Official Name: | Sedenions |
Symbol: | S |
Type: | nonassociative algebra |
Units: | e0, ..., e15 |
Identity: | e0 |
Properties: | power associativity distributivity |
S
The term sedenion is also used for other 16-dimensional algebraic structures, such as a tensor product of two copies of the biquaternions, or the algebra of 4 × 4 matrices over the real numbers, or that studied by .
Like octonions, multiplication of sedenions is neither commutative nor associative.But in contrast to the octonions, the sedenions do not even have the property of being alternative.They do, however, have the property of power associativity, which can be stated as that, for any element x of
S
xn
Every sedenion is a linear combination of the unit sedenions
e0
e1
e2
e3
e15
x=x0e0+x1e1+x2e2+ … +x14e14+x15e15.
Addition and subtraction are defined by the addition and subtraction of corresponding coefficients and multiplication is distributive over addition.
Like other algebras based on the Cayley–Dickson construction, the sedenions contain the algebra they were constructed from. So, they contain the octonions (generated by
e0
e7
e0
e3
e0
e1
e0
e0
(e3+e10)(e6-e15)
A sedenion multiplication table is shown below:
eiej | ej | |||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
e0 e1 | e2 e3 | e4 e5 | e6 e7 | e8 e9 | e10 e11 | e12 e13 | e14 e15 | |||||||||||
ei | e0 | -- color: white; when minus sign --> | e0 | e1 | e2 | e3 | e4 | e5 | e6 | e7 | e8 | e9 | e10 | e11 | e12 | e13 | e14 | e15 |
e1 | e1 | -e0 | e3 | -e2 | e5 | -e4 | -e7 | e6 | e9 | -e8 | -e11 | e10 | -e13 | e12 | e15 | -e14 | ||
e2 | e2 | -e3 | -e0 | e1 | e6 | e7 | -e4 | -e5 | e10 | e11 | -e8 | -e9 | -e14 | -e15 | e12 | e13 | ||
e3 | e3 | e2 | -e1 | -e0 | e7 | -e6 | e5 | -e4 | e11 | -e10 | e9 | -e8 | -e15 | e14 | -e13 | e12 | ||
e4 | e4 | -e5 | -e6 | -e7 | -e0 | e1 | e2 | e3 | e12 | e13 | e14 | e15 | -e8 | -e9 | -e10 | -e11 | ||
e5 | e5 | e4 | -e7 | e6 | -e1 | -e0 | -e3 | e2 | e13 | -e12 | e15 | -e14 | e9 | -e8 | e11 | -e10 | ||
e6 | e6 | e7 | e4 | -e5 | -e2 | e3 | -e0 | -e1 | e14 | -e15 | -e12 | e13 | e10 | -e11 | -e8 | e9 | ||
e7 | e7 | -e6 | e5 | e4 | -e3 | -e2 | e1 | -e0 | e15 | e14 | -e13 | -e12 | e11 | e10 | -e9 | -e8 | ||
e8 | e8 | -e9 | -e10 | -e11 | -e12 | -e13 | -e14 | -e15 | -e0 | e1 | e2 | e3 | e4 | e5 | e6 | e7 | ||
e9 | e9 | e8 | -e11 | e10 | -e13 | e12 | e15 | -e14 | -e1 | -e0 | -e3 | e2 | -e5 | e4 | e7 | -e6 | ||
e10 | e10 | e11 | e8 | -e9 | -e14 | -e15 | e12 | e13 | -e2 | e3 | -e0 | -e1 | -e6 | -e7 | e4 | e5 | ||
e11 | e11 | -e10 | e9 | e8 | -e15 | e14 | -e13 | e12 | -e3 | -e2 | e1 | -e0 | -e7 | e6 | -e5 | e4 | ||
e12 | e12 | e13 | e14 | e15 | e8 | -e9 | -e10 | -e11 | -e4 | e5 | e6 | e7 | -e0 | -e1 | -e2 | -e3 | ||
e13 | e13 | -e12 | e15 | -e14 | e9 | e8 | e11 | -e10 | -e5 | -e4 | e7 | -e6 | e1 | -e0 | e3 | -e2 | ||
e14 | e14 | -e15 | -e12 | e13 | e10 | -e11 | e8 | e9 | -e6 | -e7 | -e4 | e5 | e2 | -e3 | -e0 | e1 | ||
e15 | e15 | e14 | -e13 | -e12 | e11 | e10 | -e9 | e8 | -e7 | e6 | -e5 | -e4 | e3 | e2 | -e1 | -e0 |
From the above table, we can see that:
e0ei=eie0=eiforalli,
eiei=-e0fori ≠ 0,
eiej=-ejeifori ≠ jwithi,j ≠ 0.
The sedenions are not fully anti-associative. Choose any four generators,
i,j,k
l
In particular, in the table above, using
e1,e2,e4
e8
(e1e2)e12=e1(e2e12)=-e15
The 35 triads that make up this specific sedenion multiplication table with the 7 triads of the octonions used in creating the sedenion through the Cayley–Dickson construction shown in bold:
The binary representations of the indices of these triples bitwise XOR to 0.
The list of 84 sets of zero divisors
\{ea,eb,ec,ed\}
(ea+eb)\circ(ec+ed)=0
showed that the space of pairs of norm-one sedenions that multiply to zero is homeomorphic to the compact form of the exceptional Lie group G2. (Note that in his paper, a "zero divisor" means a pair of elements that multiply to zero.)
SU(3)c x U(1)em |
C ⊗ S
\rho+=1/2(1+ie15)
e15
e7
O
C ⊗ O
Cl(6)
SU(3)c x U(1)em |
S
C ⊗ O
(C ⊗ O)L\congCl(6)
(C ⊗ S)L
Cl(2)
Sedenion neural networks provide a means of efficient and compact expression in machine learning applications and have been used in solving multiple time-series and traffic forecasting problems.[2] [3]