In multilinear algebra , a dyadic or dyadic tensor is a second order tensor written in a special notation, formed by juxtaposing pairs of vectors, along with a notation for manipulating such expressions analogous to the rules for matrix algebra . The notation and terminology is relatively obsolete today. Its uses in physics include stress analysis and electromagnetism .
Dyadic notation was first established by Josiah Willard Gibbs in 1884.
In this article, upper-case bold variables denote dyadics (including dyads) whereas lower-case bold variables denote vectors. An alternative notation uses respectively double and single over- or underbars.
Definitions and terminology
A dyad is a tensor of order two and rank one, and is the result of the dyadic product of two vectors (complex vectors in general), whereas a dyadic is a general tensor of order two.
There are several equivalent terms and notations for this product:
the dyadic product of two vectors a and b is denoted by the juxtaposition ab ,
the outer product column vectors a and b is denoted and defined as a ⊗ b or ab T , where T means transpose ,
the tensor product a and b is denoted a ⊗ b , In the dyadic context they all have the same definition and meaning, and are used synonymously, although the tensor product is an instance of the more general and abstract use of the term.
Three-dimensional Euclidean space
To illustrate the equivalent usage, consider three-dimensional Euclidean space , letting:
a
=
a
1
i
+
a
2
j
+
a
3
k
{\displaystyle \mathbf {a} =a_{1}\mathbf {i} +a_{2}\mathbf {j} +a_{3}\mathbf {k} }
b
=
b
1
i
+
b
2
j
+
b
3
k
{\displaystyle \mathbf {b} =b_{1}\mathbf {i} +b_{2}\mathbf {j} +b_{3}\mathbf {k} }
be two vectors where i , j , k (also denoted e 1 , e 2 , e 3 ) are the standard basis vectors in this vector space (see also Cartesian coordinates ). Then the dyadic product of a and b can be represented as a sum:
a
b
=
a
1
b
1
i
i
+
a
1
b
2
i
j
+
a
1
b
3
i
k
+
a
2
b
1
j
i
+
a
2
b
2
j
j
+
a
2
b
3
j
k
+
a
3
b
1
k
i
+
a
3
b
2
k
j
+
a
3
b
3
k
k
{\displaystyle {\begin{array}{llll}\mathbf {ab} =&a_{1}b_{1}\mathbf {ii} &+a_{1}b_{2}\mathbf {ij} &+a_{1}b_{3}\mathbf {ik} \\&+a_{2}b_{1}\mathbf {ji} &+a_{2}b_{2}\mathbf {jj} &+a_{2}b_{3}\mathbf {jk} \\&+a_{3}b_{1}\mathbf {ki} &+a_{3}b_{2}\mathbf {kj} &+a_{3}b_{3}\mathbf {kk} \end{array}}}
or by extension from row and column vectors, a 3×3 matrix (also the result of the outer product or tensor product of a and b ):
a
b
≡
a
⊗
b
≡
a
b
T
=
(
a
1
a
2
a
3
)
(
b
1
b
2
b
3
)
=
(
a
1
b
1
a
1
b
2
a
1
b
3
a
2
b
1
a
2
b
2
a
2
b
3
a
3
b
1
a
3
b
2
a
3
b
3
)
.
{\displaystyle \mathbf {ab} \equiv \mathbf {a} \otimes \mathbf {b} \equiv \mathbf {ab} ^{\mathrm {T} }={\begin{pmatrix}a_{1}\\a_{2}\\a_{3}\end{pmatrix}}{\begin{pmatrix}b_{1}&b_{2}&b_{3}\end{pmatrix}}={\begin{pmatrix}a_{1}b_{1}&a_{1}b_{2}&a_{1}b_{3}\\a_{2}b_{1}&a_{2}b_{2}&a_{2}b_{3}\\a_{3}b_{1}&a_{3}b_{2}&a_{3}b_{3}\end{pmatrix}}.}
A dyad is a component of the dyadic (a monomial of the sum or equivalently entry of the matrix) - the juxtaposition of a pair of basis vectors scalar multiplied by a number.
Just as the standard basis (and unit) vectors i , j , k , have the representations:
i
=
(
1
0
0
)
,
j
=
(
0
1
0
)
,
k
=
(
0
0
1
)
{\displaystyle \mathbf {i} ={\begin{pmatrix}1\\0\\0\end{pmatrix}},\mathbf {j} ={\begin{pmatrix}0\\1\\0\end{pmatrix}},\mathbf {k} ={\begin{pmatrix}0\\0\\1\end{pmatrix}}}
(which can be transposed), the standard basis (and unit) dyads have the representation:
i
i
=
(
1
0
0
0
0
0
0
0
0
)
,
⋯
j
i
=
(
0
0
0
1
0
0
0
0
0
)
,
⋯
j
k
=
(
0
0
0
0
0
1
0
0
0
)
⋯
{\displaystyle \mathbf {ii} ={\begin{pmatrix}1&0&0\\0&0&0\\0&0&0\end{pmatrix}},\cdots \mathbf {ji} ={\begin{pmatrix}0&0&0\\1&0&0\\0&0&0\end{pmatrix}},\cdots \mathbf {jk} ={\begin{pmatrix}0&0&0\\0&0&1\\0&0&0\end{pmatrix}}\cdots }
For a simple numerical example in the standard basis:
A
=
2
i
j
+
3
2
j
i
−
8
π
j
k
+
2
2
3
k
k
=
2
(
0
1
0
0
0
0
0
0
0
)
+
3
2
(
0
0
0
1
0
0
0
0
0
)
−
8
π
(
0
0
0
0
0
1
0
0
0
)
+
2
2
3
(
0
0
0
0
0
0
0
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1
)
=
(
0
2
0
3
/
2
0
−
8
π
0
0
2
2
3
)
{\displaystyle {\begin{aligned}\mathbf {A} &=2\mathbf {ij} +{\frac {\sqrt {3}}{2}}\mathbf {ji} -8\pi \mathbf {jk} +{\frac {2{\sqrt {2}}}{3}}\mathbf {kk} \\&=2{\begin{pmatrix}0&1&0\\0&0&0\\0&0&0\end{pmatrix}}+{\frac {\sqrt {3}}{2}}{\begin{pmatrix}0&0&0\\1&0&0\\0&0&0\end{pmatrix}}-8\pi {\begin{pmatrix}0&0&0\\0&0&1\\0&0&0\end{pmatrix}}+{\frac {2{\sqrt {2}}}{3}}{\begin{pmatrix}0&0&0\\0&0&0\\0&0&1\end{pmatrix}}\\&={\begin{pmatrix}0&2&0\\{\sqrt {3}}/2&0&-8\pi \\0&0&{\frac {2{\sqrt {2}}}{3}}\end{pmatrix}}\end{aligned}}}
N -dimensional Euclidean spaceIf the Euclidean space is N -dimensional , and
a
=
∑
i
=
1
N
a
i
e
i
=
a
1
e
1
+
a
2
e
2
+
⋯
a
N
e
N
{\displaystyle \mathbf {a} =\sum _{i=1}^{N}a_{i}\mathbf {e} _{i}=a_{1}\mathbf {e} _{1}+a_{2}\mathbf {e} _{2}+\cdots a_{N}\mathbf {e} _{N}}
b
=
∑
j
=
1
N
b
j
e
j
=
b
1
e
1
+
b
2
e
2
+
⋯
b
N
e
N
{\displaystyle \mathbf {b} =\sum _{j=1}^{N}b_{j}\mathbf {e} _{j}=b_{1}\mathbf {e} _{1}+b_{2}\mathbf {e} _{2}+\cdots b_{N}\mathbf {e} _{N}}
where e i e j standard basis vectors in N -dimensions (the index i on e i ai ), then in algebraic form their dyadic product is:
A
=
∑
j
=
1
N
∑
i
=
1
N
a
i
b
j
e
i
e
j
.
{\displaystyle \mathbf {A} =\sum _{j=1}^{N}\sum _{i=1}^{N}a_{i}b_{j}{\mathbf {e} }_{i}\mathbf {e} _{j}.}
This is known as the nonion form of the dyadic. Their outer/tensor product in matrix form is:
a
b
=
a
b
T
=
(
a
1
a
2
⋮
a
N
)
(
b
1
b
2
⋯
b
N
)
=
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a
1
b
1
a
1
b
2
⋯
a
1
b
N
a
2
b
1
a
2
b
2
⋯
a
2
b
N
⋮
⋮
⋱
⋮
a
N
b
1
a
N
b
2
⋯
a
N
b
N
)
.
{\displaystyle \mathbf {ab} =\mathbf {ab} ^{\mathrm {T} }={\begin{pmatrix}a_{1}\\a_{2}\\\vdots \\a_{N}\end{pmatrix}}{\begin{pmatrix}b_{1}&b_{2}&\cdots &b_{N}\end{pmatrix}}={\begin{pmatrix}a_{1}b_{1}&a_{1}b_{2}&\cdots &a_{1}b_{N}\\a_{2}b_{1}&a_{2}b_{2}&\cdots &a_{2}b_{N}\\\vdots &\vdots &\ddots &\vdots \\a_{N}b_{1}&a_{N}b_{2}&\cdots &a_{N}b_{N}\end{pmatrix}}.}
A dyadic polynomial A , otherwise known as a dyadic, is formed from multiple vectors a i b j
A
=
∑
i
a
i
b
i
=
a
1
b
1
+
a
2
b
2
+
a
3
b
3
+
⋯
{\displaystyle \mathbf {A} =\sum _{i}\mathbf {a} _{i}\mathbf {b} _{i}=\mathbf {a} _{1}\mathbf {b} _{1}+\mathbf {a} _{2}\mathbf {b} _{2}+\mathbf {a} _{3}\mathbf {b} _{3}+\cdots }
A dyadic which cannot be reduced to a sum of less than N dyads is said to be complete. In this case, the forming vectors are non-coplanar,[dubious discuss see Chen (1983) .
Classification
The following table classifies dyadics:
Determinant
Adjugate
Matrix and its rank
Zero
= 0
= 0
= 0; rank 0: all zeroes
Linear
= 0
= 0
≠ 0; rank 1: at least one non-zero element and all 2 × 2 subdeterminants zero (single dyadic)
Planar
= 0
≠ 0 (single dyadic)
≠ 0; rank 2: at least one non-zero 2 × 2 subdeterminant
Complete
≠ 0
≠ 0
≠ 0; rank 3: non-zero determinant
Identities
The following identities are a direct consequence of the definition of the dyadic product,[ 1] linearity of vectors:
Compatible with scalar multiplication :
(
α
a
)
b
=
a
(
α
b
)
=
α
(
a
b
)
{\displaystyle (\alpha \mathbf {a} )\mathbf {b} =\mathbf {a} (\alpha \mathbf {b} )=\alpha (\mathbf {a} \mathbf {b} )}
Distributive over vector addition :
a
(
b
+
c
)
=
a
b
+
a
c
{\displaystyle \mathbf {a} (\mathbf {b} +\mathbf {c} )=\mathbf {a} \mathbf {b} +\mathbf {a} \mathbf {c} }
(
a
+
b
)
c
=
a
c
+
b
c
{\displaystyle (\mathbf {a} +\mathbf {b} )\mathbf {c} =\mathbf {a} \mathbf {c} +\mathbf {b} \mathbf {c} }
Compatible with inner product :
(
a
b
)
⋅
c
=
a
(
b
⋅
c
)
{\displaystyle (\mathbf {a} \mathbf {b} )\cdot \mathbf {c} =\mathbf {a} (\mathbf {b} \cdot \mathbf {c} )}
a
⋅
(
b
c
)
=
(
a
⋅
b
)
c
{\displaystyle \mathbf {a} \cdot (\mathbf {b} \mathbf {c} )=(\mathbf {a} \cdot \mathbf {b} )\mathbf {c} }
where α is a scalar.
Dyadic algebra
Product of dyadic and vector
There are four operations defined on a vector and dyadic, constructed from the products defined on vectors.
Left
Right
Dot product
c
⋅
a
b
=
(
c
⋅
a
)
b
{\displaystyle \mathbf {c} \cdot \mathbf {a} \mathbf {b} =\left(\mathbf {c} \cdot \mathbf {a} \right)\mathbf {b} }
(
a
b
)
⋅
c
=
a
(
b
⋅
c
)
{\displaystyle \left(\mathbf {a} \mathbf {b} \right)\cdot \mathbf {c} =\mathbf {a} \left(\mathbf {b} \cdot \mathbf {c} \right)}
Cross product
c
×
(
a
b
)
=
(
c
×
a
)
b
{\displaystyle \mathbf {c} \times \left(\mathbf {ab} \right)=\left(\mathbf {c} \times \mathbf {a} \right)\mathbf {b} }
(
a
b
)
×
c
=
a
(
b
×
c
)
{\displaystyle \left(\mathbf {ab} \right)\times \mathbf {c} =\mathbf {a} \left(\mathbf {b} \times \mathbf {c} \right)}
Product of dyadic and dyadic
There are five operations for a dyadic to another dyadic. Let a , b , c , d be vectors. Then:
Dot
Cross
Dot
Dot product
(
a
b
)
⋅
(
c
d
)
=
a
(
b
⋅
c
)
d
=
(
b
⋅
c
)
a
d
{\displaystyle \left(\mathbf {a} \mathbf {b} \right)\cdot \left(\mathbf {c} \mathbf {d} \right)=\mathbf {a} \left(\mathbf {b} \cdot \mathbf {c} \right)\mathbf {d} =\left(\mathbf {b} \cdot \mathbf {c} \right)\mathbf {a} \mathbf {d} }
Double dot product
a
b
:
c
d
=
(
a
⋅
c
)
(
b
⋅
d
)
{\displaystyle \mathbf {ab} \colon \mathbf {cd} =\left(\mathbf {a} \cdot \mathbf {c} \right)\left(\mathbf {b} \cdot \mathbf {d} \right)}
or
(
a
b
)
:
(
c
d
)
=
c
⋅
(
a
b
)
⋅
d
=
(
a
⋅
c
)
(
b
⋅
d
)
{\displaystyle \left(\mathbf {ab} \right):\left(\mathbf {cd} \right)=\mathbf {c} \cdot \left(\mathbf {ab} \right)\cdot \mathbf {d} =\left(\mathbf {a} \cdot \mathbf {c} \right)\left(\mathbf {b} \cdot \mathbf {d} \right)}
Dot–cross product
(
a
b
)
⋅
×
(
c
d
)
=
(
a
⋅
c
)
(
b
×
d
)
{\displaystyle \left(\mathbf {ab} \right)\!\!\!{\begin{array}{c}_{\cdot }\\^{\times }\end{array}}\!\!\!\left(\mathbf {c} \mathbf {d} \right)=\left(\mathbf {a} \cdot \mathbf {c} \right)\left(\mathbf {b} \times \mathbf {d} \right)}
Cross
Cross–dot product
(
a
b
)
×
⋅
(
c
d
)
=
(
a
×
c
)
(
b
⋅
d
)
{\displaystyle \left(\mathbf {ab} \right)\!\!\!{\begin{array}{c}_{\times }\\^{\cdot }\end{array}}\!\!\!\left(\mathbf {cd} \right)=\left(\mathbf {a} \times \mathbf {c} \right)\left(\mathbf {b} \cdot \mathbf {d} \right)}
Double cross product
(
a
b
)
×
×
(
c
d
)
=
(
a
×
c
)
(
b
×
d
)
{\displaystyle \left(\mathbf {ab} \right)\!\!\!{\begin{array}{c}_{\times }\\^{\times }\end{array}}\!\!\!\left(\mathbf {cd} \right)=\left(\mathbf {a} \times \mathbf {c} \right)\left(\mathbf {b} \times \mathbf {d} \right)}
Letting
A
=
∑
i
a
i
b
i
B
=
∑
i
c
i
d
i
{\displaystyle \mathbf {A} =\sum _{i}\mathbf {a} _{i}\mathbf {b} _{i}\quad \mathbf {B} =\sum _{i}\mathbf {c} _{i}\mathbf {d} _{i}}
be two general dyadics, we have:
Dot
Cross
Dot
Dot product
A
⋅
B
=
∑
j
∑
i
(
b
i
⋅
c
j
)
a
i
d
j
{\displaystyle \mathbf {A} \cdot \mathbf {B} =\sum _{j}\sum _{i}\left(\mathbf {b} _{i}\cdot \mathbf {c} _{j}\right)\mathbf {a} _{i}\mathbf {d} _{j}}
Double dot product
A
:
B
=
∑
j
∑
i
(
a
i
⋅
d
j
)
(
b
i
⋅
c
j
)
{\displaystyle \mathbf {A} \colon \mathbf {B} =\sum _{j}\sum _{i}\left(\mathbf {a} _{i}\cdot \mathbf {d} _{j}\right)\left(\mathbf {b} _{i}\cdot \mathbf {c} _{j}\right)}
or
A
:
B
=
∑
j
∑
i
=
(
a
i
⋅
c
j
)
(
b
i
⋅
d
j
)
{\displaystyle \mathbf {A} \colon \mathbf {B} =\sum _{j}\sum _{i}=\left(\mathbf {a} _{i}\cdot \mathbf {c} _{j}\right)\left(\mathbf {b} _{i}\cdot \mathbf {d} _{j}\right)}
Dot–cross product
A
⋅
×
B
=
∑
j
∑
i
(
a
i
⋅
c
j
)
(
b
i
×
d
j
)
{\displaystyle \mathbf {A} \!\!\!{\begin{array}{c}_{\cdot }\\^{\times }\end{array}}\!\!\!\mathbf {B} =\sum _{j}\sum _{i}\left(\mathbf {a} _{i}\cdot \mathbf {c} _{j}\right)\left(\mathbf {b} _{i}\times \mathbf {d} _{j}\right)}
Cross
Cross–dot product
A
×
⋅
B
=
∑
j
∑
i
(
a
i
×
c
j
)
(
b
i
⋅
d
j
)
{\displaystyle \mathbf {A} \!\!\!{\begin{array}{c}_{\times }\\^{\cdot }\end{array}}\!\!\!\mathbf {B} =\sum _{j}\sum _{i}\left(\mathbf {a} _{i}\times \mathbf {c} _{j}\right)\left(\mathbf {b} _{i}\cdot \mathbf {d} _{j}\right)}
Double cross product
A
×
×
B
=
∑
i
,
j
(
a
i
×
c
j
)
(
b
i
×
d
j
)
{\displaystyle \mathbf {A} \!\!\!{\begin{array}{c}_{\times }\\^{\times }\end{array}}\!\!\!\mathbf {B} =\sum _{i,j}\left(\mathbf {a} _{i}\times \mathbf {c} _{j}\right)\left(\mathbf {b} _{i}\times \mathbf {d} _{j}\right)}
Double-dot product
There are two ways to define the double dot product, one must be careful when deciding which convention to use. As there are no analogous matrix operations for the remaining dyadic products, no ambiguities in their definitions appear.
The double-dot product is commutative due to commutativity of the normal dot-product:
A
:
B
=
B
:
A
{\displaystyle \mathbf {A} \colon \!\mathbf {B} =\mathbf {B} \colon \!\mathbf {A} }
There is a special double dot product with a transpose
A
:
B
T
=
A
T
:
B
{\displaystyle \mathbf {A} \colon \!\mathbf {B} ^{\mathrm {T} }=\mathbf {A} ^{\mathrm {T} }\colon \!\mathbf {B} }
Another identity is:
A
:
B
=
(
A
⋅
B
T
)
:
I
=
(
B
⋅
A
T
)
:
I
{\displaystyle \mathbf {A} \colon \mathbf {B} =\left(\mathbf {A} \cdot \mathbf {B} ^{\mathrm {T} }\right)\colon \mathbf {I} =\left(\mathbf {B} \cdot \mathbf {A} ^{\mathrm {T} }\right)\colon \mathbf {I} }
Double-cross product
We can see that, for any dyad formed from two vectors a and b , its double cross product is zero.
(
a
b
)
×
×
(
a
b
)
=
(
a
×
a
)
(
b
×
b
)
=
0
{\displaystyle \left(\mathbf {ab} \right)\!\!\!{\begin{array}{c}_{\times }\\^{\times }\end{array}}\!\!\!\left(\mathbf {ab} \right)=\left(\mathbf {a} \times \mathbf {a} \right)\left(\mathbf {b} \times \mathbf {b} \right)=0}
However, by definition, a dyadic double-cross product on itself will generally be non-zero. For example, a dyadic A composed of six different vectors
A
=
∑
i
=
1
3
a
i
b
i
{\displaystyle \mathbf {A} =\sum _{i=1}^{3}\mathbf {a} _{i}\mathbf {b} _{i}}
has a non-zero self-double-cross product of
A
×
×
A
=
2
[
(
a
1
×
a
2
)
(
b
1
×
b
2
)
+
(
a
2
×
a
3
)
(
b
2
×
b
3
)
+
(
a
3
×
a
1
)
(
b
3
×
b
1
)
]
{\displaystyle \mathbf {A} \!\!\!{\begin{array}{c}_{\times }\\^{\times }\end{array}}\!\!\!\mathbf {A} =2\left[\left(\mathbf {a} _{1}\times \mathbf {a} _{2}\right)\left(\mathbf {b} _{1}\times \mathbf {b} _{2}\right)+\left(\mathbf {a} _{2}\times \mathbf {a} _{3}\right)\left(\mathbf {b} _{2}\times \mathbf {b} _{3}\right)+\left(\mathbf {a} _{3}\times \mathbf {a} _{1}\right)\left(\mathbf {b} _{3}\times \mathbf {b} _{1}\right)\right]}
Tensor contraction
The spur or expansion factor arises from the formal expansion of the dyadic in a coordinate basis by replacing each juxtaposition by a dot product of vectors:
|
A
|
=
A
11
i
⋅
i
+
A
12
i
⋅
j
+
A
31
i
⋅
k
+
A
21
j
⋅
i
+
A
22
j
⋅
j
+
A
23
j
⋅
k
+
A
31
k
⋅
i
+
A
32
k
⋅
j
+
A
33
k
⋅
k
=
A
11
+
A
22
+
A
33
{\displaystyle {\begin{array}{llll}|\mathbf {A} |&=A_{11}\mathbf {i} \cdot \mathbf {i} +A_{12}\mathbf {i} \cdot \mathbf {j} +A_{31}\mathbf {i} \cdot \mathbf {k} \\&+A_{21}\mathbf {j} \cdot \mathbf {i} +A_{22}\mathbf {j} \cdot \mathbf {j} +A_{23}\mathbf {j} \cdot \mathbf {k} \\&+A_{31}\mathbf {k} \cdot \mathbf {i} +A_{32}\mathbf {k} \cdot \mathbf {j} +A_{33}\mathbf {k} \cdot \mathbf {k} \\\\&=A_{11}+A_{22}+A_{33}\\\end{array}}}
in index notation this is the contraction of indices on the dyadic:
|
A
|
=
∑
i
A
i
i
{\displaystyle |\mathbf {A} |=\sum _{i}A_{i}{}^{i}}
In three dimensions only, the rotation factor arises by replacing every juxtaposition by a cross product
⟨
A
⟩
=
A
11
i
×
i
+
A
12
i
×
j
+
A
31
i
×
k
+
A
21
j
×
i
+
A
22
j
×
j
+
A
23
j
×
k
+
A
31
k
×
i
+
A
32
k
×
j
+
A
33
k
×
k
=
A
12
k
−
A
31
j
−
A
21
k
+
A
23
i
+
A
31
j
−
A
32
i
=
(
A
23
−
A
32
)
i
+
(
A
31
−
A
13
)
j
+
(
A
12
−
A
21
)
k
{\displaystyle {\begin{array}{llll}\langle \mathbf {A} \rangle &=A_{11}\mathbf {i} \times \mathbf {i} +A_{12}\mathbf {i} \times \mathbf {j} +A_{31}\mathbf {i} \times \mathbf {k} \\&+A_{21}\mathbf {j} \times \mathbf {i} +A_{22}\mathbf {j} \times \mathbf {j} +A_{23}\mathbf {j} \times \mathbf {k} \\&+A_{31}\mathbf {k} \times \mathbf {i} +A_{32}\mathbf {k} \times \mathbf {j} +A_{33}\mathbf {k} \times \mathbf {k} \\\\&=A_{12}\mathbf {k} -A_{31}\mathbf {j} -A_{21}\mathbf {k} \\&+A_{23}\mathbf {i} +A_{31}\mathbf {j} -A_{32}\mathbf {i} \\\\&=(A_{23}-A_{32})\mathbf {i} +(A_{31}-A_{13})\mathbf {j} +(A_{12}-A_{21})\mathbf {k} \\\end{array}}}
In index notation this is the contraction of A with the Levi-Civita tensor
⟨
A
⟩
=
∑
j
k
ϵ
i
j
k
A
j
k
.
{\displaystyle \langle \mathbf {A} \rangle =\sum _{jk}{\epsilon _{i}}^{jk}A_{jk}.}
Special dyadics
Unit dyadic
For any vector a , there exist a unit dyadic I , such that
I
⋅
a
=
a
⋅
I
=
a
{\displaystyle \mathbf {I} \cdot \mathbf {a} =\mathbf {a} \cdot \mathbf {I} =\mathbf {a} }
For any basis of 3 vectors a , b and c , with reciprocal basis
a
^
,
b
^
,
c
^
{\displaystyle {\hat {\mathbf {a} }},{\hat {\mathbf {b} }},{\hat {\mathbf {c} }}}
I
=
a
a
^
+
b
b
^
+
c
c
^
{\displaystyle \mathbf {I} =\mathbf {a} {\hat {\mathbf {a} }}+\mathbf {b} {\hat {\mathbf {b} }}+\mathbf {c} {\hat {\mathbf {c} }}}
In the standard basis,
I
=
i
i
+
j
j
+
k
k
{\displaystyle \mathbf {I} =\mathbf {ii} +\mathbf {jj} +\mathbf {kk} }
The corresponding matrix is
I
=
(
1
0
0
0
1
0
0
0
1
)
{\displaystyle \mathbf {I} ={\begin{pmatrix}1&0&0\\0&1&0\\0&0&1\\\end{pmatrix}}}
This can be put on more careful foundations (explaining what the logical content of "juxtaposing notation" could possibly mean) using the language of tensor products. If V is a finite-dimensional vector space , a dyadic tensor on V is an elementary tensor in the tensor product of V with its dual space .
The tensor product of V and its dual space is isomorphic to the space of linear maps from V to V : a dyadic tensor vf is simply the linear map sending any w in V to f (w )v . When V is Euclidean n -space, we can use the inner product to identify the dual space with V itself, making a dyadic tensor an elementary tensor product of two vectors in Euclidean space.
In this sense, the unit dyadic ij is the function from 3-space to itself sending a 1 i + a 2 j + a 3 k to a 2 i , and jj sends this sum to a 2 j . Now it is revealed in what (precise) sense ii + jj + kk is the identity: it sends a 1 i + a 2 j + a 3 k to itself because its effect is to sum each unit vector in the standard basis scaled by the coefficient of the vector in that basis.
Properties of unit dyadics
(
a
×
I
)
⋅
(
b
×
I
)
=
a
b
−
(
a
⋅
b
)
I
{\displaystyle \left(\mathbf {a} \times \mathbf {I} \right)\cdot \left(\mathbf {b} \times \mathbf {I} \right)=\mathbf {ab} -\left(\mathbf {a} \cdot \mathbf {b} \right)\mathbf {I} }
I
×
⋅
(
a
b
)
=
b
×
a
{\displaystyle \mathbf {I} \!\!{\begin{array}{c}_{\times }\\^{\cdot }\end{array}}\!\!\!\left(\mathbf {ab} \right)=\mathbf {b} \times \mathbf {a} }
I
×
×
A
=
(
A
×
×
I
)
I
−
A
T
{\displaystyle \mathbf {I} \!\!{\begin{array}{c}_{\times }\\^{\times }\end{array}}\!\!\mathbf {A} =(\mathbf {A} \!\!{\begin{array}{c}_{\times }\\^{\times }\end{array}}\!\!\mathbf {I} )\mathbf {I} -\mathbf {A} ^{\mathrm {T} }}
I
:
(
a
b
)
=
(
I
⋅
a
)
⋅
b
=
a
⋅
b
=
t
r
(
a
b
)
{\displaystyle \mathbf {I} \;\colon \left(\mathbf {ab} \right)=\left(\mathbf {I} \cdot \mathbf {a} \right)\cdot \mathbf {b} =\mathbf {a} \cdot \mathbf {b} =\mathrm {tr} \left(\mathbf {ab} \right)}
where "tr" denotes the trace .
Rotation dyadic
For any vector a in two dimensions, the left-cross product with the identity dyad I :
a
×
I
{\displaystyle \mathbf {a} \times \mathbf {I} }
is a 90 degree anticlockwise rotation dyadic around a . Alternatively the dyadic tensor
J = ji − ij =
(
0
−
1
1
0
)
{\displaystyle {\begin{pmatrix}0&-1\\1&0\end{pmatrix}}}
is a 90° anticlockwise rotation operator in 2d. It can be left-dotted with a vector to produce the rotation:
(
j
i
−
i
j
)
⋅
(
x
i
+
y
j
)
=
x
j
i
⋅
i
−
x
i
j
⋅
i
+
y
j
i
⋅
j
−
y
i
j
⋅
j
=
−
y
i
+
x
j
,
{\displaystyle (\mathbf {ji} -\mathbf {ij} )\cdot (x\mathbf {i} +y\mathbf {j} )=x\mathbf {ji} \cdot \mathbf {i} -x\mathbf {ij} \cdot \mathbf {i} +y\mathbf {ji} \cdot \mathbf {j} -y\mathbf {ij} \cdot \mathbf {j} =-y\mathbf {i} +x\mathbf {j} ,}
or in matrix notation
(
0
−
1
1
0
)
(
x
y
)
=
(
−
y
x
)
.
{\displaystyle {\begin{pmatrix}0&-1\\1&0\end{pmatrix}}{\begin{pmatrix}x\\y\end{pmatrix}}={\begin{pmatrix}-y\\x\end{pmatrix}}.}
A general 2d rotation dyadic for θ angle anti-clockwise is
I
cos
θ
+
J
sin
θ
=
(
cos
θ
−
sin
θ
sin
θ
cos
θ
)
{\displaystyle \mathbf {I} \cos \theta +\mathbf {J} \sin \theta ={\begin{pmatrix}\cos \theta &-\sin \theta \\\sin \theta &\;\cos \theta \end{pmatrix}}}
where I and J are as above.
Some authors generalize from the term dyadic to related terms triadic , tetradic and polyadic .[ 2]
See also
References
^ Spencer (1992), page 19.
^ For example, "POLYADIC METHODS IN ELASTODYNAMICS". Progress In Electromagnetics Research, PIER 31 : 113– 154. 2001. [1]
P. Mitiguy (2009). "Vectors and dyadics" (PDF) . Stanford , USA. Spiegel, M.R.; Lipschutz, S.; Spellman, D. (2009). Vector analysis, Schaum's outlines . McGraw Hill. ISBN 978-0-07-161545-7 A.J.M. Spencer (1992). Continuum Mechanics . Dover Publications. ISBN 0-486-43594-6 Morse, Philip M.; Feshbach, Herman (1953), "§1.6: Dyadics and other vector operators", Methods of theoretical physics, Volume 1 , New York: McGraw-Hill , pp. 54– 92, MR 0059774 ISMO V. Lindell (1992). Methods for Electromagnetic Field Analysis . ISBN 019856239 Hollis C. Chen (1983). Theory of Electromagnetic Wave - A Coordinate-free approach . ISBN 0-07-010688-6
External links
![{\displaystyle \mathbf {A} \!\!\!{\begin{array}{c}_{\times }\\^{\times }\end{array}}\!\!\!\mathbf {A} =2\left[\left(\mathbf {a} _{1}\times \mathbf {a} _{2}\right)\left(\mathbf {b} _{1}\times \mathbf {b} _{2}\right)+\left(\mathbf {a} _{2}\times \mathbf {a} _{3}\right)\left(\mathbf {b} _{2}\times \mathbf {b} _{3}\right)+\left(\mathbf {a} _{3}\times \mathbf {a} _{1}\right)\left(\mathbf {b} _{3}\times \mathbf {b} _{1}\right)\right]}](https://wikimedia.org/enwiki/api/rest_v1/media/math/render/svg/d232adc3299c5aa77e03ede3e58a5e580579d50d)
