Linear Operator

Get Linear Operator essential facts below. View Videos or join the Linear Operator discussion. Add Linear Operator to your PopFlock.com topic list for future reference or share this resource on social media.
## Definition and first consequences

## Examples

## Matrices

### Examples of linear transformation matrices

## Forming new linear maps from given ones

## Endomorphisms and automorphisms

## Kernel, image and the rank-nullity theorem

## Cokernel

### Index

## Algebraic classifications of linear transformations

## Change of basis

## Continuity

## Applications

## See also

## Notes

## Bibliography

This article uses material from the Wikipedia page available here. It is released under the Creative Commons Attribution-Share-Alike License 3.0.

Linear Operator

In mathematics, a **linear map** (also called a **linear mapping**, **linear transformation** or, in some contexts, **linear function**) is a mapping *V* -> *W* between two modules (for example, two vector spaces) that preserves (in the sense defined below) the operations of addition and scalar multiplication. If a linear map is a bijection then it is called a **linear isomorphism**.

An important special case is when *V* = *W*, in which case a linear map is called a (linear) *endomorphism* of *V*. Sometimes the term **linear operator** refers to this case.^{[1]} In another convention, *linear operator* allows V and W to differ, while requiring them to be real vector spaces.^{[2]} Sometimes the term *linear function* has the same meaning as *linear map*, while in analytic geometry it does not.

A linear map always maps linear subspaces onto linear subspaces (possibly of a lower dimension);^{[3]} for instance it maps a plane through the origin to a plane, straight line or point. Linear maps can often be represented as matrices, and simple examples include rotation and reflection linear transformations.

In the language of abstract algebra, a linear map is a module homomorphism. In the language of category theory, it is a morphism in the category of modules over a given ring.

Let V and W be vector spaces over the same field K.
A function *f* : *V* -> *W* is said to be a *linear map* if for any two vectors and any scalar *c* ∈ *K* 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]}

Denoting the zero elements of the vector spaces V and W by and respectively, it follows that
Let *c* = 0 and in the equation for homogeneity of degree 1:

Occasionally, V and W can be vector spaces over different fields. It is then necessary to specify which of these ground fields is being used in the definition of "linear". If V and W are spaces over the same field K as above, then we talk about K-linear maps. For example, the conjugation of complex numbers is an R-linear map C -> C, but it is not C-linear, where R and C are symbols representing the sets of real numbers and complex numbers, respectively.

A linear map *V* -> *K* with K 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 R without modification, and to any right-module upon reversing of the scalar multiplication.

- The prototypical example that gives linear maps their name is the function , of which the graph is a line through the origin.
^{[7]} - More generally, any homothety centered in the origin of a vector space, where
*c*is a scalar, is a linear operator. This does not hold in general for modules, where such a map might only be semilinear. - The zero map between two left-modules (or two right-modules) over the same ring is always 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; is a linear equation, as the term is used in analytic geometry.)
- If
*A*is a real matrix, then*A*defines a linear map from R^{n}to R^{m}by sending the column vector to the column vector . Conversely, any linear map between finite-dimensional vector spaces can be represented in this manner; see the following section. - If
*F*:*X*->*Y*is an isometry between real normed spaces such that*F*(0) = 0 then F 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 where the domain and codomain of it is the same). An example is . - A definite integral over some interval
*I*is a linear map from the space of all real-valued integrable functions on*I*to R. For example,. - 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 R to the space of all real-valued, differentiable functions on R. Without a fixed starting point, an exercise in group theory will show that the antiderivative maps to the quotient space of the differentiables over the equivalence relation "differ by a constant", which yields an identity class of the constant valued functions .
- If
*V*and*W*are finite-dimensional vector spaces over a field*F*, then functions that send linear maps to matrices in the way described in the sequel are themselves linear maps (indeed linear isomorphisms). - The expected value of a random variable (which is in fact a function, and as such a member of a vector space) is linear, as for random variables
*X*and*Y*we have and , but the variance of a random variable is not linear.

If *V* and *W* are finite-dimensional vector spaces and a basis is defined for each vector space, then every linear map from *V* to *W* can be represented by a matrix.^{[9]} This is useful because it allows concrete calculations. Matrices yield examples of linear maps: if *A* is a real matrix, then describes a linear map (see Euclidean space).

Let {**v**_{1}, ..., **v**_{n}} be a basis for *V*. Then every vector **v** in *V* is uniquely determined by the coefficients *c*_{1}, ..., *c*_{n} in the field **R**:

If is a linear map,

which implies that the function *f* is entirely determined by the vectors *f*(**v**_{1}), ..., *f*(**v**_{n}). Now let be a basis for *W*. Then we can represent each vector *f*(**v**_{j}) as

Thus, the function *f* is entirely determined by the values of *a*_{ij}. If we put these values into an matrix *M*, then we can conveniently use it to compute the vector output of *f* for any vector in *V*. To get *M*, every column *j* of *M* is a vector

corresponding to *f*(**v**_{j}) as defined above. To define it more clearly, for some column *j* that corresponds to the mapping *f*(**v**_{j}),

where **M** is the matrix of *f*. In other words, every column has a corresponding vector *f*(**v**_{j}) whose coordinates *a*_{1j}, ..., *a*_{mj} are the elements of column *j*. 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 real matrices. These are some examples:

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

- by 90 degrees counterclockwise:
- reflection
- through the
*x*axis: - through the
*y*axis: - through a line making an angle
*?*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 *a* is an element of the ground field *K*, then the map *af*, defined by , is also linear.

Thus the set of linear maps from *V* to *W* itself forms a vector space over *K*, sometimes denoted .
Furthermore, in the case that , this vector space (denoted End(*V*)) 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 *f*: *V* -> *V* is an endomorphism of *V*; the set of all such endomorphisms End(*V*) together with addition, composition and scalar multiplication as defined above forms an associative algebra with identity element over the field *K* (and in particular a ring). The multiplicative identity element of this algebra is the identity map id: *V* -> *V*.

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

If *V* has finite dimension *n*, then End(*V*) is isomorphic to the associative algebra of all *n* × *n* matrices with entries in *K*. The automorphism group of *V* is isomorphic to the general linear group GL(*n*, *K*) of all *n* × *n* invertible matrices with entries in *K*.

If *f* : *V* -> *W* is linear, we define the kernel and the image or range of *f* by

ker(*f*) is a subspace of *V* and im(*f*) is a subspace of *W*. The following dimension formula is known as the rank-nullity theorem:

^{[10]}

The number dim(im(*f*)) is also called the *rank of f* and written as rank(*f*), or sometimes, ?(*f*); the number dim(ker(*f*)) is called the *nullity of f* and written as null(*f*) or ?(*f*). If *V* and *W* are finite-dimensional, bases have been chosen and *f* is represented by the matrix *A*, then the rank and nullity of *f* are equal to the rank and nullity of the matrix *A*, 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 a solution, if it exists; - the co-kernel is the space of
*constraints*that must be satisfied if the equation is to have a solution, and its dimension is the number of constraints that must be satisfied for the equation to have a solution.

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.^{[11]}

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.

**Definition**: 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.

**Definition**: 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.

**Definition**: 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.^{[12]}
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.

- Antilinear map
- Bent function
- Bounded operator
- Continuous linear operator
- Linear functional
- Linear isometry

**^**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 ?. 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:
*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*.**^**https://math.stackexchange.com/a/62791/401895**^**Wilansky 2013, pp. 21-26.**^**Rudin 1976, p. 210 Suppose and are bases of vector spaces X and Y, respectively. Then every determines a set of numbers such that*by*n*matrix*:*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*.**^**Horn & Johnson 2013, 0.2.3 Vector spaces associated with a matrix or linear transformation, p. 6**^**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.

- Bronshtein, I. N.; Semendyayev, K. A. (2004).
*Handbook of Mathematics*(4th ed.). New York: Springer-Verlag. ISBN 3-540-43491-7. - Halmos, Paul R. (1974).
*Finite-Dimensional Vector Spaces*. New York: Springer-Verlag. 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. - 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.**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.**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.**8**(Second ed.). New York, NY: Springer New York Imprint Springer. ISBN 978-1-4612-7155-0. OCLC 840278135. - Swartz, Charles (1992).
*An introduction to Functional Analysis*. New York: M. Dekker. ISBN 978-0-8247-8643-4. OCLC 24909067. - Wilansky, Albert (2013).
*Modern Methods in Topological Vector Spaces*. Mineola, New York: Dover Publications, Inc. ISBN 978-0-486-49353-4. OCLC 849801114.

This article uses material from the Wikipedia page available here. It is released under the Creative Commons Attribution-Share-Alike License 3.0.

Popular Products

Music Scenes

Popular Artists