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In mathematics, and more specifically in linear algebra, a **linear map** (also called a **linear mapping**, **linear transformation**, **vector space homomorphism**, or in some contexts **linear function**) is a mapping between two vector spaces that preserves the operations of vector addition and scalar multiplication. The same names and the same definition are also used for the more general case of modules over a ring; see Module homomorphism.

- Definition and first consequences
- Examples
- Linear extensions
- Matrices
- Examples in two dimensions
- Vector space of linear maps
- Endomorphisms and automorphisms
- Kernel, image and the rank–nullity theorem
- Cokernel
- Index
- Algebraic classifications of linear transformations
- Monomorphism
- Epimorphism
- Isomorphism
- Change of basis
- Continuity
- Applications
- See also
- Notes
- Bibliography

If a linear map is a bijection then it is called a **linear isomorphism**. In the case where , a linear map is called a **linear endomorphism**. Sometimes the term **linear operator** refers to this case,^{ [1] } but the term "linear operator" can have different meanings for different conventions: for example, it can be used to emphasize that and are real vector spaces (not necessarily with ),^{[ citation needed ]} or it can be used to emphasize that is a function space, which is a common convention in functional analysis.^{ [2] } Sometimes the term * linear function * has the same meaning as *linear map*, while in analysis it does not.

A linear map from *V* to *W* always maps the origin of *V* to the origin of *W*. Moreover, it maps linear subspaces in *V* onto linear subspaces in *W* (possibly of a lower dimension);^{ [3] } for example, it maps a plane through the origin in *V* to either a plane through the origin in *W*, a line through the origin in *W*, or just the origin in *W*. Linear maps can often be represented as matrices, and simple examples include rotation and reflection linear transformations.

In the language of category theory, linear maps are the morphisms of vector spaces.

Let and be vector spaces over the same field . A function is said to be a *linear map* if for any two vectors and any scalar the following two conditions are satisfied:

- Additivity / operation of addition
- Homogeneity of degree 1 / operation of scalar multiplication

Thus, a linear map is said to be *operation preserving*. In other words, it does not matter whether the linear map is applied before (the right hand sides of the above examples) or after (the left hand sides of the examples) the operations of addition and scalar multiplication.

By the associativity of the addition operation denoted as +, for any vectors and scalars the following equality holds:^{ [4] }^{ [5] }

Thus a linear map is one which preserves linear combinations.

Denoting the zero elements of the vector spaces and by and respectively, it follows that Let and in the equation for homogeneity of degree 1:

A linear map with viewed as a one-dimensional vector space over itself is called a linear functional.^{ [6] }

These statements generalize to any left-module over a ring without modification, and to any right-module upon reversing of the scalar multiplication.

- A prototypical example that gives linear maps their name is a function , of which the graph is a line through the origin.
^{ [7] } - More generally, any homothety centered in the origin of a vector space is a linear map (here c is a scalar).
- The zero map between two vector spaces (over the same field) is linear.
- The identity map on any module is a linear operator.
- For real numbers, the map is not linear.
- For real numbers, the map is not linear (but is an affine transformation).
- If is a real matrix, then defines a linear map from to by sending a column vector to the column vector . Conversely, any linear map between finite-dimensional vector spaces can be represented in this manner; see the § Matrices, below.
- If is an isometry between real normed spaces such that then is a linear map. This result is not necessarily true for complex normed space.
^{ [8] } - Differentiation defines a linear map from the space of all differentiable functions to the space of all functions. It also defines a linear operator on the space of all smooth functions (a linear operator is a linear endomorphism, that is, a linear map with the same domain and codomain). Indeed,
- A definite integral over some interval I is a linear map from the space of all real-valued integrable functions on I to . Indeed,
- An indefinite integral (or antiderivative) with a fixed integration starting point defines a linear map from the space of all real-valued integrable functions on to the space of all real-valued, differentiable functions on . Without a fixed starting point, the antiderivative maps to the quotient space of the differentiable functions by the linear space of constant functions.
- If and are finite-dimensional vector spaces over a field F, of respective dimensions m and n, then the function that maps linear maps to
*n*×*m*matrices in the way described in § Matrices (below) is a linear map, and even a linear isomorphism. - The expected value of a random variable (which is in fact a function, and as such a element of a vector space) is linear, as for random variables and we have and , but the variance of a random variable is not linear.

- The function with is a linear map. This function scales the component of a vector by the factor .
- The function is additive: It does not matter whether vectors are first added and then mapped or whether they are mapped and finally added:
- The function is homogeneous: It does not matter whether a vector is first scaled and then mapped or first mapped and then scaled:

Often, a linear map is constructed by defining it on a subset of a vector space and then *extending by linearity* to the linear span of the domain. Suppose and are vector spaces and is a function defined on some subset Then a *linear extension of to * if it exists, is a linear map defined on that extends ^{ [note 1] } (meaning that for all ) and takes its values from the codomain of ^{ [9] } When the subset is a vector subspace of then a (-valued) linear extension of to all of is guaranteed to exist if (and only if) is a linear map.^{ [9] } In particular, if has a linear extension to then it has a linear extension to all of

The map can be extended to a linear map if and only if whenever is an integer, are scalars, and are vectors such that then necessarily ^{ [10] } If a linear extension of exists then the linear extension is unique and

holds for all and as above.^{ [10] } If is linearly independent then every function into any vector space has a linear extension to a (linear) map (the converse is also true).

For example, if and then the assignment and can be linearly extended from the linearly independent set of vectors to a linear map on The unique linear extension is the map that sends to

Every (scalar-valued) linear functional defined on a vector subspace of a real or complex vector space has a linear extension to all of Indeed, the Hahn–Banach dominated extension theorem even guarantees that when this linear functional is dominated by some given seminorm (meaning that holds for all in the domain of ) then there exists a linear extension to that is also dominated by

If and are finite-dimensional vector spaces and a basis is defined for each vector space, then every linear map from to can be represented by a matrix.^{ [11] } This is useful because it allows concrete calculations. Matrices yield examples of linear maps: if is a real matrix, then describes a linear map (see Euclidean space).

Let be a basis for . Then every vector is uniquely determined by the coefficients in the field :

If is a linear map,

which implies that the function *f* is entirely determined by the vectors . Now let be a basis for . Then we can represent each vector as

Thus, the function is entirely determined by the values of . If we put these values into an matrix , then we can conveniently use it to compute the vector output of for any vector in . To get , every column of is a vector

corresponding to as defined above. To define it more clearly, for some column that corresponds to the mapping ,

where is the matrix of . In other words, every column has a corresponding vector whose coordinates are the elements of column . A single linear map may be represented by many matrices. This is because the values of the elements of a matrix depend on the bases chosen.

The matrices of a linear transformation can be represented visually:

- Matrix for relative to :
- Matrix for relative to :
- Transition matrix from to :
- Transition matrix from to :

Such that starting in the bottom left corner and looking for the bottom right corner , one would left-multiply—that is, . The equivalent method would be the "longer" method going clockwise from the same point such that is left-multiplied with , or .

In two-dimensional space **R**^{2} linear maps are described by 2 × 2 matrices. These are some examples:

- rotation
- by 90 degrees counterclockwise:
- by an angle
*θ*counterclockwise:

- by 90 degrees counterclockwise:
- reflection
- through the
*x*axis: - through the
*y*axis: - through a line making an angle
*θ*with the origin:

- through the
- scaling by 2 in all directions:
- horizontal shear mapping:
- squeeze mapping:
- projection onto the
*y*axis:

The composition of linear maps is linear: if and are linear, then so is their composition . It follows from this that the class of all vector spaces over a given field *K*, together with *K*-linear maps as morphisms, forms a category.

The inverse of a linear map, when defined, is again a linear map.

If and are linear, then so is their pointwise sum , which is defined by .

If is linear and is an element of the ground field , then the map , defined by , is also linear.

Thus the set of linear maps from to itself forms a vector space over ,^{ [12] } sometimes denoted .^{ [13] } Furthermore, in the case that , this vector space, denoted , is an associative algebra under composition of maps, since the composition of two linear maps is again a linear map, and the composition of maps is always associative. This case is discussed in more detail below.

Given again the finite-dimensional case, if bases have been chosen, then the composition of linear maps corresponds to the matrix multiplication, the addition of linear maps corresponds to the matrix addition, and the multiplication of linear maps with scalars corresponds to the multiplication of matrices with scalars.

A linear transformation is an endomorphism of ; the set of all such endomorphisms together with addition, composition and scalar multiplication as defined above forms an associative algebra with identity element over the field (and in particular a ring). The multiplicative identity element of this algebra is the identity map .

An endomorphism of that is also an isomorphism is called an automorphism of . The composition of two automorphisms is again an automorphism, and the set of all automorphisms of forms a group, the automorphism group of which is denoted by or . Since the automorphisms are precisely those endomorphisms which possess inverses under composition, is the group of units in the ring .

If has finite dimension , then is isomorphic to the associative algebra of all matrices with entries in . The automorphism group of is isomorphic to the general linear group of all invertible matrices with entries in .

If is linear, we define the kernel and the image or range of by

is a subspace of and is a subspace of . The following dimension formula is known as the rank–nullity theorem:^{ [14] }

The number is also called the rank of and written as , or sometimes, ;^{ [15] }^{ [16] } the number is called the nullity of and written as or .^{ [15] }^{ [16] } If and are finite-dimensional, bases have been chosen and is represented by the matrix , then the rank and nullity of are equal to the rank and nullity of the matrix , respectively.

A subtler invariant of a linear transformation is the *co*kernel, which is defined as

This is the *dual* notion to the kernel: just as the kernel is a *sub*space of the *domain,* the co-kernel is a *quotient* space of the *target.* Formally, one has the exact sequence

These can be interpreted thus: given a linear equation *f*(**v**) = **w** to solve,

- the kernel is the space of
*solutions*to the*homogeneous*equation*f*(**v**) = 0, and its dimension is the number of degrees of freedom in the space of solutions, if it is not empty; - the co-kernel is the space of constraints that the solutions must satisfy, and its dimension is the maximal number of independent constraints.

The dimension of the co-kernel and the dimension of the image (the rank) add up to the dimension of the target space. For finite dimensions, this means that the dimension of the quotient space *W*/*f*(*V*) is the dimension of the target space minus the dimension of the image.

As a simple example, consider the map *f*: **R**^{2} → **R**^{2}, given by *f*(*x*, *y*) = (0, *y*). Then for an equation *f*(*x*, *y*) = (*a*, *b*) to have a solution, we must have *a* = 0 (one constraint), and in that case the solution space is (*x*, *b*) or equivalently stated, (0, *b*) + (*x*, 0), (one degree of freedom). The kernel may be expressed as the subspace (*x*, 0) < *V*: the value of *x* is the freedom in a solution – while the cokernel may be expressed via the map *W* → **R**, : given a vector (*a*, *b*), the value of *a* is the *obstruction* to there being a solution.

An example illustrating the infinite-dimensional case is afforded by the map *f*: **R**^{∞} → **R**^{∞}, with *b*_{1} = 0 and *b*_{n + 1} = *a _{n}* for

For a linear operator with finite-dimensional kernel and co-kernel, one may define *index* as:

namely the degrees of freedom minus the number of constraints.

For a transformation between finite-dimensional vector spaces, this is just the difference dim(*V*) − dim(*W*), by rank–nullity. This gives an indication of how many solutions or how many constraints one has: if mapping from a larger space to a smaller one, the map may be onto, and thus will have degrees of freedom even without constraints. Conversely, if mapping from a smaller space to a larger one, the map cannot be onto, and thus one will have constraints even without degrees of freedom.

The index of an operator is precisely the Euler characteristic of the 2-term complex 0 → *V* → *W* → 0. In operator theory, the index of Fredholm operators is an object of study, with a major result being the Atiyah–Singer index theorem.^{ [17] }

No classification of linear maps could be exhaustive. The following incomplete list enumerates some important classifications that do not require any additional structure on the vector space.

Let V and W denote vector spaces over a field F and let *T*: *V* → *W* be a linear map.

T is said to be * injective * or a * monomorphism * if any of the following equivalent conditions are true:

- T is one-to-one as a map of sets.
- ker
*T*= {0_{V}} - dim(ker
*T*) = 0 - T is monic or left-cancellable, which is to say, for any vector space U and any pair of linear maps
*R*:*U*→*V*and*S*:*U*→*V*, the equation*TR*=*TS*implies*R*=*S*. - T is left-invertible, which is to say there exists a linear map
*S*:*W*→*V*such that*ST*is the identity map on V.

T is said to be * surjective * or an * epimorphism * if any of the following equivalent conditions are true:

- T is onto as a map of sets.
- coker
*T*= {0_{W}} - T is epic or right-cancellable, which is to say, for any vector space U and any pair of linear maps
*R*:*W*→*U*and*S*:*W*→*U*, the equation*RT*=*ST*implies*R*=*S*. - T is right-invertible, which is to say there exists a linear map
*S*:*W*→*V*such that*TS*is the identity map on W.

T is said to be an * isomorphism * if it is both left- and right-invertible. This is equivalent to T being both one-to-one and onto (a bijection of sets) or also to T being both epic and monic, and so being a bimorphism.

If *T*: *V* → *V* is an endomorphism, then:

- If, for some positive integer n, the n-th iterate of T,
*T*^{n}, is identically zero, then T is said to be nilpotent. - If
*T*^{2}=*T*, then T is said to be idempotent - If
*T*=*kI*, where k is some scalar, then T is said to be a scaling transformation or scalar multiplication map; see scalar matrix.

Given a linear map which is an endomorphism whose matrix is *A*, in the basis *B* of the space it transforms vector coordinates [u] as [v] = *A*[u]. As vectors change with the inverse of *B* (vectors are contravariant) its inverse transformation is [v] = *B*[v'].

Substituting this in the first expression

hence

Therefore, the matrix in the new basis is *A′* = *B*^{−1}*AB*, being *B* the matrix of the given basis.

Therefore, linear maps are said to be 1-co- 1-contra-variant objects, or type (1, 1) tensors.

A *linear transformation* between topological vector spaces, for example normed spaces, may be continuous. If its domain and codomain are the same, it will then be a continuous linear operator. A linear operator on a normed linear space is continuous if and only if it is bounded, for example, when the domain is finite-dimensional.^{ [18] } An infinite-dimensional domain may have discontinuous linear operators.

An example of an unbounded, hence discontinuous, linear transformation is differentiation on the space of smooth functions equipped with the supremum norm (a function with small values can have a derivative with large values, while the derivative of 0 is 0). For a specific example, sin(*nx*)/*n* converges to 0, but its derivative cos(*nx*) does not, so differentiation is not continuous at 0 (and by a variation of this argument, it is not continuous anywhere).

A specific application of linear maps is for geometric transformations, such as those performed in computer graphics, where the translation, rotation and scaling of 2D or 3D objects is performed by the use of a transformation matrix. Linear mappings also are used as a mechanism for describing change: for example in calculus correspond to derivatives; or in relativity, used as a device to keep track of the local transformations of reference frames.

Another application of these transformations is in compiler optimizations of nested-loop code, and in parallelizing compiler techniques.

Wikibooks has a book on the topic of: ** Linear Algebra/Linear Transformations **

- Additive map – Z-module homomorphism
- Antilinear map – Conjugate homogeneous additive map
- Bent function – Special type of Boolean function
- Bounded operator – Linear transformation between topological vector spaces
- Cauchy's functional equation – Functional equation
- Continuous linear operator
- Linear functional – Linear map from a vector space to its field of scalars
- Linear isometry – Distance-preserving mathematical transformation

- ↑ "Linear transformations of V into V are often called
*linear operators*on V." Rudin 1976 , p. 207 - ↑ Let V and W be two real vector spaces. A mapping a from V into W Is called a 'linear mapping' or 'linear transformation' or 'linear operator' [...] from V into W, if

for all ,

for all and all real λ. Bronshtein & Semendyayev 2004 , p. 316 - ↑ Rudin 1991 , p. 14

Here are some properties of linear mappings whose proofs are so easy that we omit them; it is assumed that and :- If A is a subspace (or a convex set, or a balanced set) the same is true of
- If B is a subspace (or a convex set, or a balanced set) the same is true of
- In particular, the set: is a subspace of X, called the
*null space*of .

- ↑ Rudin 1991 , p. 14. Suppose now that X and Y are vector spaces
*over the same scalar field*. A mapping is said to be*linear*if for all and all scalars and . Note that one often writes , rather than , when is linear. - ↑ Rudin 1976 , p. 206. A mapping A of a vector space X into a vector space Y is said to be a
*linear transformation*if: for all and all scalars c. Note that one often writes instead of if A is linear. - ↑ Rudin 1991 , p. 14. Linear mappings of X onto its scalar field are called
*linear functionals*. - ↑ "terminology - What does 'linear' mean in Linear Algebra?".
*Mathematics Stack Exchange*. Retrieved 2021-02-17. - ↑ Wilansky 2013, pp. 21–26.
- 1 2 Kubrusly 2001, p. 57.
- 1 2 Schechter 1996, pp. 277–280.
- ↑ Rudin 1976 , p. 210 Suppose and are bases of vector spaces X and Y, respectively. Then every determines a set of numbers such that It is convenient to represent these numbers in a rectangular array of m rows and n columns, called an m
*by*n*matrix*:Observe that the coordinates of the vector (with respect to the basis ) appear in the*j*^{th}column of . The vectors are therefore sometimes called the*column vectors*of . With this terminology, the*range*of A*is spanned by the column vectors of*. - ↑ Axler (2015) p. 52, § 3.3
- ↑ Tu (2011), p. 19, § 3.1
- ↑ Horn & Johnson 2013 , 0.2.3 Vector spaces associated with a matrix or linear transformation, p. 6
- 1 2 Katznelson & Katznelson (2008) p. 52, § 2.5.1
- 1 2 Halmos (1974) p. 90, § 50
- ↑ Nistor, Victor (2001) [1994], "Index theory",
*Encyclopedia of Mathematics*, EMS Press : "The main question in index theory is to provide index formulas for classes of Fredholm operators ... Index theory has become a subject on its own only after M. F. Atiyah and I. Singer published their index theorems" - ↑ Rudin 1991 , p. 15
**1.18 Theorem***Let be a linear functional on a topological vector space X. Assume for some . Then each of the following four properties implies the other three:*- is continuous
- The null space is closed.
- is not dense in X.
- is bounded in some neighbourhood V of 0.

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*Handbook of Mathematics*(4th ed.). New York: Springer-Verlag. ISBN 3-540-43491-7. - Halmos, Paul Richard (1974) [1958].
*Finite-Dimensional Vector Spaces*(2nd ed.). Springer. ISBN 0-387-90093-4. - Horn, Roger A.; Johnson, Charles R. (2013).
*Matrix Analysis*(Second ed.). Cambridge University Press. ISBN 978-0-521-83940-2. - Katznelson, Yitzhak; Katznelson, Yonatan R. (2008).
*A (Terse) Introduction to Linear Algebra*. American Mathematical Society. ISBN 978-0-8218-4419-9. - Kubrusly, Carlos (2001).
*Elements of operator theory*. Boston: Birkhäuser. ISBN 978-1-4757-3328-0. OCLC 754555941. - Lang, Serge (1987),
*Linear Algebra*(Third ed.), New York: Springer-Verlag, ISBN 0-387-96412-6 - Rudin, Walter (1973).
*Functional Analysis*. International Series in Pure and Applied Mathematics. Vol. 25 (First ed.). New York, NY: McGraw-Hill Science/Engineering/Math. ISBN 9780070542259. - Rudin, Walter (1976).
*Principles of Mathematical Analysis*. Walter Rudin Student Series in Advanced Mathematics (3rd ed.). New York: McGraw–Hill. ISBN 978-0-07-054235-8. - Rudin, Walter (1991).
*Functional Analysis*. International Series in Pure and Applied Mathematics. Vol. 8 (Second ed.). New York, NY: McGraw-Hill Science/Engineering/Math. ISBN 978-0-07-054236-5. OCLC 21163277. - Schaefer, Helmut H.; Wolff, Manfred P. (1999).
*Topological Vector Spaces*. GTM. Vol. 8 (Second ed.). New York, NY: Springer New York Imprint Springer. ISBN 978-1-4612-7155-0. OCLC 840278135. - Schechter, Eric (1996).
*Handbook of Analysis and Its Foundations*. San Diego, CA: Academic Press. ISBN 978-0-12-622760-4. OCLC 175294365. - Swartz, Charles (1992).
*An introduction to Functional Analysis*. New York: M. Dekker. ISBN 978-0-8247-8643-4. OCLC 24909067. - Tu, Loring W. (2011).
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In mathematical physics and mathematics, the **Pauli matrices** are a set of three 2 × 2 complex matrices which are Hermitian, involutory and unitary. Usually indicated by the Greek letter sigma, they are occasionally denoted by tau when used in connection with isospin symmetries.

In Euclidean geometry, an **affine transformation** or **affinity** is a geometric transformation that preserves lines and parallelism, but not necessarily Euclidean distances and angles.

In linear algebra, the **trace** of a square matrix **A**, denoted tr(**A**), is defined to be the sum of elements on the main diagonal of **A**. The trace is only defined for a square matrix.

In probability theory and statistics, the **multivariate normal distribution**, **multivariate Gaussian distribution**, or **joint normal distribution** is a generalization of the one-dimensional (univariate) normal distribution to higher dimensions. One definition is that a random vector is said to be *k*-variate normally distributed if every linear combination of its *k* components has a univariate normal distribution. Its importance derives mainly from the multivariate central limit theorem. The multivariate normal distribution is often used to describe, at least approximately, any set of (possibly) correlated real-valued random variables each of which clusters around a mean value.

In mathematics, the **linear span** (also called the **linear hull** or just **span**) of a set S of vectors (from a vector space), denoted span(*S*), is defined as the set of all linear combinations of the vectors in S. For example, two linearly independent vectors span a plane. The linear span can be characterized either as the intersection of all linear subspaces that contain S, or as the smallest subspace containing S. The linear span of a set of vectors is therefore a vector space itself. Spans can be generalized to matroids and modules.

In machine learning, **support vector machines** are supervised learning models with associated learning algorithms that analyze data for classification and regression analysis. Developed at AT&T Bell Laboratories by Vladimir Vapnik with colleagues SVMs are one of the most robust prediction methods, being based on statistical learning frameworks or VC theory proposed by Vapnik and Chervonenkis (1974). Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non-probabilistic binary linear classifier. SVM maps training examples to points in space so as to maximise the width of the gap between the two categories. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall.

In mathematics, particularly in linear algebra, a **skew-symmetric****matrix** is a square matrix whose transpose equals its negative. That is, it satisfies the condition

In mathematics, a **self-adjoint operator** on an infinite-dimensional complex vector space *V* with inner product is a linear map *A* that is its own adjoint. If *V* is finite-dimensional with a given orthonormal basis, this is equivalent to the condition that the matrix of *A* is a Hermitian matrix, i.e., equal to its conjugate transpose *A*^{∗}. By the finite-dimensional spectral theorem, *V* has an orthonormal basis such that the matrix of *A* relative to this basis is a diagonal matrix with entries in the real numbers. This article deals with applying generalizations of this concept to operators on Hilbert spaces of arbitrary dimension.

In mathematics, a **linear form** is a linear map from a vector space to its field of scalars.

In linear algebra, the **characteristic polynomial** of a square matrix is a polynomial which is invariant under matrix similarity and has the eigenvalues as roots. It has the determinant and the trace of the matrix among its coefficients. The **characteristic polynomial** of an endomorphism of a finite-dimensional vector space is the characteristic polynomial of the matrix of that endomorphism over any base. The **characteristic equation**, also known as the **determinantal equation**, is the equation obtained by equating the characteristic polynomial to zero.

In mathematics, a function between two complex vector spaces is said to be **antilinear** or **conjugate-linear** if

The **rank–nullity theorem** is a theorem in linear algebra, which asserts

- of a matrix
*M*that its rank + its nullity = the number of columns, and - of a linear transformation that the dimension of the domain is the sum of the transformation's rank and its nullity.

In functional analysis, a branch of mathematics, a **compact operator** is a linear operator , where are normed vector spaces, with the property that maps bounded subsets of to relatively compact subsets of . Such an operator is necessarily a bounded operator, and so continuous. Some authors require that are Banach, but the definition can be extended to more general spaces.

In mathematics, an **invariant subspace** of a linear mapping *T* : *V* → *V * i.e. from some vector space *V* to itself, is a subspace *W* of *V* that is preserved by *T*; that is, *T*(*W*) ⊆ *W*.

In geometry, a **barycentric coordinate system** is a coordinate system in which the location of a point is specified by reference to a simplex. The **barycentric coordinates** of a point can be interpreted as masses placed at the vertices of the simplex, such that the point is the center of mass of these masses. These masses can be zero or negative; they are all positive if and only if the point is inside the simplex.

In mathematics, a **canonical basis** is a basis of an algebraic structure that is canonical in a sense that depends on the precise context:

In linear algebra, a **generalized eigenvector** of an matrix is a vector which satisfies certain criteria which are more relaxed than those for an (ordinary) eigenvector.

In mathematics, a complex structure on a real vector space *V* is an automorphism of *V* that squares to the minus identity, −*I*. Such a structure on *V* allows one to define multiplication by complex scalars in a canonical fashion so as to regard *V* as a complex vector space.

In mathematics, particularly in linear algebra and applications, **matrix analysis** is the study of matrices and their algebraic properties. Some particular topics out of many include; operations defined on matrices, functions of matrices, and the eigenvalues of matrices.

In mathematics, **nuclear operators** are an important class of linear operators introduced by Alexander Grothendieck in his doctoral dissertation. Nuclear operators are intimately tied to the projective tensor product of two topological vector spaces (TVSs).

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