2 Euclidean Jordan Algebras. These are formally-real Jordan Algebras;
3 specifically those where u^2 + v^2 = 0 implies that u = v = 0. They
4 are used in optimization, and have some additional nice methods beyond
5 what can be supported in a general Jordan Algebra.
8 from itertools
import repeat
10 from sage
.algebras
.quatalg
.quaternion_algebra
import QuaternionAlgebra
11 from sage
.categories
.magmatic_algebras
import MagmaticAlgebras
12 from sage
.combinat
.free_module
import CombinatorialFreeModule
13 from sage
.matrix
.constructor
import matrix
14 from sage
.matrix
.matrix_space
import MatrixSpace
15 from sage
.misc
.cachefunc
import cached_method
16 from sage
.misc
.lazy_import
import lazy_import
17 from sage
.misc
.prandom
import choice
18 from sage
.misc
.table
import table
19 from sage
.modules
.free_module
import FreeModule
, VectorSpace
20 from sage
.rings
.all
import (ZZ
, QQ
, RR
, RLF
, CLF
,
23 from mjo
.eja
.eja_element
import FiniteDimensionalEuclideanJordanAlgebraElement
24 lazy_import('mjo.eja.eja_subalgebra',
25 'FiniteDimensionalEuclideanJordanSubalgebra')
26 from mjo
.eja
.eja_utils
import _mat2vec
28 class FiniteDimensionalEuclideanJordanAlgebra(CombinatorialFreeModule
):
30 def _coerce_map_from_base_ring(self
):
32 Disable the map from the base ring into the algebra.
34 Performing a nonsense conversion like this automatically
35 is counterpedagogical. The fallback is to try the usual
36 element constructor, which should also fail.
40 sage: from mjo.eja.eja_algebra import random_eja
44 sage: set_random_seed()
45 sage: J = random_eja()
47 Traceback (most recent call last):
49 ValueError: not a naturally-represented algebra element
65 sage: from mjo.eja.eja_algebra import (JordanSpinEJA, random_eja)
69 By definition, Jordan multiplication commutes::
71 sage: set_random_seed()
72 sage: J = random_eja()
73 sage: x,y = J.random_elements(2)
79 The ``field`` we're given must be real::
81 sage: JordanSpinEJA(2,QQbar)
82 Traceback (most recent call last):
84 ValueError: field is not real
88 if not field
.is_subring(RR
):
89 # Note: this does return true for the real algebraic
90 # field, and any quadratic field where we've specified
92 raise ValueError('field is not real')
95 self
._natural
_basis
= natural_basis
98 category
= MagmaticAlgebras(field
).FiniteDimensional()
99 category
= category
.WithBasis().Unital()
101 fda
= super(FiniteDimensionalEuclideanJordanAlgebra
, self
)
103 range(len(mult_table
)),
106 self
.print_options(bracket
='')
108 # The multiplication table we're given is necessarily in terms
109 # of vectors, because we don't have an algebra yet for
110 # anything to be an element of. However, it's faster in the
111 # long run to have the multiplication table be in terms of
112 # algebra elements. We do this after calling the superclass
113 # constructor so that from_vector() knows what to do.
114 self
._multiplication
_table
= [
115 list(map(lambda x
: self
.from_vector(x
), ls
))
120 def _element_constructor_(self
, elt
):
122 Construct an element of this algebra from its natural
125 This gets called only after the parent element _call_ method
126 fails to find a coercion for the argument.
130 sage: from mjo.eja.eja_algebra import (JordanSpinEJA,
132 ....: RealSymmetricEJA)
136 The identity in `S^n` is converted to the identity in the EJA::
138 sage: J = RealSymmetricEJA(3)
139 sage: I = matrix.identity(QQ,3)
140 sage: J(I) == J.one()
143 This skew-symmetric matrix can't be represented in the EJA::
145 sage: J = RealSymmetricEJA(3)
146 sage: A = matrix(QQ,3, lambda i,j: i-j)
148 Traceback (most recent call last):
150 ArithmeticError: vector is not in free module
154 Ensure that we can convert any element of the two non-matrix
155 simple algebras (whose natural representations are their usual
156 vector representations) back and forth faithfully::
158 sage: set_random_seed()
159 sage: J = HadamardEJA.random_instance()
160 sage: x = J.random_element()
161 sage: J(x.to_vector().column()) == x
163 sage: J = JordanSpinEJA.random_instance()
164 sage: x = J.random_element()
165 sage: J(x.to_vector().column()) == x
169 msg
= "not a naturally-represented algebra element"
171 # The superclass implementation of random_element()
172 # needs to be able to coerce "0" into the algebra.
174 elif elt
in self
.base_ring():
175 # Ensure that no base ring -> algebra coercion is performed
176 # by this method. There's some stupidity in sage that would
177 # otherwise propagate to this method; for example, sage thinks
178 # that the integer 3 belongs to the space of 2-by-2 matrices.
179 raise ValueError(msg
)
181 natural_basis
= self
.natural_basis()
182 basis_space
= natural_basis
[0].matrix_space()
183 if elt
not in basis_space
:
184 raise ValueError(msg
)
186 # Thanks for nothing! Matrix spaces aren't vector spaces in
187 # Sage, so we have to figure out its natural-basis coordinates
188 # ourselves. We use the basis space's ring instead of the
189 # element's ring because the basis space might be an algebraic
190 # closure whereas the base ring of the 3-by-3 identity matrix
191 # could be QQ instead of QQbar.
192 V
= VectorSpace(basis_space
.base_ring(), elt
.nrows()*elt
.ncols())
193 W
= V
.span_of_basis( _mat2vec(s
) for s
in natural_basis
)
194 coords
= W
.coordinate_vector(_mat2vec(elt
))
195 return self
.from_vector(coords
)
200 Return a string representation of ``self``.
204 sage: from mjo.eja.eja_algebra import JordanSpinEJA
208 Ensure that it says what we think it says::
210 sage: JordanSpinEJA(2, field=QQ)
211 Euclidean Jordan algebra of dimension 2 over Rational Field
212 sage: JordanSpinEJA(3, field=RDF)
213 Euclidean Jordan algebra of dimension 3 over Real Double Field
216 fmt
= "Euclidean Jordan algebra of dimension {} over {}"
217 return fmt
.format(self
.dimension(), self
.base_ring())
219 def product_on_basis(self
, i
, j
):
220 return self
._multiplication
_table
[i
][j
]
222 def _a_regular_element(self
):
224 Guess a regular element. Needed to compute the basis for our
225 characteristic polynomial coefficients.
229 sage: from mjo.eja.eja_algebra import random_eja
233 Ensure that this hacky method succeeds for every algebra that we
234 know how to construct::
236 sage: set_random_seed()
237 sage: J = random_eja()
238 sage: J._a_regular_element().is_regular()
243 z
= self
.sum( (i
+1)*gs
[i
] for i
in range(len(gs
)) )
244 if not z
.is_regular():
245 raise ValueError("don't know a regular element")
250 def _charpoly_basis_space(self
):
252 Return the vector space spanned by the basis used in our
253 characteristic polynomial coefficients. This is used not only to
254 compute those coefficients, but also any time we need to
255 evaluate the coefficients (like when we compute the trace or
258 z
= self
._a
_regular
_element
()
259 # Don't use the parent vector space directly here in case this
260 # happens to be a subalgebra. In that case, we would be e.g.
261 # two-dimensional but span_of_basis() would expect three
263 V
= VectorSpace(self
.base_ring(), self
.vector_space().dimension())
264 basis
= [ (z
**k
).to_vector() for k
in range(self
.rank()) ]
265 V1
= V
.span_of_basis( basis
)
266 b
= (V1
.basis() + V1
.complement().basis())
267 return V
.span_of_basis(b
)
272 def _charpoly_coeff(self
, i
):
274 Return the coefficient polynomial "a_{i}" of this algebra's
275 general characteristic polynomial.
277 Having this be a separate cached method lets us compute and
278 store the trace/determinant (a_{r-1} and a_{0} respectively)
279 separate from the entire characteristic polynomial.
281 (A_of_x
, x
, xr
, detA
) = self
._charpoly
_matrix
_system
()
282 R
= A_of_x
.base_ring()
287 # Guaranteed by theory
290 # Danger: the in-place modification is done for performance
291 # reasons (reconstructing a matrix with huge polynomial
292 # entries is slow), but I don't know how cached_method works,
293 # so it's highly possible that we're modifying some global
294 # list variable by reference, here. In other words, you
295 # probably shouldn't call this method twice on the same
296 # algebra, at the same time, in two threads
297 Ai_orig
= A_of_x
.column(i
)
298 A_of_x
.set_column(i
,xr
)
299 numerator
= A_of_x
.det()
300 A_of_x
.set_column(i
,Ai_orig
)
302 # We're relying on the theory here to ensure that each a_i is
303 # indeed back in R, and the added negative signs are to make
304 # the whole charpoly expression sum to zero.
305 return R(-numerator
/detA
)
309 def _charpoly_matrix_system(self
):
311 Compute the matrix whose entries A_ij are polynomials in
312 X1,...,XN, the vector ``x`` of variables X1,...,XN, the vector
313 corresponding to `x^r` and the determinent of the matrix A =
314 [A_ij]. In other words, all of the fixed (cachable) data needed
315 to compute the coefficients of the characteristic polynomial.
320 # Turn my vector space into a module so that "vectors" can
321 # have multivatiate polynomial entries.
322 names
= tuple('X' + str(i
) for i
in range(1,n
+1))
323 R
= PolynomialRing(self
.base_ring(), names
)
325 # Using change_ring() on the parent's vector space doesn't work
326 # here because, in a subalgebra, that vector space has a basis
327 # and change_ring() tries to bring the basis along with it. And
328 # that doesn't work unless the new ring is a PID, which it usually
332 # Now let x = (X1,X2,...,Xn) be the vector whose entries are
336 # And figure out the "left multiplication by x" matrix in
339 monomial_matrices
= [ self
.monomial(i
).operator().matrix()
340 for i
in range(n
) ] # don't recompute these!
342 ek
= self
.monomial(k
).to_vector()
344 sum( x
[i
]*(monomial_matrices
[i
]*ek
)
345 for i
in range(n
) ) )
346 Lx
= matrix
.column(R
, lmbx_cols
)
348 # Now we can compute powers of x "symbolically"
349 x_powers
= [self
.one().to_vector(), x
]
350 for d
in range(2, r
+1):
351 x_powers
.append( Lx
*(x_powers
[-1]) )
353 idmat
= matrix
.identity(R
, n
)
355 W
= self
._charpoly
_basis
_space
()
356 W
= W
.change_ring(R
.fraction_field())
358 # Starting with the standard coordinates x = (X1,X2,...,Xn)
359 # and then converting the entries to W-coordinates allows us
360 # to pass in the standard coordinates to the charpoly and get
361 # back the right answer. Specifically, with x = (X1,X2,...,Xn),
364 # W.coordinates(x^2) eval'd at (standard z-coords)
368 # W-coords of (standard coords of x^2 eval'd at std-coords of z)
370 # We want the middle equivalent thing in our matrix, but use
371 # the first equivalent thing instead so that we can pass in
372 # standard coordinates.
373 x_powers
= [ W
.coordinate_vector(xp
) for xp
in x_powers
]
374 l2
= [idmat
.column(k
-1) for k
in range(r
+1, n
+1)]
375 A_of_x
= matrix
.column(R
, n
, (x_powers
[:r
] + l2
))
376 return (A_of_x
, x
, x_powers
[r
], A_of_x
.det())
380 def characteristic_polynomial(self
):
382 Return a characteristic polynomial that works for all elements
385 The resulting polynomial has `n+1` variables, where `n` is the
386 dimension of this algebra. The first `n` variables correspond to
387 the coordinates of an algebra element: when evaluated at the
388 coordinates of an algebra element with respect to a certain
389 basis, the result is a univariate polynomial (in the one
390 remaining variable ``t``), namely the characteristic polynomial
395 sage: from mjo.eja.eja_algebra import JordanSpinEJA, TrivialEJA
399 The characteristic polynomial in the spin algebra is given in
400 Alizadeh, Example 11.11::
402 sage: J = JordanSpinEJA(3)
403 sage: p = J.characteristic_polynomial(); p
404 X1^2 - X2^2 - X3^2 + (-2*t)*X1 + t^2
405 sage: xvec = J.one().to_vector()
409 By definition, the characteristic polynomial is a monic
410 degree-zero polynomial in a rank-zero algebra. Note that
411 Cayley-Hamilton is indeed satisfied since the polynomial
412 ``1`` evaluates to the identity element of the algebra on
415 sage: J = TrivialEJA()
416 sage: J.characteristic_polynomial()
423 # The list of coefficient polynomials a_0, a_1, a_2, ..., a_n.
424 a
= [ self
._charpoly
_coeff
(i
) for i
in range(r
+1) ]
426 # We go to a bit of trouble here to reorder the
427 # indeterminates, so that it's easier to evaluate the
428 # characteristic polynomial at x's coordinates and get back
429 # something in terms of t, which is what we want.
431 S
= PolynomialRing(self
.base_ring(),'t')
433 S
= PolynomialRing(S
, R
.variable_names())
436 return sum( a
[k
]*(t
**k
) for k
in range(len(a
)) )
439 def inner_product(self
, x
, y
):
441 The inner product associated with this Euclidean Jordan algebra.
443 Defaults to the trace inner product, but can be overridden by
444 subclasses if they are sure that the necessary properties are
449 sage: from mjo.eja.eja_algebra import random_eja
453 Our inner product is "associative," which means the following for
454 a symmetric bilinear form::
456 sage: set_random_seed()
457 sage: J = random_eja()
458 sage: x,y,z = J.random_elements(3)
459 sage: (x*y).inner_product(z) == y.inner_product(x*z)
463 X
= x
.natural_representation()
464 Y
= y
.natural_representation()
465 return self
.natural_inner_product(X
,Y
)
468 def is_trivial(self
):
470 Return whether or not this algebra is trivial.
472 A trivial algebra contains only the zero element.
476 sage: from mjo.eja.eja_algebra import (ComplexHermitianEJA,
481 sage: J = ComplexHermitianEJA(3)
487 sage: J = TrivialEJA()
492 return self
.dimension() == 0
495 def multiplication_table(self
):
497 Return a visual representation of this algebra's multiplication
498 table (on basis elements).
502 sage: from mjo.eja.eja_algebra import JordanSpinEJA
506 sage: J = JordanSpinEJA(4)
507 sage: J.multiplication_table()
508 +----++----+----+----+----+
509 | * || e0 | e1 | e2 | e3 |
510 +====++====+====+====+====+
511 | e0 || e0 | e1 | e2 | e3 |
512 +----++----+----+----+----+
513 | e1 || e1 | e0 | 0 | 0 |
514 +----++----+----+----+----+
515 | e2 || e2 | 0 | e0 | 0 |
516 +----++----+----+----+----+
517 | e3 || e3 | 0 | 0 | e0 |
518 +----++----+----+----+----+
521 M
= list(self
._multiplication
_table
) # copy
522 for i
in range(len(M
)):
523 # M had better be "square"
524 M
[i
] = [self
.monomial(i
)] + M
[i
]
525 M
= [["*"] + list(self
.gens())] + M
526 return table(M
, header_row
=True, header_column
=True, frame
=True)
529 def natural_basis(self
):
531 Return a more-natural representation of this algebra's basis.
533 Every finite-dimensional Euclidean Jordan Algebra is a direct
534 sum of five simple algebras, four of which comprise Hermitian
535 matrices. This method returns the original "natural" basis
536 for our underlying vector space. (Typically, the natural basis
537 is used to construct the multiplication table in the first place.)
539 Note that this will always return a matrix. The standard basis
540 in `R^n` will be returned as `n`-by-`1` column matrices.
544 sage: from mjo.eja.eja_algebra import (JordanSpinEJA,
545 ....: RealSymmetricEJA)
549 sage: J = RealSymmetricEJA(2)
551 Finite family {0: e0, 1: e1, 2: e2}
552 sage: J.natural_basis()
554 [1 0] [ 0 1/2*sqrt2] [0 0]
555 [0 0], [1/2*sqrt2 0], [0 1]
560 sage: J = JordanSpinEJA(2)
562 Finite family {0: e0, 1: e1}
563 sage: J.natural_basis()
570 if self
._natural
_basis
is None:
571 M
= self
.natural_basis_space()
572 return tuple( M(b
.to_vector()) for b
in self
.basis() )
574 return self
._natural
_basis
577 def natural_basis_space(self
):
579 Return the matrix space in which this algebra's natural basis
582 if self
._natural
_basis
is None or len(self
._natural
_basis
) == 0:
583 return MatrixSpace(self
.base_ring(), self
.dimension(), 1)
585 return self
._natural
_basis
[0].matrix_space()
589 def natural_inner_product(X
,Y
):
591 Compute the inner product of two naturally-represented elements.
593 For example in the real symmetric matrix EJA, this will compute
594 the trace inner-product of two n-by-n symmetric matrices. The
595 default should work for the real cartesian product EJA, the
596 Jordan spin EJA, and the real symmetric matrices. The others
597 will have to be overridden.
599 return (X
.conjugate_transpose()*Y
).trace()
605 Return the unit element of this algebra.
609 sage: from mjo.eja.eja_algebra import (HadamardEJA,
614 sage: J = HadamardEJA(5)
616 e0 + e1 + e2 + e3 + e4
620 The identity element acts like the identity::
622 sage: set_random_seed()
623 sage: J = random_eja()
624 sage: x = J.random_element()
625 sage: J.one()*x == x and x*J.one() == x
628 The matrix of the unit element's operator is the identity::
630 sage: set_random_seed()
631 sage: J = random_eja()
632 sage: actual = J.one().operator().matrix()
633 sage: expected = matrix.identity(J.base_ring(), J.dimension())
634 sage: actual == expected
638 # We can brute-force compute the matrices of the operators
639 # that correspond to the basis elements of this algebra.
640 # If some linear combination of those basis elements is the
641 # algebra identity, then the same linear combination of
642 # their matrices has to be the identity matrix.
644 # Of course, matrices aren't vectors in sage, so we have to
645 # appeal to the "long vectors" isometry.
646 oper_vecs
= [ _mat2vec(g
.operator().matrix()) for g
in self
.gens() ]
648 # Now we use basis linear algebra to find the coefficients,
649 # of the matrices-as-vectors-linear-combination, which should
650 # work for the original algebra basis too.
651 A
= matrix
.column(self
.base_ring(), oper_vecs
)
653 # We used the isometry on the left-hand side already, but we
654 # still need to do it for the right-hand side. Recall that we
655 # wanted something that summed to the identity matrix.
656 b
= _mat2vec( matrix
.identity(self
.base_ring(), self
.dimension()) )
658 # Now if there's an identity element in the algebra, this should work.
659 coeffs
= A
.solve_right(b
)
660 return self
.linear_combination(zip(self
.gens(), coeffs
))
663 def peirce_decomposition(self
, c
):
665 The Peirce decomposition of this algebra relative to the
668 In the future, this can be extended to a complete system of
669 orthogonal idempotents.
673 - ``c`` -- an idempotent of this algebra.
677 A triple (J0, J5, J1) containing two subalgebras and one subspace
680 - ``J0`` -- the algebra on the eigenspace of ``c.operator()``
681 corresponding to the eigenvalue zero.
683 - ``J5`` -- the eigenspace (NOT a subalgebra) of ``c.operator()``
684 corresponding to the eigenvalue one-half.
686 - ``J1`` -- the algebra on the eigenspace of ``c.operator()``
687 corresponding to the eigenvalue one.
689 These are the only possible eigenspaces for that operator, and this
690 algebra is a direct sum of them. The spaces ``J0`` and ``J1`` are
691 orthogonal, and are subalgebras of this algebra with the appropriate
696 sage: from mjo.eja.eja_algebra import random_eja, RealSymmetricEJA
700 The canonical example comes from the symmetric matrices, which
701 decompose into diagonal and off-diagonal parts::
703 sage: J = RealSymmetricEJA(3)
704 sage: C = matrix(QQ, [ [1,0,0],
708 sage: J0,J5,J1 = J.peirce_decomposition(c)
710 Euclidean Jordan algebra of dimension 1...
712 Vector space of degree 6 and dimension 2...
714 Euclidean Jordan algebra of dimension 3...
718 Every algebra decomposes trivially with respect to its identity
721 sage: set_random_seed()
722 sage: J = random_eja()
723 sage: J0,J5,J1 = J.peirce_decomposition(J.one())
724 sage: J0.dimension() == 0 and J5.dimension() == 0
726 sage: J1.superalgebra() == J and J1.dimension() == J.dimension()
729 The identity elements in the two subalgebras are the
730 projections onto their respective subspaces of the
731 superalgebra's identity element::
733 sage: set_random_seed()
734 sage: J = random_eja()
735 sage: x = J.random_element()
736 sage: if not J.is_trivial():
737 ....: while x.is_nilpotent():
738 ....: x = J.random_element()
739 sage: c = x.subalgebra_idempotent()
740 sage: J0,J5,J1 = J.peirce_decomposition(c)
741 sage: J1(c) == J1.one()
743 sage: J0(J.one() - c) == J0.one()
747 if not c
.is_idempotent():
748 raise ValueError("element is not idempotent: %s" % c
)
750 # Default these to what they should be if they turn out to be
751 # trivial, because eigenspaces_left() won't return eigenvalues
752 # corresponding to trivial spaces (e.g. it returns only the
753 # eigenspace corresponding to lambda=1 if you take the
754 # decomposition relative to the identity element).
755 trivial
= FiniteDimensionalEuclideanJordanSubalgebra(self
, ())
756 J0
= trivial
# eigenvalue zero
757 J5
= VectorSpace(self
.base_ring(), 0) # eigenvalue one-half
758 J1
= trivial
# eigenvalue one
760 for (eigval
, eigspace
) in c
.operator().matrix().left_eigenspaces():
761 if eigval
== ~
(self
.base_ring()(2)):
764 gens
= tuple( self
.from_vector(b
) for b
in eigspace
.basis() )
765 subalg
= FiniteDimensionalEuclideanJordanSubalgebra(self
, gens
)
771 raise ValueError("unexpected eigenvalue: %s" % eigval
)
776 def a_jordan_frame(self
):
778 Generate a Jordan frame for this algebra.
780 This implementation is based on the so-called "central
781 orthogonal idempotents" implemented for (semisimple) centers
782 of SageMath ``FiniteDimensionalAlgebrasWithBasis``. Since all
783 Euclidean Jordan algebas are commutative (and thus equal to
784 their own centers) and semisimple, the method should work more
785 or less as implemented, if it ever worked in the first place.
786 (I don't know the justification for the original implementation.
789 How it works: we loop through the algebras generators, looking
790 for their eigenspaces. If there's more than one eigenspace,
791 and if they result in more than one subalgebra, then we split
792 those subalgebras recursively until we get to subalgebras of
793 dimension one (whose idempotent is the unit element). Why does
794 some generator have to produce at least two subalgebras? I
795 dunno. But it seems to work.
797 Beware that Koecher defines the "center" of a Jordan algebra to
798 be something else, because the usual definition is stupid in a
799 (necessarily commutative) Jordan algebra.
803 sage: from mjo.eja.eja_algebra import (random_eja,
809 A Jordan frame for the trivial algebra has to be empty
810 (zero-length) since its rank is zero. More to the point, there
811 are no non-zero idempotents in the trivial EJA. This does not
812 cause any problems so long as we adopt the convention that the
813 empty sum is zero, since then the sole element of the trivial
814 EJA has an (empty) spectral decomposition::
816 sage: J = TrivialEJA()
817 sage: J.a_jordan_frame()
820 A one-dimensional algebra has rank one (equal to its dimension),
821 and only one primitive idempotent, namely the algebra's unit
824 sage: J = JordanSpinEJA(1)
825 sage: J.a_jordan_frame()
830 sage: J = random_eja()
831 sage: c = J.a_jordan_frame()
832 sage: all( x^2 == x for x in c )
835 sage: all( c[i]*c[j] == c[i]*(i==j) for i in range(r)
836 ....: for j in range(r) )
840 if self
.dimension() == 0:
842 if self
.dimension() == 1:
845 for g
in self
.gens():
846 eigenpairs
= g
.operator().matrix().right_eigenspaces()
847 if len(eigenpairs
) >= 2:
849 for eigval
, eigspace
in eigenpairs
:
850 # Make sub-EJAs from the matrix eigenspaces...
851 sb
= tuple( self
.from_vector(b
) for b
in eigspace
.basis() )
853 # This will fail if e.g. the eigenspace basis
854 # contains two elements and their product
855 # isn't a linear combination of the two of
856 # them (i.e. the generated EJA isn't actually
858 s
= FiniteDimensionalEuclideanJordanSubalgebra(self
, sb
)
859 subalgebras
.append(s
)
862 if len(subalgebras
) >= 2:
863 # apply this method recursively.
864 return tuple( c
.superalgebra_element()
865 for subalgebra
in subalgebras
866 for c
in subalgebra
.a_jordan_frame() )
868 # If we got here, the algebra didn't decompose, at least not when we looked at
869 # the eigenspaces corresponding only to basis elements of the algebra. The
870 # implementation I stole says that this should work because of Schur's Lemma,
871 # so I personally blame Schur's Lemma if it does not.
872 raise Exception("Schur's Lemma didn't work!")
875 def random_elements(self
, count
):
877 Return ``count`` random elements as a tuple.
881 sage: from mjo.eja.eja_algebra import JordanSpinEJA
885 sage: J = JordanSpinEJA(3)
886 sage: x,y,z = J.random_elements(3)
887 sage: all( [ x in J, y in J, z in J ])
889 sage: len( J.random_elements(10) ) == 10
893 return tuple( self
.random_element() for idx
in range(count
) )
898 Return the rank of this EJA.
902 The author knows of no algorithm to compute the rank of an EJA
903 where only the multiplication table is known. In lieu of one, we
904 require the rank to be specified when the algebra is created,
905 and simply pass along that number here.
909 sage: from mjo.eja.eja_algebra import (JordanSpinEJA,
910 ....: RealSymmetricEJA,
911 ....: ComplexHermitianEJA,
912 ....: QuaternionHermitianEJA,
917 The rank of the Jordan spin algebra is always two::
919 sage: JordanSpinEJA(2).rank()
921 sage: JordanSpinEJA(3).rank()
923 sage: JordanSpinEJA(4).rank()
926 The rank of the `n`-by-`n` Hermitian real, complex, or
927 quaternion matrices is `n`::
929 sage: RealSymmetricEJA(4).rank()
931 sage: ComplexHermitianEJA(3).rank()
933 sage: QuaternionHermitianEJA(2).rank()
938 Ensure that every EJA that we know how to construct has a
939 positive integer rank, unless the algebra is trivial in
940 which case its rank will be zero::
942 sage: set_random_seed()
943 sage: J = random_eja()
947 sage: r > 0 or (r == 0 and J.is_trivial())
954 def vector_space(self
):
956 Return the vector space that underlies this algebra.
960 sage: from mjo.eja.eja_algebra import RealSymmetricEJA
964 sage: J = RealSymmetricEJA(2)
965 sage: J.vector_space()
966 Vector space of dimension 3 over...
969 return self
.zero().to_vector().parent().ambient_vector_space()
972 Element
= FiniteDimensionalEuclideanJordanAlgebraElement
975 class KnownRankEJA(object):
977 A class for algebras that we actually know we can construct. The
978 main issue is that, for most of our methods to make sense, we need
979 to know the rank of our algebra. Thus we can't simply generate a
980 "random" algebra, or even check that a given basis and product
981 satisfy the axioms; because even if everything looks OK, we wouldn't
982 know the rank we need to actuallty build the thing.
984 Not really a subclass of FDEJA because doing that causes method
985 resolution errors, e.g.
987 TypeError: Error when calling the metaclass bases
988 Cannot create a consistent method resolution
989 order (MRO) for bases FiniteDimensionalEuclideanJordanAlgebra,
994 def _max_test_case_size():
996 Return an integer "size" that is an upper bound on the size of
997 this algebra when it is used in a random test
998 case. Unfortunately, the term "size" is quite vague -- when
999 dealing with `R^n` under either the Hadamard or Jordan spin
1000 product, the "size" refers to the dimension `n`. When dealing
1001 with a matrix algebra (real symmetric or complex/quaternion
1002 Hermitian), it refers to the size of the matrix, which is
1003 far less than the dimension of the underlying vector space.
1005 We default to five in this class, which is safe in `R^n`. The
1006 matrix algebra subclasses (or any class where the "size" is
1007 interpreted to be far less than the dimension) should override
1008 with a smaller number.
1013 def random_instance(cls
, field
=QQ
, **kwargs
):
1015 Return a random instance of this type of algebra.
1017 Beware, this will crash for "most instances" because the
1018 constructor below looks wrong.
1020 if cls
is TrivialEJA
:
1021 # The TrivialEJA class doesn't take an "n" argument because
1025 n
= ZZ
.random_element(cls
._max
_test
_case
_size
()) + 1
1026 return cls(n
, field
, **kwargs
)
1029 class HadamardEJA(FiniteDimensionalEuclideanJordanAlgebra
, KnownRankEJA
):
1031 Return the Euclidean Jordan Algebra corresponding to the set
1032 `R^n` under the Hadamard product.
1034 Note: this is nothing more than the Cartesian product of ``n``
1035 copies of the spin algebra. Once Cartesian product algebras
1036 are implemented, this can go.
1040 sage: from mjo.eja.eja_algebra import HadamardEJA
1044 This multiplication table can be verified by hand::
1046 sage: J = HadamardEJA(3)
1047 sage: e0,e1,e2 = J.gens()
1063 We can change the generator prefix::
1065 sage: HadamardEJA(3, prefix='r').gens()
1069 def __init__(self
, n
, field
=QQ
, **kwargs
):
1070 V
= VectorSpace(field
, n
)
1071 mult_table
= [ [ V
.gen(i
)*(i
== j
) for j
in range(n
) ]
1074 fdeja
= super(HadamardEJA
, self
)
1075 return fdeja
.__init
__(field
, mult_table
, rank
=n
, **kwargs
)
1077 def inner_product(self
, x
, y
):
1079 Faster to reimplement than to use natural representations.
1083 sage: from mjo.eja.eja_algebra import HadamardEJA
1087 Ensure that this is the usual inner product for the algebras
1090 sage: set_random_seed()
1091 sage: J = HadamardEJA.random_instance()
1092 sage: x,y = J.random_elements(2)
1093 sage: X = x.natural_representation()
1094 sage: Y = y.natural_representation()
1095 sage: x.inner_product(y) == J.natural_inner_product(X,Y)
1099 return x
.to_vector().inner_product(y
.to_vector())
1102 def random_eja(field
=QQ
, nontrivial
=False):
1104 Return a "random" finite-dimensional Euclidean Jordan Algebra.
1108 sage: from mjo.eja.eja_algebra import random_eja
1113 Euclidean Jordan algebra of dimension...
1116 eja_classes
= KnownRankEJA
.__subclasses
__()
1118 eja_classes
.remove(TrivialEJA
)
1119 classname
= choice(eja_classes
)
1120 return classname
.random_instance(field
=field
)
1127 class MatrixEuclideanJordanAlgebra(FiniteDimensionalEuclideanJordanAlgebra
):
1129 def _max_test_case_size():
1130 # Play it safe, since this will be squared and the underlying
1131 # field can have dimension 4 (quaternions) too.
1134 def __init__(self
, field
, basis
, rank
, normalize_basis
=True, **kwargs
):
1136 Compared to the superclass constructor, we take a basis instead of
1137 a multiplication table because the latter can be computed in terms
1138 of the former when the product is known (like it is here).
1140 # Used in this class's fast _charpoly_coeff() override.
1141 self
._basis
_normalizers
= None
1143 # We're going to loop through this a few times, so now's a good
1144 # time to ensure that it isn't a generator expression.
1145 basis
= tuple(basis
)
1147 if rank
> 1 and normalize_basis
:
1148 # We'll need sqrt(2) to normalize the basis, and this
1149 # winds up in the multiplication table, so the whole
1150 # algebra needs to be over the field extension.
1151 R
= PolynomialRing(field
, 'z')
1154 if p
.is_irreducible():
1155 field
= field
.extension(p
, 'sqrt2', embedding
=RLF(2).sqrt())
1156 basis
= tuple( s
.change_ring(field
) for s
in basis
)
1157 self
._basis
_normalizers
= tuple(
1158 ~
(self
.natural_inner_product(s
,s
).sqrt()) for s
in basis
)
1159 basis
= tuple(s
*c
for (s
,c
) in zip(basis
,self
._basis
_normalizers
))
1161 Qs
= self
.multiplication_table_from_matrix_basis(basis
)
1163 fdeja
= super(MatrixEuclideanJordanAlgebra
, self
)
1164 return fdeja
.__init
__(field
,
1167 natural_basis
=basis
,
1172 def _charpoly_coeff(self
, i
):
1174 Override the parent method with something that tries to compute
1175 over a faster (non-extension) field.
1177 if self
._basis
_normalizers
is None:
1178 # We didn't normalize, so assume that the basis we started
1179 # with had entries in a nice field.
1180 return super(MatrixEuclideanJordanAlgebra
, self
)._charpoly
_coeff
(i
)
1182 basis
= ( (b
/n
) for (b
,n
) in zip(self
.natural_basis(),
1183 self
._basis
_normalizers
) )
1185 # Do this over the rationals and convert back at the end.
1186 J
= MatrixEuclideanJordanAlgebra(QQ
,
1189 normalize_basis
=False)
1190 (_
,x
,_
,_
) = J
._charpoly
_matrix
_system
()
1191 p
= J
._charpoly
_coeff
(i
)
1192 # p might be missing some vars, have to substitute "optionally"
1193 pairs
= zip(x
.base_ring().gens(), self
._basis
_normalizers
)
1194 substitutions
= { v: v*c for (v,c) in pairs }
1195 result
= p
.subs(substitutions
)
1197 # The result of "subs" can be either a coefficient-ring
1198 # element or a polynomial. Gotta handle both cases.
1200 return self
.base_ring()(result
)
1202 return result
.change_ring(self
.base_ring())
1206 def multiplication_table_from_matrix_basis(basis
):
1208 At least three of the five simple Euclidean Jordan algebras have the
1209 symmetric multiplication (A,B) |-> (AB + BA)/2, where the
1210 multiplication on the right is matrix multiplication. Given a basis
1211 for the underlying matrix space, this function returns a
1212 multiplication table (obtained by looping through the basis
1213 elements) for an algebra of those matrices.
1215 # In S^2, for example, we nominally have four coordinates even
1216 # though the space is of dimension three only. The vector space V
1217 # is supposed to hold the entire long vector, and the subspace W
1218 # of V will be spanned by the vectors that arise from symmetric
1219 # matrices. Thus for S^2, dim(V) == 4 and dim(W) == 3.
1220 field
= basis
[0].base_ring()
1221 dimension
= basis
[0].nrows()
1223 V
= VectorSpace(field
, dimension
**2)
1224 W
= V
.span_of_basis( _mat2vec(s
) for s
in basis
)
1226 mult_table
= [[W
.zero() for j
in range(n
)] for i
in range(n
)]
1229 mat_entry
= (basis
[i
]*basis
[j
] + basis
[j
]*basis
[i
])/2
1230 mult_table
[i
][j
] = W
.coordinate_vector(_mat2vec(mat_entry
))
1238 Embed the matrix ``M`` into a space of real matrices.
1240 The matrix ``M`` can have entries in any field at the moment:
1241 the real numbers, complex numbers, or quaternions. And although
1242 they are not a field, we can probably support octonions at some
1243 point, too. This function returns a real matrix that "acts like"
1244 the original with respect to matrix multiplication; i.e.
1246 real_embed(M*N) = real_embed(M)*real_embed(N)
1249 raise NotImplementedError
1253 def real_unembed(M
):
1255 The inverse of :meth:`real_embed`.
1257 raise NotImplementedError
1261 def natural_inner_product(cls
,X
,Y
):
1262 Xu
= cls
.real_unembed(X
)
1263 Yu
= cls
.real_unembed(Y
)
1264 tr
= (Xu
*Yu
).trace()
1267 # It's real already.
1270 # Otherwise, try the thing that works for complex numbers; and
1271 # if that doesn't work, the thing that works for quaternions.
1273 return tr
.vector()[0] # real part, imag part is index 1
1274 except AttributeError:
1275 # A quaternions doesn't have a vector() method, but does
1276 # have coefficient_tuple() method that returns the
1277 # coefficients of 1, i, j, and k -- in that order.
1278 return tr
.coefficient_tuple()[0]
1281 class RealMatrixEuclideanJordanAlgebra(MatrixEuclideanJordanAlgebra
):
1285 The identity function, for embedding real matrices into real
1291 def real_unembed(M
):
1293 The identity function, for unembedding real matrices from real
1299 class RealSymmetricEJA(RealMatrixEuclideanJordanAlgebra
, KnownRankEJA
):
1301 The rank-n simple EJA consisting of real symmetric n-by-n
1302 matrices, the usual symmetric Jordan product, and the trace inner
1303 product. It has dimension `(n^2 + n)/2` over the reals.
1307 sage: from mjo.eja.eja_algebra import RealSymmetricEJA
1311 sage: J = RealSymmetricEJA(2)
1312 sage: e0, e1, e2 = J.gens()
1320 In theory, our "field" can be any subfield of the reals::
1322 sage: RealSymmetricEJA(2, AA)
1323 Euclidean Jordan algebra of dimension 3 over Algebraic Real Field
1324 sage: RealSymmetricEJA(2, RR)
1325 Euclidean Jordan algebra of dimension 3 over Real Field with
1326 53 bits of precision
1330 The dimension of this algebra is `(n^2 + n) / 2`::
1332 sage: set_random_seed()
1333 sage: n_max = RealSymmetricEJA._max_test_case_size()
1334 sage: n = ZZ.random_element(1, n_max)
1335 sage: J = RealSymmetricEJA(n)
1336 sage: J.dimension() == (n^2 + n)/2
1339 The Jordan multiplication is what we think it is::
1341 sage: set_random_seed()
1342 sage: J = RealSymmetricEJA.random_instance()
1343 sage: x,y = J.random_elements(2)
1344 sage: actual = (x*y).natural_representation()
1345 sage: X = x.natural_representation()
1346 sage: Y = y.natural_representation()
1347 sage: expected = (X*Y + Y*X)/2
1348 sage: actual == expected
1350 sage: J(expected) == x*y
1353 We can change the generator prefix::
1355 sage: RealSymmetricEJA(3, prefix='q').gens()
1356 (q0, q1, q2, q3, q4, q5)
1358 Our natural basis is normalized with respect to the natural inner
1359 product unless we specify otherwise::
1361 sage: set_random_seed()
1362 sage: J = RealSymmetricEJA.random_instance()
1363 sage: all( b.norm() == 1 for b in J.gens() )
1366 Since our natural basis is normalized with respect to the natural
1367 inner product, and since we know that this algebra is an EJA, any
1368 left-multiplication operator's matrix will be symmetric because
1369 natural->EJA basis representation is an isometry and within the EJA
1370 the operator is self-adjoint by the Jordan axiom::
1372 sage: set_random_seed()
1373 sage: x = RealSymmetricEJA.random_instance().random_element()
1374 sage: x.operator().matrix().is_symmetric()
1379 def _denormalized_basis(cls
, n
, field
):
1381 Return a basis for the space of real symmetric n-by-n matrices.
1385 sage: from mjo.eja.eja_algebra import RealSymmetricEJA
1389 sage: set_random_seed()
1390 sage: n = ZZ.random_element(1,5)
1391 sage: B = RealSymmetricEJA._denormalized_basis(n,QQ)
1392 sage: all( M.is_symmetric() for M in B)
1396 # The basis of symmetric matrices, as matrices, in their R^(n-by-n)
1400 for j
in range(i
+1):
1401 Eij
= matrix(field
, n
, lambda k
,l
: k
==i
and l
==j
)
1405 Sij
= Eij
+ Eij
.transpose()
1411 def _max_test_case_size():
1412 return 4 # Dimension 10
1415 def __init__(self
, n
, field
=QQ
, **kwargs
):
1416 basis
= self
._denormalized
_basis
(n
, field
)
1417 super(RealSymmetricEJA
, self
).__init
__(field
, basis
, n
, **kwargs
)
1420 class ComplexMatrixEuclideanJordanAlgebra(MatrixEuclideanJordanAlgebra
):
1424 Embed the n-by-n complex matrix ``M`` into the space of real
1425 matrices of size 2n-by-2n via the map the sends each entry `z = a +
1426 bi` to the block matrix ``[[a,b],[-b,a]]``.
1430 sage: from mjo.eja.eja_algebra import \
1431 ....: ComplexMatrixEuclideanJordanAlgebra
1435 sage: F = QuadraticField(-1, 'i')
1436 sage: x1 = F(4 - 2*i)
1437 sage: x2 = F(1 + 2*i)
1440 sage: M = matrix(F,2,[[x1,x2],[x3,x4]])
1441 sage: ComplexMatrixEuclideanJordanAlgebra.real_embed(M)
1450 Embedding is a homomorphism (isomorphism, in fact)::
1452 sage: set_random_seed()
1453 sage: n_max = ComplexMatrixEuclideanJordanAlgebra._max_test_case_size()
1454 sage: n = ZZ.random_element(n_max)
1455 sage: F = QuadraticField(-1, 'i')
1456 sage: X = random_matrix(F, n)
1457 sage: Y = random_matrix(F, n)
1458 sage: Xe = ComplexMatrixEuclideanJordanAlgebra.real_embed(X)
1459 sage: Ye = ComplexMatrixEuclideanJordanAlgebra.real_embed(Y)
1460 sage: XYe = ComplexMatrixEuclideanJordanAlgebra.real_embed(X*Y)
1467 raise ValueError("the matrix 'M' must be square")
1469 # We don't need any adjoined elements...
1470 field
= M
.base_ring().base_ring()
1474 a
= z
.list()[0] # real part, I guess
1475 b
= z
.list()[1] # imag part, I guess
1476 blocks
.append(matrix(field
, 2, [[a
,b
],[-b
,a
]]))
1478 return matrix
.block(field
, n
, blocks
)
1482 def real_unembed(M
):
1484 The inverse of _embed_complex_matrix().
1488 sage: from mjo.eja.eja_algebra import \
1489 ....: ComplexMatrixEuclideanJordanAlgebra
1493 sage: A = matrix(QQ,[ [ 1, 2, 3, 4],
1494 ....: [-2, 1, -4, 3],
1495 ....: [ 9, 10, 11, 12],
1496 ....: [-10, 9, -12, 11] ])
1497 sage: ComplexMatrixEuclideanJordanAlgebra.real_unembed(A)
1499 [ 10*i + 9 12*i + 11]
1503 Unembedding is the inverse of embedding::
1505 sage: set_random_seed()
1506 sage: F = QuadraticField(-1, 'i')
1507 sage: M = random_matrix(F, 3)
1508 sage: Me = ComplexMatrixEuclideanJordanAlgebra.real_embed(M)
1509 sage: ComplexMatrixEuclideanJordanAlgebra.real_unembed(Me) == M
1515 raise ValueError("the matrix 'M' must be square")
1516 if not n
.mod(2).is_zero():
1517 raise ValueError("the matrix 'M' must be a complex embedding")
1519 # If "M" was normalized, its base ring might have roots
1520 # adjoined and they can stick around after unembedding.
1521 field
= M
.base_ring()
1522 R
= PolynomialRing(field
, 'z')
1524 F
= field
.extension(z
**2 + 1, 'i', embedding
=CLF(-1).sqrt())
1527 # Go top-left to bottom-right (reading order), converting every
1528 # 2-by-2 block we see to a single complex element.
1530 for k
in range(n
/2):
1531 for j
in range(n
/2):
1532 submat
= M
[2*k
:2*k
+2,2*j
:2*j
+2]
1533 if submat
[0,0] != submat
[1,1]:
1534 raise ValueError('bad on-diagonal submatrix')
1535 if submat
[0,1] != -submat
[1,0]:
1536 raise ValueError('bad off-diagonal submatrix')
1537 z
= submat
[0,0] + submat
[0,1]*i
1540 return matrix(F
, n
/2, elements
)
1544 def natural_inner_product(cls
,X
,Y
):
1546 Compute a natural inner product in this algebra directly from
1551 sage: from mjo.eja.eja_algebra import ComplexHermitianEJA
1555 This gives the same answer as the slow, default method implemented
1556 in :class:`MatrixEuclideanJordanAlgebra`::
1558 sage: set_random_seed()
1559 sage: J = ComplexHermitianEJA.random_instance()
1560 sage: x,y = J.random_elements(2)
1561 sage: Xe = x.natural_representation()
1562 sage: Ye = y.natural_representation()
1563 sage: X = ComplexHermitianEJA.real_unembed(Xe)
1564 sage: Y = ComplexHermitianEJA.real_unembed(Ye)
1565 sage: expected = (X*Y).trace().vector()[0]
1566 sage: actual = ComplexHermitianEJA.natural_inner_product(Xe,Ye)
1567 sage: actual == expected
1571 return RealMatrixEuclideanJordanAlgebra
.natural_inner_product(X
,Y
)/2
1574 class ComplexHermitianEJA(ComplexMatrixEuclideanJordanAlgebra
, KnownRankEJA
):
1576 The rank-n simple EJA consisting of complex Hermitian n-by-n
1577 matrices over the real numbers, the usual symmetric Jordan product,
1578 and the real-part-of-trace inner product. It has dimension `n^2` over
1583 sage: from mjo.eja.eja_algebra import ComplexHermitianEJA
1587 In theory, our "field" can be any subfield of the reals::
1589 sage: ComplexHermitianEJA(2, AA)
1590 Euclidean Jordan algebra of dimension 4 over Algebraic Real Field
1591 sage: ComplexHermitianEJA(2, RR)
1592 Euclidean Jordan algebra of dimension 4 over Real Field with
1593 53 bits of precision
1597 The dimension of this algebra is `n^2`::
1599 sage: set_random_seed()
1600 sage: n_max = ComplexHermitianEJA._max_test_case_size()
1601 sage: n = ZZ.random_element(1, n_max)
1602 sage: J = ComplexHermitianEJA(n)
1603 sage: J.dimension() == n^2
1606 The Jordan multiplication is what we think it is::
1608 sage: set_random_seed()
1609 sage: J = ComplexHermitianEJA.random_instance()
1610 sage: x,y = J.random_elements(2)
1611 sage: actual = (x*y).natural_representation()
1612 sage: X = x.natural_representation()
1613 sage: Y = y.natural_representation()
1614 sage: expected = (X*Y + Y*X)/2
1615 sage: actual == expected
1617 sage: J(expected) == x*y
1620 We can change the generator prefix::
1622 sage: ComplexHermitianEJA(2, prefix='z').gens()
1625 Our natural basis is normalized with respect to the natural inner
1626 product unless we specify otherwise::
1628 sage: set_random_seed()
1629 sage: J = ComplexHermitianEJA.random_instance()
1630 sage: all( b.norm() == 1 for b in J.gens() )
1633 Since our natural basis is normalized with respect to the natural
1634 inner product, and since we know that this algebra is an EJA, any
1635 left-multiplication operator's matrix will be symmetric because
1636 natural->EJA basis representation is an isometry and within the EJA
1637 the operator is self-adjoint by the Jordan axiom::
1639 sage: set_random_seed()
1640 sage: x = ComplexHermitianEJA.random_instance().random_element()
1641 sage: x.operator().matrix().is_symmetric()
1647 def _denormalized_basis(cls
, n
, field
):
1649 Returns a basis for the space of complex Hermitian n-by-n matrices.
1651 Why do we embed these? Basically, because all of numerical linear
1652 algebra assumes that you're working with vectors consisting of `n`
1653 entries from a field and scalars from the same field. There's no way
1654 to tell SageMath that (for example) the vectors contain complex
1655 numbers, while the scalar field is real.
1659 sage: from mjo.eja.eja_algebra import ComplexHermitianEJA
1663 sage: set_random_seed()
1664 sage: n = ZZ.random_element(1,5)
1665 sage: field = QuadraticField(2, 'sqrt2')
1666 sage: B = ComplexHermitianEJA._denormalized_basis(n, field)
1667 sage: all( M.is_symmetric() for M in B)
1671 R
= PolynomialRing(field
, 'z')
1673 F
= field
.extension(z
**2 + 1, 'I')
1676 # This is like the symmetric case, but we need to be careful:
1678 # * We want conjugate-symmetry, not just symmetry.
1679 # * The diagonal will (as a result) be real.
1683 for j
in range(i
+1):
1684 Eij
= matrix(F
, n
, lambda k
,l
: k
==i
and l
==j
)
1686 Sij
= cls
.real_embed(Eij
)
1689 # The second one has a minus because it's conjugated.
1690 Sij_real
= cls
.real_embed(Eij
+ Eij
.transpose())
1692 Sij_imag
= cls
.real_embed(I
*Eij
- I
*Eij
.transpose())
1695 # Since we embedded these, we can drop back to the "field" that we
1696 # started with instead of the complex extension "F".
1697 return ( s
.change_ring(field
) for s
in S
)
1700 def __init__(self
, n
, field
=QQ
, **kwargs
):
1701 basis
= self
._denormalized
_basis
(n
,field
)
1702 super(ComplexHermitianEJA
,self
).__init
__(field
, basis
, n
, **kwargs
)
1705 class QuaternionMatrixEuclideanJordanAlgebra(MatrixEuclideanJordanAlgebra
):
1709 Embed the n-by-n quaternion matrix ``M`` into the space of real
1710 matrices of size 4n-by-4n by first sending each quaternion entry `z
1711 = a + bi + cj + dk` to the block-complex matrix ``[[a + bi,
1712 c+di],[-c + di, a-bi]]`, and then embedding those into a real
1717 sage: from mjo.eja.eja_algebra import \
1718 ....: QuaternionMatrixEuclideanJordanAlgebra
1722 sage: Q = QuaternionAlgebra(QQ,-1,-1)
1723 sage: i,j,k = Q.gens()
1724 sage: x = 1 + 2*i + 3*j + 4*k
1725 sage: M = matrix(Q, 1, [[x]])
1726 sage: QuaternionMatrixEuclideanJordanAlgebra.real_embed(M)
1732 Embedding is a homomorphism (isomorphism, in fact)::
1734 sage: set_random_seed()
1735 sage: n_max = QuaternionMatrixEuclideanJordanAlgebra._max_test_case_size()
1736 sage: n = ZZ.random_element(n_max)
1737 sage: Q = QuaternionAlgebra(QQ,-1,-1)
1738 sage: X = random_matrix(Q, n)
1739 sage: Y = random_matrix(Q, n)
1740 sage: Xe = QuaternionMatrixEuclideanJordanAlgebra.real_embed(X)
1741 sage: Ye = QuaternionMatrixEuclideanJordanAlgebra.real_embed(Y)
1742 sage: XYe = QuaternionMatrixEuclideanJordanAlgebra.real_embed(X*Y)
1747 quaternions
= M
.base_ring()
1750 raise ValueError("the matrix 'M' must be square")
1752 F
= QuadraticField(-1, 'i')
1757 t
= z
.coefficient_tuple()
1762 cplxM
= matrix(F
, 2, [[ a
+ b
*i
, c
+ d
*i
],
1763 [-c
+ d
*i
, a
- b
*i
]])
1764 realM
= ComplexMatrixEuclideanJordanAlgebra
.real_embed(cplxM
)
1765 blocks
.append(realM
)
1767 # We should have real entries by now, so use the realest field
1768 # we've got for the return value.
1769 return matrix
.block(quaternions
.base_ring(), n
, blocks
)
1774 def real_unembed(M
):
1776 The inverse of _embed_quaternion_matrix().
1780 sage: from mjo.eja.eja_algebra import \
1781 ....: QuaternionMatrixEuclideanJordanAlgebra
1785 sage: M = matrix(QQ, [[ 1, 2, 3, 4],
1786 ....: [-2, 1, -4, 3],
1787 ....: [-3, 4, 1, -2],
1788 ....: [-4, -3, 2, 1]])
1789 sage: QuaternionMatrixEuclideanJordanAlgebra.real_unembed(M)
1790 [1 + 2*i + 3*j + 4*k]
1794 Unembedding is the inverse of embedding::
1796 sage: set_random_seed()
1797 sage: Q = QuaternionAlgebra(QQ, -1, -1)
1798 sage: M = random_matrix(Q, 3)
1799 sage: Me = QuaternionMatrixEuclideanJordanAlgebra.real_embed(M)
1800 sage: QuaternionMatrixEuclideanJordanAlgebra.real_unembed(Me) == M
1806 raise ValueError("the matrix 'M' must be square")
1807 if not n
.mod(4).is_zero():
1808 raise ValueError("the matrix 'M' must be a quaternion embedding")
1810 # Use the base ring of the matrix to ensure that its entries can be
1811 # multiplied by elements of the quaternion algebra.
1812 field
= M
.base_ring()
1813 Q
= QuaternionAlgebra(field
,-1,-1)
1816 # Go top-left to bottom-right (reading order), converting every
1817 # 4-by-4 block we see to a 2-by-2 complex block, to a 1-by-1
1820 for l
in range(n
/4):
1821 for m
in range(n
/4):
1822 submat
= ComplexMatrixEuclideanJordanAlgebra
.real_unembed(
1823 M
[4*l
:4*l
+4,4*m
:4*m
+4] )
1824 if submat
[0,0] != submat
[1,1].conjugate():
1825 raise ValueError('bad on-diagonal submatrix')
1826 if submat
[0,1] != -submat
[1,0].conjugate():
1827 raise ValueError('bad off-diagonal submatrix')
1828 z
= submat
[0,0].vector()[0] # real part
1829 z
+= submat
[0,0].vector()[1]*i
# imag part
1830 z
+= submat
[0,1].vector()[0]*j
# real part
1831 z
+= submat
[0,1].vector()[1]*k
# imag part
1834 return matrix(Q
, n
/4, elements
)
1838 def natural_inner_product(cls
,X
,Y
):
1840 Compute a natural inner product in this algebra directly from
1845 sage: from mjo.eja.eja_algebra import QuaternionHermitianEJA
1849 This gives the same answer as the slow, default method implemented
1850 in :class:`MatrixEuclideanJordanAlgebra`::
1852 sage: set_random_seed()
1853 sage: J = QuaternionHermitianEJA.random_instance()
1854 sage: x,y = J.random_elements(2)
1855 sage: Xe = x.natural_representation()
1856 sage: Ye = y.natural_representation()
1857 sage: X = QuaternionHermitianEJA.real_unembed(Xe)
1858 sage: Y = QuaternionHermitianEJA.real_unembed(Ye)
1859 sage: expected = (X*Y).trace().coefficient_tuple()[0]
1860 sage: actual = QuaternionHermitianEJA.natural_inner_product(Xe,Ye)
1861 sage: actual == expected
1865 return RealMatrixEuclideanJordanAlgebra
.natural_inner_product(X
,Y
)/4
1868 class QuaternionHermitianEJA(QuaternionMatrixEuclideanJordanAlgebra
,
1871 The rank-n simple EJA consisting of self-adjoint n-by-n quaternion
1872 matrices, the usual symmetric Jordan product, and the
1873 real-part-of-trace inner product. It has dimension `2n^2 - n` over
1878 sage: from mjo.eja.eja_algebra import QuaternionHermitianEJA
1882 In theory, our "field" can be any subfield of the reals::
1884 sage: QuaternionHermitianEJA(2, AA)
1885 Euclidean Jordan algebra of dimension 6 over Algebraic Real Field
1886 sage: QuaternionHermitianEJA(2, RR)
1887 Euclidean Jordan algebra of dimension 6 over Real Field with
1888 53 bits of precision
1892 The dimension of this algebra is `2*n^2 - n`::
1894 sage: set_random_seed()
1895 sage: n_max = QuaternionHermitianEJA._max_test_case_size()
1896 sage: n = ZZ.random_element(1, n_max)
1897 sage: J = QuaternionHermitianEJA(n)
1898 sage: J.dimension() == 2*(n^2) - n
1901 The Jordan multiplication is what we think it is::
1903 sage: set_random_seed()
1904 sage: J = QuaternionHermitianEJA.random_instance()
1905 sage: x,y = J.random_elements(2)
1906 sage: actual = (x*y).natural_representation()
1907 sage: X = x.natural_representation()
1908 sage: Y = y.natural_representation()
1909 sage: expected = (X*Y + Y*X)/2
1910 sage: actual == expected
1912 sage: J(expected) == x*y
1915 We can change the generator prefix::
1917 sage: QuaternionHermitianEJA(2, prefix='a').gens()
1918 (a0, a1, a2, a3, a4, a5)
1920 Our natural basis is normalized with respect to the natural inner
1921 product unless we specify otherwise::
1923 sage: set_random_seed()
1924 sage: J = QuaternionHermitianEJA.random_instance()
1925 sage: all( b.norm() == 1 for b in J.gens() )
1928 Since our natural basis is normalized with respect to the natural
1929 inner product, and since we know that this algebra is an EJA, any
1930 left-multiplication operator's matrix will be symmetric because
1931 natural->EJA basis representation is an isometry and within the EJA
1932 the operator is self-adjoint by the Jordan axiom::
1934 sage: set_random_seed()
1935 sage: x = QuaternionHermitianEJA.random_instance().random_element()
1936 sage: x.operator().matrix().is_symmetric()
1941 def _denormalized_basis(cls
, n
, field
):
1943 Returns a basis for the space of quaternion Hermitian n-by-n matrices.
1945 Why do we embed these? Basically, because all of numerical
1946 linear algebra assumes that you're working with vectors consisting
1947 of `n` entries from a field and scalars from the same field. There's
1948 no way to tell SageMath that (for example) the vectors contain
1949 complex numbers, while the scalar field is real.
1953 sage: from mjo.eja.eja_algebra import QuaternionHermitianEJA
1957 sage: set_random_seed()
1958 sage: n = ZZ.random_element(1,5)
1959 sage: B = QuaternionHermitianEJA._denormalized_basis(n,QQ)
1960 sage: all( M.is_symmetric() for M in B )
1964 Q
= QuaternionAlgebra(QQ
,-1,-1)
1967 # This is like the symmetric case, but we need to be careful:
1969 # * We want conjugate-symmetry, not just symmetry.
1970 # * The diagonal will (as a result) be real.
1974 for j
in range(i
+1):
1975 Eij
= matrix(Q
, n
, lambda k
,l
: k
==i
and l
==j
)
1977 Sij
= cls
.real_embed(Eij
)
1980 # The second, third, and fourth ones have a minus
1981 # because they're conjugated.
1982 Sij_real
= cls
.real_embed(Eij
+ Eij
.transpose())
1984 Sij_I
= cls
.real_embed(I
*Eij
- I
*Eij
.transpose())
1986 Sij_J
= cls
.real_embed(J
*Eij
- J
*Eij
.transpose())
1988 Sij_K
= cls
.real_embed(K
*Eij
- K
*Eij
.transpose())
1991 # Since we embedded these, we can drop back to the "field" that we
1992 # started with instead of the quaternion algebra "Q".
1993 return ( s
.change_ring(field
) for s
in S
)
1996 def __init__(self
, n
, field
=QQ
, **kwargs
):
1997 basis
= self
._denormalized
_basis
(n
,field
)
1998 super(QuaternionHermitianEJA
,self
).__init
__(field
, basis
, n
, **kwargs
)
2001 class BilinearFormEJA(FiniteDimensionalEuclideanJordanAlgebra
, KnownRankEJA
):
2003 The rank-2 simple EJA consisting of real vectors ``x=(x0, x_bar)``
2004 with the half-trace inner product and jordan product ``x*y =
2005 (x0*y0 + <B*x_bar,y_bar>, x0*y_bar + y0*x_bar)`` where ``B`` is a
2006 symmetric positive-definite "bilinear form" matrix. It has
2007 dimension `n` over the reals, and reduces to the ``JordanSpinEJA``
2008 when ``B`` is the identity matrix of order ``n-1``.
2012 sage: from mjo.eja.eja_algebra import (BilinearFormEJA,
2013 ....: JordanSpinEJA)
2017 When no bilinear form is specified, the identity matrix is used,
2018 and the resulting algebra is the Jordan spin algebra::
2020 sage: J0 = BilinearFormEJA(3)
2021 sage: J1 = JordanSpinEJA(3)
2022 sage: J0.multiplication_table() == J0.multiplication_table()
2027 We can create a zero-dimensional algebra::
2029 sage: J = BilinearFormEJA(0)
2033 We can check the multiplication condition given in the Jordan, von
2034 Neumann, and Wigner paper (and also discussed on my "On the
2035 symmetry..." paper). Note that this relies heavily on the standard
2036 choice of basis, as does anything utilizing the bilinear form matrix::
2038 sage: set_random_seed()
2039 sage: n = ZZ.random_element(5)
2040 sage: M = matrix.random(QQ, max(0,n-1), algorithm='unimodular')
2041 sage: B = M.transpose()*M
2042 sage: J = BilinearFormEJA(n, B=B)
2043 sage: eis = VectorSpace(M.base_ring(), M.ncols()).basis()
2044 sage: V = J.vector_space()
2045 sage: sis = [ J.from_vector(V([0] + (M.inverse()*ei).list()))
2046 ....: for ei in eis ]
2047 sage: actual = [ sis[i]*sis[j]
2048 ....: for i in range(n-1)
2049 ....: for j in range(n-1) ]
2050 sage: expected = [ J.one() if i == j else J.zero()
2051 ....: for i in range(n-1)
2052 ....: for j in range(n-1) ]
2053 sage: actual == expected
2056 def __init__(self
, n
, field
=QQ
, B
=None, **kwargs
):
2058 self
._B
= matrix
.identity(field
, max(0,n
-1))
2062 V
= VectorSpace(field
, n
)
2063 mult_table
= [[V
.zero() for j
in range(n
)] for i
in range(n
)]
2072 z0
= x0
*y0
+ (self
._B
*xbar
).inner_product(ybar
)
2073 zbar
= y0
*xbar
+ x0
*ybar
2074 z
= V([z0
] + zbar
.list())
2075 mult_table
[i
][j
] = z
2077 # The rank of this algebra is two, unless we're in a
2078 # one-dimensional ambient space (because the rank is bounded
2079 # by the ambient dimension).
2080 fdeja
= super(BilinearFormEJA
, self
)
2081 return fdeja
.__init
__(field
, mult_table
, rank
=min(n
,2), **kwargs
)
2083 def inner_product(self
, x
, y
):
2085 Half of the trace inner product.
2087 This is defined so that the special case of the Jordan spin
2088 algebra gets the usual inner product.
2092 sage: from mjo.eja.eja_algebra import BilinearFormEJA
2096 Ensure that this is one-half of the trace inner-product when
2097 the algebra isn't just the reals (when ``n`` isn't one). This
2098 is in Faraut and Koranyi, and also my "On the symmetry..."
2101 sage: set_random_seed()
2102 sage: n = ZZ.random_element(2,5)
2103 sage: M = matrix.random(QQ, max(0,n-1), algorithm='unimodular')
2104 sage: B = M.transpose()*M
2105 sage: J = BilinearFormEJA(n, B=B)
2106 sage: x = J.random_element()
2107 sage: y = J.random_element()
2108 sage: x.inner_product(y) == (x*y).trace()/2
2112 xvec
= x
.to_vector()
2114 yvec
= y
.to_vector()
2116 return x
[0]*y
[0] + (self
._B
*xbar
).inner_product(ybar
)
2119 class JordanSpinEJA(BilinearFormEJA
):
2121 The rank-2 simple EJA consisting of real vectors ``x=(x0, x_bar)``
2122 with the usual inner product and jordan product ``x*y =
2123 (<x,y>, x0*y_bar + y0*x_bar)``. It has dimension `n` over
2128 sage: from mjo.eja.eja_algebra import JordanSpinEJA
2132 This multiplication table can be verified by hand::
2134 sage: J = JordanSpinEJA(4)
2135 sage: e0,e1,e2,e3 = J.gens()
2151 We can change the generator prefix::
2153 sage: JordanSpinEJA(2, prefix='B').gens()
2158 Ensure that we have the usual inner product on `R^n`::
2160 sage: set_random_seed()
2161 sage: J = JordanSpinEJA.random_instance()
2162 sage: x,y = J.random_elements(2)
2163 sage: X = x.natural_representation()
2164 sage: Y = y.natural_representation()
2165 sage: x.inner_product(y) == J.natural_inner_product(X,Y)
2169 def __init__(self
, n
, field
=QQ
, **kwargs
):
2170 # This is a special case of the BilinearFormEJA with the identity
2171 # matrix as its bilinear form.
2172 return super(JordanSpinEJA
, self
).__init
__(n
, field
, **kwargs
)
2175 class TrivialEJA(FiniteDimensionalEuclideanJordanAlgebra
, KnownRankEJA
):
2177 The trivial Euclidean Jordan algebra consisting of only a zero element.
2181 sage: from mjo.eja.eja_algebra import TrivialEJA
2185 sage: J = TrivialEJA()
2192 sage: 7*J.one()*12*J.one()
2194 sage: J.one().inner_product(J.one())
2196 sage: J.one().norm()
2198 sage: J.one().subalgebra_generated_by()
2199 Euclidean Jordan algebra of dimension 0 over Rational Field
2204 def __init__(self
, field
=QQ
, **kwargs
):
2206 fdeja
= super(TrivialEJA
, self
)
2207 # The rank is zero using my definition, namely the dimension of the
2208 # largest subalgebra generated by any element.
2209 return fdeja
.__init
__(field
, mult_table
, rank
=0, **kwargs
)