Linear transformation

In mathematics, a linear transformation (also called linear operator or linear map) is a function between two vector spaces which respects the arithmetical operations addition and scalar multiplication defined on vector spaces, or, in other words, it "preserves linear combinations".

Table of contents 1 Definition and first consequences 2 Examples and matrices 3 Forming new linear transformations from given ones 4 Endomorphisms and automorphisms 5 Kernel and image

Definition and first consequences

Formally, if V and W are vector spaces over the same ground field K, we say that f : V → W is a linear transformation if for any two vectors x and y in V and any scalar a in K, we have

<math>f(x+y)=f(x)+f(y)\quad{\rm (additivity)}</math>

<math>f(ax)=af(x)\quad{\rm (homogeneity)}.</math>

This is equivalent to saying that f "preserves linear combinations", i.e., for any vectors x₁, ..., x_m and scalars a₁, ..., a_m, we have

<math>f(a_1 x_1+\cdots+a_m x_m)=a_1 f(x_1)+\cdots+a_m f(x_m).</math>

Occasionally, V and W can be considered as vector spaces over different ground fields, and it is then important to specify which field was used for the definition of "linear". If V and W are considered as spaces over the field K as above, 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.

Examples and matrices

If V and W are finite dimensional and bases have been chosen, then every linear transformation from V to W can be represented as a matrix; this is useful because it allows concrete calculations. Conversely, matrices yield examples of linear transformations: if A is a real m-by-n matrix, then the rule f(x) = Ax describes a linear transformation Rⁿ → R^m (see Euclidean space).

There are also important examples of linear transformation involving infinite-dimensional spaces. For instance, the integral yields a linear map from the space of all real-valued integrable functions on some interval to R, while differentiation is a linear transformation from the space of all differentiable functions to the space of all functions.

Forming new linear transformations from given ones

The composition of linear transformations is linear: if f : V → W and g : W → Z are linear, then so is g o f : V → Z.

If f₁ : V → W and f₂ : V → W are linear, then so is their sum f₁ + f₂ (which is defined by (f₁ + f₂)(x) = f₁(x) + f₂(x)).

If f : V → W is linear and a is an element of the ground field K, then the map af, defined by (af)(x) = a (f(x)), is also linear.

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

Endomorphisms and automorphisms

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 identity element of this algebra is the identity map id : V → V.

A bijective endomorphism of V 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).

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

Kernel and image

If f : V → W is linear, we define the kernel and the image of f by

<math>\operatorname{ker}(f)=\{\,x\in V:f(x)=0\,\}</math>

<math>\operatorname{im}(f)=\{\,f(x):x\in V\,\}</math>

ker(f) is a subspace of V and im(f) is a subspace of W. The following dimension formula is often useful:

dim(ker(f)) + dim(im(f)) = dim(V)

The number dim(im(f)) is also called the rank of f and written as rk(f). If V and W are finite dimensional, bases have been chosen and f is represented by the matrix A, then the rank of f is equal to the rank of the matrix A.