Glossary of linear algebra
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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
[edit]- 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
[edit]- 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]
C
[edit]- column vector
- A matrix with only one column.[3]
- 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
[edit]- 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 unit matrix.
- diagonal matrix
- A matrix in which only the entries on the main diagonal are non-zero.[4]
- dimension
- The number of elements of any basis of a vector space.[2]
- dual space
- The vector space of all linear forms on a given vector space.[5]
E
[edit]- elementary matrix
- Square matrix that differs from the identity matrix by at most one entry
I
[edit]- identity matrix
- A diagonal matrix all of the diagonal elements of which are equal to .[4]
- inverse matrix
- Of a matrix , another matrix such that multiplied by and multiplied by both equal the identity matrix.[4]
- 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
[edit]- linear algebra
- The branch of mathematics that deals with vectors, vector spaces, linear transformations and systems of linear equations.
- linear combination
- A sum, each of whose summands is an appropriate vector times an appropriate scalar (or ring element).[6]
- 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 ).[7]
- linear form
- A linear map from a vector space to its field of scalars[8]
- linear independence
- Property of being not linearly dependent.[9]
- 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
[edit]- matrix
- Rectangular arrangement of numbers or other mathematical objects.[4]
N
[edit]- null vector
- 1. Another term for an isotropic vector.
- 2. Another term for a zero vector.
R
[edit]- row vector
- A matrix with only one row.[4]
S
[edit]- 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.[10]
- spectrum
- Set of the eigenvalues of a matrix.[11]
- square matrix
- A matrix having the same number of rows as columns.[4]
U
[edit]- unit vector
- a vector in a normed vector space whose norm is 1, or a Euclidean vector of length one.[12]
V
[edit]- vector
- 1. A directed quantity, one with both magnitude and direction.
- 2. An element of a vector space.[13]
- 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.[14]
Z
[edit]- 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.[15]
Notes
[edit]- ^ James & James 1992, p. 7.
- ^ a b c James & James 1992, p. 27.
- ^ James & James 1992, p. 66.
- ^ a b c d e f James & James 1992, p. 263.
- ^ James & James 1992, pp. 80, 135.
- ^ James & James 1992, p. 251.
- ^ James & James 1992, p. 252.
- ^ Bourbaki 1989, p. 232.
- ^ James & James 1992, p. 111.
- ^ Williams 2014, p. 407.
- ^ James & James 1992, p. 389.
- ^ James & James 1992, p. 463.
- ^ James & James 1992, p. 441.
- ^ James & James 1992, p. 442.
- ^ James & James 1992, p. 452.
References
[edit]- James, Robert C.; James, Glenn (1992). Mathematics Dictionary (5th ed.). Chapman and Hall. ISBN 978-0442007416.
- Bourbaki, Nicolas (1989). Algebra I. Springer. ISBN 978-3540193739.
- Williams, Gareth (2014). Linear algebra with applications (8th ed.). Jones & Bartlett Learning.