lacunary - Mathnotes

Linear Transformations

Definition: Linear Transformation (also: linear) (also defines: additivity, homogeniety) \@{linear-transformation}

Let V and W be vector spaces over a field F. We call a function

T:V→W a linear transformation from V to W if for all x,y∈V and all c∈F, we have

  1. (a) T(x+y)=T(x)+T(y) and
  2. (b) T(cx)=cT(x)

The first condition is called additivity and the second is called homogeniety.

Definition: Null Space (also: kernel) (also defines: range of a linear transformation) \@{null-space}

Let V and W be vector spaces and let T:V→W be linear.

We define the null space (also: kernel) N(T) of T to be the set of all vectors x∈V such that T(x)=0, that is, N(T)={x∈V:T(x)=0.}

We define the range of a linear transformation (also: image of a linear transformation) R(T) of T to be the subset of W consisting of all @images (under T) of vectors in V, that is, R(T)={T(x):x∈V}.

Referenced by (2 direct)
Theorem: Null Space and Range are Subspaces \@{null-space-and-range-are-subspaces}

Let V and W be vector spaces and let T:V→W be linear. Then the null space and range of T are @subspaces of V and W, respectively.

Theorem: Range is span of transformation of domain basis \@{range-is-span-of-transformation-of-domain-basis}

Let V and W be vector spaces and let T:V→W be linear. If β={v1,v2,…,vn} is a @basis for V, then

R(T)=span(T(β))=span({T(v1),T(v2),…,T(vn)}).

Definition: rank (also defines: nullity) \@{rank}

Let V and W be vector spaces and let T:V→W be linear. If N(T) and R(T) (null space and range of T, respectively) are @finite dimensional, the we define the nullity of T, denoted nullity(T), and the rank of T, denoted rank(T), to be the @dimensions of N(T) and R(T), respectively.

Theorem: Rank Nullity Theorem \@{rank-nullity-theorem}

Let V and W be vector spaces and let T:V→W be linear. If V is @finite-dimensional, then

rank(T)+nullity(T)=dim(V).

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