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A two-vector or bivector [1] is a tensor of type () and it is the dual of a two-form, meaning that it is a linear functional which maps two-forms to the real numbers (or more generally, to scalars). The tensor product of a pair of vectors is a two-vector. Then, any two-form can be expressed as a linear combination of tensor products of pairs of ...
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 ...
A pairing function can usually be defined inductively – that is, given the n th pair, what is the (n+1) th pair? The way Cantor's function progresses diagonally across the plane can be expressed as (,) + = (, +).
This vector length is equivalent to the dimensions of the original matrix output , making converting back to a matrix a direct transformation. Thus, the vector, Z ″ {\displaystyle Z''} , is converted back to matrix form, which produces the output of the two-dimensional discrete convolution.
Basis Decomposition of a 2-vector. For vectors in R 3, the exterior algebra is closely related to the cross product and triple product.Using the standard basis {e 1, e 2, e 3}, the exterior product of a pair of vectors
In linear recurrences, the n th term is equated to a linear function of the previous terms. A famous example is the recurrence for the Fibonacci numbers , F n = F n − 1 + F n − 2 {\displaystyle F_{n}=F_{n-1}+F_{n-2}} where the order k {\displaystyle k} is two and the linear function merely adds the two previous terms.
In vector calculus the derivative of a vector y with respect to a scalar x is known as the tangent vector of the vector y, . Notice here that y : R 1 → R m . Example Simple examples of this include the velocity vector in Euclidean space , which is the tangent vector of the position vector (considered as a function of time).
Formally, a parity check matrix H of a linear code C is a generator matrix of the dual code, C ⊥. This means that a codeword c is in C if and only if the matrix-vector product Hc ⊤ = 0 (some authors [1] would write this in an equivalent form, cH ⊤ = 0.) The rows of a parity check matrix are the coefficients of the parity check equations. [2]