Minimal polynomial and diagonalization of a block matrix

In summary: A representative for each class could be a matrix with a single 1 in the upper left corner and 0's everywhere else. In summary, the problem is asking for a representative for each equivalence class in the set of rank-1 matrices under the equivalence relation of "similarity". While there are an infinite number of equivalence classes, one possible representative for each class could be a matrix with a single 1 in the upper left corner and 0's everywhere else.
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mahler1
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Homework Statement .

Let ##X:=\{A \in \mathbb C^{n\times n} : rank(A)=1\}##. Determine a representative for each equivalence class, for the equivalence relation "similarity" in ##X##.

The attempt at a solution.

I am a pretty lost with this problem: I know that, thinking in terms of columns ##X## is the set of matrices with just one linearly independent column. In an ##n\times n## matrix there are ##n## columns, so I thought that maybe there could be ##n## representatives of this equivalence relation, but I couldn't prove it and in fact I am not at all convinced this is true. I would appreciate suggestions to solve the problem.
 
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I know that, thinking in terms of columns X is the set of matrices with just one linearly independent column.

That is correct, but rank-1 matrices have other properties that could be more useful to answer the question. Reading http://en.wikipedia.org/wiki/Matrix_similarity should give you some ideas.

I thought that maybe there could be n representatives of this equivalence relation, but I couldn't prove it and in fact I am not at all convinced this is true.
It's not true. There are an infinite number of equivalence classes, and an infinite number of matrices in each class.
 
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1. What is a minimal polynomial?

The minimal polynomial of a matrix is the polynomial of lowest degree that, when evaluated at the matrix, equals zero. It is the smallest polynomial that has the matrix as a root.

2. Why is the minimal polynomial important in diagonalization of a block matrix?

The minimal polynomial helps determine the diagonal form of a block matrix, as it is a key factor in finding the eigenvalues and eigenvectors of the matrix. It also helps in determining the algebraic and geometric multiplicities of the eigenvalues.

3. Can a block matrix have multiple minimal polynomials?

Yes, a block matrix can have multiple minimal polynomials. This is because the minimal polynomial depends on the specific block structure and arrangement of the matrix, and different arrangements can result in different minimal polynomials.

4. How is the minimal polynomial related to the characteristic polynomial?

The minimal polynomial is a factor of the characteristic polynomial. This means that the roots of the minimal polynomial are also roots of the characteristic polynomial. However, the characteristic polynomial may have additional factors that are not present in the minimal polynomial.

5. Can all block matrices be diagonalized?

No, not all block matrices can be diagonalized. Some block matrices may have repeated eigenvalues or may not have enough linearly independent eigenvectors, making them not diagonalizable. The minimal polynomial can help determine if a block matrix is diagonalizable or not.

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