Understanding the Minimal Polynomial: Clarifying Confusion on p(T)(v)

In summary: T)(v)=0" for all v.In summary, the minimal polynomial is defined as the least degree monic polynomial that annihilates a given linear transformation T. The book provides a proof for this definition, which involves showing that the minimal polynomial is the least common multiple of all the other polynomials that also annihilate T. This is demonstrated through the use of a basis and minimal polynomials for each basis element.
  • #1
kidsmoker
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I'm just learning a bit about the "minimal polynomial" today but there was a section from the book which I didn't understand. This is the section, and I've circled the bit I'm having trouble with.

http://img15.imageshack.us/img15/1825/97503873.jpg (sorry, it won't let me post an image for some reason??)

Firstly it's a bit unclear to me what they mean by p(T)(v). Would this mean that you take the linear transformation T (or equivalently its matrix), stick it in the polynomial p to obtain a new linear transformation p(T), then perform this transformation on v?

Okay, assuming that's correct I can understand that p(T)=0 <=> p(T)(v)=0. But then how does this imply that the minimal polynomial is the least common multiple of all those other ones?! They say it like it's completely obvious!

Thanks.
 
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  • #2
Your interpretation of p(T) is correct. Take note of this, since it's a rather useful technique. As for the least common multiple thing, how obvious it is depends on how much algebra you've been doing lately. Your text probably should have supplied a proof, unless they already have results to that effect.

I'll adapt the notation given in your text: let bi be a basis for V and ui be corresponding minimal polynomials killing each bi (bear with me, I don't know how to type this properly! I'll always use "i" as an index). We need to prove two directions. First, assume ui divides p for all i, say (fixing i) p=f*gi. Then p(T)(v)=f(T)*gi(T)(v)=f(T)(0)=0, so p(T) kills all of V. Conversely, say bi does not divide p, but p(bi)=0. Then p=f*ui+r, for some f and r with degree less than that of ui. But then p(T)(bi)=r(bi)=0, contradicting the minimality of ui.
 
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  • #3
These posts are written in a hurry. If anything needs clarification, please ask. Above, I used two facts implicitly: the result given as an excercise immediately below the red box in the scan, and the fact that (f*g)(T)=f(T)*g(T). These are both easy.
 
  • #4
kidsmoker said:
I can understand that p(T)=0 <=> p(T)(v)=0.
That's not correct.

p(T)=0 <=> { p(T)(v) = 0 for all v }
 

What is the minimal polynomial?

The minimal polynomial is a concept in mathematics, specifically in linear algebra, that refers to the smallest degree polynomial that has the given matrix as its root. It is a monic polynomial, meaning its leading coefficient is 1, and it is irreducible, meaning it cannot be factored into smaller polynomials.

Why is the minimal polynomial important?

The minimal polynomial is important because it helps us understand the properties and behavior of a given matrix. It can provide information about the eigenvalues and eigenvectors of a matrix, and can also be used to determine the similarity between two matrices.

How is the minimal polynomial calculated?

The minimal polynomial can be calculated using various methods, such as the Cayley-Hamilton theorem, which states that every square matrix satisfies its own characteristic polynomial. Other methods include using the matrix's Jordan canonical form or using the matrix's power series representation.

What is the relationship between the minimal polynomial and the characteristic polynomial?

The minimal polynomial is a factor of the characteristic polynomial, meaning it divides the characteristic polynomial without leaving a remainder. This also means that the roots of the minimal polynomial are also roots of the characteristic polynomial. However, the converse is not always true, as a matrix may have repeated eigenvalues, but only one minimal polynomial.

Can the minimal polynomial be used to determine the diagonalizability of a matrix?

Yes, the minimal polynomial can be used to determine the diagonalizability of a matrix. If the minimal polynomial has distinct linear factors, then the matrix is diagonalizable. If the minimal polynomial has repeated factors, then the matrix is not diagonalizable. This is because the minimal polynomial provides information about the eigenvalues and multiplicities of a matrix, which are crucial in determining its diagonalizability.

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