X-Git-Url: http://gitweb.michael.orlitzky.com/?a=blobdiff_plain;f=mjo%2Feja%2Feja_algebra.py;h=3361bfaaeb6490f425ac383d0b14904b2e02b76c;hb=b1fed66884abd74c4c2456d56389a64fe5491ab4;hp=845503b081ce7a9282128886fa595ca4bd7027a6;hpb=2a534368e635eb94c2c866dbc35e9a8ef9d539d4;p=sage.d.git diff --git a/mjo/eja/eja_algebra.py b/mjo/eja/eja_algebra.py index 845503b..3361bfa 100644 --- a/mjo/eja/eja_algebra.py +++ b/mjo/eja/eja_algebra.py @@ -170,6 +170,17 @@ from mjo.eja.eja_element import FiniteDimensionalEJAElement from mjo.eja.eja_operator import FiniteDimensionalEJAOperator from mjo.eja.eja_utils import _all2list, _mat2vec +def EuclideanJordanAlgebras(field): + r""" + The category of Euclidean Jordan algebras over ``field``, which + must be a subfield of the real numbers. For now this is just a + convenient wrapper around all of the other category axioms that + apply to all EJAs. + """ + category = MagmaticAlgebras(field).FiniteDimensional() + category = category.WithBasis().Unital().Commutative() + return category + class FiniteDimensionalEJA(CombinatorialFreeModule): r""" A finite-dimensional Euclidean Jordan algebra. @@ -228,6 +239,26 @@ class FiniteDimensionalEJA(CombinatorialFreeModule): """ Element = FiniteDimensionalEJAElement + @staticmethod + def _check_input_field(field): + if not field.is_subring(RR): + # Note: this does return true for the real algebraic + # field, the rationals, and any quadratic field where + # we've specified a real embedding. + raise ValueError("scalar field is not real") + + @staticmethod + def _check_input_axioms(basis, jordan_product, inner_product): + if not all( jordan_product(bi,bj) == jordan_product(bj,bi) + for bi in basis + for bj in basis ): + raise ValueError("Jordan product is not commutative") + + if not all( inner_product(bi,bj) == inner_product(bj,bi) + for bi in basis + for bj in basis ): + raise ValueError("inner-product is not commutative") + def __init__(self, basis, jordan_product, @@ -236,7 +267,6 @@ class FiniteDimensionalEJA(CombinatorialFreeModule): matrix_space=None, orthonormalize=True, associative=None, - cartesian_product=False, check_field=True, check_axioms=True, prefix="b"): @@ -244,30 +274,14 @@ class FiniteDimensionalEJA(CombinatorialFreeModule): n = len(basis) if check_field: - if not field.is_subring(RR): - # Note: this does return true for the real algebraic - # field, the rationals, and any quadratic field where - # we've specified a real embedding. - raise ValueError("scalar field is not real") + self._check_input_field(field) if check_axioms: # Check commutativity of the Jordan and inner-products. # This has to be done before we build the multiplication # and inner-product tables/matrices, because we take # advantage of symmetry in the process. - if not all( jordan_product(bi,bj) == jordan_product(bj,bi) - for bi in basis - for bj in basis ): - raise ValueError("Jordan product is not commutative") - - if not all( inner_product(bi,bj) == inner_product(bj,bi) - for bi in basis - for bj in basis ): - raise ValueError("inner-product is not commutative") - - - category = MagmaticAlgebras(field).FiniteDimensional() - category = category.WithBasis().Unital().Commutative() + self._check_input_axioms(basis, jordan_product, inner_product) if n <= 1: # All zero- and one-dimensional algebras are just the real @@ -286,14 +300,11 @@ class FiniteDimensionalEJA(CombinatorialFreeModule): for bj in basis for bk in basis) + category = EuclideanJordanAlgebras(field) + if associative: # Element subalgebras can take advantage of this. category = category.Associative() - if cartesian_product: - # Use join() here because otherwise we only get the - # "Cartesian product of..." and not the things themselves. - category = category.join([category, - category.CartesianProducts()]) # Call the superclass constructor so that we can use its from_vector() # method to build our multiplication table. @@ -309,7 +320,6 @@ class FiniteDimensionalEJA(CombinatorialFreeModule): # as well as a subspace W of V spanned by those (vectorized) # basis elements. The W-coordinates are the coefficients that # we see in things like x = 1*b1 + 2*b2. - vector_basis = basis degree = 0 if n > 0: @@ -348,14 +358,19 @@ class FiniteDimensionalEJA(CombinatorialFreeModule): # its own set of non-ambient coordinates (in terms of the # supplied basis). vector_basis = tuple( V(_all2list(b)) for b in basis ) - W = V.span_of_basis( vector_basis, check=check_axioms) + + # Save the span of our matrix basis (when written out as long + # vectors) because otherwise we'll have to reconstruct it + # every time we want to coerce a matrix into the algebra. + self._matrix_span = V.span_of_basis( vector_basis, check=check_axioms) if orthonormalize: - # Now "W" is the vector space of our algebra coordinates. The - # variables "X1", "X2",... refer to the entries of vectors in - # W. Thus to convert back and forth between the orthonormal - # coordinates and the given ones, we need to stick the original - # basis in W. + # Now "self._matrix_span" is the vector space of our + # algebra coordinates. The variables "X1", "X2",... refer + # to the entries of vectors in self._matrix_span. Thus to + # convert back and forth between the orthonormal + # coordinates and the given ones, we need to stick the + # original basis in self._matrix_span. U = V.span_of_basis( deortho_vector_basis, check=check_axioms) self._deortho_matrix = matrix.column( U.coordinate_vector(q) for q in vector_basis ) @@ -364,7 +379,8 @@ class FiniteDimensionalEJA(CombinatorialFreeModule): # Now we actually compute the multiplication and inner-product # tables/matrices using the possibly-orthonormalized basis. self._inner_product_matrix = matrix.identity(field, n) - self._multiplication_table = [ [0 for j in range(i+1)] + zed = self.zero() + self._multiplication_table = [ [zed for j in range(i+1)] for i in range(n) ] # Note: the Jordan and inner-products are defined in terms @@ -379,7 +395,7 @@ 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(_all2list(elt))) + elt = self._matrix_span.coordinate_vector(V(_all2list(elt))) self._multiplication_table[i][j] = self.from_vector(elt) if not orthonormalize: @@ -686,8 +702,8 @@ class FiniteDimensionalEJA(CombinatorialFreeModule): def _element_constructor_(self, elt): """ - Construct an element of this algebra from its vector or matrix - representation. + Construct an element of this algebra or a subalgebra from its + EJA element, vector, or matrix representation. This gets called only after the parent element _call_ method fails to find a coercion for the argument. @@ -726,6 +742,16 @@ class FiniteDimensionalEJA(CombinatorialFreeModule): sage: J( (J1.matrix_basis()[1], J2.matrix_basis()[2]) ) b1 + b5 + Subalgebra elements are embedded into the superalgebra:: + + sage: J = JordanSpinEJA(3) + sage: J.one() + b0 + sage: x = sum(J.gens()) + sage: A = x.subalgebra_generated_by() + sage: J(A.one()) + b0 + TESTS: Ensure that we can convert any element back and forth @@ -750,6 +776,7 @@ class FiniteDimensionalEJA(CombinatorialFreeModule): Traceback (most recent call last): ... ValueError: not an element of this algebra + """ msg = "not an element of this algebra" if elt in self.base_ring(): @@ -759,13 +786,16 @@ class FiniteDimensionalEJA(CombinatorialFreeModule): # that the integer 3 belongs to the space of 2-by-2 matrices. raise ValueError(msg) - try: - # Try to convert a vector into a column-matrix... + if hasattr(elt, 'superalgebra_element'): + # Handle subalgebra elements + if elt.parent().superalgebra() == self: + return elt.superalgebra_element() + + if hasattr(elt, 'sparse_vector'): + # Convert a vector into a column-matrix. We check for + # "sparse_vector" and not "column" because matrices also + # have a "column" method. elt = elt.column() - except (AttributeError, TypeError): - # and ignore failure, because we weren't really expecting - # a vector as an argument anyway. - pass if elt not in self.matrix_space(): raise ValueError(msg) @@ -782,15 +812,10 @@ class FiniteDimensionalEJA(CombinatorialFreeModule): # 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. - 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) + elt = self._matrix_span.ambient_vector_space()(_all2list(elt)) try: - coords = W.coordinate_vector(V(elt)) + coords = self._matrix_span.coordinate_vector(elt) except ArithmeticError: # vector is not in free module raise ValueError(msg) @@ -1396,7 +1421,7 @@ class FiniteDimensionalEJA(CombinatorialFreeModule): # 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 = self.subalgebra(()) + trivial = self.subalgebra((), check_axioms=False) J0 = trivial # eigenvalue zero J5 = VectorSpace(self.base_ring(), 0) # eigenvalue one-half J1 = trivial # eigenvalue one @@ -1828,11 +1853,36 @@ class ConcreteEJA(FiniteDimensionalEJA): def random_instance(cls, max_dimension=None, *args, **kwargs): """ Return a random instance of this type of algebra whose dimension - is less than or equal to ``max_dimension``. If the dimension bound - is omitted, then the ``_max_random_instance_dimension()`` is used - to get a suitable bound. + is less than or equal to the lesser of ``max_dimension`` and + the value returned by ``_max_random_instance_dimension()``. If + the dimension bound is omitted, then only the + ``_max_random_instance_dimension()`` is used as a bound. This method should be implemented in each subclass. + + SETUP:: + + sage: from mjo.eja.eja_algebra import ConcreteEJA + + TESTS: + + Both the class bound and the ``max_dimension`` argument are upper + bounds on the dimension of the algebra returned:: + + sage: from sage.misc.prandom import choice + sage: eja_class = choice(ConcreteEJA.__subclasses__()) + sage: class_max_d = eja_class._max_random_instance_dimension() + sage: J = eja_class.random_instance(max_dimension=20, + ....: field=QQ, + ....: orthonormalize=False) + sage: J.dimension() <= class_max_d + True + sage: J = eja_class.random_instance(max_dimension=2, + ....: field=QQ, + ....: orthonormalize=False) + sage: J.dimension() <= 2 + True + """ from sage.misc.prandom import choice eja_class = choice(cls.__subclasses__()) @@ -1840,7 +1890,7 @@ class ConcreteEJA(FiniteDimensionalEJA): # These all bubble up to the RationalBasisEJA superclass # constructor, so any (kw)args valid there are also valid # here. - return eja_class.random_instance(*args, **kwargs) + return eja_class.random_instance(max_dimension, *args, **kwargs) class MatrixEJA(FiniteDimensionalEJA): @@ -2072,14 +2122,15 @@ class RealSymmetricEJA(MatrixEJA, RationalBasisEJA, ConcreteEJA): return ZZ(int(ZZ(8*max_dimension + 1).sqrt()/2 - 1/2)) @classmethod - def random_instance(cls, max_dimension=None, **kwargs): + def random_instance(cls, max_dimension=None, *args, **kwargs): """ Return a random instance of this type of algebra. """ - if max_dimension is None: - max_dimension = cls._max_random_instance_dimension() - max_size = cls._max_random_instance_size(max_dimension) + 1 - n = ZZ.random_element(max_size) + class_max_d = cls._max_random_instance_dimension() + if (max_dimension is None or max_dimension > class_max_d): + max_dimension = class_max_d + max_size = cls._max_random_instance_size(max_dimension) + n = ZZ.random_element(max_size + 1) return cls(n, **kwargs) def __init__(self, n, field=AA, **kwargs): @@ -2196,13 +2247,15 @@ class ComplexHermitianEJA(MatrixEJA, RationalBasisEJA, ConcreteEJA): return ZZ(int(ZZ(max_dimension).sqrt())) @classmethod - def random_instance(cls, max_dimension=None, **kwargs): + def random_instance(cls, max_dimension=None, *args, **kwargs): """ Return a random instance of this type of algebra. """ - if max_dimension is None: - max_dimension = cls._max_random_instance_dimension() - n = ZZ.random_element(cls._max_random_instance_size(max_dimension) + 1) + class_max_d = cls._max_random_instance_dimension() + if (max_dimension is None or max_dimension > class_max_d): + max_dimension = class_max_d + max_size = cls._max_random_instance_size(max_dimension) + n = ZZ.random_element(max_size + 1) return cls(n, **kwargs) @@ -2292,13 +2345,15 @@ class QuaternionHermitianEJA(MatrixEJA, RationalBasisEJA, ConcreteEJA): return ZZ(int(ZZ(8*max_dimension + 1).sqrt()/4 + 1/4)) @classmethod - def random_instance(cls, max_dimension=None, **kwargs): + def random_instance(cls, max_dimension=None, *args, **kwargs): """ Return a random instance of this type of algebra. """ - if max_dimension is None: - max_dimension = cls._max_random_instance_dimension() - n = ZZ.random_element(cls._max_random_instance_size(max_dimension) + 1) + class_max_d = cls._max_random_instance_dimension() + if (max_dimension is None or max_dimension > class_max_d): + max_dimension = class_max_d + max_size = cls._max_random_instance_size(max_dimension) + n = ZZ.random_element(max_size + 1) return cls(n, **kwargs) class OctonionHermitianEJA(MatrixEJA, RationalBasisEJA, ConcreteEJA): @@ -2405,13 +2460,15 @@ class OctonionHermitianEJA(MatrixEJA, RationalBasisEJA, ConcreteEJA): return 0 @classmethod - def random_instance(cls, max_dimension=None, **kwargs): + def random_instance(cls, max_dimension=None, *args, **kwargs): """ Return a random instance of this type of algebra. """ - if max_dimension is None: - max_dimension = cls._max_random_instance_dimension() - n = ZZ.random_element(cls._max_random_instance_size(max_dimension) + 1) + class_max_d = cls._max_random_instance_dimension() + if (max_dimension is None or max_dimension > class_max_d): + max_dimension = class_max_d + max_size = cls._max_random_instance_size(max_dimension) + n = ZZ.random_element(max_size + 1) return cls(n, **kwargs) def __init__(self, n, field=AA, **kwargs): @@ -2547,13 +2604,15 @@ class HadamardEJA(RationalBasisEJA, ConcreteEJA): return max_dimension @classmethod - def random_instance(cls, max_dimension=None, **kwargs): + def random_instance(cls, max_dimension=None, *args, **kwargs): """ Return a random instance of this type of algebra. """ - if max_dimension is None: - max_dimension = cls._max_random_instance_dimension() - n = ZZ.random_element(cls._max_random_instance_size(max_dimension) + 1) + class_max_d = cls._max_random_instance_dimension() + if (max_dimension is None or max_dimension > class_max_d): + max_dimension = class_max_d + max_size = cls._max_random_instance_size(max_dimension) + n = ZZ.random_element(max_size + 1) return cls(n, **kwargs) @@ -2708,13 +2767,16 @@ class BilinearFormEJA(RationalBasisEJA, ConcreteEJA): return max_dimension @classmethod - def random_instance(cls, max_dimension=None, **kwargs): + def random_instance(cls, max_dimension=None, *args, **kwargs): """ Return a random instance of this algebra. """ - if max_dimension is None: - max_dimension = cls._max_random_instance_dimension() - n = ZZ.random_element(cls._max_random_instance_size(max_dimension) + 1) + class_max_d = cls._max_random_instance_dimension() + if (max_dimension is None or max_dimension > class_max_d): + max_dimension = class_max_d + max_size = cls._max_random_instance_size(max_dimension) + n = ZZ.random_element(max_size + 1) + if n.is_zero(): B = matrix.identity(ZZ, n) return cls(B, **kwargs) @@ -2725,6 +2787,7 @@ class BilinearFormEJA(RationalBasisEJA, ConcreteEJA): alpha = ZZ.zero() while alpha.is_zero(): alpha = ZZ.random_element().abs() + B22 = M.transpose()*M + alpha*I from sage.matrix.special import block_matrix @@ -2798,15 +2861,17 @@ class JordanSpinEJA(BilinearFormEJA): super().__init__(B, *args, **kwargs) @classmethod - def random_instance(cls, max_dimension=None, **kwargs): + def random_instance(cls, max_dimension=None, *args, **kwargs): """ Return a random instance of this type of algebra. Needed here to override the implementation for ``BilinearFormEJA``. """ - if max_dimension is None: - max_dimension = cls._max_random_instance_dimension() - n = ZZ.random_element(cls._max_random_instance_size(max_dimension) + 1) + class_max_d = cls._max_random_instance_dimension() + if (max_dimension is None or max_dimension > class_max_d): + max_dimension = class_max_d + max_size = cls._max_random_instance_size(max_dimension) + n = ZZ.random_element(max_size + 1) return cls(n, **kwargs) @@ -2863,9 +2928,12 @@ class TrivialEJA(RationalBasisEJA, ConcreteEJA): self.one.set_cache( self.zero() ) @classmethod - def random_instance(cls, **kwargs): + def random_instance(cls, max_dimension=None, *args, **kwargs): # We don't take a "size" argument so the superclass method is - # inappropriate for us. + # inappropriate for us. The ``max_dimension`` argument is + # included so that if this method is called generically with a + # ``max_dimension=`` argument, we don't try to pass + # it on to the algebra constructor. return cls(**kwargs) @@ -2881,6 +2949,7 @@ class CartesianProductEJA(FiniteDimensionalEJA): sage: from mjo.eja.eja_algebra import (random_eja, ....: CartesianProductEJA, + ....: ComplexHermitianEJA, ....: HadamardEJA, ....: JordanSpinEJA, ....: RealSymmetricEJA) @@ -2992,6 +3061,28 @@ class CartesianProductEJA(FiniteDimensionalEJA): | b2 || 0 | 0 | b2 | +----++----+----+----+ + The "matrix space" of a Cartesian product always consists of + ordered pairs (or triples, or...) whose components are the + matrix spaces of its factors:: + + sage: J1 = HadamardEJA(2) + sage: J2 = ComplexHermitianEJA(2) + sage: J = cartesian_product([J1,J2]) + sage: J.matrix_space() + The Cartesian product of (Full MatrixSpace of 2 by 1 dense + matrices over Algebraic Real Field, Module of 2 by 2 matrices + with entries in Algebraic Field over the scalar ring Algebraic + Real Field) + sage: J.one().to_matrix()[0] + [1] + [1] + sage: J.one().to_matrix()[1] + +---+---+ + | 1 | 0 | + +---+---+ + | 0 | 1 | + +---+---+ + TESTS: All factors must share the same base field:: @@ -3014,11 +3105,7 @@ class CartesianProductEJA(FiniteDimensionalEJA): sage: expected = J.one() # long time sage: actual == expected # long time True - """ - Element = FiniteDimensionalEJAElement - - def __init__(self, factors, **kwargs): m = len(factors) if m == 0: @@ -3030,63 +3117,91 @@ class CartesianProductEJA(FiniteDimensionalEJA): if not all( J.base_ring() == field for J in factors ): raise ValueError("all factors must share the same base field") + # Figure out the category to use. associative = all( f.is_associative() for f in factors ) - - # Compute my matrix space. This category isn't perfect, but - # is good enough for what we need to do. + category = EuclideanJordanAlgebras(field) + if associative: category = category.Associative() + category = category.join([category, category.CartesianProducts()]) + + # Compute my matrix space. We don't simply use the + # ``cartesian_product()`` functor here because it acts + # differently on SageMath MatrixSpaces and our custom + # MatrixAlgebras, which are CombinatorialFreeModules. We + # always want the result to be represented (and indexed) as an + # ordered tuple. This category isn't perfect, but is good + # enough for what we need to do. MS_cat = MagmaticAlgebras(field).FiniteDimensional().WithBasis() MS_cat = MS_cat.Unital().CartesianProducts() MS_factors = tuple( J.matrix_space() for J in factors ) from sage.sets.cartesian_product import CartesianProduct - MS = CartesianProduct(MS_factors, MS_cat) + self._matrix_space = CartesianProduct(MS_factors, MS_cat) - basis = [] - zero = MS.zero() + self._matrix_basis = [] + zero = self._matrix_space.zero() for i in range(m): for b in factors[i].matrix_basis(): z = list(zero) z[i] = b - basis.append(z) + self._matrix_basis.append(z) - basis = tuple( MS(b) for b in basis ) + self._matrix_basis = tuple( self._matrix_space(b) + for b in self._matrix_basis ) + n = len(self._matrix_basis) - # Define jordan/inner products that operate on that matrix_basis. - def jordan_product(x,y): - return MS(tuple( - (factors[i](x[i])*factors[i](y[i])).to_matrix() - for i in range(m) - )) - - def inner_product(x, y): - return sum( - factors[i](x[i]).inner_product(factors[i](y[i])) - for i in range(m) - ) + # We already have what we need for the super-superclass constructor. + CombinatorialFreeModule.__init__(self, + field, + range(n), + prefix="b", + category=category, + bracket=False) - # 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, - matrix_space=MS, - orthonormalize=False, - associative=associative, - cartesian_product=True, - check_field=False, - check_axioms=False) + # Now create the vector space for the algebra, which will have + # its own set of non-ambient coordinates (in terms of the + # supplied basis). + degree = sum( f._matrix_span.ambient_vector_space().degree() + for f in factors ) + V = VectorSpace(field, degree) + vector_basis = tuple( V(_all2list(b)) for b in self._matrix_basis ) + + # Save the span of our matrix basis (when written out as long + # vectors) because otherwise we'll have to reconstruct it + # every time we want to coerce a matrix into the algebra. + self._matrix_span = V.span_of_basis( vector_basis, check=False) # Since we don't (re)orthonormalize the basis, the FDEJA # constructor is going to set self._deortho_matrix to the # identity matrix. Here we set it to the correct value using # the deortho matrices from our factors. - self._deortho_matrix = matrix.block_diagonal( [J._deortho_matrix - for J in factors] ) + self._deortho_matrix = matrix.block_diagonal( + [J._deortho_matrix for J in factors] + ) + + self._inner_product_matrix = matrix.block_diagonal( + [J._inner_product_matrix for J in factors] + ) + + # Building the multiplication table is a bit more tricky + # because we have to embed the entries of the factors' + # multiplication tables into the product EJA. + zed = self.zero() + self._multiplication_table = [ [zed for j in range(i+1)] + for i in range(n) ] + + # Keep track of an offset that tallies the dimensions of all + # previous factors. If the second factor is dim=2 and if the + # first one is dim=3, then we want to skip the first 3x3 block + # when copying the multiplication table for the second factor. + offset = 0 + for f in range(m): + phi_f = self.cartesian_embedding(f) + factor_dim = factors[f].dimension() + for i in range(factor_dim): + for j in range(i+1): + f_ij = factors[f]._multiplication_table[i][j] + e = phi_f(f_ij) + self._multiplication_table[offset+i][offset+j] = e + offset += factor_dim self.rank.set_cache(sum(J.rank() for J in factors)) ones = tuple(J.one().to_matrix() for J in factors) @@ -3108,65 +3223,6 @@ class CartesianProductEJA(FiniteDimensionalEJA): return cartesian_product.symbol.join("%s" % factor for factor in self._sets) - def matrix_space(self): - r""" - Return the space that our matrix basis lives in as a Cartesian - product. - - We don't simply use the ``cartesian_product()`` functor here - because it acts differently on SageMath MatrixSpaces and our - custom MatrixAlgebras, which are CombinatorialFreeModules. We - always want the result to be represented (and indexed) as - an ordered tuple. - - SETUP:: - - sage: from mjo.eja.eja_algebra import (ComplexHermitianEJA, - ....: HadamardEJA, - ....: OctonionHermitianEJA, - ....: 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) - - :: - - sage: J1 = ComplexHermitianEJA(1) - sage: J2 = ComplexHermitianEJA(1) - sage: J = cartesian_product([J1,J2]) - sage: J.one().to_matrix()[0] - +---+ - | 1 | - +---+ - sage: J.one().to_matrix()[1] - +---+ - | 1 | - +---+ - - :: - - sage: J1 = OctonionHermitianEJA(1) - sage: J2 = OctonionHermitianEJA(1) - sage: J = cartesian_product([J1,J2]) - sage: J.one().to_matrix()[0] - +----+ - | e0 | - +----+ - sage: J.one().to_matrix()[1] - +----+ - | e0 | - +----+ - - """ - return super().matrix_space() - @cached_method def cartesian_projection(self, i): @@ -3418,6 +3474,21 @@ class RationalBasisCartesianProductEJA(CartesianProductEJA, RationalBasisEJA.CartesianProduct = RationalBasisCartesianProductEJA def random_eja(max_dimension=None, *args, **kwargs): + r""" + + SETUP:: + + sage: from mjo.eja.eja_algebra import random_eja + + TESTS:: + + sage: set_random_seed() + sage: n = ZZ.random_element(1,5) + sage: J = random_eja(max_dimension=n, field=QQ, orthonormalize=False) + sage: J.dimension() <= n + True + + """ # Use the ConcreteEJA default as the total upper bound (regardless # of any whether or not any individual factors set a lower limit). if max_dimension is None: