Linear Transformations

Def: Linear Transformation

If V and W are vector spaces, a function T: VW is called a linear transformation if:

  1. v1,v2V,T(v1Vv2)=T(v1)WT(v2)
  2. cR,vV,T(cVv)=cWT(v)

if T:VV then it is often called a linear operator

Proposition: if T is linear then:

  1. T(0V)=0W
  2. vV,T(v)=T(v)
  3. If c1,,cnR and v1,,vnV then: T(i=1ncivi)=i=1nciT(vi)

Thm: Linear Extension

Suppose V fdvs and W is a vector space. Let B={v1,,vn} be a basis for V and for each i=1,...,n let wiW. There is a unique linear transformation such that T(vi)=wi.

Def: Kernel

ker(T)={vV:T(v)=0V}

Def: Image

Im(T)={T(v):vV}

Thm: Kernel and Image are subspaces

TW is linear then ker(T) and im(T) are subspaces

Thm: Injective if ket(T)= {0}

T:VW is linear, then T is injective if and only if ker(T)={0}
maps linearly independent vectors to linearly independent vectors
if no basis is in the kernel then it is injective

Thm: im(T) = span(T(basis of pre-image))

Suppose V is a fdvs with basis B=v1,,vn.
If T:VW is linear, then im(T)=span(T(B))

Def: Rank and Nullspace of T

If T:VW is linear, then its

  1. rank is rank(T) = dim(im(T))
  2. nullity is nullity(T)=dim(ker(T))

Thm: Rank-Nullity

If V and W are vector spaces and T:VW is linear. If rank(T), nullity(T) < then dim(V)=rank(T)+null(T)

pf. define a basis for the image and a map from the pre-image to that basis. Define a basis for the kernel. Show the elements that map to the basis of the image and also the basis of the kernel form a basis for V.

Corollary from Rank-Nullity

If T: VW is linear and dim(V)=dim(W) then T is injective T is surjective T is bijective. Leading to the concept of isomorphisms.