Lecture 27 - Finishing Minimal Polynomials & Starting Jordon Block Decomposition

Recall that pT(z) is the characteristic polynomial of T while μT(z) is the characteristic polynomial of T.

We derived some facts:

Fact

  1. deg(μT)deg(pT)=dim(V)
  2. Any polynomial s with s(T)=0T can only happen iff s is a multiple of μT.
  3. pT is a multiple of μT
  4. The zeroes of μT are precisely the eigenvalues of T.

The proof is as follows:

Proof
(1): From the definition.

(2): is trivial. For suppose s such that s(T)=0. Write:

s=pμT+r

using the Division algorithm for polynomials. Plug in T on both sides:

s(T)=p(T)μT(T)+r(T)=p(T)0+r(T)=r(T)=0

Thus r(T)=0 so then s is a multiple of μT.

(3): Apply (2)

(4): Suppose λ is a zero of μT(z)=zm+αm1zm1++α1z+α0. Thus zλ is a factor of μT so then:

μT(z)=(zλ)q(z),deg(q)=deg(μT)1

Plugging in T:

q(T)0

since μT is a minimal polynomial. But then:

μT(T)=(TλI)q(T)=0

Thus q(T)0, so there's some vector v such that q(T)v0. As such, then:

0=(TλI)q(T)v0

So λ is an eigenvalue of v via T. This shows . For suppose λ is an eigenvalue. Then Tv=λv for some v0. Then:

μT(T)=Tmαm1Tm1++α0I

Plug in v:

0=λmv+αm1λm1v++α0v=(λm+αm1λm1++α0)v0

Thus μT(λ)=0.

This implies that if you have the characteristic polynomial pT, then you kinda know what the minimal polynomial μT should look like. Suppose:

pT(z)=i=1m(zλi)di

Then μT(z) is:

μT(z)=i=1m(zλi)ci

where 1cidi for all i.

Example

Suppose TL(R3) where:

M(T)=[51239523310]

Let v1=(1,4,1), v2=(0,1,0) and v3=(2,1,3) compose the basis β. Then:

M(T,β)=[111016002]

Thi is an UT matrix so λ1=1 with d1=2 and λ2=2 with d2=1. We could further find the basis β such that we have block-diagonal matrices. However, here:

pT(z)=(z+1)2(z2)

But:

μT(z)=(z+1)α(z2)

where α{1,2}. But which one? We calculate for the α=1 case:

(T+I)(T2I)=T2T2I

We could find the matrix representation and find that T2T2I0, so then (+1)(z2) is not the minimal polynomial. Thus, the characteristic polynomial in this case is the minimal polynomial.

8.D: Jordan Decomposition

While Axler doesn't use a proof that uses quotient spaces, we'll use them here since we are good at them.

Recall that for any operator TL(V) that there is some k such that:

null(T0)null(Tk)=null(Tk+1)=

where if T is nilpotent then:

null(Tk)==V

We know that we can find a basis β such that M(T,β) is "extra upper triangular" where we have upper triangular sub-matrices in the matrix.

The question here is: can we do better? Namely, can we get more zeroes in M(T,β)?

Goal

We want to produce a basis such that M(T,β) has many zeros.

An Example

Let's do an example to get our feet wet. Suppose TL(V) with dim(V)=9. Further, we suppose that:

null(T0)dim=0null(T1)dim=4null(T2)dim=7null(T3)dim=8null(T4)dim=9==V

So since T0,T1,T2,T3 have non-trivial nullspaces, we can choose vectors from each of them:

In general:

  1. Choose v1V such that Tn1v0 for n=dim(V).
  2. Find a basis for null(Tnk)/null(Tnk1) where k1.
  3. Repeat for growing values of k, moving over the difference in dimensions of our nullspace chain.