Linear Algebra -- Projection matrix question

In summary, the conversation discusses the most general polynomial in an n×n matrix A that has the property A^2 = A. The most general polynomial is found to be of 1st order, with the inclusion of A^0 = I.
  • #1
Physgeek64
247
11

Homework Statement


Let A be an n×n matrix which has the property that A^2 =A.
(i) Write down the most general polynomial in A

Homework Equations

The Attempt at a Solution


My biggest problem is that I don't even understand what the question is asking

Is it just sum (alphaA^n)=0

but A^n=A

sum(alpha A)=0 ?

I know its not the equation to find the eigenvalues as that follows, and I'm fine with that

Av=pv where p are the eigenvalues, and v the corresponding eigenvectors
A^2v=Apv
Av=p(Av)
pv=p^2v
v(p)(p-1)=0 and hence p= 0 or 1

But I just don't understand the first bit

Many thanks in advance :)
 
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  • #2
The absolute most general polynomial, degree N, in some arbitrary A would be ##P_N(A)=\sum_{n=0}^N a_nA^n## wouldn't it?
What is special about this polynomial?
 
  • #3
Simon Bridge said:
The absolute most general polynomial, degree N, in some arbitrary A would be ##P_N(A)=\sum_{n=0}^N a_nA^n## wouldn't it?
What is special about this polynomial?
Well since A is a projection matrix, surely that would imply than ##A^n=A## and hence ##P_N(A)=\sum_{n=0}^N a_nA^n## -> ##P_N(A)=\sum_{n=0}^N a_nA##
 
  • #4
i.e. the most general polynomial is 1st order. Well done.
 
  • #5
Wouldn't you want to include ##A^0 = I##?
 
  • #6
LCKurtz said:
Wouldn't you want to include ##A^0 = I##?
Yes, yes I would ;) Thank you
 

Related to Linear Algebra -- Projection matrix question

1. What is a projection matrix in linear algebra?

A projection matrix is a square matrix that maps vectors onto a subspace by projecting them onto a lower-dimensional subspace. It is used to simplify calculations and to represent transformations in linear algebra.

2. How is a projection matrix calculated?

A projection matrix is calculated by taking the dot product of the vector to be projected and the unit vector of the subspace onto which it is being projected. This dot product is then multiplied by the unit vector to obtain the projection matrix.

3. What is the purpose of a projection matrix?

The purpose of a projection matrix is to simplify calculations and representations of transformations in linear algebra. It is also used to project vectors onto lower-dimensional subspaces, making it easier to analyze and understand data.

4. What is the difference between an orthogonal and non-orthogonal projection matrix?

An orthogonal projection matrix is a square matrix that projects vectors onto a subspace that is orthogonal to the subspace being projected onto. A non-orthogonal projection matrix, on the other hand, projects vectors onto a subspace that may not be orthogonal to the subspace being projected onto.

5. How is a projection matrix used in data analysis?

A projection matrix is used in data analysis to simplify and represent transformations in linear algebra. It is also used to project high-dimensional data onto lower-dimensional subspaces for easier analysis and visualization. In machine learning, projection matrices are used for dimensionality reduction techniques such as principal component analysis (PCA).

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