Glossary of linear algebra
From Wikipedia, the free encyclopedia
This glossary of linear algebra is a list of definitions and terms relevant to the field of linear algebra, the branch of mathematics concerned with linear equations and their representations as vector spaces.
For a glossary related to the generalization of vector spaces through modules, see glossary of module theory.
A
- affine transformation
- A composition of functions consisting of a linear transformation between vector spaces followed by a translation.[1] Equivalently, a function between vector spaces that preserves affine combinations.
- affine combination
- A linear combination in which the sum of the coefficients is 1.
B
- basis
- In a vector space, a linearly independent set of vectors spanning the whole vector space.[2]
- basis vector
- An element of a given basis of a vector space.[2]
- bilinear form
- On vector space V over field K, a bilinear form is a function that is linear in each variable.
C
- column vector
- A matrix with only one column.[3]
- complex number
- An element of a complex plane
- complex plane
- A linear algebra over the real numbers with basis {1, i }, where i is an imaginary unit[4]
- coordinate vector
- The tuple of the coordinates of a vector on a basis.
- covector
- An element of the dual space of a vector space, (that is a linear form), identified to an element of the vector space through an inner product.
D
- determinant
- The unique scalar function over square matrices which is distributive over matrix multiplication, multilinear in the rows and columns, and takes the value of for the identity matrix.[5]
- diagonal matrix
- A matrix in which only the entries on the main diagonal are non-zero.[6]
- dimension
- The number of elements of any basis of a vector space.[2]
- dot product
- Given two vectors of the same length, the dot product is the sum of the products of their corresponding indices.
- dual space
- The vector space of all linear forms on a given vector space.[7]
E
- elementary matrix
- Square matrix that differs from the identity matrix by at most one entry
H
I
- identity matrix
- A diagonal matrix all of the diagonal elements of which are equal to .[6]
- imaginary unit
- 1. An operator (x, y) → (y, –x), rotating the plane 90° counterclockwise
- 2. In a linear algebra, a linear map which when composed with itself produces the negative of the identity
- inverse matrix
- Of a matrix , another matrix such that multiplied by and multiplied by both equal the identity matrix.[6]
- isotropic vector
- In a vector space with a quadratic form, a non-zero vector for which the form is zero.
- isotropic quadratic form
- A vector space with a quadratic form which has a null vector.
L
- linear algebra
- 1. The branch of mathematics that deals with vectors, vector spaces, linear transformations and systems of linear equations.
- 2. A vector space that has a binary operation making it a ring. This linear algebra is also known as an algebra over a field.[8]
- linear combination
- A sum, each of whose summands is an appropriate vector times an appropriate scalar (or ring element).[9]
- linear dependence
- A linear dependence of a tuple of vectors is a nonzero tuple of scalar coefficients for which the linear combination equals .
- linear equation
- A polynomial equation of degree one (such as ).[10]
- linear form
- A linear map from a vector space to its field of scalars[11]
- linear independence
- Property of being not linearly dependent.[12]
- linear map
- A function between vector spaces which respects addition and scalar multiplication.
- linear transformation
- A linear map whose domain and codomain are equal; it is generally supposed to be invertible.
M
- matrix
- Rectangular arrangement of numbers or other mathematical objects.[6] A matrix is written A = (ai, j), where ai, j is the entry at row i and column j.
- matrix multiplication
- If a matrix A has the same number of columns as does matrix B of rows, then a product C = AB may be formed with ci, j equal to the dot product of row i of A with column j of B.
N
- null vector
- 1. Another term for an isotropic vector.
- 2. Another term for a zero vector.
O
- orthogonality
- Two vectors u and v are orthogonal with respect to a bilinear form B when B(u,v) = 0.
- orthonormality
- A set of vectors is orthonormal when they are all unit vectors and are pairwise orthogonal.
- orthogonal matrix
- A real square matrix with rows (or columns) that form an orthonormal set.
R
- row vector
- A matrix with only one row.[6]
S
- scalar
- A scalar is an element of a field used in the definition of a vector space.
- singular-value decomposition
- a factorization of an complex matrix M as , where U is an complex unitary matrix, is an rectangular diagonal matrix with non-negative real numbers on the diagonal, and V is an complex unitary matrix.[13]
- spectrum
- Set of the eigenvalues of a matrix.[14]
- split-complex number
- An element of a split-complex plane
- split-complex plane
- A linear algebra over the real numbers with basis {1, j }, where j is a hyperbolic unit
- square matrix
- A matrix having the same number of rows as columns.[6]
T
- transpose
- The transpose of an n × m matrix M is an m × n matrix M T obtained by using the rows of M for the columns of M T.
U
- unit vector
- a vector in a normed vector space whose norm is 1, or a Euclidean vector of length one.[15]
V
- vector
- 1. A directed quantity, one with both magnitude and direction.
- 2. An element of a vector space.[16]
- vector space
- A set, whose elements can be added together, and multiplied by elements of a field (this is scalar multiplication); the set must be an abelian group under addition, and the scalar multiplication must be a linear map.[17]
Z
- zero vector
- The additive identity in a vector space. In a normed vector space, it is the unique vector of norm zero. In a Euclidean vector space, it is the unique vector of length zero.[18]
Notes
References
Wikiwand - on
Seamless Wikipedia browsing. On steroids.