# Linear map

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.

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.

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

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.

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 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.

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

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

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.

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'].

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.