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In [[multilinear algebra]], a '''dyadic''' or '''dyadic tensor''' is a second [[Tensor (intrinsic definition)#Definition via tensor products of vector spaces|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 (mathematics)|matrix algebra]]. The notation and terminology is relatively obsolete today. Its uses in physics include [[stress analysis]] and [[electromagnetism]].


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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==
 
===Dyadic, outer, and tensor products===
 
A ''dyad'' is a [[tensor]] of [[Tensor order|order]] two and [[Tensor rank|rank]] one, and is the result of the dyadic product of two [[Euclidean vector|vector]]s ([[complex vector]]s in general), whereas a ''dyadic'' is a general [[tensor]] of [[Tensor order|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]]''' of two [[column vector]]s '''a''' and '''b''' is denoted and defined as '''a''' &otimes; '''b''' or '''ab'''<sup>T</sup>, where T means [[transpose]],
*the '''[[tensor product]]''' of two vectors '''a''' and '''b''' is denoted '''a''' &otimes; '''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 space|three-dimensional]] [[Euclidean space]], letting:
 
:<math>\mathbf{a} = a_1 \mathbf{i} + a_2 \mathbf{j} + a_3 \mathbf{k}</math>
:<math>\mathbf{b} = b_1 \mathbf{i} + b_2 \mathbf{j} + b_3 \mathbf{k}</math>
 
be two vectors where '''i''', '''j''', '''k''' (also denoted '''e'''<sub>1</sub>, '''e'''<sub>2</sub>, '''e'''<sub>3</sub>) 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:
 
:<math> \begin{array}{llll}
\mathbf{ab} = & a_1 b_1 \mathbf{i i} & + a_1 b_2 \mathbf{i j} & + a_1 b_3 \mathbf{i k} \\
&+ a_2 b_1 \mathbf{j i} & + a_2 b_2 \mathbf{j j} & + a_2 b_3 \mathbf{j k}\\
&+ a_3 b_1 \mathbf{k i} & + a_3 b_2 \mathbf{k j} & + a_3 b_3 \mathbf{k k}
\end{array}</math>
 
or by extension from row and column vectors, a 3&times;3 matrix (also the result of the outer product or tensor product of '''a''' and '''b'''):
 
:<math>\mathbf{a b} \equiv \mathbf{a}\otimes\mathbf{b} \equiv \mathbf{a b}^\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_1b_1 & a_1b_2 & a_1b_3 \\
a_2b_1 & a_2b_2 & a_2b_3 \\
a_3b_1 & a_3b_2 & a_3b_3
\end{pmatrix}.</math>
 
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 vector]]s [[scalar multiplication|scalar multiplied]] by a number.
 
Just as the standard basis (and unit) vectors '''i''', '''j''', '''k''', have the representations:
 
:<math>\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}
</math>
 
(which can be transposed), the ''standard basis (and unit) dyads'' have the representation:
 
:<math>\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
</math>
 
For a simple numerical example in the standard basis:
 
:<math>\begin{align}
\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{align}</math>
 
====''N''-dimensional Euclidean space====
 
If the Euclidean space is ''N''-[[dimension]]al, and
 
:<math> \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</math>
:<math>\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</math>
 
where '''e'''<sub>''i''</sub> and '''e'''<sub>''j''</sub> are the [[standard basis]] vectors in ''N''-dimensions (the index ''i'' on '''e'''<sub>''i''</sub> selects a specific vector, not a component of the vector as in ''a<sub>i</sub>''), then in algebraic form their dyadic product is:
 
:<math> \mathbf{A} = \sum _{j=1}^N\sum_{i=1}^N a_ib_j{\mathbf{e}}_i\mathbf{e}_j.</math>
 
This is known as the ''nonion form'' of the dyadic. Their outer/tensor product in matrix form is:
 
:<math>
\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_1b_1 & a_1b_2 & \cdots & a_1b_N \\
a_2b_1 & a_2b_2 & \cdots & a_2b_N \\
\vdots & \vdots & \ddots & \vdots \\
a_Nb_1 & a_Nb_2 & \cdots & a_Nb_N
\end{pmatrix}.</math>
 
A ''dyadic polynomial'' '''A''', otherwise known as a dyadic, is formed from multiple vectors '''a'''<sub>''i''</sub> and '''b'''<sub>''j''</sub>:
 
:<math> \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 </math>
 
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|date=October 2012}} see [[#Chen|Chen (1983)]].
 
===Classification===
 
The following table classifies dyadics:
 
:{| class="wikitable"
|-
|
! [[Determinant]]
! [[Adjugate]]
! [[Matrix (mathematics)|Matrix]] and its [[Rank (linear algebra)|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)
|-
! [[Plane (geometry)|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 tensor product:<ref>Spencer (1992), page 19.</ref>
 
{{ordered list
|1= '''Compatible with [[scalar multiplication]]:'''
:<math>(\alpha \mathbf{a})  \mathbf{b} =\mathbf{a}  (\alpha \mathbf{b}) = \alpha (\mathbf{a}  \mathbf{b})</math>
for any scalar <math>\alpha</math>.
 
|2= '''[[Distributive property|Distributive]] over [[vector addition]]:'''
:<math>\mathbf{a}  (\mathbf{b} + \mathbf{c}) =\mathbf{a}  \mathbf{b} + \mathbf{a}  \mathbf{c}</math>
:<math>(\mathbf{a} + \mathbf{b})  \mathbf{c} =\mathbf{a}  \mathbf{c} + \mathbf{b}  \mathbf{c}</math>
}}
 
== Dyadic algebra ==
 
=== Product of dyadic and vector ===
 
There are four operations defined on a vector and dyadic, constructed from the products defined on vectors.
 
:{| class="wikitable"
|-valign="top"
!
! Left
! Right
|-valign="top"
! [[Dot product]]
|
<math> \mathbf{c}\cdot \mathbf{a} \mathbf{b} = \left(\mathbf{c}\cdot\mathbf{a}\right)\mathbf{b}</math>
|
<math> \left(\mathbf{a}\mathbf{b}\right)\cdot \mathbf{c} = \mathbf{a}\left(\mathbf{b}\cdot\mathbf{c}\right) </math>
|-valign="top"
! [[Cross product]]
|
<math> \mathbf{c} \times \left(\mathbf{ab}\right) = \left(\mathbf{c}\times\mathbf{a}\right)\mathbf{b} </math>
|
<math> \left(\mathbf{ab}\right)\times\mathbf{c} = \mathbf{a}\left(\mathbf{b}\times\mathbf{c}\right)</math>
|-
|}
 
=== Product of dyadic and dyadic ===
 
There are five operations for a dyadic to another dyadic. Let '''a''', '''b''', '''c''', '''d''' be vectors. Then:
 
:{| class="wikitable"
|-
!
!
! Dot
! Cross
|-valign="top"
! Dot
|| ''Dot product''
<math>\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}</math>
|| ''Double dot product''
 
<math>\mathbf{ab}\colon\mathbf{cd}=\left(\mathbf{a}\cdot\mathbf{d}\right)\left(\mathbf{b}\cdot\mathbf{c}\right)</math>
 
or
 
<math> \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) </math>
 
|| ''Dot–cross product''
<math> \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)</math>
|-valign="top"
! Cross
||
|| ''Cross–dot product''
 
<math> \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)</math>
|| ''Double cross product''
 
<math> \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)</math>
|-
|}
 
Letting
 
:<math> \mathbf{A}=\sum _i \mathbf{a}_i\mathbf{b}_i \quad \mathbf{B}=\sum _i \mathbf{c}_i\mathbf{d}_i </math>
 
be two general dyadics, we have:
 
:{| class="wikitable"
|-
!
!
! Dot
! Cross
|-valign="top"
! Dot
|| ''Dot product''
 
<math> \mathbf{A}\cdot\mathbf{B} = \sum_j\sum _i\left(\mathbf{b}_i\cdot\mathbf{c}_j\right)\mathbf{a}_i\mathbf{d}_j </math>
|| ''Double dot product''
 
<math>\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)</math>
 
or
 
<math> \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) </math>
 
|| ''Dot–cross product''
<math> \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) </math>
|-valign="top"
! Cross
||
|| ''Cross–dot product''
 
<math> \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) </math>  
|| ''Double cross product''
<math> \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) </math>
|}
 
==== 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:
 
:<math> \mathbf{A} \colon \! \mathbf{B} = \mathbf{B} \colon \! \mathbf{A} </math>
 
There is a special double dot product with a [[transpose]]
 
:<math> \mathbf{A} \colon \! \mathbf{B}^\mathrm{T} = \mathbf{A}^\mathrm{T} \colon \! \mathbf{B} </math>
 
Another identity is:
 
:<math>\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} </math>
 
==== Double-cross product ====
 
We can see that, for any dyad formed from two vectors '''a''' and '''b''', its double cross product is zero.
 
:<math> \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</math>
 
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
 
:<math>\mathbf{A}=\sum _{i=1}^3 \mathbf{a}_i\mathbf{b}_i </math>
 
has a non-zero self-double-cross product of
 
:<math> \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] </math>
 
====Tensor contraction====
 
{{main|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:
 
:<math> \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}</math>
 
in index notation this is the contraction of indices on the dyadic:
 
:<math>|\mathbf{A}| = \sum_i A_i{}^i</math>
 
In three dimensions only, the ''rotation factor'' arises by replacing every juxtaposition by a [[cross product]]
 
:<math> \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}</math>
 
In index notation this is the contraction of '''A''' with the [[Levi-Civita tensor]]
:<math>\langle\mathbf{A}\rangle=\sum_{jk}{\epsilon_i}^{jk}A_{jk}.</math>
 
==Special dyadics==
 
===Unit dyadic===
 
For any vector '''a''', there exist a unit dyadic '''I''', such that
 
:<math> \mathbf{I}\cdot\mathbf{a}=\mathbf{a}\cdot\mathbf{I}= \mathbf{a} </math>
 
For any basis of 3 vectors '''a''', '''b''' and '''c''', with [[Multiplicative inverse|reciprocal]] basis <math>\hat{{\mathbf{a}}}, \hat{\mathbf{b}}, \hat{\mathbf{c}}</math>, the unit dyadic is defined by
 
:<math>\mathbf{I} = \mathbf{a}\hat{\mathbf{a}} + \mathbf{b}\hat{\mathbf{b}} + \mathbf{c}\hat{\mathbf{c}}</math>
 
In the standard basis,
 
:<math> \mathbf{I} = \mathbf{ii} + \mathbf{jj} + \mathbf{kk} </math>
 
The corresponding matrix is
 
:<math>\mathbf{I}=\begin{pmatrix}
1 & 0 & 0\\
0 & 1 & 0\\
0 & 0 & 1\\
\end{pmatrix}</math>
 
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 map]]s 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''<sub>1</sub>'''i''' + ''a''<sub>2</sub>'''j''' + ''a''<sub>3</sub>'''k''' to ''a''<sub>2</sub>'''i''', and '''jj''' sends this sum to ''a''<sub>2</sub>'''j'''. Now it is revealed in what (precise) sense  '''ii''' + '''jj''' + '''kk''' is the identity:  it sends ''a''<sub>1</sub>'''i''' + ''a''<sub>2</sub>'''j''' + ''a''<sub>3</sub>'''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
 
:<math> \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}</math>
 
:<math>\mathbf{I}
\!\!\begin{array}{c}
_\times  \\
^\cdot
\end{array}\!\!\!
\left(\mathbf{ab}\right)=\mathbf{b}\times\mathbf{a} </math>
 
:<math> \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}</math>
 
:<math>\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)</math>
 
where "tr" denotes the [[Trace (linear algebra)|trace]].
 
===Rotation dyadic===
 
For any vector '''a''' in two dimensions, the left-cross product with the identity dyad '''I''':
 
:<math> \mathbf{a}\times \mathbf{I}</math>
 
is a 90 degree anticlockwise rotation dyadic around ''a''. Alternatively the dyadic tensor
 
:'''J'''  =  '''ji &minus; ij''' = <math> \begin{pmatrix}
0 & -1 \\
1 & 0
\end{pmatrix}</math>
 
is a 90° anticlockwise [[Rotation operator (vector space)|rotation operator]] in 2d. It can be left-dotted with a vector to produce the rotation:
:<math> (\mathbf{j i} - \mathbf{i j}) \cdot (x \mathbf{i} + y \mathbf{j}) =
x \mathbf{j i} \cdot \mathbf{i} - x \mathbf{i j} \cdot \mathbf{i} + y \mathbf{j i} \cdot \mathbf{j} - y \mathbf{i j} \cdot \mathbf{j} =
-y \mathbf{i} + x \mathbf{j},</math>
or in matrix notation
:<math>
\begin{pmatrix}
0 & -1 \\
1 & 0
\end{pmatrix}
\begin{pmatrix}
x \\
y
\end{pmatrix}=
\begin{pmatrix}
-y \\
x
\end{pmatrix}.</math>
 
A general 2d rotation dyadic for θ angle anti-clockwise is
 
:<math>\mathbf{I}\cos\theta + \mathbf{J}\sin\theta =
\begin{pmatrix}
  \cos\theta &-\sin\theta \\
  \sin\theta &\;\cos\theta
\end{pmatrix}
</math>
 
where '''I''' and '''J''' are as above.
 
==Related terms==
Some authors generalize from the term ''dyadic'' to related terms ''triadic'', ''tetradic'' and ''polyadic''.<ref>For example, {{cite journal |authors=I. V. Lindell and A. P. Kiselev |title=POLYADIC METHODS IN ELASTODYNAMICS |year=2001 |journal=Progress In Electromagnetics Research, PIER 31 |pages=113–154 }} [http://www.jpier.org/PIER/pier31/06.0005171.Lindell.K.pdf]</ref>
 
==See also==
* [[Kronecker product]]
* [[Polyadic algebra]]
* [[Unit vector]]
* [[Multivector]]
* [[Differential form]]
* [[Quaternions]]
* [[Field (mathematics)]]
 
==References==
{{reflist}}
 
* {{cite news|url=http://www.stanford.edu/class/me331b/documents/VectorBasisIndependent.pdf|author=P. Mitiguy|year=2009|title=Vectors and dyadics|location=[[Stanford]], USA}} Chapter 2
* {{cite book | title=Vector analysis, Schaum's outlines|first1=M.R.|last1=Spiegel|first2=S.|last2=Lipschutz|first3=D.|last3=Spellman| year=2009 | publisher=McGraw Hill|isbn=978-0-07-161545-7}}
* {{cite book | title=Continuum Mechanics | author=A.J.M. Spencer | year=1992 | publisher=Dover Publications | isbn=0-486-43594-6 }}.
* {{Citation | last1=Morse | first1=Philip M. | last2=Feshbach | first2=Herman | title=Methods of theoretical physics, Volume 1 | publisher=[[McGraw-Hill]] | location=New York | mr=0059774 |isbn=978-0-07-043316-8 | year=1953 | chapter=§1.6: Dyadics and other vector operators|pages=54&ndash;92}}.
*{{cite book | title=Methods for Electromagnetic Field Analysis | author=Ismo V. Lindell | publisher=Wiley-Blackwell |year=1996 | isbn=978-0-7803-6039-6 }}.
*<cite id=Chen>{{cite book | title=Theory of Electromagnetic Wave - A Coordinate-free approach | author=Hollis C. Chen | publisher=McGraw Hill |year=1983 | isbn=978-0-07-010688-8 }}.</cite>
 
==External links==
* [http://www.ismolindell.com/publications/monographs/pdf/Aftis.pdf Advanced Field Theory, I.V.Lindel]
* [http://my.ece.ucsb.edu/bobsclass/201B/W01/vectors.pdf Vector and Dyadic Analysis]
* [http://chem4823.usask.ca/nmr/tensor.pdf Introductory Tensor Analysis]
* [http://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20050175884_2005173651.pdf Nasa.gov, Foundations of Tensor Analysis for students of Physics and Engineering with an Introduction to the Theory of Relativity, J.C. Kolecki]
* [http://www.grc.nasa.gov/WWW/k-12/Numbers/Math/documents/Tensors_TM2002211716.pdf Nasa.gov, An introduction to Tensors for students of Physics and Engineering, J.C. Kolecki]
 
{{tensor}}
 
[[Category:Tensors]]

Latest revision as of 19:23, 24 December 2013

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

Dyadic, outer, and tensor products

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 of two column vectors a and b is denoted and defined as ab or abT, where T means transpose,
  • the tensor product of two vectors a and b is denoted ab,

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=a1i+a2j+a3k
b=b1i+b2j+b3k

be two vectors where i, j, k (also denoted e1, e2, e3) 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:

ab=a1b1ii+a1b2ij+a1b3ik+a2b1ji+a2b2jj+a2b3jk+a3b1ki+a3b2kj+a3b3kk

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):

abababT=(a1a2a3)(b1b2b3)=(a1b1a1b2a1b3a2b1a2b2a2b3a3b1a3b2a3b3).

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=(100),j=(010),k=(001)

(which can be transposed), the standard basis (and unit) dyads have the representation:

ii=(100000000),ji=(000100000),jk=(000001000)

For a simple numerical example in the standard basis:

A=2ij+32ji8πjk+223kk=2(010000000)+32(000100000)8π(000001000)+223(000000001)=(0203/208π00223)

N-dimensional Euclidean space

If the Euclidean space is N-dimensional, and

a=i=1Naiei=a1e1+a2e2+aNeN
b=j=1Nbjej=b1e1+b2e2+bNeN

where ei and ej are the standard basis vectors in N-dimensions (the index i on ei selects a specific vector, not a component of the vector as in ai), then in algebraic form their dyadic product is:

A=j=1Ni=1Naibjeiej.

This is known as the nonion form of the dyadic. Their outer/tensor product in matrix form is:

ab=abT=(a1a2aN)(b1b2bN)=(a1b1a1b2a1bNa2b1a2b2a2bNaNb1aNb2aNbN).

A dyadic polynomial A, otherwise known as a dyadic, is formed from multiple vectors ai and bj:

A=iaibi=a1b1+a2b2+a3b3+

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,To succeed in selling a home, it is advisable be competent in real estate advertising and marketing, authorized, monetary, operational aspects, and other information and skills. This is essential as a result of you want to negotiate with more and more sophisticated buyers. You could outperform rivals, use latest technologies, and stay ahead of the fast altering market.

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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 tensor product:[1]

Template:Ordered list

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

cab=(ca)b

(ab)c=a(bc)

Cross product

c×(ab)=(c×a)b

(ab)×c=a(b×c)

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

(ab)(cd)=a(bc)d=(bc)ad

Double dot product

ab:cd=(ad)(bc)

or

(ab):(cd)=c(ab)d=(ac)(bd)

Dot–cross product

(ab)×(cd)=(ac)(b×d)

Cross Cross–dot product

(ab)×(cd)=(a×c)(bd)

Double cross product

(ab)××(cd)=(a×c)(b×d)

Letting

A=iaibiB=icidi

be two general dyadics, we have:

Dot Cross
Dot Dot product

AB=ji(bicj)aidj

Double dot product

A:B=ji(aidj)(bicj)

or

A:B=ji=(aicj)(bidj)

Dot–cross product

A×B=ji(aicj)(bi×dj)

Cross Cross–dot product

A×B=ji(ai×cj)(bidj)

Double cross product

A××B=i,j(ai×cj)(bi×dj)

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

There is a special double dot product with a transpose

A:BT=AT:B

Another identity is:

A:B=(ABT):I=(BAT):I

Double-cross product

We can see that, for any dyad formed from two vectors a and b, its double cross product is zero.

(ab)××(ab)=(a×a)(b×b)=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=13aibi

has a non-zero self-double-cross product of

A××A=2[(a1×a2)(b1×b2)+(a2×a3)(b2×b3)+(a3×a1)(b3×b1)]

Tensor contraction

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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|=A11ii+A12ij+A31ik+A21ji+A22jj+A23jk+A31ki+A32kj+A33kk=A11+A22+A33

in index notation this is the contraction of indices on the dyadic:

|A|=iAii

In three dimensions only, the rotation factor arises by replacing every juxtaposition by a cross product

A=A11i×i+A12i×j+A31i×k+A21j×i+A22j×j+A23j×k+A31k×i+A32k×j+A33k×k=A12kA31jA21k+A23i+A31jA32i=(A23A32)i+(A31A13)j+(A12A21)k

In index notation this is the contraction of A with the Levi-Civita tensor

A=jkϵijkAjk.

Special dyadics

Unit dyadic

For any vector a, there exist a unit dyadic I, such that

Ia=aI=a

For any basis of 3 vectors a, b and c, with reciprocal basis a^,b^,c^, the unit dyadic is defined by

I=aa^+bb^+cc^

In the standard basis,

I=ii+jj+kk

The corresponding matrix is

I=(100010001)

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 a1i + a2j + a3k to a2i, and jj sends this sum to a2j. Now it is revealed in what (precise) sense ii + jj + kk is the identity: it sends a1i + a2j + a3k 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)=ab(ab)I
I×(ab)=b×a
I××A=(A××I)IAT
I:(ab)=(Ia)b=ab=tr(ab)

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

is a 90 degree anticlockwise rotation dyadic around a. Alternatively the dyadic tensor

J = ji − ij = (0110)

is a 90° anticlockwise rotation operator in 2d. It can be left-dotted with a vector to produce the rotation:

(jiij)(xi+yj)=xjiixiji+yjijyijj=yi+xj,

or in matrix notation

(0110)(xy)=(yx).

A general 2d rotation dyadic for θ angle anti-clockwise is

Icosθ+Jsinθ=(cosθsinθsinθcosθ)

where I and J are as above.

Related terms

Some authors generalize from the term dyadic to related terms triadic, tetradic and polyadic.[2]

See also

References

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External links

Template:Tensor

  1. Spencer (1992), page 19.
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    A vendor's stamp duty has been launched on industrial property for the primary time, at rates ranging from 5 per cent to 15 per cent. The Authorities might be trying to reassure the market that they aren't in opposition to foreigners and PRs investing in Singapore's property market. They imposed these measures because of extenuating components available in the market." The sale of new dual-key EC models will even be restricted to multi-generational households only. The models have two separate entrances, permitting grandparents, for example, to dwell separately. The vendor's stamp obligation takes effect right this moment and applies to industrial property and plots which might be offered inside three years of the date of buy. JLL named Best Performing Property Brand for second year running

    The data offered is for normal info purposes only and isn't supposed to be personalised investment or monetary advice. Motley Fool Singapore contributor Stanley Lim would not personal shares in any corporations talked about. Singapore private home costs increased by 1.eight% within the fourth quarter of 2012, up from 0.6% within the earlier quarter. Resale prices of government-built HDB residences which are usually bought by Singaporeans, elevated by 2.5%, quarter on quarter, the quickest acquire in five quarters. And industrial property, prices are actually double the levels of three years ago. No withholding tax in the event you sell your property. All your local information regarding vital HDB policies, condominium launches, land growth, commercial property and more

    There are various methods to go about discovering the precise property. Some local newspapers (together with the Straits Instances ) have categorised property sections and many local property brokers have websites. Now there are some specifics to consider when buying a 'new launch' rental. Intended use of the unit Every sale begins with 10 p.c low cost for finish of season sale; changes to 20 % discount storewide; follows by additional reduction of fiftyand ends with last discount of 70 % or extra. Typically there is even a warehouse sale or transferring out sale with huge mark-down of costs for stock clearance. Deborah Regulation from Expat Realtor shares her property market update, plus prime rental residences and houses at the moment available to lease Esparina EC @ Sengkang [1]