) + sage: CP1.is_associative() + True + sage: CP2 = cartesian_product([J1,J3]) + sage: CP2.is_associative() + False + + TESTS: + + All factors must share the same base field:: + + sage: J1 = HadamardEJA(2, field=QQ) + sage: J2 = RealSymmetricEJA(2) + sage: CartesianProductEJA((J1,J2)) + Traceback (most recent call last): + ... + ValueError: all factors must share the same base field + + The cached unit element is the same one that would be computed:: + + sage: set_random_seed() # long time + sage: J1 = random_eja() # long time + sage: J2 = random_eja() # long time + sage: J = cartesian_product([J1,J2]) # long time + sage: actual = J.one() # long time + sage: J.one.clear_cache() # long time + sage: expected = J.one() # long time + sage: actual == expected # long time + True + + """ + Element = FiniteDimensionalEJAElement + + + def __init__(self, algebras, **kwargs): + CombinatorialFreeModule_CartesianProduct.__init__(self, + algebras, + **kwargs) + field = algebras[0].base_ring() + if not all( J.base_ring() == field for J in algebras ): + raise ValueError("all factors must share the same base field") + + associative = all( m.is_associative() for m in algebras ) + + # The definition of matrix_space() and self.basis() relies + # only on the stuff in the CFM_CartesianProduct class, which + # we've already initialized. + Js = self.cartesian_factors() + m = len(Js) + MS = self.matrix_space() + basis = tuple( + MS(tuple( self.cartesian_projection(i)(b).to_matrix() + for i in range(m) )) + for b in self.basis() + ) + + # Define jordan/inner products that operate on that matrix_basis. + def jordan_product(x,y): + return MS(tuple( + (Js[i](x[i])*Js[i](y[i])).to_matrix() for i in range(m) + )) + + def inner_product(x, y): + return sum( + Js[i](x[i]).inner_product(Js[i](y[i])) for i in range(m) + ) + + # There's no need to check the field since it already came + # from an EJA. Likewise the axioms are guaranteed to be + # satisfied, unless the guy writing this class sucks. + # + # If you want the basis to be orthonormalized, orthonormalize + # the factors. + FiniteDimensionalEJA.__init__(self, + basis, + jordan_product, + inner_product, + field=field, + orthonormalize=False, + associative=associative, + cartesian_product=True, + check_field=False, + check_axioms=False) + + ones = tuple(J.one() for J in algebras) + self.one.set_cache(self._cartesian_product_of_elements(ones)) + self.rank.set_cache(sum(J.rank() for J in algebras)) + + def matrix_space(self): + r""" + Return the space that our matrix basis lives in as a Cartesian + product. + + SETUP:: + + sage: from mjo.eja.eja_algebra import (HadamardEJA, + ....: RealSymmetricEJA) + + EXAMPLES:: + + sage: J1 = HadamardEJA(1) + sage: J2 = RealSymmetricEJA(2) + sage: J = cartesian_product([J1,J2]) + sage: J.matrix_space() + The Cartesian product of (Full MatrixSpace of 1 by 1 dense + matrices over Algebraic Real Field, Full MatrixSpace of 2 + by 2 dense matrices over Algebraic Real Field) + + """ + from sage.categories.cartesian_product import cartesian_product + return cartesian_product( [J.matrix_space() + for J in self.cartesian_factors()] ) + + @cached_method + def cartesian_projection(self, i): + r""" + SETUP:: + + sage: from mjo.eja.eja_algebra import (random_eja, + ....: JordanSpinEJA, + ....: HadamardEJA, + ....: RealSymmetricEJA, + ....: ComplexHermitianEJA) + + EXAMPLES: + + The projection morphisms are Euclidean Jordan algebra + operators:: + + sage: J1 = HadamardEJA(2) + sage: J2 = RealSymmetricEJA(2) + sage: J = cartesian_product([J1,J2]) + sage: J.cartesian_projection(0) + Linear operator between finite-dimensional Euclidean Jordan + algebras represented by the matrix: + [1 0 0 0 0] + [0 1 0 0 0] + Domain: Euclidean Jordan algebra of dimension 2 over Algebraic + Real Field (+) Euclidean Jordan algebra of dimension 3 over + Algebraic Real Field + Codomain: Euclidean Jordan algebra of dimension 2 over Algebraic + Real Field + sage: J.cartesian_projection(1) + Linear operator between finite-dimensional Euclidean Jordan + algebras represented by the matrix: + [0 0 1 0 0] + [0 0 0 1 0] + [0 0 0 0 1] + Domain: Euclidean Jordan algebra of dimension 2 over Algebraic + Real Field (+) Euclidean Jordan algebra of dimension 3 over + Algebraic Real Field + Codomain: Euclidean Jordan algebra of dimension 3 over Algebraic + Real Field + + The projections work the way you'd expect on the vector + representation of an element:: + + sage: J1 = JordanSpinEJA(2) + sage: J2 = ComplexHermitianEJA(2) + sage: J = cartesian_product([J1,J2]) + sage: pi_left = J.cartesian_projection(0) + sage: pi_right = J.cartesian_projection(1) + sage: pi_left(J.one()).to_vector() + (1, 0) + sage: pi_right(J.one()).to_vector() + (1, 0, 0, 1) + sage: J.one().to_vector() + (1, 0, 1, 0, 0, 1) + + TESTS: + + The answer never changes:: + + sage: set_random_seed() + sage: J1 = random_eja() + sage: J2 = random_eja() + sage: J = cartesian_product([J1,J2]) + sage: P0 = J.cartesian_projection(0) + sage: P1 = J.cartesian_projection(0) + sage: P0 == P1 + True + + """ + Ji = self.cartesian_factors()[i] + # Requires the fix on Trac 31421/31422 to work! + Pi = super().cartesian_projection(i) + return FiniteDimensionalEJAOperator(self,Ji,Pi.matrix()) + + @cached_method + def cartesian_embedding(self, i): + r""" + SETUP:: + + sage: from mjo.eja.eja_algebra import (random_eja, + ....: JordanSpinEJA, + ....: HadamardEJA, + ....: RealSymmetricEJA) + + EXAMPLES: + + The embedding morphisms are Euclidean Jordan algebra + operators:: + + sage: J1 = HadamardEJA(2) + sage: J2 = RealSymmetricEJA(2) + sage: J = cartesian_product([J1,J2]) + sage: J.cartesian_embedding(0) + Linear operator between finite-dimensional Euclidean Jordan + algebras represented by the matrix: + [1 0] + [0 1] + [0 0] + [0 0] + [0 0] + Domain: Euclidean Jordan algebra of dimension 2 over + Algebraic Real Field + Codomain: Euclidean Jordan algebra of dimension 2 over + Algebraic Real Field (+) Euclidean Jordan algebra of + dimension 3 over Algebraic Real Field + sage: J.cartesian_embedding(1) + Linear operator between finite-dimensional Euclidean Jordan + algebras represented by the matrix: + [0 0 0] + [0 0 0] + [1 0 0] + [0 1 0] + [0 0 1] + Domain: Euclidean Jordan algebra of dimension 3 over + Algebraic Real Field + Codomain: Euclidean Jordan algebra of dimension 2 over + Algebraic Real Field (+) Euclidean Jordan algebra of + dimension 3 over Algebraic Real Field + + The embeddings work the way you'd expect on the vector + representation of an element:: + + sage: J1 = JordanSpinEJA(3) + sage: J2 = RealSymmetricEJA(2) + sage: J = cartesian_product([J1,J2]) + sage: iota_left = J.cartesian_embedding(0) + sage: iota_right = J.cartesian_embedding(1) + sage: iota_left(J1.zero()) == J.zero() + True + sage: iota_right(J2.zero()) == J.zero() + True + sage: J1.one().to_vector() + (1, 0, 0) + sage: iota_left(J1.one()).to_vector() + (1, 0, 0, 0, 0, 0) + sage: J2.one().to_vector() + (1, 0, 1) + sage: iota_right(J2.one()).to_vector() + (0, 0, 0, 1, 0, 1) + sage: J.one().to_vector() + (1, 0, 0, 1, 0, 1) + + TESTS: + + The answer never changes:: + + sage: set_random_seed() + sage: J1 = random_eja() + sage: J2 = random_eja() + sage: J = cartesian_product([J1,J2]) + sage: E0 = J.cartesian_embedding(0) + sage: E1 = J.cartesian_embedding(0) + sage: E0 == E1 + True + + Composing a projection with the corresponding inclusion should + produce the identity map, and mismatching them should produce + the zero map:: + + sage: set_random_seed() + sage: J1 = random_eja() + sage: J2 = random_eja() + sage: J = cartesian_product([J1,J2]) + sage: iota_left = J.cartesian_embedding(0) + sage: iota_right = J.cartesian_embedding(1) + sage: pi_left = J.cartesian_projection(0) + sage: pi_right = J.cartesian_projection(1) + sage: pi_left*iota_left == J1.one().operator() + True + sage: pi_right*iota_right == J2.one().operator() + True + sage: (pi_left*iota_right).is_zero() + True + sage: (pi_right*iota_left).is_zero() + True + + """ + Ji = self.cartesian_factors()[i] + # Requires the fix on Trac 31421/31422 to work! + Ei = super().cartesian_embedding(i) + return FiniteDimensionalEJAOperator(Ji,self,Ei.matrix()) + + + +FiniteDimensionalEJA.CartesianProduct = CartesianProductEJA + +class RationalBasisCartesianProductEJA(CartesianProductEJA, + RationalBasisEJA): + r""" + A separate class for products of algebras for which we know a + rational basis. + + SETUP:: + + sage: from mjo.eja.eja_algebra import (JordanSpinEJA, + ....: RealSymmetricEJA) + + EXAMPLES: + + This gives us fast characteristic polynomial computations in + product algebras, too:: + + + sage: J1 = JordanSpinEJA(2) + sage: J2 = RealSymmetricEJA(3) + sage: J = cartesian_product([J1,J2]) + sage: J.characteristic_polynomial_of().degree() + 5 + sage: J.rank() + 5 + + """ + def __init__(self, algebras, **kwargs): + CartesianProductEJA.__init__(self, algebras, **kwargs) + + self._rational_algebra = None + if self.vector_space().base_field() is not QQ: + self._rational_algebra = cartesian_product([ + r._rational_algebra for r in algebras + ]) +RationalBasisEJA.CartesianProduct = RationalBasisCartesianProductEJA random_eja = ConcreteEJA.random_instance