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eja: fix the dimension of the complex Hermitian simple EJA.
[sage.d.git] / mjo / eja / euclidean_jordan_algebra.py
1 """
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.
6 """
7
8 from sage.categories.magmatic_algebras import MagmaticAlgebras
9 from sage.structure.element import is_Matrix
10 from sage.structure.category_object import normalize_names
11
12 from sage.algebras.finite_dimensional_algebras.finite_dimensional_algebra import FiniteDimensionalAlgebra
13 from sage.algebras.finite_dimensional_algebras.finite_dimensional_algebra_element import FiniteDimensionalAlgebraElement
14
15 class FiniteDimensionalEuclideanJordanAlgebra(FiniteDimensionalAlgebra):
16 @staticmethod
17 def __classcall_private__(cls,
18 field,
19 mult_table,
20 names='e',
21 assume_associative=False,
22 category=None,
23 rank=None):
24 n = len(mult_table)
25 mult_table = [b.base_extend(field) for b in mult_table]
26 for b in mult_table:
27 b.set_immutable()
28 if not (is_Matrix(b) and b.dimensions() == (n, n)):
29 raise ValueError("input is not a multiplication table")
30 mult_table = tuple(mult_table)
31
32 cat = MagmaticAlgebras(field).FiniteDimensional().WithBasis()
33 cat.or_subcategory(category)
34 if assume_associative:
35 cat = cat.Associative()
36
37 names = normalize_names(n, names)
38
39 fda = super(FiniteDimensionalEuclideanJordanAlgebra, cls)
40 return fda.__classcall__(cls,
41 field,
42 mult_table,
43 assume_associative=assume_associative,
44 names=names,
45 category=cat,
46 rank=rank)
47
48
49 def __init__(self, field,
50 mult_table,
51 names='e',
52 assume_associative=False,
53 category=None,
54 rank=None):
55 self._rank = rank
56 fda = super(FiniteDimensionalEuclideanJordanAlgebra, self)
57 fda.__init__(field,
58 mult_table,
59 names=names,
60 category=category)
61
62
63 def _repr_(self):
64 """
65 Return a string representation of ``self``.
66 """
67 fmt = "Euclidean Jordan algebra of degree {} over {}"
68 return fmt.format(self.degree(), self.base_ring())
69
70 def rank(self):
71 """
72 Return the rank of this EJA.
73 """
74 if self._rank is None:
75 raise ValueError("no rank specified at genesis")
76 else:
77 return self._rank
78
79
80 class Element(FiniteDimensionalAlgebraElement):
81 """
82 An element of a Euclidean Jordan algebra.
83 """
84
85 def __pow__(self, n):
86 """
87 Return ``self`` raised to the power ``n``.
88
89 Jordan algebras are always power-associative; see for
90 example Faraut and Koranyi, Proposition II.1.2 (ii).
91
92 .. WARNING:
93
94 We have to override this because our superclass uses row vectors
95 instead of column vectors! We, on the other hand, assume column
96 vectors everywhere.
97
98 EXAMPLES:
99
100 sage: set_random_seed()
101 sage: x = random_eja().random_element()
102 sage: x.matrix()*x.vector() == (x**2).vector()
103 True
104
105 """
106 A = self.parent()
107 if n == 0:
108 return A.one()
109 elif n == 1:
110 return self
111 else:
112 return A.element_class(A, (self.matrix()**(n-1))*self.vector())
113
114
115 def characteristic_polynomial(self):
116 """
117 Return my characteristic polynomial (if I'm a regular
118 element).
119
120 Eventually this should be implemented in terms of the parent
121 algebra's characteristic polynomial that works for ALL
122 elements.
123 """
124 if self.is_regular():
125 return self.minimal_polynomial()
126 else:
127 raise NotImplementedError('irregular element')
128
129
130 def det(self):
131 """
132 Return my determinant, the product of my eigenvalues.
133
134 EXAMPLES::
135
136 sage: J = JordanSpinSimpleEJA(2)
137 sage: e0,e1 = J.gens()
138 sage: x = e0 + e1
139 sage: x.det()
140 0
141 sage: J = JordanSpinSimpleEJA(3)
142 sage: e0,e1,e2 = J.gens()
143 sage: x = e0 + e1 + e2
144 sage: x.det()
145 -1
146
147 """
148 cs = self.characteristic_polynomial().coefficients(sparse=False)
149 r = len(cs) - 1
150 if r >= 0:
151 return cs[0] * (-1)**r
152 else:
153 raise ValueError('charpoly had no coefficients')
154
155
156 def is_nilpotent(self):
157 """
158 Return whether or not some power of this element is zero.
159
160 The superclass method won't work unless we're in an
161 associative algebra, and we aren't. However, we generate
162 an assocoative subalgebra and we're nilpotent there if and
163 only if we're nilpotent here (probably).
164
165 TESTS:
166
167 The identity element is never nilpotent::
168
169 sage: set_random_seed()
170 sage: random_eja().one().is_nilpotent()
171 False
172
173 The additive identity is always nilpotent::
174
175 sage: set_random_seed()
176 sage: random_eja().zero().is_nilpotent()
177 True
178
179 """
180 # The element we're going to call "is_nilpotent()" on.
181 # Either myself, interpreted as an element of a finite-
182 # dimensional algebra, or an element of an associative
183 # subalgebra.
184 elt = None
185
186 if self.parent().is_associative():
187 elt = FiniteDimensionalAlgebraElement(self.parent(), self)
188 else:
189 V = self.span_of_powers()
190 assoc_subalg = self.subalgebra_generated_by()
191 # Mis-design warning: the basis used for span_of_powers()
192 # and subalgebra_generated_by() must be the same, and in
193 # the same order!
194 elt = assoc_subalg(V.coordinates(self.vector()))
195
196 # Recursive call, but should work since elt lives in an
197 # associative algebra.
198 return elt.is_nilpotent()
199
200
201 def is_regular(self):
202 """
203 Return whether or not this is a regular element.
204
205 EXAMPLES:
206
207 The identity element always has degree one, but any element
208 linearly-independent from it is regular::
209
210 sage: J = JordanSpinSimpleEJA(5)
211 sage: J.one().is_regular()
212 False
213 sage: e0, e1, e2, e3, e4 = J.gens() # e0 is the identity
214 sage: for x in J.gens():
215 ....: (J.one() + x).is_regular()
216 False
217 True
218 True
219 True
220 True
221
222 """
223 return self.degree() == self.parent().rank()
224
225
226 def degree(self):
227 """
228 Compute the degree of this element the straightforward way
229 according to the definition; by appending powers to a list
230 and figuring out its dimension (that is, whether or not
231 they're linearly dependent).
232
233 EXAMPLES::
234
235 sage: J = JordanSpinSimpleEJA(4)
236 sage: J.one().degree()
237 1
238 sage: e0,e1,e2,e3 = J.gens()
239 sage: (e0 - e1).degree()
240 2
241
242 In the spin factor algebra (of rank two), all elements that
243 aren't multiples of the identity are regular::
244
245 sage: set_random_seed()
246 sage: n = ZZ.random_element(1,10).abs()
247 sage: J = JordanSpinSimpleEJA(n)
248 sage: x = J.random_element()
249 sage: x == x.coefficient(0)*J.one() or x.degree() == 2
250 True
251
252 """
253 return self.span_of_powers().dimension()
254
255
256 def matrix(self):
257 """
258 Return the matrix that represents left- (or right-)
259 multiplication by this element in the parent algebra.
260
261 We have to override this because the superclass method
262 returns a matrix that acts on row vectors (that is, on
263 the right).
264 """
265 fda_elt = FiniteDimensionalAlgebraElement(self.parent(), self)
266 return fda_elt.matrix().transpose()
267
268
269 def minimal_polynomial(self):
270 """
271 EXAMPLES::
272
273 sage: set_random_seed()
274 sage: x = random_eja().random_element()
275 sage: x.degree() == x.minimal_polynomial().degree()
276 True
277
278 ::
279
280 sage: set_random_seed()
281 sage: x = random_eja().random_element()
282 sage: x.degree() == x.minimal_polynomial().degree()
283 True
284
285 The minimal polynomial and the characteristic polynomial coincide
286 and are known (see Alizadeh, Example 11.11) for all elements of
287 the spin factor algebra that aren't scalar multiples of the
288 identity::
289
290 sage: set_random_seed()
291 sage: n = ZZ.random_element(2,10).abs()
292 sage: J = JordanSpinSimpleEJA(n)
293 sage: y = J.random_element()
294 sage: while y == y.coefficient(0)*J.one():
295 ....: y = J.random_element()
296 sage: y0 = y.vector()[0]
297 sage: y_bar = y.vector()[1:]
298 sage: actual = y.minimal_polynomial()
299 sage: x = SR.symbol('x', domain='real')
300 sage: expected = x^2 - 2*y0*x + (y0^2 - norm(y_bar)^2)
301 sage: bool(actual == expected)
302 True
303
304 """
305 # The element we're going to call "minimal_polynomial()" on.
306 # Either myself, interpreted as an element of a finite-
307 # dimensional algebra, or an element of an associative
308 # subalgebra.
309 elt = None
310
311 if self.parent().is_associative():
312 elt = FiniteDimensionalAlgebraElement(self.parent(), self)
313 else:
314 V = self.span_of_powers()
315 assoc_subalg = self.subalgebra_generated_by()
316 # Mis-design warning: the basis used for span_of_powers()
317 # and subalgebra_generated_by() must be the same, and in
318 # the same order!
319 elt = assoc_subalg(V.coordinates(self.vector()))
320
321 # Recursive call, but should work since elt lives in an
322 # associative algebra.
323 return elt.minimal_polynomial()
324
325
326 def quadratic_representation(self):
327 """
328 Return the quadratic representation of this element.
329
330 EXAMPLES:
331
332 The explicit form in the spin factor algebra is given by
333 Alizadeh's Example 11.12::
334
335 sage: n = ZZ.random_element(1,10).abs()
336 sage: J = JordanSpinSimpleEJA(n)
337 sage: x = J.random_element()
338 sage: x_vec = x.vector()
339 sage: x0 = x_vec[0]
340 sage: x_bar = x_vec[1:]
341 sage: A = matrix(QQ, 1, [x_vec.inner_product(x_vec)])
342 sage: B = 2*x0*x_bar.row()
343 sage: C = 2*x0*x_bar.column()
344 sage: D = identity_matrix(QQ, n-1)
345 sage: D = (x0^2 - x_bar.inner_product(x_bar))*D
346 sage: D = D + 2*x_bar.tensor_product(x_bar)
347 sage: Q = block_matrix(2,2,[A,B,C,D])
348 sage: Q == x.quadratic_representation()
349 True
350
351 """
352 return 2*(self.matrix()**2) - (self**2).matrix()
353
354
355 def span_of_powers(self):
356 """
357 Return the vector space spanned by successive powers of
358 this element.
359 """
360 # The dimension of the subalgebra can't be greater than
361 # the big algebra, so just put everything into a list
362 # and let span() get rid of the excess.
363 V = self.vector().parent()
364 return V.span( (self**d).vector() for d in xrange(V.dimension()) )
365
366
367 def subalgebra_generated_by(self):
368 """
369 Return the associative subalgebra of the parent EJA generated
370 by this element.
371
372 TESTS::
373
374 sage: set_random_seed()
375 sage: x = random_eja().random_element()
376 sage: x.subalgebra_generated_by().is_associative()
377 True
378
379 Squaring in the subalgebra should be the same thing as
380 squaring in the superalgebra::
381
382 sage: set_random_seed()
383 sage: x = random_eja().random_element()
384 sage: u = x.subalgebra_generated_by().random_element()
385 sage: u.matrix()*u.vector() == (u**2).vector()
386 True
387
388 """
389 # First get the subspace spanned by the powers of myself...
390 V = self.span_of_powers()
391 F = self.base_ring()
392
393 # Now figure out the entries of the right-multiplication
394 # matrix for the successive basis elements b0, b1,... of
395 # that subspace.
396 mats = []
397 for b_right in V.basis():
398 eja_b_right = self.parent()(b_right)
399 b_right_rows = []
400 # The first row of the right-multiplication matrix by
401 # b1 is what we get if we apply that matrix to b1. The
402 # second row of the right multiplication matrix by b1
403 # is what we get when we apply that matrix to b2...
404 #
405 # IMPORTANT: this assumes that all vectors are COLUMN
406 # vectors, unlike our superclass (which uses row vectors).
407 for b_left in V.basis():
408 eja_b_left = self.parent()(b_left)
409 # Multiply in the original EJA, but then get the
410 # coordinates from the subalgebra in terms of its
411 # basis.
412 this_row = V.coordinates((eja_b_left*eja_b_right).vector())
413 b_right_rows.append(this_row)
414 b_right_matrix = matrix(F, b_right_rows)
415 mats.append(b_right_matrix)
416
417 # It's an algebra of polynomials in one element, and EJAs
418 # are power-associative.
419 #
420 # TODO: choose generator names intelligently.
421 return FiniteDimensionalEuclideanJordanAlgebra(F, mats, assume_associative=True, names='f')
422
423
424 def subalgebra_idempotent(self):
425 """
426 Find an idempotent in the associative subalgebra I generate
427 using Proposition 2.3.5 in Baes.
428
429 TESTS::
430
431 sage: set_random_seed()
432 sage: J = eja_rn(5)
433 sage: c = J.random_element().subalgebra_idempotent()
434 sage: c^2 == c
435 True
436 sage: J = JordanSpinSimpleEJA(5)
437 sage: c = J.random_element().subalgebra_idempotent()
438 sage: c^2 == c
439 True
440
441 """
442 if self.is_nilpotent():
443 raise ValueError("this only works with non-nilpotent elements!")
444
445 V = self.span_of_powers()
446 J = self.subalgebra_generated_by()
447 # Mis-design warning: the basis used for span_of_powers()
448 # and subalgebra_generated_by() must be the same, and in
449 # the same order!
450 u = J(V.coordinates(self.vector()))
451
452 # The image of the matrix of left-u^m-multiplication
453 # will be minimal for some natural number s...
454 s = 0
455 minimal_dim = V.dimension()
456 for i in xrange(1, V.dimension()):
457 this_dim = (u**i).matrix().image().dimension()
458 if this_dim < minimal_dim:
459 minimal_dim = this_dim
460 s = i
461
462 # Now minimal_matrix should correspond to the smallest
463 # non-zero subspace in Baes's (or really, Koecher's)
464 # proposition.
465 #
466 # However, we need to restrict the matrix to work on the
467 # subspace... or do we? Can't we just solve, knowing that
468 # A(c) = u^(s+1) should have a solution in the big space,
469 # too?
470 #
471 # Beware, solve_right() means that we're using COLUMN vectors.
472 # Our FiniteDimensionalAlgebraElement superclass uses rows.
473 u_next = u**(s+1)
474 A = u_next.matrix()
475 c_coordinates = A.solve_right(u_next.vector())
476
477 # Now c_coordinates is the idempotent we want, but it's in
478 # the coordinate system of the subalgebra.
479 #
480 # We need the basis for J, but as elements of the parent algebra.
481 #
482 basis = [self.parent(v) for v in V.basis()]
483 return self.parent().linear_combination(zip(c_coordinates, basis))
484
485
486 def trace(self):
487 """
488 Return my trace, the sum of my eigenvalues.
489
490 EXAMPLES::
491
492 sage: J = JordanSpinSimpleEJA(3)
493 sage: e0,e1,e2 = J.gens()
494 sage: x = e0 + e1 + e2
495 sage: x.trace()
496 2
497
498 """
499 cs = self.characteristic_polynomial().coefficients(sparse=False)
500 if len(cs) >= 2:
501 return -1*cs[-2]
502 else:
503 raise ValueError('charpoly had fewer than 2 coefficients')
504
505
506 def trace_inner_product(self, other):
507 """
508 Return the trace inner product of myself and ``other``.
509 """
510 if not other in self.parent():
511 raise ArgumentError("'other' must live in the same algebra")
512
513 return (self*other).trace()
514
515
516 def eja_rn(dimension, field=QQ):
517 """
518 Return the Euclidean Jordan Algebra corresponding to the set
519 `R^n` under the Hadamard product.
520
521 EXAMPLES:
522
523 This multiplication table can be verified by hand::
524
525 sage: J = eja_rn(3)
526 sage: e0,e1,e2 = J.gens()
527 sage: e0*e0
528 e0
529 sage: e0*e1
530 0
531 sage: e0*e2
532 0
533 sage: e1*e1
534 e1
535 sage: e1*e2
536 0
537 sage: e2*e2
538 e2
539
540 """
541 # The FiniteDimensionalAlgebra constructor takes a list of
542 # matrices, the ith representing right multiplication by the ith
543 # basis element in the vector space. So if e_1 = (1,0,0), then
544 # right (Hadamard) multiplication of x by e_1 picks out the first
545 # component of x; and likewise for the ith basis element e_i.
546 Qs = [ matrix(field, dimension, dimension, lambda k,j: 1*(k == j == i))
547 for i in xrange(dimension) ]
548
549 return FiniteDimensionalEuclideanJordanAlgebra(field,Qs,rank=dimension)
550
551
552
553 def random_eja():
554 """
555 Return a "random" finite-dimensional Euclidean Jordan Algebra.
556
557 ALGORITHM:
558
559 For now, we choose a random natural number ``n`` (greater than zero)
560 and then give you back one of the following:
561
562 * The cartesian product of the rational numbers ``n`` times; this is
563 ``QQ^n`` with the Hadamard product.
564
565 * The Jordan spin algebra on ``QQ^n``.
566
567 * The ``n``-by-``n`` rational symmetric matrices with the symmetric
568 product.
569
570 Later this might be extended to return Cartesian products of the
571 EJAs above.
572
573 TESTS::
574
575 sage: random_eja()
576 Euclidean Jordan algebra of degree...
577
578 """
579 n = ZZ.random_element(1,5).abs()
580 constructor = choice([eja_rn,
581 JordanSpinSimpleEJA,
582 RealSymmetricSimpleEJA,
583 ComplexHermitianSimpleEJA])
584 return constructor(n, field=QQ)
585
586
587
588 def _real_symmetric_basis(n, field=QQ):
589 """
590 Return a basis for the space of real symmetric n-by-n matrices.
591 """
592 # The basis of symmetric matrices, as matrices, in their R^(n-by-n)
593 # coordinates.
594 S = []
595 for i in xrange(n):
596 for j in xrange(i+1):
597 Eij = matrix(field, n, lambda k,l: k==i and l==j)
598 if i == j:
599 Sij = Eij
600 else:
601 # Beware, orthogonal but not normalized!
602 Sij = Eij + Eij.transpose()
603 S.append(Sij)
604 return S
605
606
607 def _complex_hermitian_basis(n, field=QQ):
608 """
609 Returns a basis for the space of complex Hermitian n-by-n matrices.
610
611 TESTS::
612
613 sage: set_random_seed()
614 sage: n = ZZ.random_element(1,5).abs()
615 sage: all( M.is_symmetric() for M in _complex_hermitian_basis(n) )
616 True
617
618 """
619 F = QuadraticField(-1, 'I')
620 I = F.gen()
621
622 # This is like the symmetric case, but we need to be careful:
623 #
624 # * We want conjugate-symmetry, not just symmetry.
625 # * The diagonal will (as a result) be real.
626 #
627 S = []
628 for i in xrange(n):
629 for j in xrange(i+1):
630 Eij = matrix(field, n, lambda k,l: k==i and l==j)
631 if i == j:
632 Sij = _embed_complex_matrix(Eij)
633 S.append(Sij)
634 else:
635 # Beware, orthogonal but not normalized! The second one
636 # has a minus because it's conjugated.
637 Sij_real = _embed_complex_matrix(Eij + Eij.transpose())
638 S.append(Sij_real)
639 Sij_imag = _embed_complex_matrix(I*Eij - I*Eij.transpose())
640 S.append(Sij_imag)
641 return S
642
643
644 def _multiplication_table_from_matrix_basis(basis):
645 """
646 At least three of the five simple Euclidean Jordan algebras have the
647 symmetric multiplication (A,B) |-> (AB + BA)/2, where the
648 multiplication on the right is matrix multiplication. Given a basis
649 for the underlying matrix space, this function returns a
650 multiplication table (obtained by looping through the basis
651 elements) for an algebra of those matrices.
652 """
653 # In S^2, for example, we nominally have four coordinates even
654 # though the space is of dimension three only. The vector space V
655 # is supposed to hold the entire long vector, and the subspace W
656 # of V will be spanned by the vectors that arise from symmetric
657 # matrices. Thus for S^2, dim(V) == 4 and dim(W) == 3.
658 field = basis[0].base_ring()
659 dimension = basis[0].nrows()
660
661 def mat2vec(m):
662 return vector(field, m.list())
663
664 def vec2mat(v):
665 return matrix(field, dimension, v.list())
666
667 V = VectorSpace(field, dimension**2)
668 W = V.span( mat2vec(s) for s in basis )
669
670 # Taking the span above reorders our basis (thanks, jerk!) so we
671 # need to put our "matrix basis" in the same order as the
672 # (reordered) vector basis.
673 S = [ vec2mat(b) for b in W.basis() ]
674
675 Qs = []
676 for s in S:
677 # Brute force the multiplication-by-s matrix by looping
678 # through all elements of the basis and doing the computation
679 # to find out what the corresponding row should be. BEWARE:
680 # these multiplication tables won't be symmetric! It therefore
681 # becomes REALLY IMPORTANT that the underlying algebra
682 # constructor uses ROW vectors and not COLUMN vectors. That's
683 # why we're computing rows here and not columns.
684 Q_rows = []
685 for t in S:
686 this_row = mat2vec((s*t + t*s)/2)
687 Q_rows.append(W.coordinates(this_row))
688 Q = matrix(field, W.dimension(), Q_rows)
689 Qs.append(Q)
690
691 return Qs
692
693
694 def _embed_complex_matrix(M):
695 """
696 Embed the n-by-n complex matrix ``M`` into the space of real
697 matrices of size 2n-by-2n via the map the sends each entry `z = a +
698 bi` to the block matrix ``[[a,b],[-b,a]]``.
699
700 EXAMPLES::
701
702 sage: F = QuadraticField(-1,'i')
703 sage: x1 = F(4 - 2*i)
704 sage: x2 = F(1 + 2*i)
705 sage: x3 = F(-i)
706 sage: x4 = F(6)
707 sage: M = matrix(F,2,[x1,x2,x3,x4])
708 sage: _embed_complex_matrix(M)
709 [ 4 2| 1 -2]
710 [-2 4| 2 1]
711 [-----+-----]
712 [ 0 1| 6 0]
713 [-1 0| 0 6]
714
715 """
716 n = M.nrows()
717 if M.ncols() != n:
718 raise ArgumentError("the matrix 'M' must be square")
719 field = M.base_ring()
720 blocks = []
721 for z in M.list():
722 a = z.real()
723 b = z.imag()
724 blocks.append(matrix(field, 2, [[a,-b],[b,a]]))
725
726 # We can drop the imaginaries here.
727 return block_matrix(field.base_ring(), n, blocks)
728
729
730 def _unembed_complex_matrix(M):
731 """
732 The inverse of _embed_complex_matrix().
733
734 EXAMPLES::
735
736 sage: A = matrix(QQ,[ [ 1, 2, 3, 4],
737 ....: [-2, 1, -4, 3],
738 ....: [ 9, 10, 11, 12],
739 ....: [-10, 9, -12, 11] ])
740 sage: _unembed_complex_matrix(A)
741 [ -2*i + 1 -4*i + 3]
742 [ -10*i + 9 -12*i + 11]
743 """
744 n = ZZ(M.nrows())
745 if M.ncols() != n:
746 raise ArgumentError("the matrix 'M' must be square")
747 if not n.mod(2).is_zero():
748 raise ArgumentError("the matrix 'M' must be a complex embedding")
749
750 F = QuadraticField(-1, 'i')
751 i = F.gen()
752
753 # Go top-left to bottom-right (reading order), converting every
754 # 2-by-2 block we see to a single complex element.
755 elements = []
756 for k in xrange(n/2):
757 for j in xrange(n/2):
758 submat = M[2*k:2*k+2,2*j:2*j+2]
759 if submat[0,0] != submat[1,1]:
760 raise ArgumentError('bad real submatrix')
761 if submat[0,1] != -submat[1,0]:
762 raise ArgumentError('bad imag submatrix')
763 z = submat[0,0] + submat[1,0]*i
764 elements.append(z)
765
766 return matrix(F, n/2, elements)
767
768
769 def RealSymmetricSimpleEJA(n, field=QQ):
770 """
771 The rank-n simple EJA consisting of real symmetric n-by-n
772 matrices, the usual symmetric Jordan product, and the trace inner
773 product. It has dimension `(n^2 + n)/2` over the reals.
774
775 EXAMPLES::
776
777 sage: J = RealSymmetricSimpleEJA(2)
778 sage: e0, e1, e2 = J.gens()
779 sage: e0*e0
780 e0
781 sage: e1*e1
782 e0 + e2
783 sage: e2*e2
784 e2
785
786 TESTS:
787
788 The degree of this algebra is `(n^2 + n) / 2`::
789
790 sage: set_random_seed()
791 sage: n = ZZ.random_element(1,5).abs()
792 sage: J = RealSymmetricSimpleEJA(n)
793 sage: J.degree() == (n^2 + n)/2
794 True
795
796 """
797 S = _real_symmetric_basis(n, field=field)
798 Qs = _multiplication_table_from_matrix_basis(S)
799
800 return FiniteDimensionalEuclideanJordanAlgebra(field,Qs,rank=n)
801
802
803 def ComplexHermitianSimpleEJA(n, field=QQ):
804 """
805 The rank-n simple EJA consisting of complex Hermitian n-by-n
806 matrices over the real numbers, the usual symmetric Jordan product,
807 and the real-part-of-trace inner product. It has dimension `n^2` over
808 the reals.
809
810 TESTS:
811
812 The degree of this algebra is `n^2`::
813
814 sage: set_random_seed()
815 sage: n = ZZ.random_element(1,5).abs()
816 sage: J = ComplexHermitianSimpleEJA(n)
817 sage: J.degree() == n^2
818 True
819
820 """
821 S = _complex_hermitian_basis(n)
822 Qs = _multiplication_table_from_matrix_basis(S)
823 return FiniteDimensionalEuclideanJordanAlgebra(field, Qs, rank=n)
824
825
826 def QuaternionHermitianSimpleEJA(n):
827 """
828 The rank-n simple EJA consisting of self-adjoint n-by-n quaternion
829 matrices, the usual symmetric Jordan product, and the
830 real-part-of-trace inner product. It has dimension `2n^2 - n` over
831 the reals.
832 """
833 pass
834
835 def OctonionHermitianSimpleEJA(n):
836 """
837 This shit be crazy. It has dimension 27 over the reals.
838 """
839 n = 3
840 pass
841
842 def JordanSpinSimpleEJA(n, field=QQ):
843 """
844 The rank-2 simple EJA consisting of real vectors ``x=(x0, x_bar)``
845 with the usual inner product and jordan product ``x*y =
846 (<x_bar,y_bar>, x0*y_bar + y0*x_bar)``. It has dimension `n` over
847 the reals.
848
849 EXAMPLES:
850
851 This multiplication table can be verified by hand::
852
853 sage: J = JordanSpinSimpleEJA(4)
854 sage: e0,e1,e2,e3 = J.gens()
855 sage: e0*e0
856 e0
857 sage: e0*e1
858 e1
859 sage: e0*e2
860 e2
861 sage: e0*e3
862 e3
863 sage: e1*e2
864 0
865 sage: e1*e3
866 0
867 sage: e2*e3
868 0
869
870 In one dimension, this is the reals under multiplication::
871
872 sage: J1 = JordanSpinSimpleEJA(1)
873 sage: J2 = eja_rn(1)
874 sage: J1 == J2
875 True
876
877 """
878 Qs = []
879 id_matrix = identity_matrix(field, n)
880 for i in xrange(n):
881 ei = id_matrix.column(i)
882 Qi = zero_matrix(field, n)
883 Qi.set_row(0, ei)
884 Qi.set_column(0, ei)
885 Qi += diagonal_matrix(n, [ei[0]]*n)
886 # The addition of the diagonal matrix adds an extra ei[0] in the
887 # upper-left corner of the matrix.
888 Qi[0,0] = Qi[0,0] * ~field(2)
889 Qs.append(Qi)
890
891 # The rank of the spin factor algebra is two, UNLESS we're in a
892 # one-dimensional ambient space (the rank is bounded by the
893 # ambient dimension).
894 return FiniteDimensionalEuclideanJordanAlgebra(field, Qs, rank=min(n,2))