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In mathematics, especially in linear algebra and matrix theory, the vectorization of a matrix is a linear transformation which converts the matrix into a vector.
Vectorization (mathematics), a linear transformation that converts a matrix into a column vector; Vector autoregression, an econometric model used to capture the evolution and the interdependencies between multiple time series; Vector boson, a boson with the spin quantum number equal to 1
Related titles should be described in Vectorization, ... Vectorization (mathematics), a linear transformation which converts a matrix into a column vector;
In mathematics, the Kronecker product, sometimes denoted by ⊗, is an operation on two matrices of arbitrary size resulting in a block matrix.It is a specialization of the tensor product (which is denoted by the same symbol) from vectors to matrices and gives the matrix of the tensor product linear map with respect to a standard choice of basis.
In mathematics and physics, a vector space (also called a linear space) is a set whose elements, often called vectors, can be added together and multiplied ("scaled") by numbers called scalars. The operations of vector addition and scalar multiplication must satisfy certain requirements, called vector axioms.
Function rank is an important concept to array programming languages in general, by analogy to tensor rank in mathematics: functions that operate on data may be classified by the number of dimensions they act on. Ordinary multiplication, for example, is a scalar ranked function because it operates on zero-dimensional data (individual numbers).
Today's NYT Connections puzzle for Wednesday, February 19, 2025The New York Times
In the case of column vectors, the Kronecker product can be viewed as a form of vectorization (or flattening) of the outer product. In particular, for two column vectors u {\displaystyle \mathbf {u} } and v {\displaystyle \mathbf {v} } , we can write: