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In
mathematics Mathematics (from Greek: ) includes the study of such topics as numbers (arithmetic and number theory), formulas and related structures (algebra), shapes and spaces in which they are contained (geometry), and quantities and their changes (cal ...
, specifically
multilinear algebra In mathematics, multilinear algebra extends the methods of linear algebra. Just as linear algebra is built on the concept of a vector space, vector and develops the theory of vector spaces, multilinear algebra builds on the concepts of Multivect ...
, a dyadic or dyadic tensor is a second
order Order, ORDER or Orders may refer to: * Orderliness Orderliness is a quality that is characterized by a person’s interest in keeping their surroundings and themselves well organized, and is associated with other qualities such as cleanliness a ...
tensor In mathematics Mathematics (from Greek: ) includes the study of such topics as numbers (arithmetic and number theory), formulas and related structures (algebra), shapes and spaces in which they are contained (geometry), and quantities a ...

tensor
, written in a notation that fits in with
vector algebra In mathematics, vector algebra may mean: * Linear algebra Linear algebra is the branch of mathematics concerning linear equations such as: :a_1x_1+\cdots +a_nx_n=b, linear maps such as: :(x_1, \ldots, x_n) \mapsto a_1x_1+\cdots +a_nx_n, and their ...
. There are numerous ways to multiply two
Euclidean vector In mathematics Mathematics (from Greek: ) includes the study of such topics as numbers (arithmetic and number theory), formulas and related structures (algebra), shapes and spaces in which they are contained (geometry), and quantities and ...
s. The
dot product In mathematics, the dot product or scalar productThe term ''scalar product'' is often also used more generally to mean a symmetric bilinear form, for example for a pseudo-Euclidean space. is an algebraic operation that takes two equal-length seque ...
takes in two vectors and returns a
scalar Scalar may refer to: *Scalar (mathematics), an element of a field, which is used to define a vector space, usually the field of real numbers *Scalar (physics), a physical quantity that can be described by a single element of a number field such as ...
, while the
cross product In , the cross product or vector product (occasionally directed area product, to emphasize its geometric significance) is a on two s in a three-dimensional (named here E), and is denoted by the symbol \times. Given two and , the cross produc ...

cross product
returns a
pseudovector In physics and mathematics, a pseudovector (or axial vector) is a quantity that is defined as a function (mathematics), function of some vector (geometry), vectors or other geometric shapes, that resembles a vector, and behaves like a vector in ma ...

pseudovector
. Both of these have various significant geometric interpretations and are widely used in mathematics,
physics Physics is the natural science that studies matter, its Elementary particle, fundamental constituents, its Motion (physics), motion and behavior through Spacetime, space and time, and the related entities of energy and force. "Physical scie ...

physics
, and
engineering Engineering is the use of scientific principles to design and build machines, structures, and other items, including bridges, tunnels, roads, vehicles, and buildings. The discipline of engineering encompasses a broad range of more speciali ...

engineering
. The dyadic product takes in two vectors and returns a second order tensor called a ''dyadic'' in this context. A dyadic can be used to contain physical or geometric information, although in general there is no direct way of geometrically interpreting it. The dyadic product is distributive over
vector addition Vector may refer to: Biology *Vector (epidemiology) In epidemiology, a disease vector is any living agent that carries and transmits an infectious pathogen to another living organism; agents regarded as vectors are organisms, such as Para ...

vector addition
, and
associative In mathematics Mathematics (from Ancient Greek, Greek: ) includes the study of such topics as quantity (number theory), mathematical structure, structure (algebra), space (geometry), and calculus, change (mathematical analysis, analysis). ...
with scalar multiplication. Therefore, the dyadic product is linear in both of its operands. In general, two dyadics can be added to get another dyadic, and scalar multiplication, multiplied by numbers to scale the dyadic. However, the product is not commutative; changing the order of the vectors results in a different dyadic. The formalism of dyadic algebra is an extension of vector algebra to include the dyadic product of vectors. The dyadic product is also associative with the dot and cross products with other vectors, which allows the dot, cross, and dyadic products to be combined to obtain other scalars, vectors, or dyadics. It also has some aspects of matrix algebra, as the numerical components of vectors can be arranged into row and column vectors, and those of second order tensors in square matrix, square matrices. Also, the dot, cross, and dyadic products can all be expressed in matrix form. Dyadic expressions may closely resemble the matrix equivalents. The dot product of a dyadic with a vector gives another vector, and taking the dot product of this result gives a scalar derived from the dyadic. The effect that a given dyadic has on other vectors can provide indirect physical or geometric interpretations. Dyadic notation was first established by Josiah Willard Gibbs in 1884. The notation and terminology are relatively obsolete today. Its uses in physics include continuum mechanics and electromagnetism. 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 In mathematics Mathematics (from Greek: ) includes the study of such topics as numbers (arithmetic and number theory), formulas and related structures (algebra), shapes and spaces in which they are contained (geometry), and quantities a ...

tensor
of Tensor order, order two and Tensor rank, rank one, and is the dyadic product of two Euclidean vector, vectors (complex vectors in general), whereas a ''dyadic'' is a general
tensor In mathematics Mathematics (from Greek: ) includes the study of such topics as numbers (arithmetic and number theory), formulas and related structures (algebra), shapes and spaces in which they are contained (geometry), and quantities a ...

tensor
of Tensor order, order two (which may be full rank or not). There are several equivalent terms and notations for this product: * the dyadic product of two vectors \mathbf and \mathbf is denoted by \mathbf\mathbf (juxtaposed; no symbols, multiplication signs, crosses, dots, etc.) * the outer product of two column vectors \mathbf and \mathbf is denoted and defined as \mathbf \otimes \mathbf or \mathbf\mathbf^\mathsf, where \mathsf means transpose, * the tensor product of two vectors \mathbf and \mathbf is denoted \mathbf \otimes \mathbf, 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. Dirac's bra–ket notation makes the use of dyads and dyadics intuitively clear, see #Cahill, Cahill (2013).


Three-dimensional Euclidean space

To illustrate the equivalent usage, consider Three-dimensional space, three-dimensional Euclidean space, letting: :\begin \mathbf &= a_1 \mathbf + a_2 \mathbf + a_3 \mathbf \\ \mathbf &= b_1 \mathbf + b_2 \mathbf + b_3 \mathbf \end 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: :\begin \mathbf =\qquad &a_1 b_1 \mathbf + a_1 b_2 \mathbf + a_1 b_3 \mathbf \\ + &a_2 b_1 \mathbf + a_2 b_2 \mathbf + a_2 b_3 \mathbf \\ + &a_3 b_1 \mathbf + a_3 b_2 \mathbf + a_3 b_3 \mathbf \end 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): : \mathbf \equiv \mathbf\otimes\mathbf \equiv \mathbf^\mathsf = \begin a_1 \\ a_2 \\ a_3 \end\begin b_1 & b_2 & b_3 \end = \begin 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. A ''dyad'' is a component of the dyadic (a monomial of the sum or equivalently an entry of the matrix) — the dyadic product of a pair of basis vectors scalar multiplication, scalar multiplied by a number. Just as the standard basis (and unit) vectors i, j, k, have the representations: :\begin \mathbf &= \begin 1 \\ 0 \\ 0 \end,& \mathbf &= \begin 0 \\ 1 \\ 0 \end,& \mathbf &= \begin 0 \\ 0 \\ 1 \end \end (which can be transposed), the ''standard basis (and unit) dyads'' have the representation: :\begin \mathbf &= \begin 1 & 0 & 0 \\ 0 & 0 & 0 \\ 0 & 0 & 0 \end, & \mathbf &= \begin 0 & 1 & 0 \\ 0 & 0 & 0 \\ 0 & 0 & 0 \end, & \mathbf &= \begin 0 & 0 & 1 \\ 0 & 0 & 0 \\ 0 & 0 & 0 \end \\ \mathbf &= \begin 0 & 0 & 0 \\ 1 & 0 & 0 \\ 0 & 0 & 0 \end, & \mathbf &= \begin 0 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 0 \end, & \mathbf &= \begin 0 & 0 & 0 \\ 0 & 0 & 1 \\ 0 & 0 & 0 \end \\ \mathbf &= \begin 0 & 0 & 0 \\ 0 & 0 & 0 \\ 1 & 0 & 0 \end, & \mathbf &= \begin 0 & 0 & 0 \\ 0 & 0 & 0 \\ 0 & 1 & 0 \end, & \mathbf &= \begin 0 & 0 & 0 \\ 0 & 0 & 0 \\ 0 & 0 & 1 \end \end For a simple numerical example in the standard basis: :\begin \mathbf &= 2\mathbf + \frac\mathbf - 8\pi\mathbf + \frac\mathbf \\[2pt] &= 2 \begin 0 & 1 & 0 \\ 0 & 0 & 0 \\ 0 & 0 & 0 \end + \frac\begin 0 & 0 & 0 \\ 1 & 0 & 0 \\ 0 & 0 & 0 \end - 8\pi \begin 0 & 0 & 0 \\ 0 & 0 & 1 \\ 0 & 0 & 0 \end + \frac\begin 0 & 0 & 0 \\ 0 & 0 & 0 \\ 0 & 0 & 1 \end \\[2pt] &= \begin 0 & 2 & 0 \\ \frac & 0 & -8\pi \\ 0 & 0 & \frac \end \end


''N''-dimensional Euclidean space

If the Euclidean space is ''N''-dimensional, and :\begin \mathbf &= \sum_^N a_i\mathbf_i = a_1 \mathbf_1 + a_2 \mathbf_2 + + a_N \mathbf_N \\ \mathbf &= \sum_^N b_j\mathbf_j = b_1 \mathbf_1 + b_2 \mathbf_2 + \ldots + b_N \mathbf_N \end where e''i'' and e''j'' are the standard basis vectors in ''N''-dimensions (the index ''i'' on e''i'' selects a specific vector, not a component of the vector as in ''ai''), then in algebraic form their dyadic product is: :\mathbf = \sum_^N \sum_^N a_i b_j \mathbf_i \mathbf_j. This is known as the ''nonion form'' of the dyadic. Their outer/tensor product in matrix form is: : \mathbf = \mathbf^\mathsf = \begin a_1 \\ a_2 \\ \vdots \\ a_N \end\begin b_1 & b_2 & \cdots & b_N \end = \begin 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. A ''dyadic polynomial'' A, otherwise known as a dyadic, is formed from multiple vectors a''i'' and b''j'': : \mathbf = \sum_i\mathbf_i\mathbf_i = \mathbf_1\mathbf_1 + \mathbf_2\mathbf_2 + \mathbf_3\mathbf_3 + \ldots 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, see #Chen, Chen (1983).


Classification

The following table classifies dyadics: :


Identities

The following identities are a direct consequence of the definition of the tensor product:


Dyadic algebra


Product of dyadic and vector

There are four operations defined on a vector and dyadic, constructed from the products defined on vectors. :


Product of dyadic and dyadic

There are five operations for a dyadic to another dyadic. Let a, b, c, d be real vectors. Then: : Letting :\mathbf = \sum_i \mathbf_i\mathbf_i,\quad \mathbf = \sum_j \mathbf_j\mathbf_j be two general dyadics, we have: :


Double-dot product

The first definition of the double-dot product is the Frobenius inner product, : \begin \operatorname\left(\mathbf\mathbf^\mathsf\right) &=\sum_ \operatorname\left(\mathbf_i \mathbf_i^\mathsf \mathbf_j \mathbf_j^\mathsf\right) \\ &=\sum_ \operatorname\left(\mathbf_j^\mathsf \mathbf_i \mathbf_i^\mathsf \mathbf_j\right) \\ &=\sum_ (\mathbf_i\cdot\mathbf_j)(\mathbf_i\cdot\mathbf_j) \\ &=\mathbf _\centerdot^\centerdot \mathbf \end Furthermore, since, : \begin \mathbf^\mathsf &=\sum_ \left(\mathbf_i\mathbf_j^\mathsf\right)^\mathsf \\ &=\sum_ \mathbf_i\mathbf_j^\mathsf \end we get that, : \mathbf _\centerdot^\centerdot \mathbf = \mathbf \underline \mathbf^\mathsf so the second possible definition of the double-dot product is just the first with an additional transposition on the second dyadic. For these reasons, the first definition of the double-dot product is preferred, though some authors still use the second.


Double-cross product

We can see that, for any dyad formed from two vectors a and b, its double cross product is zero. : \left(\mathbf\right) _\times^\times \left(\mathbf\right) = \left(\mathbf\times\mathbf\right)\left(\mathbf\times\mathbf\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 :\mathbf = \sum_^3 \mathbf_i\mathbf_i has a non-zero self-double-cross product of : \mathbf _\times^\times \mathbf = 2\left[ \left(\mathbf_1 \times \mathbf_2\right)\left(\mathbf_1 \times \mathbf_2\right) + \left(\mathbf_2 \times \mathbf_3\right)\left(\mathbf_2 \times \mathbf_3\right) + \left(\mathbf_3 \times \mathbf_1\right)\left(\mathbf_3 \times \mathbf_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 dyadic product by a dot product of vectors: :\begin , \mathbf, =\qquad &A_ \mathbf\cdot\mathbf + A_ \mathbf\cdot\mathbf + A_ \mathbf\cdot\mathbf \\ + &A_ \mathbf\cdot\mathbf + A_ \mathbf\cdot\mathbf + A_ \mathbf\cdot\mathbf \\ + &A_ \mathbf\cdot\mathbf + A_ \mathbf\cdot\mathbf + A_ \mathbf\cdot\mathbf \\[6pt] =\qquad &A_ + A_ + A_ \end in index notation this is the contraction of indices on the dyadic: :, \mathbf, = \sum_i A_i^i In three dimensions only, the ''rotation factor'' arises by replacing every dyadic product by a
cross product In , the cross product or vector product (occasionally directed area product, to emphasize its geometric significance) is a on two s in a three-dimensional (named here E), and is denoted by the symbol \times. Given two and , the cross produc ...

cross product
:\begin \langle\mathbf\rangle =\qquad &A_ \mathbf\times\mathbf + A_ \mathbf\times\mathbf + A_ \mathbf\times\mathbf \\ + &A_ \mathbf\times\mathbf + A_ \mathbf\times\mathbf + A_ \mathbf\times\mathbf\\ + &A_ \mathbf\times\mathbf + A_ \mathbf\times\mathbf + A_ \mathbf\times\mathbf \\[6pt] =\qquad &A_ \mathbf - A_ \mathbf - A_ \mathbf \\ + &A_ \mathbf + A_ \mathbf - A_ \mathbf \\[6pt] =\qquad &\left(A_ - A_\right)\mathbf + \left(A_ - A_\right)\mathbf + \left(A_ - A_\right)\mathbf\\ \end In index notation this is the contraction of A with the Levi-Civita tensor :\langle\mathbf\rangle = \sum_^A_.


Unit dyadic

There exists a unit dyadic, denoted by I, such that, for any vector a, : \mathbf\cdot\mathbf=\mathbf\cdot\mathbf= \mathbf Given a basis of 3 vectors a, b and c, with Dual basis, reciprocal basis \hat, \hat, \hat, the unit dyadic is expressed by :\mathbf = \mathbf\hat + \mathbf\hat + \mathbf\hat In the standard basis, : \mathbf = \mathbf + \mathbf + \mathbf Explicitly, the dot product to the right of the unit dyadic is : \begin \mathbf \cdot \mathbf & = (\mathbf\mathbf + \mathbf\mathbf + \mathbf\mathbf)\cdot \mathbf \\ & = \mathbf(\mathbf \cdot \mathbf) + \mathbf(\mathbf \cdot \mathbf) + \mathbf (\mathbf \cdot \mathbf) \\ & = \mathbf a_x + \mathbf a_y + \mathbf a_z \\ & = \mathbf \end and to the left : \begin \mathbf \cdot \mathbf & = \mathbf \cdot (\mathbf\mathbf + \mathbf\mathbf + \mathbf\mathbf)\\ & = (\mathbf\cdot \mathbf)\mathbf + (\mathbf\cdot \mathbf)\mathbf + (\mathbf\cdot \mathbf)\mathbf \\ & = a_x \mathbf + a_y \mathbf + a_z \mathbf \\ & = \mathbf \end The corresponding matrix is :\mathbf=\begin 1 & 0 & 0\\ 0 & 1 & 0\\ 0 & 0 & 1\\ \end 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''1i + ''a''2j + ''a''3k to ''a''2i, and jj sends this sum to ''a''2j. Now it is revealed in what (precise) sense ii + jj + kk is the identity: it sends ''a''1i + ''a''2j + ''a''3k 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

:\begin \left(\mathbf\times\mathbf\right)\cdot\left(\mathbf\times\mathbf\right) &= \mathbf - \left(\mathbf\cdot\mathbf\right)\mathbf \\ \mathbf _\times^ \left(\mathbf\right) &= \mathbf\times\mathbf \\ \mathbf _\times^\times \mathbf &= (\mathbf _^ \mathbf)\mathbf - \mathbf^\mathsf \\ \mathbf _^ \left(\mathbf\right) &= \left(\mathbf\cdot\mathbf\right)\cdot\mathbf = \mathbf\cdot\mathbf = \mathrm\left(\mathbf\right) \end where "tr" denotes the Trace (linear algebra), trace.


Examples


Vector projection and rejection

A nonzero vector a can always be split into two perpendicular components, one parallel (‖) to the direction of a unit vector n, and one perpendicular (⊥) to it; :\mathbf = \mathbf_\parallel + \mathbf_\perp The parallel component is found by vector projection, which is equivalent to the dot product of a with the dyadic nn, :\mathbf_\parallel = \mathbf(\mathbf\cdot\mathbf) = (\mathbf)\cdot\mathbf and the perpendicular component is found from vector rejection, which is equivalent to the dot product of a with the dyadic , :\mathbf_\perp = \mathbf - \mathbf(\mathbf\cdot\mathbf) = (\mathbf - \mathbf)\cdot\mathbf


Rotation dyadic


2d rotations

The dyadic : \mathbf = \mathbf - \mathbf = \begin 0 & -1 \\ 1 & 0 \end is a 90° anticlockwise Rotation operator (vector space), rotation operator in 2d. It can be left-dotted with a vector r = ''x''i + ''y''j to produce the vector, : (\mathbf - \mathbf) \cdot (x \mathbf + y \mathbf) = x \mathbf \cdot \mathbf - x \mathbf \cdot \mathbf + y \mathbf \cdot \mathbf - y \mathbf \cdot \mathbf = -y \mathbf + x \mathbf, in summary : \mathbf\cdot\mathbf = \mathbf_\mathrm or in matrix notation : \begin 0 & -1 \\ 1 & 0 \end \begin x \\ y \end= \begin -y \\ x \end. For any angle ''θ'', the 2d rotation dyadic for a rotation anti-clockwise in the plane is :\mathbf = \mathbf\cos\theta + \mathbf\sin\theta = (\mathbf+\mathbf)\cos\theta + (\mathbf-\mathbf)\sin\theta = \begin \cos\theta &-\sin\theta \\ \sin\theta &\;\cos\theta \end where I and J are as above, and the rotation of any 2d vector a = ''ax''i + ''ay''j is :\mathbf_\mathrm = \mathbf\cdot\mathbf


3d rotations

A general 3d rotation of a vector a, about an axis in the direction of a unit vector ω and anticlockwise through angle ''θ'', can be performed using Rodrigues' rotation formula in the dyadic form :\mathbf_\mathrm = \mathbf \cdot \mathbf \,, where the rotation dyadic is :\mathbf = \mathbf \cos\theta + \sin\theta \boldsymbol + (1 - \cos\theta) \boldsymbol \,, and the Cartesian entries of ω also form those of the dyadic :\boldsymbol = \omega_x( \mathbf -\mathbf ) + \omega_y( \mathbf -\mathbf ) + \omega_z( \mathbf - \mathbf ) \,, The effect of Ω on a is the cross product :\boldsymbol\cdot\mathbf = \boldsymbol\times \mathbf which is the dyadic form the cross product matrix with a column vector.


Lorentz transformation

In special relativity, the Lorentz boost with speed ''v'' in the direction of a unit vector n can be expressed as :t' = \gamma\left(t - \frac \right) :\mathbf' = [\mathbf + (\gamma-1) \mathbf]\cdot \mathbf - \gamma v \mathbft where :\gamma=\frac is the Lorentz factor.


Related terms

Some authors generalize from the term ''dyadic'' to related terms ''triadic'', ''tetradic'' and ''polyadic''.For example,


See also

* Kronecker product * Polyadic algebra * Unit vector * Multivector * Differential form * Quaternions * Field (mathematics)


Notes


References

* Chapter 2 * * . * . * . * . *


External links


Vector Analysis, a Text-Book for the use of Students of Mathematics and Physics, Founded upon the Lectures of J. Willard Gibbs PhD LLD, Edwind Bidwell Wilson PhD

Advanced Field Theory, I.V.Lindel

Vector and Dyadic Analysis

Introductory Tensor Analysis

Nasa.gov, Foundations of Tensor Analysis for students of Physics and Engineering with an Introduction to the Theory of Relativity, J.C. Kolecki

Nasa.gov, An introduction to Tensors for students of Physics and Engineering, J.C. Kolecki
{{Tensors Tensors