+r"""
+Representations and constructions for Euclidean Jordan algebras.
+
+A Euclidean Jordan algebra is a Jordan algebra that has some
+additional properties:
+
+ 1. It is finite-dimensional.
+ 2. Its scalar field is the real numbers.
+ 3a. An inner product is defined on it, and...
+ 3b. That inner product is compatible with the Jordan product
+ in the sense that `<x*y,z> = <y,x*z>` for all elements
+ `x,y,z` in the algebra.
+
+Every Euclidean Jordan algebra is formally-real: for any two elements
+`x` and `y` in the algebra, `x^{2} + y^{2} = 0` implies that `x = y =
+0`. Conversely, every finite-dimensional formally-real Jordan algebra
+can be made into a Euclidean Jordan algebra with an appropriate choice
+of inner-product.
+
+Formally-real Jordan algebras were originally studied as a framework
+for quantum mechanics. Today, Euclidean Jordan algebras are crucial in
+symmetric cone optimization, since every symmetric cone arises as the
+cone of squares in some Euclidean Jordan algebra.
+
+It is known that every Euclidean Jordan algebra decomposes into an
+orthogonal direct sum (essentially, a Cartesian product) of simple
+algebras, and that moreover, up to Jordan-algebra isomorphism, there
+are only five families of simple algebras. We provide constructions
+for these simple algebras:
+
+ * :class:`BilinearFormEJA`
+ * :class:`RealSymmetricEJA`
+ * :class:`ComplexHermitianEJA`
+ * :class:`QuaternionHermitianEJA`
+ * :class:`OctonionHermitianEJA`
+
+In addition to these, we provide a few other example constructions,
+
+ * :class:`JordanSpinEJA`
+ * :class:`HadamardEJA`
+ * :class:`AlbertEJA`
+ * :class:`TrivialEJA`
+ * :class:`ComplexSkewSymmetricEJA`
+
+The Jordan spin algebra is a bilinear form algebra where the bilinear
+form is the identity. The Hadamard EJA is simply a Cartesian product
+of one-dimensional spin algebras. The Albert EJA is simply a special
+case of the :class:`OctonionHermitianEJA` where the matrices are
+three-by-three and the resulting space has dimension 27. And
+last/least, the trivial EJA is exactly what you think it is; it could
+also be obtained by constructing a dimension-zero instance of any of
+the other algebras. Cartesian products of these are also supported
+using the usual ``cartesian_product()`` function; as a result, we
+support (up to isomorphism) all Euclidean Jordan algebras.
+
+At a minimum, the following are required to construct a Euclidean
+Jordan algebra:
+
+ * A basis of matrices, column vectors, or MatrixAlgebra elements
+ * A Jordan product defined on the basis
+ * Its inner product defined on the basis
+
+The real numbers form a Euclidean Jordan algebra when both the Jordan
+and inner products are the usual multiplication. We use this as our
+example, and demonstrate a few ways to construct an EJA.
+
+First, we can use one-by-one SageMath matrices with algebraic real
+entries to represent real numbers. We define the Jordan and inner
+products to be essentially real-number multiplication, with the only
+difference being that the Jordan product again returns a one-by-one
+matrix, whereas the inner product must return a scalar. Our basis for
+the one-by-one matrices is of course the set consisting of a single
+matrix with its sole entry non-zero::
+
+ sage: from mjo.eja.eja_algebra import EJA
+ sage: jp = lambda X,Y: X*Y
+ sage: ip = lambda X,Y: X[0,0]*Y[0,0]
+ sage: b1 = matrix(AA, [[1]])
+ sage: J1 = EJA((b1,), jp, ip)
+ sage: J1
+ Euclidean Jordan algebra of dimension 1 over Algebraic Real Field
+
+In fact, any positive scalar multiple of that inner-product would work::
+
+ sage: ip2 = lambda X,Y: 16*ip(X,Y)
+ sage: J2 = EJA((b1,), jp, ip2)
+ sage: J2
+ Euclidean Jordan algebra of dimension 1 over Algebraic Real Field
+
+But beware that your basis will be orthonormalized _with respect to the
+given inner-product_ unless you pass ``orthonormalize=False`` to the
+constructor. For example::
+
+ sage: J3 = EJA((b1,), jp, ip2, orthonormalize=False)
+ sage: J3
+ Euclidean Jordan algebra of dimension 1 over Algebraic Real Field
+
+To see the difference, you can take the first and only basis element
+of the resulting algebra, and ask for it to be converted back into
+matrix form::
+
+ sage: J1.basis()[0].to_matrix()
+ [1]
+ sage: J2.basis()[0].to_matrix()
+ [1/4]
+ sage: J3.basis()[0].to_matrix()
+ [1]
+
+Since square roots are used in that process, the default scalar field
+that we use is the field of algebraic real numbers, ``AA``. You can
+also Use rational numbers, but only if you either pass
+``orthonormalize=False`` or know that orthonormalizing your basis
+won't stray beyond the rational numbers. The example above would
+have worked only because ``sqrt(16) == 4`` is rational.
+
+Another option for your basis is to use elemebts of a
+:class:`MatrixAlgebra`::
+
+ sage: from mjo.matrix_algebra import MatrixAlgebra
+ sage: A = MatrixAlgebra(1,AA,AA)
+ sage: J4 = EJA(A.gens(), jp, ip)
+ sage: J4
+ Euclidean Jordan algebra of dimension 1 over Algebraic Real Field
+ sage: J4.basis()[0].to_matrix()
+ +---+
+ | 1 |
+ +---+
+
+An easier way to view the entire EJA basis in its original (but
+perhaps orthonormalized) matrix form is to use the ``matrix_basis``
+method::
+
+ sage: J4.matrix_basis()
+ (+---+
+ | 1 |
+ +---+,)
+
+In particular, a :class:`MatrixAlgebra` is needed to work around the
+fact that matrices in SageMath must have entries in the same
+(commutative and associative) ring as its scalars. There are many
+Euclidean Jordan algebras whose elements are matrices that violate
+those assumptions. The complex, quaternion, and octonion Hermitian
+matrices all have entries in a ring (the complex numbers, quaternions,
+or octonions...) that differs from the algebra's scalar ring (the real
+numbers). Quaternions are also non-commutative; the octonions are
+neither commutative nor associative.
+
+SETUP::
+
+ sage: from mjo.eja.eja_algebra import random_eja
+
+EXAMPLES::
+
+ sage: random_eja()
+ Euclidean Jordan algebra of dimension...
"""
-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 itertools import repeat
from sage.algebras.quatalg.quaternion_algebra import QuaternionAlgebra
from sage.categories.magmatic_algebras import MagmaticAlgebras
+from sage.categories.sets_cat import cartesian_product
from sage.combinat.free_module import CombinatorialFreeModule
from sage.matrix.constructor import matrix
from sage.matrix.matrix_space import MatrixSpace
from sage.misc.cachefunc import cached_method
-from sage.misc.prandom import choice
from sage.misc.table import table
from sage.modules.free_module import FreeModule, VectorSpace
-from sage.rings.integer_ring import ZZ
-from sage.rings.number_field.number_field import NumberField, QuadraticField
-from sage.rings.polynomial.polynomial_ring_constructor import PolynomialRing
-from sage.rings.rational_field import QQ
-from sage.rings.real_lazy import CLF, RLF
-from sage.structure.element import is_Matrix
-
-from mjo.eja.eja_element import FiniteDimensionalEuclideanJordanAlgebraElement
-from mjo.eja.eja_utils import _mat2vec
-
-class FiniteDimensionalEuclideanJordanAlgebra(CombinatorialFreeModule):
- # This is an ugly hack needed to prevent the category framework
- # from implementing a coercion from our base ring (e.g. the
- # rationals) into the algebra. First of all -- such a coercion is
- # nonsense to begin with. But more importantly, it tries to do so
- # in the category of rings, and since our algebras aren't
- # associative they generally won't be rings.
- _no_generic_basering_coercion = True
+from sage.rings.all import (ZZ, QQ, AA, QQbar, RR, RLF, CLF,
+ PolynomialRing,
+ QuadraticField)
+from mjo.eja.eja_element import (CartesianProductEJAElement,
+ EJAElement)
+from mjo.eja.eja_operator import EJAOperator
+from mjo.eja.eja_utils import _all2list
+
+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 EJA(CombinatorialFreeModule):
+ r"""
+ A finite-dimensional Euclidean Jordan algebra.
+
+ INPUT:
+
+ - ``basis`` -- a tuple; 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`` -- a function; afunction 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`` -- a function; a 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.
+
+ - ``matrix_space`` -- the space that your matrix basis lives in,
+ or ``None`` (the default). So long as your basis does not have
+ length zero you can omit this. But in trivial algebras, it is
+ required.
+
+ - ``field`` -- a subfield of the reals (default: ``AA``); the scalar
+ field for the algebra.
+
+ - ``orthonormalize`` -- boolean (default: ``True``); whether or
+ not to orthonormalize the basis. Doing so is expensive and
+ generally rules out using the rationals as your ``field``, but
+ is required for spectral decompositions.
+
+ SETUP::
+
+ sage: from mjo.eja.eja_algebra import random_eja
+
+ TESTS:
+
+ We should compute that an element subalgebra is associative even
+ if we circumvent the element method::
+
+ sage: J = random_eja(field=QQ,orthonormalize=False)
+ sage: x = J.random_element()
+ sage: A = x.subalgebra_generated_by(orthonormalize=False)
+ sage: basis = tuple(b.superalgebra_element() for b in A.basis())
+ sage: J.subalgebra(basis, orthonormalize=False).is_associative()
+ True
+ """
+ Element = EJAElement
+
+ @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,
- field,
- mult_table,
- rank,
- prefix='e',
- category=None,
- natural_basis=None):
+ basis,
+ jordan_product,
+ inner_product,
+ field=AA,
+ matrix_space=None,
+ orthonormalize=True,
+ associative=None,
+ check_field=True,
+ check_axioms=True,
+ prefix="b"):
+
+ n = len(basis)
+
+ if check_field:
+ 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.
+ self._check_input_axioms(basis, jordan_product, inner_product)
+
+ if n <= 1:
+ # All zero- and one-dimensional algebras are just the real
+ # numbers with (some positive multiples of) the usual
+ # multiplication as its Jordan and inner-product.
+ associative = True
+ if associative is None:
+ # We should figure it out. As with check_axioms, we have to do
+ # this without the help of the _jordan_product_is_associative()
+ # method because we need to know the category before we
+ # initialize the algebra.
+ associative = all( jordan_product(jordan_product(bi,bj),bk)
+ ==
+ jordan_product(bi,jordan_product(bj,bk))
+ for bi in basis
+ for bj in basis
+ for bk in basis)
+
+ category = EuclideanJordanAlgebras(field)
+
+ if associative:
+ # Element subalgebras can take advantage of this.
+ category = category.Associative()
+
+ # Call the superclass constructor so that we can use its from_vector()
+ # method to build our multiplication table.
+ 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,
+ # 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.
+
+ degree = 0
+ if n > 0:
+ degree = len(_all2list(basis[0]))
+
+ # Build an ambient space that fits our matrix basis when
+ # written out as "long vectors."
+ V = VectorSpace(field, degree)
+
+ # The matrix that will hold the orthonormal -> unorthonormal
+ # coordinate transformation. Default to an identity matrix of
+ # the appropriate size to avoid special cases for None
+ # everywhere.
+ self._deortho_matrix = matrix.identity(field,n)
+
+ if orthonormalize:
+ # 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(_all2list(b)) for b in basis )
+
+ from mjo.eja.eja_utils import gram_schmidt
+ basis = tuple(gram_schmidt(basis, inner_product))
+
+ # Save the (possibly orthonormalized) matrix basis for
+ # later, as well as the space that its elements live in.
+ # In most cases we can deduce the matrix space, but when
+ # n == 0 (that is, there are no basis elements) we cannot.
+ self._matrix_basis = basis
+ if matrix_space is None:
+ self._matrix_space = self._matrix_basis[0].parent()
+ else:
+ self._matrix_space = matrix_space
+
+ # 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(_all2list(b)) for b in 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=check_axioms)
+
+ if orthonormalize:
+ # Now "self._matrix_span" is the vector space of our
+ # algebra coordinates. The variables "X0", "X1",... 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 )
+
+
+ # Now we actually compute the multiplication and inner-product
+ # tables/matrices using the possibly-orthonormalized basis.
+ self._inner_product_matrix = matrix.identity(field, n)
+ 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
+ # of the ambient basis. It's important that their arguments
+ # are in ambient coordinates as well.
+ for i in range(n):
+ for j in range(i+1):
+ # ortho basis w.r.t. ambient coords
+ q_i = basis[i]
+ q_j = basis[j]
+
+ # 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 = self._matrix_span.coordinate_vector(V(_all2list(elt)))
+ self._multiplication_table[i][j] = self.from_vector(elt)
+
+ if not orthonormalize:
+ # If we're orthonormalizing the basis with respect
+ # to an inner-product, then the inner-product
+ # matrix with respect to the resulting basis is
+ # just going to be the identity.
+ ip = inner_product(q_i, q_j)
+ self._inner_product_matrix[i,j] = ip
+ self._inner_product_matrix[j,i] = ip
+
+ self._inner_product_matrix._cache = {'hermitian': True}
+ self._inner_product_matrix.set_immutable()
+
+ if check_axioms:
+ if not self._is_jordanian():
+ raise ValueError("Jordan identity does not hold")
+ if not self._inner_product_is_associative():
+ raise ValueError("inner product is not associative")
+
+
+ def _coerce_map_from_base_ring(self):
+ """
+ Disable the map from the base ring into the algebra.
+
+ Performing a nonsense conversion like this automatically
+ is counterpedagogical. The fallback is to try the usual
+ element constructor, which should also fail.
+
+ SETUP::
+
+ sage: from mjo.eja.eja_algebra import random_eja
+
+ TESTS::
+
+ sage: J = random_eja()
+ sage: J(1)
+ Traceback (most recent call last):
+ ...
+ ValueError: not an element of this algebra
+
"""
+ return None
+
+
+ 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: J = random_eja()
+ sage: n = J.dimension()
+ sage: bi = J.zero()
+ sage: bj = J.zero()
+ sage: bi_bj = J.zero()*J.zero()
+ sage: if n > 0:
+ ....: i = ZZ.random_element(n)
+ ....: j = ZZ.random_element(n)
+ ....: bi = J.monomial(i)
+ ....: bj = J.monomial(j)
+ ....: bi_bj = J.product_on_basis(i,j)
+ sage: bi*bj == bi_bj
+ True
+
+ """
+ # We only stored the lower-triangular portion of the
+ # multiplication table.
+ if j <= i:
+ return self._multiplication_table[i][j]
+ else:
+ return self._multiplication_table[j][i]
+
+ def inner_product(self, x, y):
+ """
+ The inner product associated with this Euclidean Jordan algebra.
+
+ Defaults to the trace inner product, but can be overridden by
+ subclasses if they are sure that the necessary properties are
+ satisfied.
+
+ SETUP::
+
+ sage: from mjo.eja.eja_algebra import (random_eja,
+ ....: HadamardEJA,
+ ....: BilinearFormEJA)
+
EXAMPLES:
- By definition, Jordan multiplication commutes::
+ Our inner product is "associative," which means the following for
+ a symmetric bilinear form::
- sage: set_random_seed()
sage: J = random_eja()
+ sage: x,y,z = J.random_elements(3)
+ sage: (x*y).inner_product(z) == y.inner_product(x*z)
+ True
+
+ TESTS:
+
+ Ensure that this is the usual inner product for the algebras
+ over `R^n`::
+
+ sage: J = HadamardEJA.random_instance()
sage: x,y = J.random_elements(2)
- sage: x*y == y*x
+ sage: actual = x.inner_product(y)
+ sage: expected = x.to_vector().inner_product(y.to_vector())
+ sage: actual == expected
+ True
+
+ Ensure that this is one-half of the trace inner-product in a
+ BilinearFormEJA that isn't just the reals (when ``n`` isn't
+ one). This is in Faraut and Koranyi, and also my "On the
+ symmetry..." paper::
+
+ sage: J = BilinearFormEJA.random_instance()
+ sage: n = J.dimension()
+ sage: x = J.random_element()
+ sage: y = J.random_element()
+ sage: (n == 1) or (x.inner_product(y) == (x*y).trace()/2)
True
"""
- self._rank = rank
- self._natural_basis = natural_basis
+ B = self._inner_product_matrix
+ return (B*x.to_vector()).inner_product(y.to_vector())
+
+
+ def is_associative(self):
+ r"""
+ 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
+
+ """
+ return "Associative" in self.category().axioms()
+
+ def _is_commutative(self):
+ r"""
+ Whether or not this algebra's multiplication table 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( x*y == y*x for x in self.gens() for y in self.gens() )
- # TODO: HACK for the charpoly.. needs redesign badly.
- self._basis_normalizers = None
+ def _is_jordanian(self):
+ r"""
+ Whether or not this algebra's multiplication table respects the
+ Jordan identity `(x^{2})(xy) = x(x^{2}y)`.
- if category is None:
- category = MagmaticAlgebras(field).FiniteDimensional()
- category = category.WithBasis().Unital()
+ 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
+ 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))
+ ==
+ (self.monomial(i))*((self.monomial(i)**2)*self.monomial(j))
+ for i in range(self.dimension())
+ for j in range(self.dimension()) )
+
+ def _jordan_product_is_associative(self):
+ r"""
+ Return whether or not this algebra's Jordan product is
+ associative; that is, whether or not `x*(y*z) = (x*y)*z`
+ for all `x,y,x`.
+
+ This method should agree with :meth:`is_associative` unless
+ you lied about the value of the ``associative`` parameter
+ when you constructed the algebra.
+
+ SETUP::
+
+ sage: from mjo.eja.eja_algebra import (random_eja,
+ ....: RealSymmetricEJA,
+ ....: ComplexHermitianEJA,
+ ....: QuaternionHermitianEJA)
+
+ EXAMPLES::
+
+ sage: J = RealSymmetricEJA(4, orthonormalize=False)
+ sage: J._jordan_product_is_associative()
+ False
+ sage: x = sum(J.gens())
+ sage: A = x.subalgebra_generated_by()
+ sage: A._jordan_product_is_associative()
+ True
+
+ ::
+
+ sage: J = ComplexHermitianEJA(2,field=QQ,orthonormalize=False)
+ sage: J._jordan_product_is_associative()
+ False
+ sage: x = sum(J.gens())
+ sage: A = x.subalgebra_generated_by(orthonormalize=False)
+ sage: A._jordan_product_is_associative()
+ True
+
+ ::
+
+ sage: J = QuaternionHermitianEJA(2)
+ sage: J._jordan_product_is_associative()
+ False
+ sage: x = sum(J.gens())
+ sage: A = x.subalgebra_generated_by()
+ sage: A._jordan_product_is_associative()
+ True
+
+ TESTS:
- fda = super(FiniteDimensionalEuclideanJordanAlgebra, self)
- fda.__init__(field,
- range(len(mult_table)),
- prefix=prefix,
- category=category)
- self.print_options(bracket='')
+ The values we've presupplied to the constructors agree with
+ the computation::
- # The multiplication table we're given is necessarily in terms
- # of vectors, because we don't have an algebra yet for
- # anything to be an element of. However, it's faster in the
- # long run to have the multiplication table be in terms of
- # algebra elements. We do this after calling the superclass
- # constructor so that from_vector() knows what to do.
- self._multiplication_table = [ map(lambda x: self.from_vector(x), ls)
- for ls in mult_table ]
+ sage: J = random_eja()
+ sage: J.is_associative() == J._jordan_product_is_associative()
+ True
+ """
+ R = self.base_ring()
+
+ # Used to check whether or not something is zero.
+ epsilon = R.zero()
+ if not R.is_exact():
+ # I don't know of any examples that make this magnitude
+ # necessary because I don't know how to make an
+ # associative algebra when the element subalgebra
+ # construction is unreliable (as it is over RDF; we can't
+ # find the degree of an element because we can't compute
+ # the rank of a matrix). But even multiplication of floats
+ # is non-associative, so *some* epsilon is needed... let's
+ # just take the one from _inner_product_is_associative?
+ epsilon = 1e-15
+
+ 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)
+ diff = (x*y)*z - x*(y*z)
+
+ if diff.norm() > epsilon:
+ return False
+
+ return True
+
+ def _inner_product_is_associative(self):
+ r"""
+ Return whether or not this algebra's inner product `B` is
+ associative; that is, whether or not `B(xy,z) = B(x,yz)`.
+
+ This method should of course always return ``True``, unless
+ this algebra was constructed with ``check_axioms=False`` and
+ passed an invalid Jordan or inner-product.
+ """
+ R = self.base_ring()
+
+ # Used to check whether or not something is zero.
+ epsilon = R.zero()
+ if not R.is_exact():
+ # This choice is sufficient to allow the construction of
+ # QuaternionHermitianEJA(2, field=RDF) with check_axioms=True.
+ epsilon = 1e-15
+
+ 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)
+ diff = (x*y).inner_product(z) - x.inner_product(y*z)
+
+ if diff.abs() > epsilon:
+ return False
+
+ return True
def _element_constructor_(self, elt):
"""
- Construct an element of this algebra from its natural
- 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.
SETUP::
- sage: from mjo.eja.eja_algebra import (JordanSpinEJA,
- ....: RealCartesianProductEJA,
+ sage: from mjo.eja.eja_algebra import (random_eja,
+ ....: JordanSpinEJA,
+ ....: HadamardEJA,
....: RealSymmetricEJA)
EXAMPLES:
sage: J(A)
Traceback (most recent call last):
...
- ArithmeticError: vector is not in free module
+ 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]) )
+ 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 of the two non-matrix
- simple algebras (whose natural representations are their usual
- vector representations) back and forth faithfully::
+ Ensure that we can convert any element back and forth
+ faithfully between its matrix and algebra representations::
- sage: set_random_seed()
- sage: J = RealCartesianProductEJA.random_instance()
- sage: x = J.random_element()
- sage: J(x.to_vector().column()) == x
- True
- sage: J = JordanSpinEJA.random_instance()
+ sage: J = random_eja()
sage: x = J.random_element()
- sage: J(x.to_vector().column()) == x
+ sage: J(x.to_matrix()) == 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
+
"""
- if elt == 0:
- # The superclass implementation of random_element()
- # needs to be able to coerce "0" into the algebra.
- return self.zero()
+ msg = "not an element of this algebra"
+ 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
+ # that the integer 3 belongs to the space of 2-by-2 matrices.
+ raise ValueError(msg)
+
+ if hasattr(elt, 'superalgebra_element'):
+ # Handle subalgebra elements
+ if elt.parent().superalgebra() == self:
+ return elt.superalgebra_element()
- natural_basis = self.natural_basis()
- basis_space = natural_basis[0].matrix_space()
- if elt not in basis_space:
- raise ValueError("not a naturally-represented algebra 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()
+
+ if elt not in self.matrix_space():
+ raise ValueError(msg)
# Thanks for nothing! Matrix spaces aren't vector spaces in
- # Sage, so we have to figure out its natural-basis coordinates
+ # Sage, so we have to figure out its matrix-basis coordinates
# ourselves. We use the basis space's ring instead of the
# element's ring because the basis space might be an algebraic
# closure whereas the base ring of the 3-by-3 identity matrix
# could be QQ instead of QQbar.
- V = VectorSpace(basis_space.base_ring(), elt.nrows()*elt.ncols())
- W = V.span_of_basis( _mat2vec(s) for s in natural_basis )
- coords = W.coordinate_vector(_mat2vec(elt))
- return self.from_vector(coords)
-
+ #
+ # 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.
+ #
+ elt = self._matrix_span.ambient_vector_space()(_all2list(elt))
- @staticmethod
- def _max_test_case_size():
- """
- Return an integer "size" that is an upper bound on the size of
- this algebra when it is used in a random test
- case. Unfortunately, the term "size" is quite vague -- when
- dealing with `R^n` under either the Hadamard or Jordan spin
- product, the "size" refers to the dimension `n`. When dealing
- with a matrix algebra (real symmetric or complex/quaternion
- Hermitian), it refers to the size of the matrix, which is
- far less than the dimension of the underlying vector space.
-
- We default to five in this class, which is safe in `R^n`. The
- matrix algebra subclasses (or any class where the "size" is
- interpreted to be far less than the dimension) should override
- with a smaller number.
- """
- return 5
+ try:
+ coords = self._matrix_span.coordinate_vector(elt)
+ except ArithmeticError: # vector is not in free module
+ raise ValueError(msg)
+ return self.from_vector(coords)
def _repr_(self):
"""
Ensure that it says what we think it says::
- sage: JordanSpinEJA(2, field=QQ)
- Euclidean Jordan algebra of dimension 2 over Rational Field
+ sage: JordanSpinEJA(2, field=AA)
+ Euclidean Jordan algebra of dimension 2 over Algebraic Real Field
sage: JordanSpinEJA(3, field=RDF)
Euclidean Jordan algebra of dimension 3 over Real Double Field
fmt = "Euclidean Jordan algebra of dimension {} over {}"
return fmt.format(self.dimension(), self.base_ring())
- def product_on_basis(self, i, j):
- return self._multiplication_table[i][j]
-
- def _a_regular_element(self):
- """
- Guess a regular element. Needed to compute the basis for our
- characteristic polynomial coefficients.
-
- SETUP::
-
- sage: from mjo.eja.eja_algebra import random_eja
-
- TESTS:
-
- Ensure that this hacky method succeeds for every algebra that we
- know how to construct::
-
- sage: set_random_seed()
- sage: J = random_eja()
- sage: J._a_regular_element().is_regular()
- True
-
- """
- 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()
- # Don't use the parent vector space directly here in case this
- # happens to be a subalgebra. In that case, we would be e.g.
- # two-dimensional but span_of_basis() would expect three
- # coordinates.
- V = VectorSpace(self.base_ring(), self.vector_space().dimension())
- basis = [ (z**k).to_vector() for k in range(self.rank()) ]
- V1 = V.span_of_basis( basis )
- b = (V1.basis() + V1.complement().basis())
- return V.span_of_basis(b)
-
-
@cached_method
- def _charpoly_coeff(self, i):
- """
- Return the coefficient polynomial "a_{i}" of this algebra's
- general characteristic polynomial.
-
- Having this be a separate cached method lets us compute and
- store the trace/determinant (a_{r-1} and a_{0} respectively)
- separate from the entire characteristic polynomial.
- """
- if self._basis_normalizers is not None:
- # Must be a matrix class?
- # WARNING/TODO: this whole mess is mis-designed.
- n = self.natural_basis_space().nrows()
- field = self.base_ring().base_ring() # yeeeeaaaahhh
- J = self.__class__(n, field, False)
- (_,x,_,_) = J._charpoly_matrix_system()
- p = J._charpoly_coeff(i)
- # p might be missing some vars, have to substitute "optionally"
- pairs = zip(x.base_ring().gens(), self._basis_normalizers)
- substitutions = { v: v*c for (v,c) in pairs }
- return p.subs(substitutions)
-
- (A_of_x, x, xr, detA) = self._charpoly_matrix_system()
- R = A_of_x.base_ring()
- 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/detA)
-
-
- @cached_method
- def _charpoly_matrix_system(self):
- """
- Compute the matrix whose entries A_ij are polynomials in
- 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()
-
- # Turn my vector space into a module so that "vectors" can
- # have multivatiate polynomial entries.
- names = tuple('X' + str(i) for i in range(1,n+1))
- R = PolynomialRing(self.base_ring(), names)
-
- # Using change_ring() on the parent's vector space doesn't work
- # here because, in a subalgebra, that vector space has a basis
- # and change_ring() tries to bring the basis along with it. And
- # that doesn't work unless the new ring is a PID, which it usually
- # won't be.
- V = FreeModule(R,n)
-
- # Now let x = (X1,X2,...,Xn) be the vector whose entries are
- # indeterminates...
- x = V(names)
-
- # And figure out the "left multiplication by x" matrix in
- # that setting.
- lmbx_cols = []
- monomial_matrices = [ self.monomial(i).operator().matrix()
- for i in range(n) ] # don't recompute these!
- for k in range(n):
- ek = self.monomial(k).to_vector()
- lmbx_cols.append(
- sum( x[i]*(monomial_matrices[i]*ek)
- for i in range(n) ) )
- Lx = matrix.column(R, lmbx_cols)
-
- # Now we can compute powers of x "symbolically"
- x_powers = [self.one().to_vector(), x]
- for d in range(2, r+1):
- x_powers.append( Lx*(x_powers[-1]) )
-
- idmat = matrix.identity(R, 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_powers = [ W.coordinate_vector(xp) for xp in x_powers ]
- l2 = [idmat.column(k-1) for k in range(r+1, n+1)]
- A_of_x = matrix.column(R, n, (x_powers[:r] + l2))
- return (A_of_x, x, x_powers[r], A_of_x.det())
-
-
- @cached_method
- def characteristic_polynomial(self):
+ def characteristic_polynomial_of(self):
"""
- Return a characteristic polynomial that works for all elements
- of this algebra.
+ Return the algebra's "characteristic polynomial of" function,
+ which is itself a multivariate polynomial that, when evaluated
+ at the coordinates of some algebra element, returns that
+ element's characteristic polynomial.
The resulting polynomial has `n+1` variables, where `n` is the
dimension of this algebra. The first `n` variables correspond to
SETUP::
- sage: from mjo.eja.eja_algebra import JordanSpinEJA
+ sage: from mjo.eja.eja_algebra import JordanSpinEJA, TrivialEJA
EXAMPLES:
Alizadeh, Example 11.11::
sage: J = JordanSpinEJA(3)
- sage: p = J.characteristic_polynomial(); p
- X1^2 - X2^2 - X3^2 + (-2*t)*X1 + t^2
+ sage: p = J.characteristic_polynomial_of(); p
+ X0^2 - X1^2 - X2^2 + (-2*t)*X0 + t^2
sage: xvec = J.one().to_vector()
sage: p(*xvec)
t^2 - 2*t + 1
+ By definition, the characteristic polynomial is a monic
+ degree-zero polynomial in a rank-zero algebra. Note that
+ Cayley-Hamilton is indeed satisfied since the polynomial
+ ``1`` evaluates to the identity element of the algebra on
+ any argument::
+
+ sage: J = TrivialEJA()
+ sage: J.characteristic_polynomial_of()
+ 1
+
"""
r = self.rank()
n = self.dimension()
- # The list of coefficient polynomials a_1, a_2, ..., a_n.
- a = [ self._charpoly_coeff(i) for i in range(n) ]
+ # The list of coefficient polynomials a_0, a_1, a_2, ..., a_(r-1).
+ a = self._charpoly_coefficients()
# We go to a bit of trouble here to reorder the
# indeterminates, so that it's easier to evaluate the
# characteristic polynomial at x's coordinates and get back
# something in terms of t, which is what we want.
- R = a[0].parent()
S = PolynomialRing(self.base_ring(),'t')
t = S.gen(0)
- S = PolynomialRing(S, R.variable_names())
- t = S(t)
-
- # Note: all entries past the rth should be zero. The
- # coefficient of the highest power (x^r) is 1, but it doesn't
- # appear in the solution vector which contains coefficients
- # for the other powers (to make them sum to x^r).
- if (r < n):
- a[r] = 1 # corresponds to x^r
- else:
- # When the rank is equal to the dimension, trying to
- # assign a[r] goes out-of-bounds.
- a.append(1) # corresponds to x^r
+ if r > 0:
+ R = a[0].parent()
+ S = PolynomialRing(S, R.variable_names())
+ t = S(t)
+
+ return (t**r + sum( a[k]*(t**k) for k in range(r) ))
+
+ def coordinate_polynomial_ring(self):
+ r"""
+ The multivariate polynomial ring in which this algebra's
+ :meth:`characteristic_polynomial_of` lives.
+
+ SETUP::
+
+ sage: from mjo.eja.eja_algebra import (HadamardEJA,
+ ....: RealSymmetricEJA)
+
+ EXAMPLES::
- return sum( a[k]*(t**k) for k in range(len(a)) )
+ sage: J = HadamardEJA(2)
+ sage: J.coordinate_polynomial_ring()
+ Multivariate Polynomial Ring in X0, X1...
+ sage: J = RealSymmetricEJA(3,field=QQ,orthonormalize=False)
+ sage: J.coordinate_polynomial_ring()
+ Multivariate Polynomial Ring in X0, X1, X2, X3, X4, X5...
+ """
+ var_names = tuple( "X%d" % z for z in range(self.dimension()) )
+ return PolynomialRing(self.base_ring(), var_names)
def inner_product(self, x, y):
"""
SETUP::
- sage: from mjo.eja.eja_algebra import random_eja
+ sage: from mjo.eja.eja_algebra import (random_eja,
+ ....: HadamardEJA,
+ ....: BilinearFormEJA)
EXAMPLES:
- Our inner product satisfies the Jordan axiom, which is also
- referred to as "associativity" for a symmetric bilinear form::
+ Our inner product is "associative," which means the following for
+ a symmetric bilinear form::
- sage: set_random_seed()
sage: J = random_eja()
sage: x,y,z = J.random_elements(3)
sage: (x*y).inner_product(z) == y.inner_product(x*z)
True
+ TESTS:
+
+ Ensure that this is the usual inner product for the algebras
+ over `R^n`::
+
+ sage: J = HadamardEJA.random_instance()
+ sage: x,y = J.random_elements(2)
+ sage: actual = x.inner_product(y)
+ sage: expected = x.to_vector().inner_product(y.to_vector())
+ sage: actual == expected
+ True
+
+ Ensure that this is one-half of the trace inner-product in a
+ BilinearFormEJA that isn't just the reals (when ``n`` isn't
+ one). This is in Faraut and Koranyi, and also my "On the
+ symmetry..." paper::
+
+ sage: J = BilinearFormEJA.random_instance()
+ sage: n = J.dimension()
+ sage: x = J.random_element()
+ sage: y = J.random_element()
+ sage: (n == 1) or (x.inner_product(y) == (x*y).trace()/2)
+ True
"""
- X = x.natural_representation()
- Y = y.natural_representation()
- return self.natural_inner_product(X,Y)
+ B = self._inner_product_matrix
+ return (B*x.to_vector()).inner_product(y.to_vector())
def is_trivial(self):
SETUP::
- sage: from mjo.eja.eja_algebra import ComplexHermitianEJA
+ sage: from mjo.eja.eja_algebra import (ComplexHermitianEJA,
+ ....: TrivialEJA)
EXAMPLES::
sage: J = ComplexHermitianEJA(3)
sage: J.is_trivial()
False
- sage: A = J.zero().subalgebra_generated_by()
- sage: A.is_trivial()
+
+ ::
+
+ sage: J = TrivialEJA()
+ sage: J.is_trivial()
True
"""
sage: J = JordanSpinEJA(4)
sage: J.multiplication_table()
+----++----+----+----+----+
- | * || e0 | e1 | e2 | e3 |
+ | * || b0 | b1 | b2 | b3 |
+====++====+====+====+====+
- | e0 || e0 | e1 | e2 | e3 |
+ | b0 || b0 | b1 | b2 | b3 |
+----++----+----+----+----+
- | e1 || e1 | e0 | 0 | 0 |
+ | b1 || b1 | b0 | 0 | 0 |
+----++----+----+----+----+
- | e2 || e2 | 0 | e0 | 0 |
+ | b2 || b2 | 0 | b0 | 0 |
+----++----+----+----+----+
- | e3 || e3 | 0 | 0 | e0 |
+ | b3 || b3 | 0 | 0 | b0 |
+----++----+----+----+----+
"""
- M = list(self._multiplication_table) # copy
- for i in range(len(M)):
- # M had better be "square"
- M[i] = [self.monomial(i)] + M[i]
- M = [["*"] + list(self.gens())] + M
+ n = self.dimension()
+ # Prepend the header row.
+ M = [["*"] + list(self.gens())]
+
+ # And to each subsequent row, prepend an entry that belongs to
+ # the left-side "header column."
+ M += [ [self.monomial(i)] + [ self.monomial(i)*self.monomial(j)
+ for j in range(n) ]
+ for i in range(n) ]
+
return table(M, header_row=True, header_column=True, frame=True)
- def natural_basis(self):
+ def matrix_basis(self):
"""
- Return a more-natural representation of this algebra's basis.
+ Return an (often more natural) representation of this algebras
+ basis as an ordered tuple of matrices.
+
+ Every finite-dimensional Euclidean Jordan Algebra is a, up to
+ Jordan isomorphism, a direct sum of five simple
+ algebras---four of which comprise Hermitian matrices. And the
+ last type of algebra can of course be thought of as `n`-by-`1`
+ column matrices (ambiguusly called column vectors) to avoid
+ special cases. As a result, matrices (and column vectors) are
+ a natural representation format for Euclidean Jordan algebra
+ elements.
- Every finite-dimensional Euclidean Jordan Algebra is a direct
- sum of five simple algebras, four of which comprise Hermitian
- matrices. This method returns the original "natural" basis
- for our underlying vector space. (Typically, the natural basis
- is used to construct the multiplication table in the first place.)
+ But, when we construct an algebra from a basis of matrices,
+ those matrix representations are lost in favor of coordinate
+ vectors *with respect to* that basis. We could eventually
+ convert back if we tried hard enough, but having the original
+ representations handy is valuable enough that we simply store
+ them and return them from this method.
- Note that this will always return a matrix. The standard basis
- in `R^n` will be returned as `n`-by-`1` column matrices.
+ 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:`CartesianProductEJA` can be displayed
+ nicely, without having to have special classes for direct sums
+ one of whose components was a matrix algebra.
SETUP::
sage: J = RealSymmetricEJA(2)
sage: J.basis()
- Finite family {0: e0, 1: e1, 2: e2}
- sage: J.natural_basis()
+ Finite family {0: b0, 1: b1, 2: b2}
+ sage: J.matrix_basis()
(
- [1 0] [ 0 1/2*sqrt2] [0 0]
- [0 0], [1/2*sqrt2 0], [0 1]
+ [1 0] [ 0 0.7071067811865475?] [0 0]
+ [0 0], [0.7071067811865475? 0], [0 1]
)
::
sage: J = JordanSpinEJA(2)
sage: J.basis()
- Finite family {0: e0, 1: e1}
- sage: J.natural_basis()
+ Finite family {0: b0, 1: b1}
+ sage: J.matrix_basis()
(
[1] [0]
[0], [1]
)
-
"""
- if self._natural_basis is None:
- M = self.natural_basis_space()
- return tuple( M(b.to_vector()) for b in self.basis() )
- else:
- return self._natural_basis
+ return self._matrix_basis
- def natural_basis_space(self):
+ def matrix_space(self):
"""
- Return the matrix space in which this algebra's natural basis
- elements live.
- """
- if self._natural_basis is None or len(self._natural_basis) == 0:
- return MatrixSpace(self.base_ring(), self.dimension(), 1)
- else:
- return self._natural_basis[0].matrix_space()
+ Return the matrix space in which this algebra's elements live, if
+ we think of them as matrices (including column vectors of the
+ appropriate size).
+ "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. For algebras of
+ matrices, this returns the space in which their
+ real embeddings live.
- @staticmethod
- def natural_inner_product(X,Y):
- """
- Compute the inner product of two naturally-represented elements.
+ 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()
+ Module of 2 by 2 matrices with entries in Algebraic Field over
+ the scalar ring Rational Field
+ sage: J = QuaternionHermitianEJA(1,field=QQ,orthonormalize=False)
+ sage: J.matrix_space()
+ Module of 1 by 1 matrices with entries in Quaternion
+ Algebra (-1, -1) with base ring Rational Field over
+ the scalar ring Rational Field
- For example in the real symmetric matrix EJA, this will compute
- the trace inner-product of two n-by-n symmetric matrices. The
- default should work for the real cartesian product EJA, the
- Jordan spin EJA, and the real symmetric matrices. The others
- will have to be overridden.
"""
- return (X.conjugate_transpose()*Y).trace()
+ return self._matrix_space
@cached_method
SETUP::
- sage: from mjo.eja.eja_algebra import (RealCartesianProductEJA,
+ 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()
+ b0 + b1 + b2 + b3 + b4
+
+ The unit element in the Hadamard EJA is inherited in the
+ subalgebras generated by its elements::
- sage: J = RealCartesianProductEJA(5)
+ sage: J = HadamardEJA(5)
sage: J.one()
- e0 + e1 + e2 + e3 + e4
+ b0 + b1 + b2 + b3 + b4
+ sage: x = sum(J.gens())
+ sage: A = x.subalgebra_generated_by(orthonormalize=False)
+ sage: A.one()
+ c0
+ sage: A.one().superalgebra_element()
+ b0 + b1 + b2 + b3 + b4
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(orthonormalize=False)
+ sage: y = A.random_element()
+ sage: A.one()*y == y and y*A.one() == y
+ True
+
+ ::
+
+ 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::
+ 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()
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
+
+ ::
+
+ 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::
+
+ sage: J = random_eja()
+ sage: cached = J.one()
+ sage: J.one.clear_cache()
+ sage: J.one() == cached
+ True
+
+ ::
+
+ 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
#
# Of course, matrices aren't vectors in sage, so we have to
# appeal to the "long vectors" isometry.
- oper_vecs = [ _mat2vec(g.operator().matrix()) for g in self.gens() ]
- # Now we use basis linear algebra to find the coefficients,
+ V = VectorSpace(self.base_ring(), self.dimension()**2)
+ oper_vecs = [ V(g.operator().matrix().list()) for g in self.gens() ]
+
+ # Now we use basic linear algebra to find the coefficients,
# of the matrices-as-vectors-linear-combination, which should
# work for the original algebra basis too.
- A = matrix.column(self.base_ring(), oper_vecs)
+ A = matrix(self.base_ring(), oper_vecs)
# We used the isometry on the left-hand side already, but we
# still need to do it for the right-hand side. Recall that we
# wanted something that summed to the identity matrix.
- b = _mat2vec( matrix.identity(self.base_ring(), self.dimension()) )
+ b = V( matrix.identity(self.base_ring(), self.dimension()).list() )
- # Now if there's an identity element in the algebra, this should work.
- coeffs = A.solve_right(b)
- return self.linear_combination(zip(self.gens(), coeffs))
+ # Now if there's an identity element in the algebra, this
+ # should work. We solve on the left to avoid having to
+ # transpose the matrix "A".
+ return self.from_vector(A.solve_left(b))
- def random_element(self):
- # Temporary workaround for https://trac.sagemath.org/ticket/28327
- if self.is_trivial():
- return self.zero()
- else:
- s = super(FiniteDimensionalEuclideanJordanAlgebra, self)
- return s.random_element()
+ def peirce_decomposition(self, c):
+ """
+ The Peirce decomposition of this algebra relative to the
+ idempotent ``c``.
+
+ In the future, this can be extended to a complete system of
+ orthogonal idempotents.
+
+ INPUT:
+
+ - ``c`` -- an idempotent of this algebra.
+
+ OUTPUT:
+
+ A triple (J0, J5, J1) containing two subalgebras and one subspace
+ of this algebra,
+
+ - ``J0`` -- the algebra on the eigenspace of ``c.operator()``
+ corresponding to the eigenvalue zero.
+
+ - ``J5`` -- the eigenspace (NOT a subalgebra) of ``c.operator()``
+ corresponding to the eigenvalue one-half.
+
+ - ``J1`` -- the algebra on the eigenspace of ``c.operator()``
+ corresponding to the eigenvalue one.
+
+ These are the only possible eigenspaces for that operator, and this
+ algebra is a direct sum of them. The spaces ``J0`` and ``J1`` are
+ orthogonal, and are subalgebras of this algebra with the appropriate
+ restrictions.
+
+ SETUP::
+
+ sage: from mjo.eja.eja_algebra import random_eja, RealSymmetricEJA
+
+ EXAMPLES:
+
+ The canonical example comes from the symmetric matrices, which
+ decompose into diagonal and off-diagonal parts::
+
+ sage: J = RealSymmetricEJA(3)
+ sage: C = matrix(QQ, [ [1,0,0],
+ ....: [0,1,0],
+ ....: [0,0,0] ])
+ sage: c = J(C)
+ sage: J0,J5,J1 = J.peirce_decomposition(c)
+ sage: J0
+ Euclidean Jordan algebra of dimension 1...
+ sage: J5
+ Vector space of degree 6 and dimension 2...
+ sage: J1
+ Euclidean Jordan algebra of dimension 3...
+ sage: J0.one().to_matrix()
+ [0 0 0]
+ [0 0 0]
+ [0 0 1]
+ sage: orig_df = AA.options.display_format
+ sage: AA.options.display_format = 'radical'
+ sage: J.from_vector(J5.basis()[0]).to_matrix()
+ [ 0 0 1/2*sqrt(2)]
+ [ 0 0 0]
+ [1/2*sqrt(2) 0 0]
+ sage: J.from_vector(J5.basis()[1]).to_matrix()
+ [ 0 0 0]
+ [ 0 0 1/2*sqrt(2)]
+ [ 0 1/2*sqrt(2) 0]
+ sage: AA.options.display_format = orig_df
+ sage: J1.one().to_matrix()
+ [1 0 0]
+ [0 1 0]
+ [0 0 0]
+
+ TESTS:
+
+ Every algebra decomposes trivially with respect to its identity
+ element::
+
+ sage: J = random_eja()
+ sage: J0,J5,J1 = J.peirce_decomposition(J.one())
+ sage: J0.dimension() == 0 and J5.dimension() == 0
+ True
+ sage: J1.superalgebra() == J and J1.dimension() == J.dimension()
+ True
+
+ The decomposition is into eigenspaces, and its components are
+ therefore necessarily orthogonal. Moreover, the identity
+ elements in the two subalgebras are the projections onto their
+ respective subspaces of the superalgebra's identity element::
+
+ sage: J = random_eja()
+ sage: x = J.random_element()
+ sage: if not J.is_trivial():
+ ....: while x.is_nilpotent():
+ ....: x = J.random_element()
+ sage: c = x.subalgebra_idempotent()
+ sage: J0,J5,J1 = J.peirce_decomposition(c)
+ sage: ipsum = 0
+ sage: for (w,y,z) in zip(J0.basis(), J5.basis(), J1.basis()):
+ ....: w = w.superalgebra_element()
+ ....: y = J.from_vector(y)
+ ....: z = z.superalgebra_element()
+ ....: ipsum += w.inner_product(y).abs()
+ ....: ipsum += w.inner_product(z).abs()
+ ....: ipsum += y.inner_product(z).abs()
+ sage: ipsum
+ 0
+ sage: J1(c) == J1.one()
+ True
+ sage: J0(J.one() - c) == J0.one()
+ True
+
+ """
+ if not c.is_idempotent():
+ raise ValueError("element is not idempotent: %s" % c)
+
+ # 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 = self.subalgebra((), check_axioms=False)
+ J0 = trivial # eigenvalue zero
+ J5 = VectorSpace(self.base_ring(), 0) # eigenvalue one-half
+ J1 = trivial # eigenvalue one
+
+ for (eigval, eigspace) in c.operator().matrix().right_eigenspaces():
+ if eigval == ~(self.base_ring()(2)):
+ J5 = eigspace
+ else:
+ gens = tuple( self.from_vector(b) for b in eigspace.basis() )
+ subalg = self.subalgebra(gens, check_axioms=False)
+ if eigval == 0:
+ J0 = subalg
+ elif eigval == 1:
+ J1 = subalg
+ else:
+ raise ValueError("unexpected eigenvalue: %s" % eigval)
+
+ return (J0, J5, J1)
+
+
+ def random_element(self, thorough=False):
+ r"""
+ Return a random element of this algebra.
+
+ Our algebra superclass method only returns a linear
+ combination of at most two basis elements. We instead
+ want the vector space "random element" method that
+ returns a more diverse selection.
+
+ INPUT:
+
+ - ``thorough`` -- (boolean; default False) whether or not we
+ should generate irrational coefficients for the random
+ element when our base ring is irrational; this slows the
+ algebra operations to a crawl, but any truly random method
+ should include them
+
+ """
+ # For a general base ring... maybe we can trust this to do the
+ # right thing? Unlikely, but.
+ V = self.vector_space()
+ if self.base_ring() is AA and not thorough:
+ # Now that AA generates actually random random elements
+ # (post Trac 30875), we only need to de-thorough the
+ # randomness when asked to.
+ V = V.change_ring(QQ)
+
+ v = V.random_element()
+ return self.from_vector(V.coordinate_vector(v))
- def random_elements(self, count):
+ def random_elements(self, count, thorough=False):
"""
Return ``count`` random elements as a tuple.
+ INPUT:
+
+ - ``thorough`` -- (boolean; default False) whether or not we
+ should generate irrational coefficients for the random
+ elements when our base ring is irrational; this slows the
+ algebra operations to a crawl, but any truly random method
+ should include them
+
SETUP::
sage: from mjo.eja.eja_algebra import JordanSpinEJA
True
"""
- return tuple( self.random_element() for idx in xrange(count) )
+ return tuple( self.random_element(thorough)
+ for idx in range(count) )
- @classmethod
- def random_instance(cls, field=QQ, **kwargs):
- """
- Return a random instance of this type of algebra.
- In subclasses for algebras that we know how to construct, this
- is a shortcut for constructing test cases and examples.
- """
- if cls is FiniteDimensionalEuclideanJordanAlgebra:
- # Red flag! But in theory we could do this I guess. The
- # only finite-dimensional exceptional EJA is the
- # octononions. So, we could just create an EJA from an
- # associative matrix algebra (generated by a subset of
- # elements) with the symmetric product. Or, we could punt
- # to random_eja() here, override it in our subclasses, and
- # not worry about it.
- raise NotImplementedError
+ def operator_polynomial_matrix(self):
+ r"""
+ Return the matrix of polynomials (over this algebra's
+ :meth:`coordinate_polynomial_ring`) that, when evaluated at
+ the basis coordinates of an element `x`, produces the basis
+ representation of `L_{x}`.
- n = ZZ.random_element(1, cls._max_test_case_size())
- return cls(n, field, **kwargs)
+ SETUP::
+ sage: from mjo.eja.eja_algebra import (HadamardEJA,
+ ....: JordanSpinEJA)
+
+ EXAMPLES::
+
+ sage: J = HadamardEJA(4)
+ sage: L_x = J.operator_polynomial_matrix()
+ sage: L_x
+ [X0 0 0 0]
+ [ 0 X1 0 0]
+ [ 0 0 X2 0]
+ [ 0 0 0 X3]
+ sage: x = J.one()
+ sage: d = zip(J.coordinate_polynomial_ring().gens(), x.to_vector())
+ sage: L_x.subs(dict(d))
+ [1 0 0 0]
+ [0 1 0 0]
+ [0 0 1 0]
+ [0 0 0 1]
+
+ ::
+
+ sage: J = JordanSpinEJA(4)
+ sage: L_x = J.operator_polynomial_matrix()
+ sage: L_x
+ [X0 X1 X2 X3]
+ [X1 X0 0 0]
+ [X2 0 X0 0]
+ [X3 0 0 X0]
+ sage: x = J.one()
+ sage: d = zip(J.coordinate_polynomial_ring().gens(), x.to_vector())
+ sage: L_x.subs(dict(d))
+ [1 0 0 0]
+ [0 1 0 0]
+ [0 0 1 0]
+ [0 0 0 1]
+
+ """
+ R = self.coordinate_polynomial_ring()
+
+ 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( v*self.monomial(k).operator().matrix()[i,j]
+ for (k,v) in enumerate(R.gens()) )
+
+ n = self.dimension()
+ return matrix(R, n, n, L_x_i_j)
+
+ @cached_method
+ def _charpoly_coefficients(self):
+ 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: J = random_eja()
+ sage: all(p.is_homogeneous() for p in J._charpoly_coefficients())
+ True
- def rank(self):
"""
- Return the rank of this EJA.
+ n = self.dimension()
+ R = self.coordinate_polynomial_ring()
+ F = R.fraction_field()
+
+ L_x = self.operator_polynomial_matrix()
+
+ r = None
+ if self.rank.is_in_cache():
+ r = self.rank()
+ # There's no need to pad the system with redundant
+ # columns if we *know* they'll be redundant.
+ n = r
+
+ # Compute an extra power in case the rank is equal to
+ # the dimension (otherwise, we would stop at x^(r-1)).
+ x_powers = [ (L_x**k)*self.one().to_vector()
+ for k in range(n+1) ]
+ A = matrix.column(F, x_powers[:n])
+ AE = A.extended_echelon_form()
+ E = AE[:,n:]
+ A_rref = AE[:,:n]
+ if r is None:
+ r = A_rref.rank()
+ b = x_powers[r]
+
+ # 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. 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)
- ALGORITHM:
+ @cached_method
+ def rank(self):
+ r"""
+ Return the rank of this EJA.
- The author knows of no algorithm to compute the rank of an EJA
- where only the multiplication table is known. In lieu of one, we
- require the rank to be specified when the algebra is created,
- and simply pass along that number here.
+ This is a cached method because we know the rank a priori for
+ all of the algebras we can construct. Thus we can avoid the
+ expensive ``_charpoly_coefficients()`` call unless we truly
+ need to compute the whole characteristic polynomial.
SETUP::
- sage: from mjo.eja.eja_algebra import (JordanSpinEJA,
+ sage: from mjo.eja.eja_algebra import (HadamardEJA,
+ ....: JordanSpinEJA,
....: RealSymmetricEJA,
....: ComplexHermitianEJA,
....: QuaternionHermitianEJA,
TESTS:
Ensure that every EJA that we know how to construct has a
- positive integer rank::
+ positive integer rank, unless the algebra is trivial in
+ which case its rank will be zero::
+
+ sage: J = random_eja()
+ sage: r = J.rank()
+ sage: r in ZZ
+ True
+ sage: r > 0 or (r == 0 and J.is_trivial())
+ True
+
+ Ensure that computing the rank actually works, since the ranks
+ of all simple algebras are known and will be cached by default::
- sage: set_random_seed()
- sage: r = random_eja().rank()
- sage: r in ZZ and r > 0
+ sage: J = random_eja() # long time
+ sage: cached = J.rank() # long time
+ sage: J.rank.clear_cache() # long time
+ sage: J.rank() == cached # long time
True
"""
- return self._rank
+ 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 EJASubalgebra
+ return EJASubalgebra(self, basis, **kwargs)
def vector_space(self):
return self.zero().to_vector().parent().ambient_vector_space()
- Element = FiniteDimensionalEuclideanJordanAlgebraElement
+class RationalBasisEJA(EJA):
+ r"""
+ Algebras whose supplied basis elements have all rational entries.
+
+ SETUP::
+
+ sage: from mjo.eja.eja_algebra import BilinearFormEJA
+
+ EXAMPLES:
+
+ The supplied basis is orthonormalized by default::
+
+ sage: B = matrix(QQ, [[1, 0, 0], [0, 25, -32], [0, -32, 41]])
+ sage: J = BilinearFormEJA(B)
+ sage: J.matrix_basis()
+ (
+ [1] [ 0] [ 0]
+ [0] [1/5] [32/5]
+ [0], [ 0], [ 5]
+ )
-class RealCartesianProductEJA(FiniteDimensionalEuclideanJordanAlgebra):
"""
- Return the Euclidean Jordan Algebra corresponding to the set
- `R^n` under the Hadamard product.
+ def __init__(self,
+ basis,
+ jordan_product,
+ inner_product,
+ field=AA,
+ check_field=True,
+ **kwargs):
+
+ if check_field:
+ # Abuse the check_field parameter to check that the entries of
+ # out basis (in ambient coordinates) are in the field QQ.
+ # Use _all2list to get the vector coordinates of octonion
+ # entries and not the octonions themselves (which are not
+ # rational).
+ if not all( all(b_i in QQ for b_i in _all2list(b))
+ for b in basis ):
+ raise TypeError("basis not rational")
+
+ super().__init__(basis,
+ jordan_product,
+ inner_product,
+ field=field,
+ check_field=check_field,
+ **kwargs)
+
+ self._rational_algebra = None
+ if field is not QQ:
+ # There's no point in constructing the extra algebra if this
+ # one is already rational.
+ #
+ # Note: the same Jordan and inner-products work here,
+ # because they are necessarily defined with respect to
+ # ambient coordinates and not any particular basis.
+ self._rational_algebra = EJA(
+ basis,
+ jordan_product,
+ inner_product,
+ field=QQ,
+ matrix_space=self.matrix_space(),
+ associative=self.is_associative(),
+ orthonormalize=False,
+ check_field=False,
+ check_axioms=False)
+
+ def rational_algebra(self):
+ # Using None as a flag here (rather than just assigning "self"
+ # to self._rational_algebra by default) feels a little bit
+ # more sane to me in a garbage-collected environment.
+ if self._rational_algebra is None:
+ return self
+ else:
+ return self._rational_algebra
- Note: this is nothing more than the Cartesian product of ``n``
- copies of the spin algebra. Once Cartesian product algebras
- are implemented, this can go.
+ @cached_method
+ def _charpoly_coefficients(self):
+ r"""
+ SETUP::
- SETUP::
+ sage: from mjo.eja.eja_algebra import (BilinearFormEJA,
+ ....: JordanSpinEJA)
- sage: from mjo.eja.eja_algebra import RealCartesianProductEJA
+ EXAMPLES:
- EXAMPLES:
+ The base ring of the resulting polynomial coefficients is what
+ it should be, and not the rationals (unless the algebra was
+ already over the rationals)::
- This multiplication table can be verified by hand::
+ sage: J = JordanSpinEJA(3)
+ sage: J._charpoly_coefficients()
+ (X0^2 - X1^2 - X2^2, -2*X0)
+ sage: a0 = J._charpoly_coefficients()[0]
+ sage: J.base_ring()
+ Algebraic Real Field
+ sage: a0.base_ring()
+ Algebraic Real Field
+
+ """
+ if self.rational_algebra() is self:
+ # Bypass the hijinks if they won't benefit us.
+ return super()._charpoly_coefficients()
+
+ # Do the computation over the rationals.
+ a = ( a_i.change_ring(self.base_ring())
+ for a_i in self.rational_algebra()._charpoly_coefficients() )
+
+ # Convert our coordinate variables into deorthonormalized ones
+ # and substitute them into the deorthonormalized charpoly
+ # coefficients.
+ R = self.coordinate_polynomial_ring()
+ from sage.modules.free_module_element import vector
+ X = vector(R, R.gens())
+ BX = self._deortho_matrix*X
+
+ subs_dict = { X[i]: BX[i] for i in range(len(X)) }
+ return tuple( a_i.subs(subs_dict) for a_i in a )
+
+class ConcreteEJA(EJA):
+ r"""
+ A class for the Euclidean Jordan algebras that we know by name.
+
+ These are the Jordan algebras whose basis, multiplication table,
+ rank, and so on are known a priori. More to the point, they are
+ the Euclidean Jordan algebras for which we are able to conjure up
+ a "random instance."
- sage: J = RealCartesianProductEJA(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
+ SETUP::
+
+ sage: from mjo.eja.eja_algebra import ConcreteEJA
TESTS:
- We can change the generator prefix::
+ Our basis is normalized with respect to the algebra's inner
+ product, unless we specify otherwise::
- sage: RealCartesianProductEJA(3, prefix='r').gens()
- (r0, r1, r2)
+ sage: J = ConcreteEJA.random_instance()
+ sage: all( b.norm() == 1 for b in J.gens() )
+ True
+ Since our basis is orthonormal with respect to the algebra's inner
+ product, and since we know that this algebra is an EJA, any
+ left-multiplication operator's matrix will be symmetric because
+ natural->EJA basis representation is an isometry and within the
+ EJA the operator is self-adjoint by the Jordan axiom::
+
+ sage: J = ConcreteEJA.random_instance()
+ sage: x = J.random_element()
+ sage: x.operator().is_self_adjoint()
+ True
"""
- def __init__(self, n, field=QQ, **kwargs):
- V = VectorSpace(field, n)
- mult_table = [ [ V.gen(i)*(i == j) for j in range(n) ]
- for i in range(n) ]
- fdeja = super(RealCartesianProductEJA, self)
- return fdeja.__init__(field, mult_table, rank=n, **kwargs)
+ @staticmethod
+ def _max_random_instance_dimension():
+ r"""
+ The maximum dimension of any random instance. Ten dimensions seems
+ to be about the point where everything takes a turn for the
+ worse. And dimension ten (but not nine) allows the 4-by-4 real
+ Hermitian matrices, the 2-by-2 quaternion Hermitian matrices,
+ and the 2-by-2 octonion Hermitian matrices.
+ """
+ return 10
- def inner_product(self, x, y):
+ @staticmethod
+ def _max_random_instance_size(max_dimension):
+ """
+ Return an integer "size" that is an upper bound on the size of
+ this algebra when it is used in a random test case. This size
+ (which can be passed to the algebra's constructor) is itself
+ based on the ``max_dimension`` parameter.
+
+ This method must be implemented in each subclass.
+ """
+ raise NotImplementedError
+
+ @classmethod
+ def random_instance(cls, max_dimension=None, *args, **kwargs):
"""
- Faster to reimplement than to use natural representations.
+ Return a random instance of this type of algebra whose dimension
+ 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 RealCartesianProductEJA
+ sage: from mjo.eja.eja_algebra import ConcreteEJA
TESTS:
- Ensure that this is the usual inner product for the algebras
- over `R^n`::
+ Both the class bound and the ``max_dimension`` argument are upper
+ bounds on the dimension of the algebra returned::
- sage: set_random_seed()
- sage: J = RealCartesianProductEJA.random_instance()
- sage: x,y = J.random_elements(2)
- sage: X = x.natural_representation()
- sage: Y = y.natural_representation()
- sage: x.inner_product(y) == J.natural_inner_product(X,Y)
+ 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
"""
- return x.to_vector().inner_product(y.to_vector())
-
+ from sage.misc.prandom import choice
+ eja_class = choice(cls.__subclasses__())
-def random_eja():
- """
- Return a "random" finite-dimensional Euclidean Jordan Algebra.
+ # These all bubble up to the RationalBasisEJA superclass
+ # constructor, so any (kw)args valid there are also valid
+ # here.
+ return eja_class.random_instance(max_dimension, *args, **kwargs)
- ALGORITHM:
- For now, we choose a random natural number ``n`` (greater than zero)
- and then give you back one of the following:
+class HermitianMatrixEJA(EJA):
+ @staticmethod
+ def _denormalized_basis(A):
+ """
+ Returns a basis for the given Hermitian matrix space.
- * The cartesian product of the rational numbers ``n`` times; this is
- ``QQ^n`` with the Hadamard product.
+ Why do we embed these? Basically, because all of numerical linear
+ algebra assumes that you're working with vectors consisting of `n`
+ entries from a field and scalars from the same field. There's no way
+ to tell SageMath that (for example) the vectors contain complex
+ numbers, while the scalar field is real.
- * The Jordan spin algebra on ``QQ^n``.
+ SETUP::
- * The ``n``-by-``n`` rational symmetric matrices with the symmetric
- product.
+ sage: from mjo.hurwitz import (ComplexMatrixAlgebra,
+ ....: QuaternionMatrixAlgebra,
+ ....: OctonionMatrixAlgebra)
+ sage: from mjo.eja.eja_algebra import HermitianMatrixEJA
- * The ``n``-by-``n`` complex-rational Hermitian matrices embedded
- in the space of ``2n``-by-``2n`` real symmetric matrices.
+ TESTS::
- * The ``n``-by-``n`` quaternion-rational Hermitian matrices embedded
- in the space of ``4n``-by-``4n`` real symmetric matrices.
+ sage: n = ZZ.random_element(1,5)
+ sage: A = MatrixSpace(QQ, n)
+ sage: B = HermitianMatrixEJA._denormalized_basis(A)
+ sage: all( M.is_hermitian() for M in B)
+ True
- Later this might be extended to return Cartesian products of the
- EJAs above.
+ ::
- SETUP::
+ sage: n = ZZ.random_element(1,5)
+ sage: A = ComplexMatrixAlgebra(n, scalars=QQ)
+ sage: B = HermitianMatrixEJA._denormalized_basis(A)
+ sage: all( M.is_hermitian() for M in B)
+ True
- sage: from mjo.eja.eja_algebra import random_eja
+ ::
- TESTS::
+ sage: n = ZZ.random_element(1,5)
+ sage: A = QuaternionMatrixAlgebra(n, scalars=QQ)
+ sage: B = HermitianMatrixEJA._denormalized_basis(A)
+ sage: all( M.is_hermitian() for M in B )
+ True
- sage: random_eja()
- Euclidean Jordan algebra of dimension...
+ ::
- """
- classname = choice([RealCartesianProductEJA,
- JordanSpinEJA,
- RealSymmetricEJA,
- ComplexHermitianEJA,
- QuaternionHermitianEJA])
- return classname.random_instance()
+ sage: n = ZZ.random_element(1,5)
+ sage: A = OctonionMatrixAlgebra(n, scalars=QQ)
+ sage: B = HermitianMatrixEJA._denormalized_basis(A)
+ sage: all( M.is_hermitian() for M in B )
+ True
+ """
+ # These work for real MatrixSpace, whose monomials only have
+ # two coordinates (because the last one would always be "1").
+ es = A.base_ring().gens()
+ gen = lambda A,m: A.monomial(m[:2])
+ if hasattr(A, 'entry_algebra_gens'):
+ # We've got a MatrixAlgebra, and its monomials will have
+ # three coordinates.
+ es = A.entry_algebra_gens()
+ gen = lambda A,m: A.monomial(m)
+ basis = []
+ for i in range(A.nrows()):
+ for j in range(i+1):
+ if i == j:
+ E_ii = gen(A, (i,j,es[0]))
+ basis.append(E_ii)
+ else:
+ for e in es:
+ E_ij = gen(A, (i,j,e))
+ E_ij += E_ij.conjugate_transpose()
+ basis.append(E_ij)
+ return tuple( basis )
+ @staticmethod
+ def jordan_product(X,Y):
+ return (X*Y + Y*X)/2
-class MatrixEuclideanJordanAlgebra(FiniteDimensionalEuclideanJordanAlgebra):
@staticmethod
- def _max_test_case_size():
- # Play it safe, since this will be squared and the underlying
- # field can have dimension 4 (quaternions) too.
- return 3
+ def trace_inner_product(X,Y):
+ r"""
+ A trace inner-product for matrices that aren't embedded in the
+ reals. It takes MATRICES as arguments, not EJA elements.
- @classmethod
- def _denormalized_basis(cls, n, field):
- raise NotImplementedError
+ SETUP::
- def __init__(self, n, field=QQ, normalize_basis=True, **kwargs):
- S = self._denormalized_basis(n, field)
-
- if n > 1 and normalize_basis:
- # We'll need sqrt(2) to normalize the basis, and this
- # winds up in the multiplication table, so the whole
- # algebra needs to be over the field extension.
- R = PolynomialRing(field, 'z')
- z = R.gen()
- p = z**2 - 2
- if p.is_irreducible():
- field = NumberField(p, 'sqrt2', embedding=RLF(2).sqrt())
- S = [ s.change_ring(field) for s in S ]
- self._basis_normalizers = tuple(
- ~(self.natural_inner_product(s,s).sqrt()) for s in S )
- S = tuple( s*c for (s,c) in zip(S,self._basis_normalizers) )
-
- Qs = self.multiplication_table_from_matrix_basis(S)
-
- fdeja = super(MatrixEuclideanJordanAlgebra, self)
- return fdeja.__init__(field,
- Qs,
- rank=n,
- natural_basis=S,
- **kwargs)
+ sage: from mjo.eja.eja_algebra import (RealSymmetricEJA,
+ ....: ComplexHermitianEJA,
+ ....: QuaternionHermitianEJA,
+ ....: OctonionHermitianEJA)
+ EXAMPLES::
- @staticmethod
- def multiplication_table_from_matrix_basis(basis):
- """
- At least three of the five simple Euclidean Jordan algebras have the
- symmetric multiplication (A,B) |-> (AB + BA)/2, where the
- multiplication on the right is matrix multiplication. Given a basis
- for the underlying matrix space, this function returns a
- multiplication table (obtained by looping through the basis
- elements) for an algebra of those matrices.
- """
- # In S^2, for example, we nominally have four coordinates even
- # though the space is of dimension three only. The vector space V
- # is supposed to hold the entire long vector, and the subspace W
- # of V will be spanned by the vectors that arise from symmetric
- # matrices. Thus for S^2, dim(V) == 4 and dim(W) == 3.
- field = basis[0].base_ring()
- dimension = basis[0].nrows()
-
- V = VectorSpace(field, dimension**2)
- W = V.span_of_basis( _mat2vec(s) for s in basis )
- n = len(basis)
- mult_table = [[W.zero() for j in range(n)] for i in range(n)]
- for i in range(n):
- for j in range(n):
- mat_entry = (basis[i]*basis[j] + basis[j]*basis[i])/2
- mult_table[i][j] = W.coordinate_vector(_mat2vec(mat_entry))
+ sage: J = RealSymmetricEJA(2,field=QQ,orthonormalize=False)
+ sage: I = J.one().to_matrix()
+ sage: J.trace_inner_product(I, -I)
+ -2
- return mult_table
+ ::
+ sage: J = ComplexHermitianEJA(2,field=QQ,orthonormalize=False)
+ sage: I = J.one().to_matrix()
+ sage: J.trace_inner_product(I, -I)
+ -2
- @staticmethod
- def real_embed(M):
- """
- Embed the matrix ``M`` into a space of real matrices.
+ ::
- The matrix ``M`` can have entries in any field at the moment:
- the real numbers, complex numbers, or quaternions. And although
- they are not a field, we can probably support octonions at some
- point, too. This function returns a real matrix that "acts like"
- the original with respect to matrix multiplication; i.e.
+ sage: J = QuaternionHermitianEJA(2,field=QQ,orthonormalize=False)
+ sage: I = J.one().to_matrix()
+ sage: J.trace_inner_product(I, -I)
+ -2
+
+ ::
- real_embed(M*N) = real_embed(M)*real_embed(N)
+ sage: J = OctonionHermitianEJA(2,field=QQ,orthonormalize=False)
+ sage: I = J.one().to_matrix()
+ sage: J.trace_inner_product(I, -I)
+ -2
"""
- raise NotImplementedError
+ tr = (X*Y).trace()
+ if hasattr(tr, 'coefficient'):
+ # Works for octonions, and has to come first because they
+ # also have a "real()" method that doesn't return an
+ # element of the scalar ring.
+ return tr.coefficient(0)
+ elif hasattr(tr, 'coefficient_tuple'):
+ # Works for quaternions.
+ return tr.coefficient_tuple()[0]
+ # Works for real and complex numbers.
+ return tr.real()
- @staticmethod
- def real_unembed(M):
- """
- The inverse of :meth:`real_embed`.
- """
- raise NotImplementedError
+ def __init__(self, matrix_space, **kwargs):
+ # We know this is a valid EJA, but will double-check
+ # if the user passes check_axioms=True.
+ if "check_axioms" not in kwargs: kwargs["check_axioms"] = False
- @classmethod
- def natural_inner_product(cls,X,Y):
- Xu = cls.real_unembed(X)
- Yu = cls.real_unembed(Y)
- tr = (Xu*Yu).trace()
- if tr in RLF:
- # It's real already.
- return tr
-
- # Otherwise, try the thing that works for complex numbers; and
- # if that doesn't work, the thing that works for quaternions.
- try:
- return tr.vector()[0] # real part, imag part is index 1
- except AttributeError:
- # A quaternions doesn't have a vector() method, but does
- # have coefficient_tuple() method that returns the
- # coefficients of 1, i, j, and k -- in that order.
- return tr.coefficient_tuple()[0]
+ super().__init__(self._denormalized_basis(matrix_space),
+ self.jordan_product,
+ self.trace_inner_product,
+ field=matrix_space.base_ring(),
+ matrix_space=matrix_space,
+ **kwargs)
+ self.rank.set_cache(matrix_space.nrows())
+ self.one.set_cache( self(matrix_space.one()) )
-class RealSymmetricEJA(MatrixEuclideanJordanAlgebra):
+class RealSymmetricEJA(HermitianMatrixEJA, RationalBasisEJA, ConcreteEJA):
"""
The rank-n simple EJA consisting of real symmetric n-by-n
matrices, the usual symmetric Jordan product, and the trace inner
EXAMPLES::
sage: J = RealSymmetricEJA(2)
- sage: e0, e1, e2 = J.gens()
- sage: e0*e0
- e0
- sage: e1*e1
- 1/2*e0 + 1/2*e2
- sage: e2*e2
- e2
+ sage: b0, b1, b2 = J.gens()
+ sage: b0*b0
+ b0
+ sage: b1*b1
+ 1/2*b0 + 1/2*b2
+ sage: b2*b2
+ b2
+
+ In theory, our "field" can be any subfield of the reals::
+
+ sage: RealSymmetricEJA(2, field=RDF, check_axioms=True)
+ Euclidean Jordan algebra of dimension 3 over Real Double Field
+ sage: RealSymmetricEJA(2, field=RR, check_axioms=True)
+ Euclidean Jordan algebra of dimension 3 over Real Field with
+ 53 bits of precision
TESTS:
The dimension of this algebra is `(n^2 + n) / 2`::
- sage: set_random_seed()
- sage: n_max = RealSymmetricEJA._max_test_case_size()
- sage: n = ZZ.random_element(1, n_max)
+ sage: d = RealSymmetricEJA._max_random_instance_dimension()
+ sage: n = RealSymmetricEJA._max_random_instance_size(d)
sage: J = RealSymmetricEJA(n)
sage: J.dimension() == (n^2 + n)/2
True
The Jordan multiplication is what we think it is::
- sage: set_random_seed()
sage: J = RealSymmetricEJA.random_instance()
sage: x,y = J.random_elements(2)
- sage: actual = (x*y).natural_representation()
- sage: X = x.natural_representation()
- sage: Y = y.natural_representation()
+ sage: actual = (x*y).to_matrix()
+ sage: X = x.to_matrix()
+ sage: Y = y.to_matrix()
sage: expected = (X*Y + Y*X)/2
sage: actual == expected
True
sage: RealSymmetricEJA(3, prefix='q').gens()
(q0, q1, q2, q3, q4, q5)
- Our natural basis is normalized with respect to the natural inner
- product unless we specify otherwise::
-
- sage: set_random_seed()
- sage: J = RealSymmetricEJA.random_instance()
- sage: all( b.norm() == 1 for b in J.gens() )
- True
-
- Since our natural basis is normalized with respect to the natural
- inner product, and since we know that this algebra is an EJA, any
- left-multiplication operator's matrix will be symmetric because
- natural->EJA basis representation is an isometry and within the EJA
- the operator is self-adjoint by the Jordan axiom::
+ We can construct the (trivial) algebra of rank zero::
- sage: set_random_seed()
- sage: x = RealSymmetricEJA.random_instance().random_element()
- sage: x.operator().matrix().is_symmetric()
- True
+ sage: RealSymmetricEJA(0)
+ Euclidean Jordan algebra of dimension 0 over Algebraic Real Field
"""
+ @staticmethod
+ def _max_random_instance_size(max_dimension):
+ # Obtained by solving d = (n^2 + n)/2.
+ # The ZZ-int-ZZ thing is just "floor."
+ return ZZ(int(ZZ(8*max_dimension + 1).sqrt()/2 - 1/2))
+
@classmethod
- def _denormalized_basis(cls, n, field):
+ def random_instance(cls, max_dimension=None, *args, **kwargs):
"""
- Return a basis for the space of real symmetric n-by-n matrices.
-
- SETUP::
-
- sage: from mjo.eja.eja_algebra import RealSymmetricEJA
-
- TESTS::
-
- sage: set_random_seed()
- sage: n = ZZ.random_element(1,5)
- sage: B = RealSymmetricEJA._denormalized_basis(n,QQ)
- sage: all( M.is_symmetric() for M in B)
- True
-
+ Return a random instance of this type of algebra.
"""
- # The basis of symmetric matrices, as matrices, in their R^(n-by-n)
- # coordinates.
- S = []
- for i in xrange(n):
- for j in xrange(i+1):
- Eij = matrix(field, n, lambda k,l: k==i and l==j)
- if i == j:
- Sij = Eij
- else:
- Sij = Eij + Eij.transpose()
- S.append(Sij)
- return tuple(S)
+ 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):
+ A = MatrixSpace(field, n)
+ super().__init__(A, **kwargs)
- @staticmethod
- def _max_test_case_size():
- return 5 # Dimension 10
+ from mjo.eja.eja_cache import real_symmetric_eja_coeffs
+ a = real_symmetric_eja_coeffs(self)
+ if a is not None:
+ self.rational_algebra()._charpoly_coefficients.set_cache(a)
- @staticmethod
- def real_embed(M):
- """
- Embed the matrix ``M`` into a space of real matrices.
- The matrix ``M`` can have entries in any field at the moment:
- the real numbers, complex numbers, or quaternions. And although
- they are not a field, we can probably support octonions at some
- point, too. This function returns a real matrix that "acts like"
- the original with respect to matrix multiplication; i.e.
- real_embed(M*N) = real_embed(M)*real_embed(N)
+class ComplexHermitianEJA(HermitianMatrixEJA, RationalBasisEJA, ConcreteEJA):
+ """
+ The rank-n simple EJA consisting of complex Hermitian n-by-n
+ matrices over the real numbers, the usual symmetric Jordan product,
+ and the real-part-of-trace inner product. It has dimension `n^2` over
+ the reals.
- """
- return M
+ SETUP::
+ sage: from mjo.eja.eja_algebra import ComplexHermitianEJA
- @staticmethod
- def real_unembed(M):
- """
- The inverse of :meth:`real_embed`.
- """
- return M
+ EXAMPLES:
+ In theory, our "field" can be any subfield of the reals, but we
+ can't use inexact real fields at the moment because SageMath
+ doesn't know how to convert their elements into complex numbers,
+ or even into algebraic reals::
+ sage: QQbar(RDF(1))
+ Traceback (most recent call last):
+ ...
+ TypeError: Illegal initializer for algebraic number
+ sage: AA(RR(1))
+ Traceback (most recent call last):
+ ...
+ TypeError: Illegal initializer for algebraic number
-class ComplexMatrixEuclideanJordanAlgebra(MatrixEuclideanJordanAlgebra):
- @staticmethod
- def real_embed(M):
- """
- Embed the n-by-n complex matrix ``M`` into the space of real
- matrices of size 2n-by-2n via the map the sends each entry `z = a +
- bi` to the block matrix ``[[a,b],[-b,a]]``.
+ TESTS:
- SETUP::
+ The dimension of this algebra is `n^2`::
- sage: from mjo.eja.eja_algebra import \
- ....: ComplexMatrixEuclideanJordanAlgebra
+ sage: d = ComplexHermitianEJA._max_random_instance_dimension()
+ sage: n = ComplexHermitianEJA._max_random_instance_size(d)
+ sage: J = ComplexHermitianEJA(n)
+ sage: J.dimension() == n^2
+ True
- EXAMPLES::
+ The Jordan multiplication is what we think it is::
- sage: F = QuadraticField(-1, 'i')
- sage: x1 = F(4 - 2*i)
- sage: x2 = F(1 + 2*i)
- sage: x3 = F(-i)
- sage: x4 = F(6)
- sage: M = matrix(F,2,[[x1,x2],[x3,x4]])
- sage: ComplexMatrixEuclideanJordanAlgebra.real_embed(M)
- [ 4 -2| 1 2]
- [ 2 4|-2 1]
- [-----+-----]
- [ 0 -1| 6 0]
- [ 1 0| 0 6]
-
- TESTS:
-
- Embedding is a homomorphism (isomorphism, in fact)::
-
- sage: set_random_seed()
- sage: n_max = ComplexMatrixEuclideanJordanAlgebra._max_test_case_size()
- sage: n = ZZ.random_element(n_max)
- sage: F = QuadraticField(-1, 'i')
- sage: X = random_matrix(F, n)
- sage: Y = random_matrix(F, n)
- sage: Xe = ComplexMatrixEuclideanJordanAlgebra.real_embed(X)
- sage: Ye = ComplexMatrixEuclideanJordanAlgebra.real_embed(Y)
- sage: XYe = ComplexMatrixEuclideanJordanAlgebra.real_embed(X*Y)
- sage: Xe*Ye == XYe
- True
-
- """
- n = M.nrows()
- if M.ncols() != n:
- raise ValueError("the matrix 'M' must be square")
- field = M.base_ring()
- blocks = []
- for z in M.list():
- a = z.vector()[0] # real part, I guess
- b = z.vector()[1] # imag part, I guess
- blocks.append(matrix(field, 2, [[a,b],[-b,a]]))
-
- # We can drop the imaginaries here.
- return matrix.block(field.base_ring(), n, blocks)
-
-
- @staticmethod
- def real_unembed(M):
- """
- The inverse of _embed_complex_matrix().
+ sage: J = ComplexHermitianEJA.random_instance()
+ sage: x,y = J.random_elements(2)
+ sage: actual = (x*y).to_matrix()
+ sage: X = x.to_matrix()
+ sage: Y = y.to_matrix()
+ sage: expected = (X*Y + Y*X)/2
+ sage: actual == expected
+ True
+ sage: J(expected) == x*y
+ True
- SETUP::
+ We can change the generator prefix::
- sage: from mjo.eja.eja_algebra import \
- ....: ComplexMatrixEuclideanJordanAlgebra
+ sage: ComplexHermitianEJA(2, prefix='z').gens()
+ (z0, z1, z2, z3)
- EXAMPLES::
+ We can construct the (trivial) algebra of rank zero::
- sage: A = matrix(QQ,[ [ 1, 2, 3, 4],
- ....: [-2, 1, -4, 3],
- ....: [ 9, 10, 11, 12],
- ....: [-10, 9, -12, 11] ])
- sage: ComplexMatrixEuclideanJordanAlgebra.real_unembed(A)
- [ 2*i + 1 4*i + 3]
- [ 10*i + 9 12*i + 11]
+ sage: ComplexHermitianEJA(0)
+ Euclidean Jordan algebra of dimension 0 over Algebraic Real Field
- TESTS:
+ """
+ def __init__(self, n, field=AA, **kwargs):
+ from mjo.hurwitz import ComplexMatrixAlgebra
+ A = ComplexMatrixAlgebra(n, scalars=field)
+ super().__init__(A, **kwargs)
- Unembedding is the inverse of embedding::
+ from mjo.eja.eja_cache import complex_hermitian_eja_coeffs
+ a = complex_hermitian_eja_coeffs(self)
+ if a is not None:
+ self.rational_algebra()._charpoly_coefficients.set_cache(a)
- sage: set_random_seed()
- sage: F = QuadraticField(-1, 'i')
- sage: M = random_matrix(F, 3)
- sage: Me = ComplexMatrixEuclideanJordanAlgebra.real_embed(M)
- sage: ComplexMatrixEuclideanJordanAlgebra.real_unembed(Me) == M
- True
+ @staticmethod
+ def _max_random_instance_size(max_dimension):
+ # Obtained by solving d = n^2.
+ # The ZZ-int-ZZ thing is just "floor."
+ return ZZ(int(ZZ(max_dimension).sqrt()))
+ @classmethod
+ def random_instance(cls, max_dimension=None, *args, **kwargs):
"""
- n = ZZ(M.nrows())
- if M.ncols() != n:
- raise ValueError("the matrix 'M' must be square")
- if not n.mod(2).is_zero():
- raise ValueError("the matrix 'M' must be a complex embedding")
+ Return a random instance of this type of algebra.
+ """
+ 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)
- field = M.base_ring() # This should already have sqrt2
- R = PolynomialRing(field, 'z')
- z = R.gen()
- F = NumberField(z**2 + 1,'i', embedding=CLF(-1).sqrt())
- i = F.gen()
- # Go top-left to bottom-right (reading order), converting every
- # 2-by-2 block we see to a single complex element.
- elements = []
- for k in xrange(n/2):
- for j in xrange(n/2):
- submat = M[2*k:2*k+2,2*j:2*j+2]
- if submat[0,0] != submat[1,1]:
- raise ValueError('bad on-diagonal submatrix')
- if submat[0,1] != -submat[1,0]:
- raise ValueError('bad off-diagonal submatrix')
- z = submat[0,0] + submat[0,1]*i
- elements.append(z)
+class QuaternionHermitianEJA(HermitianMatrixEJA, RationalBasisEJA, ConcreteEJA):
+ r"""
+ The rank-n simple EJA consisting of self-adjoint n-by-n quaternion
+ matrices, the usual symmetric Jordan product, and the
+ real-part-of-trace inner product. It has dimension `2n^2 - n` over
+ the reals.
- return matrix(F, n/2, elements)
+ SETUP::
+ sage: from mjo.eja.eja_algebra import QuaternionHermitianEJA
-class ComplexHermitianEJA(ComplexMatrixEuclideanJordanAlgebra):
- """
- The rank-n simple EJA consisting of complex Hermitian n-by-n
- matrices over the real numbers, the usual symmetric Jordan product,
- and the real-part-of-trace inner product. It has dimension `n^2` over
- the reals.
+ EXAMPLES:
- SETUP::
+ In theory, our "field" can be any subfield of the reals::
- sage: from mjo.eja.eja_algebra import ComplexHermitianEJA
+ sage: QuaternionHermitianEJA(2, field=RDF, check_axioms=True)
+ Euclidean Jordan algebra of dimension 6 over Real Double Field
+ sage: QuaternionHermitianEJA(2, field=RR, check_axioms=True)
+ Euclidean Jordan algebra of dimension 6 over Real Field with
+ 53 bits of precision
TESTS:
- The dimension of this algebra is `n^2`::
+ The dimension of this algebra is `2*n^2 - n`::
- sage: set_random_seed()
- sage: n_max = ComplexHermitianEJA._max_test_case_size()
- sage: n = ZZ.random_element(1, n_max)
- sage: J = ComplexHermitianEJA(n)
- sage: J.dimension() == n^2
+ sage: d = QuaternionHermitianEJA._max_random_instance_dimension()
+ sage: n = QuaternionHermitianEJA._max_random_instance_size(d)
+ sage: J = QuaternionHermitianEJA(n)
+ sage: J.dimension() == 2*(n^2) - n
True
The Jordan multiplication is what we think it is::
- sage: set_random_seed()
- sage: J = ComplexHermitianEJA.random_instance()
+ sage: J = QuaternionHermitianEJA.random_instance()
sage: x,y = J.random_elements(2)
- sage: actual = (x*y).natural_representation()
- sage: X = x.natural_representation()
- sage: Y = y.natural_representation()
+ sage: actual = (x*y).to_matrix()
+ sage: X = x.to_matrix()
+ sage: Y = y.to_matrix()
sage: expected = (X*Y + Y*X)/2
sage: actual == expected
True
We can change the generator prefix::
- sage: ComplexHermitianEJA(2, prefix='z').gens()
- (z0, z1, z2, z3)
+ sage: QuaternionHermitianEJA(2, prefix='a').gens()
+ (a0, a1, a2, a3, a4, a5)
- Our natural basis is normalized with respect to the natural inner
- product unless we specify otherwise::
+ We can construct the (trivial) algebra of rank zero::
- sage: set_random_seed()
- sage: J = ComplexHermitianEJA.random_instance()
- sage: all( b.norm() == 1 for b in J.gens() )
- True
+ sage: QuaternionHermitianEJA(0)
+ Euclidean Jordan algebra of dimension 0 over Algebraic Real Field
- Since our natural basis is normalized with respect to the natural
- inner product, and since we know that this algebra is an EJA, any
- left-multiplication operator's matrix will be symmetric because
- natural->EJA basis representation is an isometry and within the EJA
- the operator is self-adjoint by the Jordan axiom::
+ """
+ def __init__(self, n, field=AA, **kwargs):
+ from mjo.hurwitz import QuaternionMatrixAlgebra
+ A = QuaternionMatrixAlgebra(n, scalars=field)
+ super().__init__(A, **kwargs)
- sage: set_random_seed()
- sage: x = ComplexHermitianEJA.random_instance().random_element()
- sage: x.operator().matrix().is_symmetric()
- True
+ from mjo.eja.eja_cache import quaternion_hermitian_eja_coeffs
+ a = quaternion_hermitian_eja_coeffs(self)
+ if a is not None:
+ self.rational_algebra()._charpoly_coefficients.set_cache(a)
+
+
+
+ @staticmethod
+ def _max_random_instance_size(max_dimension):
+ r"""
+ The maximum rank of a random QuaternionHermitianEJA.
+ """
+ # Obtained by solving d = 2n^2 - n.
+ # The ZZ-int-ZZ thing is just "floor."
+ return ZZ(int(ZZ(8*max_dimension + 1).sqrt()/4 + 1/4))
- """
@classmethod
- def _denormalized_basis(cls, n, field):
+ def random_instance(cls, max_dimension=None, *args, **kwargs):
"""
- Returns a basis for the space of complex Hermitian n-by-n matrices.
+ Return a random instance of this type of algebra.
+ """
+ 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)
- Why do we embed these? Basically, because all of numerical linear
- algebra assumes that you're working with vectors consisting of `n`
- entries from a field and scalars from the same field. There's no way
- to tell SageMath that (for example) the vectors contain complex
- numbers, while the scalar field is real.
+class OctonionHermitianEJA(HermitianMatrixEJA, RationalBasisEJA, ConcreteEJA):
+ r"""
+ SETUP::
- SETUP::
+ sage: from mjo.eja.eja_algebra import (EJA,
+ ....: OctonionHermitianEJA)
+ sage: from mjo.hurwitz import Octonions, OctonionMatrixAlgebra
- sage: from mjo.eja.eja_algebra import ComplexHermitianEJA
+ EXAMPLES:
- TESTS::
+ The 3-by-3 algebra satisfies the axioms of an EJA::
+
+ sage: OctonionHermitianEJA(3, # long time
+ ....: field=QQ, # long time
+ ....: orthonormalize=False, # long time
+ ....: check_axioms=True) # long time
+ Euclidean Jordan algebra of dimension 27 over Rational Field
+
+ After a change-of-basis, the 2-by-2 algebra has the same
+ multiplication table as the ten-dimensional Jordan spin algebra::
+
+ sage: A = OctonionMatrixAlgebra(2,Octonions(QQ),QQ)
+ sage: b = OctonionHermitianEJA._denormalized_basis(A)
+ sage: basis = (b[0] + b[9],) + b[1:9] + (b[0] - b[9],)
+ sage: jp = OctonionHermitianEJA.jordan_product
+ sage: ip = OctonionHermitianEJA.trace_inner_product
+ sage: J = EJA(basis,
+ ....: jp,
+ ....: ip,
+ ....: field=QQ,
+ ....: orthonormalize=False)
+ sage: J.multiplication_table()
+ +----++----+----+----+----+----+----+----+----+----+----+
+ | * || b0 | b1 | b2 | b3 | b4 | b5 | b6 | b7 | b8 | b9 |
+ +====++====+====+====+====+====+====+====+====+====+====+
+ | b0 || b0 | b1 | b2 | b3 | b4 | b5 | b6 | b7 | b8 | b9 |
+ +----++----+----+----+----+----+----+----+----+----+----+
+ | b1 || b1 | b0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
+ +----++----+----+----+----+----+----+----+----+----+----+
+ | b2 || b2 | 0 | b0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
+ +----++----+----+----+----+----+----+----+----+----+----+
+ | b3 || b3 | 0 | 0 | b0 | 0 | 0 | 0 | 0 | 0 | 0 |
+ +----++----+----+----+----+----+----+----+----+----+----+
+ | b4 || b4 | 0 | 0 | 0 | b0 | 0 | 0 | 0 | 0 | 0 |
+ +----++----+----+----+----+----+----+----+----+----+----+
+ | b5 || b5 | 0 | 0 | 0 | 0 | b0 | 0 | 0 | 0 | 0 |
+ +----++----+----+----+----+----+----+----+----+----+----+
+ | b6 || b6 | 0 | 0 | 0 | 0 | 0 | b0 | 0 | 0 | 0 |
+ +----++----+----+----+----+----+----+----+----+----+----+
+ | b7 || b7 | 0 | 0 | 0 | 0 | 0 | 0 | b0 | 0 | 0 |
+ +----++----+----+----+----+----+----+----+----+----+----+
+ | b8 || b8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | b0 | 0 |
+ +----++----+----+----+----+----+----+----+----+----+----+
+ | b9 || b9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | b0 |
+ +----++----+----+----+----+----+----+----+----+----+----+
- sage: set_random_seed()
- sage: n = ZZ.random_element(1,5)
- sage: field = QuadraticField(2, 'sqrt2')
- sage: B = ComplexHermitianEJA._denormalized_basis(n, field)
- sage: all( M.is_symmetric() for M in B)
- True
+ TESTS:
+
+ We can actually construct the 27-dimensional Albert algebra,
+ and we get the right unit element if we recompute it::
+
+ sage: J = OctonionHermitianEJA(3, # long time
+ ....: field=QQ, # long time
+ ....: orthonormalize=False) # long time
+ sage: J.one.clear_cache() # long time
+ sage: J.one() # long time
+ b0 + b9 + b26
+ sage: J.one().to_matrix() # long time
+ +----+----+----+
+ | e0 | 0 | 0 |
+ +----+----+----+
+ | 0 | e0 | 0 |
+ +----+----+----+
+ | 0 | 0 | e0 |
+ +----+----+----+
+
+ The 2-by-2 algebra is isomorphic to the ten-dimensional Jordan
+ spin algebra, but just to be sure, we recompute its rank::
+
+ sage: J = OctonionHermitianEJA(2, # long time
+ ....: field=QQ, # long time
+ ....: orthonormalize=False) # long time
+ sage: J.rank.clear_cache() # long time
+ sage: J.rank() # long time
+ 2
+
+ """
+ @staticmethod
+ def _max_random_instance_size(max_dimension):
+ r"""
+ The maximum rank of a random OctonionHermitianEJA.
+ """
+ # There's certainly a formula for this, but with only four
+ # cases to worry about, I'm not that motivated to derive it.
+ if max_dimension >= 27:
+ return 3
+ elif max_dimension >= 10:
+ return 2
+ elif max_dimension >= 1:
+ return 1
+ else:
+ return 0
+ @classmethod
+ def random_instance(cls, max_dimension=None, *args, **kwargs):
"""
- R = PolynomialRing(field, 'z')
- z = R.gen()
- F = NumberField(z**2 + 1, 'I', embedding=CLF(-1).sqrt())
- I = F.gen()
+ Return a random instance of this type of algebra.
+ """
+ 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)
- # This is like the symmetric case, but we need to be careful:
- #
- # * We want conjugate-symmetry, not just symmetry.
- # * The diagonal will (as a result) be real.
- #
- S = []
- for i in xrange(n):
- for j in xrange(i+1):
- Eij = matrix(F, n, lambda k,l: k==i and l==j)
- 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())
- S.append(Sij_real)
- Sij_imag = cls.real_embed(I*Eij - I*Eij.transpose())
- S.append(Sij_imag)
+ def __init__(self, n, field=AA, **kwargs):
+ if n > 3:
+ # Otherwise we don't get an EJA.
+ raise ValueError("n cannot exceed 3")
+
+ # We know this is a valid EJA, but will double-check
+ # if the user passes check_axioms=True.
+ if "check_axioms" not in kwargs: kwargs["check_axioms"] = False
+
+ from mjo.hurwitz import OctonionMatrixAlgebra
+ A = OctonionMatrixAlgebra(n, scalars=field)
+ super().__init__(A, **kwargs)
+
+ from mjo.eja.eja_cache import octonion_hermitian_eja_coeffs
+ a = octonion_hermitian_eja_coeffs(self)
+ if a is not None:
+ self.rational_algebra()._charpoly_coefficients.set_cache(a)
- # Since we embedded these, we can drop back to the "field" that we
- # started with instead of the complex extension "F".
- return tuple( s.change_ring(field) for s in S )
+class AlbertEJA(OctonionHermitianEJA):
+ r"""
+ The Albert algebra is the algebra of three-by-three Hermitian
+ matrices whose entries are octonions.
+ SETUP::
+
+ sage: from mjo.eja.eja_algebra import AlbertEJA
+
+ EXAMPLES::
+
+ sage: AlbertEJA(field=QQ, orthonormalize=False)
+ Euclidean Jordan algebra of dimension 27 over Rational Field
+ sage: AlbertEJA() # long time
+ Euclidean Jordan algebra of dimension 27 over Algebraic Real Field
+
+ """
+ def __init__(self, *args, **kwargs):
+ super().__init__(3, *args, **kwargs)
+
+
+class HadamardEJA(RationalBasisEJA, ConcreteEJA):
+ """
+ Return the Euclidean Jordan algebra on `R^n` with the Hadamard
+ (pointwise real-number multiplication) Jordan product and the
+ usual inner-product.
+
+ This is nothing more than the Cartesian product of ``n`` copies of
+ the one-dimensional Jordan spin algebra, and is the most common
+ example of a non-simple Euclidean Jordan algebra.
+
+ SETUP::
+
+ sage: from mjo.eja.eja_algebra import HadamardEJA
+
+ EXAMPLES:
+
+ This multiplication table can be verified by hand::
+
+ sage: J = HadamardEJA(3)
+ sage: b0,b1,b2 = J.gens()
+ sage: b0*b0
+ b0
+ sage: b0*b1
+ 0
+ sage: b0*b2
+ 0
+ sage: b1*b1
+ b1
+ sage: b1*b2
+ 0
+ sage: b2*b2
+ b2
+
+ TESTS:
+
+ We can change the generator prefix::
+
+ sage: HadamardEJA(3, prefix='r').gens()
+ (r0, r1, r2)
+ """
+ def __init__(self, n, field=AA, **kwargs):
+ MS = MatrixSpace(field, n, 1)
+
+ if n == 0:
+ jordan_product = lambda x,y: x
+ inner_product = lambda x,y: x
+ else:
+ def jordan_product(x,y):
+ return MS( xi*yi for (xi,yi) in zip(x,y) )
+
+ def inner_product(x,y):
+ return (x.T*y)[0,0]
+
+ # New defaults for keyword arguments. Don't orthonormalize
+ # because our basis is already orthonormal with respect to our
+ # inner-product. Don't check the axioms, because we know this
+ # is a valid EJA... but do double-check if the user passes
+ # check_axioms=True. Note: we DON'T override the "check_field"
+ # default here, because the user can pass in a field!
+ if "orthonormalize" not in kwargs: kwargs["orthonormalize"] = False
+ if "check_axioms" not in kwargs: kwargs["check_axioms"] = False
+
+ column_basis = tuple( MS(b) for b in FreeModule(field, n).basis() )
+ super().__init__(column_basis,
+ jordan_product,
+ inner_product,
+ field=field,
+ matrix_space=MS,
+ associative=True,
+ **kwargs)
+ self.rank.set_cache(n)
+
+ self.one.set_cache( self.sum(self.gens()) )
-class QuaternionMatrixEuclideanJordanAlgebra(MatrixEuclideanJordanAlgebra):
@staticmethod
- def real_embed(M):
+ def _max_random_instance_dimension():
+ r"""
+ There's no reason to go higher than five here. That's
+ enough to get the point across.
"""
- Embed the n-by-n quaternion matrix ``M`` into the space of real
- matrices of size 4n-by-4n by first sending each quaternion entry `z
- = a + bi + cj + dk` to the block-complex matrix ``[[a + bi,
- c+di],[-c + di, a-bi]]`, and then embedding those into a real
- matrix.
+ return 5
- SETUP::
+ @staticmethod
+ def _max_random_instance_size(max_dimension):
+ r"""
+ The maximum size (=dimension) of a random HadamardEJA.
+ """
+ return max_dimension
+
+ @classmethod
+ def random_instance(cls, max_dimension=None, *args, **kwargs):
+ """
+ Return a random instance of this type of algebra.
+ """
+ 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)
- sage: from mjo.eja.eja_algebra import \
- ....: QuaternionMatrixEuclideanJordanAlgebra
- EXAMPLES::
+class BilinearFormEJA(RationalBasisEJA, ConcreteEJA):
+ r"""
+ The rank-2 simple EJA consisting of real vectors ``x=(x0, x_bar)``
+ with the half-trace inner product and jordan product ``x*y =
+ (<Bx,y>,y_bar>, x0*y_bar + y0*x_bar)`` where `B = 1 \times B22` is
+ a symmetric positive-definite "bilinear form" matrix. Its
+ dimension is the size of `B`, and it has rank two in dimensions
+ larger than two. It reduces to the ``JordanSpinEJA`` when `B` is
+ the identity matrix of order ``n``.
- sage: Q = QuaternionAlgebra(QQ,-1,-1)
- sage: i,j,k = Q.gens()
- sage: x = 1 + 2*i + 3*j + 4*k
- sage: M = matrix(Q, 1, [[x]])
- sage: QuaternionMatrixEuclideanJordanAlgebra.real_embed(M)
- [ 1 2 3 4]
- [-2 1 -4 3]
- [-3 4 1 -2]
- [-4 -3 2 1]
-
- Embedding is a homomorphism (isomorphism, in fact)::
-
- sage: set_random_seed()
- sage: n_max = QuaternionMatrixEuclideanJordanAlgebra._max_test_case_size()
- sage: n = ZZ.random_element(n_max)
- sage: Q = QuaternionAlgebra(QQ,-1,-1)
- sage: X = random_matrix(Q, n)
- sage: Y = random_matrix(Q, n)
- sage: Xe = QuaternionMatrixEuclideanJordanAlgebra.real_embed(X)
- sage: Ye = QuaternionMatrixEuclideanJordanAlgebra.real_embed(Y)
- sage: XYe = QuaternionMatrixEuclideanJordanAlgebra.real_embed(X*Y)
- sage: Xe*Ye == XYe
- True
+ We insist that the one-by-one upper-left identity block of `B` be
+ passed in as well so that we can be passed a matrix of size zero
+ to construct a trivial algebra.
- """
- quaternions = M.base_ring()
- n = M.nrows()
- if M.ncols() != n:
- raise ValueError("the matrix 'M' must be square")
+ SETUP::
+
+ sage: from mjo.eja.eja_algebra import (BilinearFormEJA,
+ ....: JordanSpinEJA)
+
+ EXAMPLES:
+
+ When no bilinear form is specified, the identity matrix is used,
+ and the resulting algebra is the Jordan spin algebra::
- F = QuadraticField(-1, 'i')
- i = F.gen()
+ sage: B = matrix.identity(AA,3)
+ sage: J0 = BilinearFormEJA(B)
+ sage: J1 = JordanSpinEJA(3)
+ sage: J0.multiplication_table() == J0.multiplication_table()
+ True
- blocks = []
- for z in M.list():
- t = z.coefficient_tuple()
- a = t[0]
- b = t[1]
- c = t[2]
- d = t[3]
- cplxM = matrix(F, 2, [[ a + b*i, c + d*i],
- [-c + d*i, a - b*i]])
- realM = ComplexMatrixEuclideanJordanAlgebra.real_embed(cplxM)
- blocks.append(realM)
+ An error is raised if the matrix `B` does not correspond to a
+ positive-definite bilinear form::
- # We should have real entries by now, so use the realest field
- # we've got for the return value.
- return matrix.block(quaternions.base_ring(), n, blocks)
+ sage: B = matrix.random(QQ,2,3)
+ sage: J = BilinearFormEJA(B)
+ Traceback (most recent call last):
+ ...
+ ValueError: bilinear form is not positive-definite
+ sage: B = matrix.zero(QQ,3)
+ sage: J = BilinearFormEJA(B)
+ Traceback (most recent call last):
+ ...
+ ValueError: bilinear form is not positive-definite
+ TESTS:
+ We can create a zero-dimensional algebra::
+
+ sage: B = matrix.identity(AA,0)
+ sage: J = BilinearFormEJA(B)
+ sage: J.basis()
+ Finite family {}
+
+ We can check the multiplication condition given in the Jordan, von
+ Neumann, and Wigner paper (and also discussed on my "On the
+ symmetry..." paper). Note that this relies heavily on the standard
+ choice of basis, as does anything utilizing the bilinear form
+ matrix. We opt not to orthonormalize the basis, because if we
+ did, we would have to normalize the `s_{i}` in a similar manner::
+
+ sage: n = ZZ.random_element(5)
+ sage: M = matrix.random(QQ, max(0,n-1), algorithm='unimodular')
+ sage: B11 = matrix.identity(QQ,1)
+ sage: B22 = M.transpose()*M
+ sage: B = block_matrix(2,2,[ [B11,0 ],
+ ....: [0, B22 ] ])
+ sage: J = BilinearFormEJA(B, orthonormalize=False)
+ sage: eis = VectorSpace(M.base_ring(), M.ncols()).basis()
+ sage: V = J.vector_space()
+ sage: sis = [ J( V([0] + (M.inverse()*ei).list()).column() )
+ ....: for ei in eis ]
+ sage: actual = [ sis[i]*sis[j]
+ ....: for i in range(n-1)
+ ....: for j in range(n-1) ]
+ sage: expected = [ J.one() if i == j else J.zero()
+ ....: for i in range(n-1)
+ ....: for j in range(n-1) ]
+ sage: actual == expected
+ True
+
+ """
+ def __init__(self, B, field=AA, **kwargs):
+ # The matrix "B" is supplied by the user in most cases,
+ # so it makes sense to check whether or not its positive-
+ # definite unless we are specifically asked not to...
+ if ("check_axioms" not in kwargs) or kwargs["check_axioms"]:
+ if not B.is_positive_definite():
+ raise ValueError("bilinear form is not positive-definite")
+
+ # However, all of the other data for this EJA is computed
+ # by us in manner that guarantees the axioms are
+ # satisfied. So, again, unless we are specifically asked to
+ # verify things, we'll skip the rest of the checks.
+ if "check_axioms" not in kwargs: kwargs["check_axioms"] = False
+
+ n = B.nrows()
+ MS = MatrixSpace(field, n, 1)
+
+ def inner_product(x,y):
+ return (y.T*B*x)[0,0]
+
+ def jordan_product(x,y):
+ x0 = x[0,0]
+ xbar = x[1:,0]
+ y0 = y[0,0]
+ ybar = y[1:,0]
+ z0 = inner_product(y,x)
+ zbar = y0*xbar + x0*ybar
+ return MS([z0] + zbar.list())
+
+ column_basis = tuple( MS(b) for b in FreeModule(field, n).basis() )
+
+ # TODO: I haven't actually checked this, but it seems legit.
+ associative = False
+ if n <= 2:
+ associative = True
+
+ super().__init__(column_basis,
+ jordan_product,
+ inner_product,
+ field=field,
+ matrix_space=MS,
+ associative=associative,
+ **kwargs)
+
+ # The rank of this algebra is two, unless we're in a
+ # one-dimensional ambient space (because the rank is bounded
+ # by the ambient dimension).
+ self.rank.set_cache(min(n,2))
+ if n == 0:
+ self.one.set_cache( self.zero() )
+ else:
+ self.one.set_cache( self.monomial(0) )
@staticmethod
- def real_unembed(M):
+ def _max_random_instance_dimension():
+ r"""
+ There's no reason to go higher than five here. That's
+ enough to get the point across.
"""
- The inverse of _embed_quaternion_matrix().
+ return 5
- SETUP::
+ @staticmethod
+ def _max_random_instance_size(max_dimension):
+ r"""
+ The maximum size (=dimension) of a random BilinearFormEJA.
+ """
+ return max_dimension
- sage: from mjo.eja.eja_algebra import \
- ....: QuaternionMatrixEuclideanJordanAlgebra
+ @classmethod
+ def random_instance(cls, max_dimension=None, *args, **kwargs):
+ """
+ Return a random instance of this algebra.
+ """
+ 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)
- EXAMPLES::
+ if n.is_zero():
+ B = matrix.identity(ZZ, n)
+ return cls(B, **kwargs)
- sage: M = matrix(QQ, [[ 1, 2, 3, 4],
- ....: [-2, 1, -4, 3],
- ....: [-3, 4, 1, -2],
- ....: [-4, -3, 2, 1]])
- sage: QuaternionMatrixEuclideanJordanAlgebra.real_unembed(M)
- [1 + 2*i + 3*j + 4*k]
+ B11 = matrix.identity(ZZ, 1)
+ M = matrix.random(ZZ, n-1)
+ I = matrix.identity(ZZ, n-1)
+ alpha = ZZ.zero()
+ while alpha.is_zero():
+ alpha = ZZ.random_element().abs()
- TESTS:
+ B22 = M.transpose()*M + alpha*I
- Unembedding is the inverse of embedding::
+ from sage.matrix.special import block_matrix
+ B = block_matrix(2,2, [ [B11, ZZ(0) ],
+ [ZZ(0), B22 ] ])
- sage: set_random_seed()
- sage: Q = QuaternionAlgebra(QQ, -1, -1)
- sage: M = random_matrix(Q, 3)
- sage: Me = QuaternionMatrixEuclideanJordanAlgebra.real_embed(M)
- sage: QuaternionMatrixEuclideanJordanAlgebra.real_unembed(Me) == M
- True
+ return cls(B, **kwargs)
- """
- n = ZZ(M.nrows())
- if M.ncols() != n:
- raise ValueError("the matrix 'M' must be square")
- if not n.mod(4).is_zero():
- raise ValueError("the matrix 'M' must be a complex embedding")
-
- # 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)
- i,j,k = Q.gens()
-
- # Go top-left to bottom-right (reading order), converting every
- # 4-by-4 block we see to a 2-by-2 complex block, to a 1-by-1
- # quaternion block.
- elements = []
- for l in xrange(n/4):
- for m in xrange(n/4):
- submat = ComplexMatrixEuclideanJordanAlgebra.real_unembed(
- M[4*l:4*l+4,4*m:4*m+4] )
- if submat[0,0] != submat[1,1].conjugate():
- raise ValueError('bad on-diagonal submatrix')
- if submat[0,1] != -submat[1,0].conjugate():
- raise ValueError('bad off-diagonal submatrix')
- z = submat[0,0].vector()[0] # real part
- z += submat[0,0].vector()[1]*i # imag part
- z += submat[0,1].vector()[0]*j # real part
- z += submat[0,1].vector()[1]*k # imag part
- elements.append(z)
-
- return matrix(Q, n/4, elements)
-
-
-
-class QuaternionHermitianEJA(QuaternionMatrixEuclideanJordanAlgebra):
+
+class JordanSpinEJA(BilinearFormEJA):
"""
- The rank-n simple EJA consisting of self-adjoint n-by-n quaternion
- matrices, the usual symmetric Jordan product, and the
- real-part-of-trace inner product. It has dimension `2n^2 - n` over
+ The rank-2 simple EJA consisting of real vectors ``x=(x0, x_bar)``
+ with the usual inner product and jordan product ``x*y =
+ (<x,y>, x0*y_bar + y0*x_bar)``. It has dimension `n` over
the reals.
SETUP::
- sage: from mjo.eja.eja_algebra import QuaternionHermitianEJA
+ sage: from mjo.eja.eja_algebra import JordanSpinEJA
+
+ EXAMPLES:
+
+ This multiplication table can be verified by hand::
+
+ sage: J = JordanSpinEJA(4)
+ sage: b0,b1,b2,b3 = J.gens()
+ sage: b0*b0
+ b0
+ sage: b0*b1
+ b1
+ sage: b0*b2
+ b2
+ sage: b0*b3
+ b3
+ sage: b1*b2
+ 0
+ sage: b1*b3
+ 0
+ sage: b2*b3
+ 0
+
+ We can change the generator prefix::
+
+ sage: JordanSpinEJA(2, prefix='B').gens()
+ (B0, B1)
TESTS:
- The dimension of this algebra is `2*n^2 - n`::
+ Ensure that we have the usual inner product on `R^n`::
- sage: set_random_seed()
- sage: n_max = QuaternionHermitianEJA._max_test_case_size()
- sage: n = ZZ.random_element(1, n_max)
- sage: J = QuaternionHermitianEJA(n)
- sage: J.dimension() == 2*(n^2) - n
- True
+ sage: J = JordanSpinEJA.random_instance()
+ sage: x,y = J.random_elements(2)
+ sage: actual = x.inner_product(y)
+ sage: expected = x.to_vector().inner_product(y.to_vector())
+ sage: actual == expected
+ True
- The Jordan multiplication is what we think it is::
+ """
+ def __init__(self, n, *args, **kwargs):
+ # This is a special case of the BilinearFormEJA with the
+ # identity matrix as its bilinear form.
+ B = matrix.identity(ZZ, n)
- sage: set_random_seed()
- sage: J = QuaternionHermitianEJA.random_instance()
+ # Don't orthonormalize because our basis is already
+ # orthonormal with respect to our inner-product.
+ if "orthonormalize" not in kwargs: kwargs["orthonormalize"] = False
+
+ # But also don't pass check_field=False here, because the user
+ # can pass in a field!
+ super().__init__(B, *args, **kwargs)
+
+ @classmethod
+ 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``.
+ """
+ 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 TrivialEJA(RationalBasisEJA, ConcreteEJA):
+ """
+ The trivial Euclidean Jordan algebra consisting of only a zero element.
+
+ SETUP::
+
+ sage: from mjo.eja.eja_algebra import TrivialEJA
+
+ EXAMPLES::
+
+ sage: J = TrivialEJA()
+ sage: J.dimension()
+ 0
+ sage: J.zero()
+ 0
+ sage: J.one()
+ 0
+ sage: 7*J.one()*12*J.one()
+ 0
+ sage: J.one().inner_product(J.one())
+ 0
+ sage: J.one().norm()
+ 0
+ sage: J.one().subalgebra_generated_by()
+ Euclidean Jordan algebra of dimension 0 over Algebraic Real Field
+ sage: J.rank()
+ 0
+
+ """
+ def __init__(self, field=AA, **kwargs):
+ jordan_product = lambda x,y: x
+ inner_product = lambda x,y: field.zero()
+ basis = ()
+ MS = MatrixSpace(field,0)
+
+ # New defaults for keyword arguments
+ if "orthonormalize" not in kwargs: kwargs["orthonormalize"] = False
+ if "check_axioms" not in kwargs: kwargs["check_axioms"] = False
+
+ super().__init__(basis,
+ jordan_product,
+ inner_product,
+ associative=True,
+ field=field,
+ matrix_space=MS,
+ **kwargs)
+
+ # The rank is zero using my definition, namely the dimension of the
+ # largest subalgebra generated by any element.
+ self.rank.set_cache(0)
+ self.one.set_cache( self.zero() )
+
+ @classmethod
+ def random_instance(cls, max_dimension=None, *args, **kwargs):
+ # We don't take a "size" argument so the superclass method is
+ # inappropriate for us. The ``max_dimension`` argument is
+ # included so that if this method is called generically with a
+ # ``max_dimension=<whatever>`` argument, we don't try to pass
+ # it on to the algebra constructor.
+ return cls(**kwargs)
+
+
+class CartesianProductEJA(EJA):
+ 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,
+ ....: ComplexHermitianEJA,
+ ....: HadamardEJA,
+ ....: JordanSpinEJA,
+ ....: RealSymmetricEJA)
+
+ EXAMPLES:
+
+ The Jordan product is inherited from our factors and implemented by
+ our CombinatorialFreeModule Cartesian product superclass::
+
+ sage: J1 = HadamardEJA(2)
+ sage: J2 = RealSymmetricEJA(2)
+ sage: J = cartesian_product([J1,J2])
sage: x,y = J.random_elements(2)
- sage: actual = (x*y).natural_representation()
- sage: X = x.natural_representation()
- sage: Y = y.natural_representation()
- sage: expected = (X*Y + Y*X)/2
- sage: actual == expected
- True
- sage: J(expected) == x*y
+ sage: x*y in J
True
- We can change the generator prefix::
+ 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
- sage: QuaternionHermitianEJA(2, prefix='a').gens()
- (a0, a1, a2, a3, a4, a5)
+ 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
- Our natural basis is normalized with respect to the natural inner
- product unless we specify otherwise::
+ The product algebra will be associative if and only if all of its
+ components are associative::
- sage: set_random_seed()
- sage: J = QuaternionHermitianEJA.random_instance()
- sage: all( b.norm() == 1 for b in J.gens() )
+ 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
+
+ Cartesian products of Cartesian products work::
+
+ sage: J1 = JordanSpinEJA(1)
+ sage: J2 = JordanSpinEJA(1)
+ sage: J3 = JordanSpinEJA(1)
+ sage: J = cartesian_product([J1,cartesian_product([J2,J3])])
+ sage: J.multiplication_table()
+ +----++----+----+----+
+ | * || b0 | b1 | b2 |
+ +====++====+====+====+
+ | b0 || b0 | 0 | 0 |
+ +----++----+----+----+
+ | b1 || 0 | b1 | 0 |
+ +----++----+----+----+
+ | b2 || 0 | 0 | b2 |
+ +----++----+----+----+
+ sage: HadamardEJA(3).multiplication_table()
+ +----++----+----+----+
+ | * || b0 | b1 | b2 |
+ +====++====+====+====+
+ | b0 || b0 | 0 | 0 |
+ +----++----+----+----+
+ | b1 || 0 | b1 | 0 |
+ +----++----+----+----+
+ | 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 |
+ +---+---+
- Since our natural basis is normalized with respect to the natural
- inner product, and since we know that this algebra is an EJA, any
- left-multiplication operator's matrix will be symmetric because
- natural->EJA basis representation is an isometry and within the EJA
- the operator is self-adjoint by the Jordan axiom::
+ TESTS:
- sage: set_random_seed()
- sage: x = QuaternionHermitianEJA.random_instance().random_element()
- sage: x.operator().matrix().is_symmetric()
- True
+ 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: 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
"""
- @classmethod
- def _denormalized_basis(cls, n, field):
+ Element = CartesianProductEJAElement
+ def __init__(self, factors, **kwargs):
+ m = len(factors)
+ if m == 0:
+ return TrivialEJA()
+
+ self._sets = factors
+
+ field = factors[0].base_ring()
+ 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 )
+ 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
+ self._matrix_space = CartesianProduct(MS_factors, MS_cat)
+
+ 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
+ self._matrix_basis.append(z)
+
+ self._matrix_basis = tuple( self._matrix_space(b)
+ for b in self._matrix_basis )
+ n = len(self._matrix_basis)
+
+ # We already have what we need for the super-superclass constructor.
+ CombinatorialFreeModule.__init__(self,
+ field,
+ range(n),
+ prefix="b",
+ category=category,
+ bracket=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._inner_product_matrix = matrix.block_diagonal(
+ [J._inner_product_matrix for J in factors]
+ )
+ self._inner_product_matrix._cache = {'hermitian': True}
+ self._inner_product_matrix.set_immutable()
+
+ # 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)
+ self.one.set_cache(self(ones))
+
+ def _sets_keys(self):
+ r"""
+
+ SETUP::
+
+ sage: from mjo.eja.eja_algebra import (ComplexHermitianEJA,
+ ....: RealSymmetricEJA)
+
+ TESTS:
+
+ The superclass uses ``_sets_keys()`` to implement its
+ ``cartesian_factors()`` method::
+
+ sage: J1 = RealSymmetricEJA(2,
+ ....: field=QQ,
+ ....: orthonormalize=False,
+ ....: prefix="a")
+ sage: J2 = ComplexHermitianEJA(2,field=QQ,orthonormalize=False)
+ sage: J = cartesian_product([J1,J2])
+ sage: x = sum(i*J.gens()[i] for i in range(len(J.gens())))
+ sage: x.cartesian_factors()
+ (a1 + 2*a2, 3*b0 + 4*b1 + 5*b2 + 6*b3)
+
+ """
+ # Copy/pasted from CombinatorialFreeModule_CartesianProduct,
+ # but returning a tuple instead of a list.
+ return tuple(range(len(self.cartesian_factors())))
+
+ def cartesian_factors(self):
+ # Copy/pasted from CombinatorialFreeModule_CartesianProduct.
+ return self._sets
+
+ def cartesian_factor(self, i):
+ r"""
+ Return the ``i``th factor of this algebra.
"""
- Returns a basis for the space of quaternion Hermitian n-by-n matrices.
+ return self._sets[i]
+
+ def _repr_(self):
+ # Copy/pasted from CombinatorialFreeModule_CartesianProduct.
+ from sage.categories.cartesian_product import cartesian_product
+ return cartesian_product.symbol.join("%s" % factor
+ for factor in self._sets)
- Why do we embed these? Basically, because all of numerical
- linear algebra assumes that you're working with vectors consisting
- of `n` entries from a field and scalars from the same field. There's
- no way to tell SageMath that (for example) the vectors contain
- complex numbers, while the scalar field is real.
+ @cached_method
+ def cartesian_projection(self, i):
+ r"""
SETUP::
- sage: from mjo.eja.eja_algebra import QuaternionHermitianEJA
+ sage: from mjo.eja.eja_algebra import (random_eja,
+ ....: JordanSpinEJA,
+ ....: HadamardEJA,
+ ....: RealSymmetricEJA,
+ ....: ComplexHermitianEJA)
- TESTS::
+ EXAMPLES:
- sage: set_random_seed()
- sage: n = ZZ.random_element(1,5)
- sage: B = QuaternionHermitianEJA._denormalized_basis(n,QQ)
- sage: all( M.is_symmetric() for M in B )
+ 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: 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
"""
- Q = QuaternionAlgebra(QQ,-1,-1)
- I,J,K = Q.gens()
+ offset = sum( self.cartesian_factor(k).dimension()
+ for k in range(i) )
+ Ji = self.cartesian_factor(i)
+ Pi = self._module_morphism(lambda j: Ji.monomial(j - offset),
+ codomain=Ji)
- # This is like the symmetric case, but we need to be careful:
- #
- # * We want conjugate-symmetry, not just symmetry.
- # * The diagonal will (as a result) be real.
- #
- S = []
- for i in xrange(n):
- for j in xrange(i+1):
- Eij = matrix(Q, n, lambda k,l: k==i and l==j)
- 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())
- S.append(Sij_real)
- Sij_I = cls.real_embed(I*Eij - I*Eij.transpose())
- S.append(Sij_I)
- Sij_J = cls.real_embed(J*Eij - J*Eij.transpose())
- S.append(Sij_J)
- Sij_K = cls.real_embed(K*Eij - K*Eij.transpose())
- S.append(Sij_K)
- return tuple(S)
+ return EJAOperator(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: 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: 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
+
+ """
+ offset = sum( self.cartesian_factor(k).dimension()
+ for k in range(i) )
+ Ji = self.cartesian_factor(i)
+ Ei = Ji._module_morphism(lambda j: self.monomial(j + offset),
+ codomain=self)
+ return EJAOperator(Ji,self,Ei.matrix())
+
+
+ def subalgebra(self, basis, **kwargs):
+ r"""
+ Create a subalgebra of this algebra from the given basis.
+
+ Only overridden to allow us to use a special Cartesian product
+ subalgebra class.
+
+ SETUP::
+
+ sage: from mjo.eja.eja_algebra import (HadamardEJA,
+ ....: QuaternionHermitianEJA)
+
+ EXAMPLES:
+
+ Subalgebras of Cartesian product EJAs have a different class
+ than those of non-Cartesian-product EJAs::
+
+ sage: J1 = HadamardEJA(2,field=QQ,orthonormalize=False)
+ sage: J2 = QuaternionHermitianEJA(0,field=QQ,orthonormalize=False)
+ sage: J = cartesian_product([J1,J2])
+ sage: K1 = J1.subalgebra((J1.one(),), orthonormalize=False)
+ sage: K = J.subalgebra((J.one(),), orthonormalize=False)
+ sage: K1.__class__ is K.__class__
+ False
+
+ """
+ from mjo.eja.eja_subalgebra import CartesianProductEJASubalgebra
+ return CartesianProductEJASubalgebra(self, basis, **kwargs)
+
+EJA.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 (EJA,
+ ....: HadamardEJA,
+ ....: 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
+
+ TESTS:
+
+ The ``cartesian_product()`` function only uses the first factor to
+ decide where the result will live; thus we have to be careful to
+ check that all factors do indeed have a ``rational_algebra()`` method
+ before we construct an algebra that claims to have a rational basis::
+
+ sage: J1 = HadamardEJA(2)
+ sage: jp = lambda X,Y: X*Y
+ sage: ip = lambda X,Y: X[0,0]*Y[0,0]
+ sage: b1 = matrix(QQ, [[1]])
+ sage: J2 = EJA((b1,), jp, ip)
+ sage: cartesian_product([J2,J1]) # factor one not RationalBasisEJA
+ Euclidean Jordan algebra of dimension 1 over Algebraic Real
+ Field (+) Euclidean Jordan algebra of dimension 2 over Algebraic
+ Real Field
+ sage: cartesian_product([J1,J2]) # factor one is RationalBasisEJA
+ Traceback (most recent call last):
+ ...
+ ValueError: factor not a RationalBasisEJA
-class JordanSpinEJA(FiniteDimensionalEuclideanJordanAlgebra):
"""
- The rank-2 simple EJA consisting of real vectors ``x=(x0, x_bar)``
- with the usual inner product and jordan product ``x*y =
- (<x_bar,y_bar>, x0*y_bar + y0*x_bar)``. It has dimension `n` over
- the reals.
+ def __init__(self, algebras, **kwargs):
+ if not all( hasattr(r, "rational_algebra") for r in algebras ):
+ raise ValueError("factor not a RationalBasisEJA")
+
+ CartesianProductEJA.__init__(self, algebras, **kwargs)
+
+ @cached_method
+ def rational_algebra(self):
+ if self.base_ring() is QQ:
+ return self
+
+ return cartesian_product([
+ r.rational_algebra() for r in self.cartesian_factors()
+ ])
+
+
+RationalBasisEJA.CartesianProduct = RationalBasisCartesianProductEJA
+
+def random_eja(max_dimension=None, *args, **kwargs):
+ r"""
SETUP::
- sage: from mjo.eja.eja_algebra import JordanSpinEJA
+ sage: from mjo.eja.eja_algebra import random_eja
+
+ TESTS::
+
+ 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:
+ max_dimension = ConcreteEJA._max_random_instance_dimension()
+ J1 = ConcreteEJA.random_instance(max_dimension, *args, **kwargs)
+
+
+ # Roll the dice to see if we attempt a Cartesian product.
+ dice_roll = ZZ.random_element(len(ConcreteEJA.__subclasses__()) + 1)
+ new_max_dimension = max_dimension - J1.dimension()
+ if new_max_dimension == 0 or dice_roll != 0:
+ # If it's already as big as we're willing to tolerate, just
+ # return it and don't worry about Cartesian products.
+ return J1
+ else:
+ # Use random_eja() again so we can get more than two factors
+ # if the sub-call also Decides on a cartesian product.
+ J2 = random_eja(new_max_dimension, *args, **kwargs)
+ return cartesian_product([J1,J2])
+
+
+class ComplexSkewSymmetricEJA(RationalBasisEJA, ConcreteEJA):
+ r"""
+ The skew-symmetric EJA of order `2n` described in Faraut and
+ Koranyi's Exercise III.1.b. It has dimension `2n^2 - n`.
+
+ It is (not obviously) isomorphic to the QuaternionHermitianEJA of
+ order `n`, as can be inferred by comparing rank/dimension or
+ explicitly from their "characteristic polynomial of" functions,
+ which just so happen to align nicely.
+
+ SETUP::
+
+ sage: from mjo.eja.eja_algebra import (ComplexSkewSymmetricEJA,
+ ....: QuaternionHermitianEJA)
+ sage: from mjo.eja.eja_operator import EJAOperator
EXAMPLES:
- This multiplication table can be verified by hand::
+ This EJA is isomorphic to the quaternions::
- sage: J = JordanSpinEJA(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
+ sage: J = ComplexSkewSymmetricEJA(2, field=QQ, orthonormalize=False)
+ sage: K = QuaternionHermitianEJA(2, field=QQ, orthonormalize=False)
+ sage: jordan_isom_matrix = matrix.diagonal(QQ,[-1,1,1,1,1,-1])
+ sage: phi = EJAOperator(J,K,jordan_isom_matrix)
+ sage: all( phi(x*y) == phi(x)*phi(y)
+ ....: for x in J.gens()
+ ....: for y in J.gens() )
+ True
+ sage: x,y = J.random_elements(2)
+ sage: phi(x*y) == phi(x)*phi(y)
+ True
- We can change the generator prefix::
+ TESTS:
- sage: JordanSpinEJA(2, prefix='B').gens()
- (B0, B1)
+ Random elements should satisfy the same conditions that the basis
+ elements do::
+
+ sage: K = ComplexSkewSymmetricEJA.random_instance(field=QQ,
+ ....: orthonormalize=False)
+ sage: x,y = K.random_elements(2)
+ sage: z = x*y
+ sage: x = x.to_matrix()
+ sage: y = y.to_matrix()
+ sage: z = z.to_matrix()
+ sage: all( e.is_skew_symmetric() for e in (x,y,z) )
+ True
+ sage: J = -K.one().to_matrix()
+ sage: all( e*J == J*e.conjugate() for e in (x,y,z) )
+ True
+
+ The power law in Faraut & Koranyi's II.7.a is satisfied.
+ We're in a subalgebra of theirs, but powers are still
+ defined the same::
+
+ sage: K = ComplexSkewSymmetricEJA.random_instance(field=QQ,
+ ....: orthonormalize=False)
+ sage: x = K.random_element()
+ sage: k = ZZ.random_element(5)
+ sage: actual = x^k
+ sage: J = -K.one().to_matrix()
+ sage: expected = K(-J*(J*x.to_matrix())^k)
+ sage: actual == expected
+ True
"""
- def __init__(self, n, field=QQ, **kwargs):
- V = VectorSpace(field, n)
- mult_table = [[V.zero() for j in range(n)] for i in range(n)]
- for i in range(n):
- for j in range(n):
- x = V.gen(i)
- y = V.gen(j)
- x0 = x[0]
- xbar = x[1:]
- y0 = y[0]
- ybar = y[1:]
- # z = x*y
- z0 = x.inner_product(y)
- zbar = y0*xbar + x0*ybar
- z = V([z0] + zbar.list())
- mult_table[i][j] = z
-
- # The rank of the spin algebra is two, unless we're in a
- # one-dimensional ambient space (because the rank is bounded by
- # the ambient dimension).
- fdeja = super(JordanSpinEJA, self)
- return fdeja.__init__(field, mult_table, rank=min(n,2), **kwargs)
+ @staticmethod
+ def _max_random_instance_size(max_dimension):
+ # Obtained by solving d = 2n^2 - n, which comes from noticing
+ # that, in 2x2 block form, any element of this algebra has a
+ # free skew-symmetric top-left block, a Hermitian top-right
+ # block, and two bottom blocks that are determined by the top.
+ # The ZZ-int-ZZ thing is just "floor."
+ return ZZ(int(ZZ(8*max_dimension + 1).sqrt()/4 + 1/4))
- def inner_product(self, x, y):
+ @classmethod
+ def random_instance(cls, max_dimension=None, *args, **kwargs):
"""
- Faster to reimplement than to use natural representations.
+ Return a random instance of this type of algebra.
+ """
+ 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)
+ @staticmethod
+ def _denormalized_basis(A):
+ """
SETUP::
- sage: from mjo.eja.eja_algebra import JordanSpinEJA
+ sage: from mjo.hurwitz import ComplexMatrixAlgebra
+ sage: from mjo.eja.eja_algebra import ComplexSkewSymmetricEJA
TESTS:
- Ensure that this is the usual inner product for the algebras
- over `R^n`::
+ The basis elements are all skew-Hermitian::
- sage: set_random_seed()
- sage: J = JordanSpinEJA.random_instance()
- sage: x,y = J.random_elements(2)
- sage: X = x.natural_representation()
- sage: Y = y.natural_representation()
- sage: x.inner_product(y) == J.natural_inner_product(X,Y)
+ sage: d_max = ComplexSkewSymmetricEJA._max_random_instance_dimension()
+ sage: n_max = ComplexSkewSymmetricEJA._max_random_instance_size(d_max)
+ sage: n = ZZ.random_element(n_max + 1)
+ sage: A = ComplexMatrixAlgebra(2*n, scalars=QQ)
+ sage: B = ComplexSkewSymmetricEJA._denormalized_basis(A)
+ sage: all( M.is_skew_symmetric() for M in B)
+ True
+
+ The basis elements ``b`` all satisfy ``b*J == J*b.conjugate()``,
+ as in the definition of the algebra::
+
+ sage: d_max = ComplexSkewSymmetricEJA._max_random_instance_dimension()
+ sage: n_max = ComplexSkewSymmetricEJA._max_random_instance_size(d_max)
+ sage: n = ZZ.random_element(n_max + 1)
+ sage: A = ComplexMatrixAlgebra(2*n, scalars=QQ)
+ sage: I_n = matrix.identity(ZZ, n)
+ sage: J = matrix.block(ZZ, 2, 2, (0, I_n, -I_n, 0), subdivide=False)
+ sage: J = A.from_list(J.rows())
+ sage: B = ComplexSkewSymmetricEJA._denormalized_basis(A)
+ sage: all( b*J == J*b.conjugate() for b in B )
True
"""
- return x.to_vector().inner_product(y.to_vector())
+ es = A.entry_algebra_gens()
+ gen = lambda A,m: A.monomial(m)
+
+ basis = []
+
+ # The size of the blocks. We're going to treat these thing as
+ # 2x2 block matrices,
+ #
+ # [ x1 x2 ]
+ # [ -x2-conj x1-conj ]
+ #
+ # where x1 is skew-symmetric and x2 is Hermitian.
+ #
+ m = A.nrows()/2
+
+ # We only loop through the top half of the matrix, because the
+ # bottom can be constructed from the top.
+ for i in range(m):
+ # First do the top-left block, which is skew-symmetric.
+ # We can compute the bottom-right block in the process.
+ for j in range(i+1):
+ if i != j:
+ # Skew-symmetry implies zeros for (i == j).
+ for e in es:
+ # Top-left block's entry.
+ E_ij = gen(A, (i,j,e))
+ E_ij -= gen(A, (j,i,e))
+
+ # Bottom-right block's entry.
+ F_ij = gen(A, (i+m,j+m,e)).conjugate()
+ F_ij -= gen(A, (j+m,i+m,e)).conjugate()
+
+ basis.append(E_ij + F_ij)
+
+ # Now do the top-right block, which is Hermitian, and compute
+ # the bottom-left block along the way.
+ for j in range(m,i+m+1):
+ if (i+m) == j:
+ # Hermitian matrices have real diagonal entries.
+ # Top-right block's entry.
+ E_ii = gen(A, (i,j,es[0]))
+
+ # Bottom-left block's entry. Don't conjugate
+ # 'cause it's real.
+ E_ii -= gen(A, (i+m,j-m,es[0]))
+ basis.append(E_ii)
+ else:
+ for e in es:
+ # Top-right block's entry. BEWARE! We're not
+ # reflecting across the main diagonal as in
+ # (i,j)~(j,i). We're only reflecting across
+ # the diagonal for the top-right block.
+ E_ij = gen(A, (i,j,e))
+
+ # Shift it back to non-offset coords, transpose,
+ # conjugate, and put it back:
+ #
+ # (i,j) -> (i,j-m) -> (j-m, i) -> (j-m, i+m)
+ E_ij += gen(A, (j-m,i+m,e)).conjugate()
+
+ # Bottom-left's block's below-diagonal entry.
+ # Just shift the top-right coords down m and
+ # left m.
+ F_ij = -gen(A, (i+m,j-m,e)).conjugate()
+ F_ij += -gen(A, (j,i,e)) # double-conjugate cancels
+
+ basis.append(E_ij + F_ij)
+
+ return tuple( basis )
+
+ @staticmethod
+ @cached_method
+ def _J_matrix(matrix_space):
+ n = matrix_space.nrows() // 2
+ F = matrix_space.base_ring()
+ I_n = matrix.identity(F, n)
+ J = matrix.block(F, 2, 2, (0, I_n, -I_n, 0), subdivide=False)
+ return matrix_space.from_list(J.rows())
+
+ def J_matrix(self):
+ return ComplexSkewSymmetricEJA._J_matrix(self.matrix_space())
+
+ def __init__(self, n, field=AA, **kwargs):
+ # New code; always check the axioms.
+ #if "check_axioms" not in kwargs: kwargs["check_axioms"] = False
+
+ from mjo.hurwitz import ComplexMatrixAlgebra
+ A = ComplexMatrixAlgebra(2*n, scalars=field)
+ J = ComplexSkewSymmetricEJA._J_matrix(A)
+
+ def jordan_product(X,Y):
+ return (X*J*Y + Y*J*X)/2
+
+ def inner_product(X,Y):
+ return (X.conjugate_transpose()*Y).trace().real()
+
+ super().__init__(self._denormalized_basis(A),
+ jordan_product,
+ inner_product,
+ field=field,
+ matrix_space=A,
+ **kwargs)
+
+ # This algebra is conjectured (by me) to be isomorphic to
+ # the quaternion Hermitian EJA of size n, and the rank
+ # would follow from that.
+ #self.rank.set_cache(n)
+ self.one.set_cache( self(-J) )
projects / sage.d.git / blobdiff