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1 1. Add cartesian products to random_eja().
2
3 2. Add references and start citing them.
4
5 3. Implement the octonion simple EJA. We don't actually need octonions
6 for this to work, only their real embedding (some 8x8 monstrosity).
7
8 4. Pre-cache charpoly for some small algebras?
9
10 RealSymmetricEJA(4):
11
12 sage: F = J.base_ring()
13 sage: a0 = (1/4)*X[4]**2*X[6]**2 - (1/2)*X[2]*X[5]*X[6]**2 - (1/2)*X[3]*X[4]*X[6]*X[7] + (F(2).sqrt()/2)*X[1]*X[5]*X[6]*X[7] + (1/4)*X[3]**2*X[7]**2 - (1/2)*X[0]*X[5]*X[7]**2 + (F(2).sqrt()/2)*X[2]*X[3]*X[6]*X[8] - (1/2)*X[1]*X[4]*X[6*X[8] - (1/2)*X[1]*X[3]*X[7]*X[8] + (F(2).sqrt()/2)*X[0]*X[4]*X[7]*X[8] + (1/4)*X[1]**2*X[8]**2 - (1/2)*X[0]*X[2]*X[8]**2 - (1/2)*X[2]*X[3]**2*X[9] + (F(2).sqrt()/2)*X[1]*X[3]*X[4]*X[9] - (1/2)*X[0]*X[4]**2*X[9] - (1/2)*X[1]**2*X[5]*X[9] + X[0]*X[2]*X[5]*X[9]
14
15 5. Profile the construction of "large" matrix algebras (like the
16 15-dimensional QuaternionHermitianAlgebra(3)) to find out why
17 they're so slow.
18
19 6. Instead of storing a basis multiplication matrix, just make
20 product_on_basis() a cached method and manually cache its
21 entries. The cython cached method lookup should be faster than a
22 python-based matrix lookup anyway. NOTE: we should still be able
23 to recompute the table somehow. Is this worth it?
24
25 7. What the ever-loving fuck is this shit?
26
27 sage: O = Octonions(QQ)
28 sage: e0 = O.monomial(0)
29 sage: e0*[[[[]]]]
30 [[[[]]]]*e0
31
32 8. In fact, could my octonion matrix algebra be generalized for any
33 algebra of matrices over the reals whose entries are not real? Then
34 we wouldn't need real embeddings at all. They might even be fricking
35 vector spaces if I did that...
36
37 9. Every once in a long while, the test
38
39 sage: set_random_seed()
40 sage: x = random_eja().random_element()
41 sage: x.is_invertible() == (x.det() != 0)
42
43 in eja_element.py returns False.