Complex and upper triangular matrices

In summary, the problem states that for all complex two by two matrices, they will be upper triangular matrices when multiplied by their inverse. The solution involves finding distinct eigenvalues and showing that the eigenvectors are not linearly independent. Another possible approach is to find a split polynomial in C[X] that cancels the matrix. The concept of endomorphism can also be used, but is not necessary for solving the problem.
  • #1
sassie
35
0

Homework Statement



Prove that for all complex two by two matrices, they will be upper triangular matrices (edit: i think what is meant by upper triangular matrices is that PAP^-1 will be upper triangular matrices - the wording of the question i was given was a little misleading it seems)

Homework Equations



A=PDP^-1

The Attempt at a Solution



The way I tried to do it was to show that A will have n distinct eigenval, whereby I let

A=[a b
c d]

i found the eigenvals for that. then and for the case a=d, tried to show that the eigenvec are not linearly independent. However, this is the correct way to do it? or is there a much easier solution to this question?
 
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  • #2
Possibly interesting:
An endomorphism is a upper triangularizable endomorphism if and only if it does cancel a split polynomn(in your case, in C[X]).
 
  • #3
penguin007 said:
Possibly interesting:
An endomorphism is a upper triangularizable endomorphism if and only if it does cancel a split polynomn(in your case, in C[X]).

oh dear. i don't even know what an endomorphism is. i probably can't use it to solve my problem!
 
  • #4
In the context of linear algebra, 'endomorphism' is just the proper (and short) way of saying 'linear transformation of a space into itself'.At any rate, I don't understand the question. An upper triangular matrix is nilpotent and certainly not all linear transformations of C2 are nilpotent.
 
  • #5
aPhilosopher said:
In the context of linear algebra, 'endomorphism' is just the proper (and short) way of saying 'linear transformation of a space into itself'.


At any rate, I don't understand the question. An upper triangular matrix is nilpotent and certainly not all linear transformations of C2 are nilpotent.


I'm not quite sure what you meant by nilpotent either (do you mean equal to zero when raised to a certain power?)

By I think what the question means is that there will be an invertible matrix P s.t. PAP^-1 is upper triangular, and is something to do with diagonalisation. not sure though.
 
  • #6
An upper triangular matrix is not always nilpotent (a "strict" upper triangular one is).
And the link between endomorphism and your problem is that every square matrix can be interpreted as the matrix of an endomorphism in different basis.
But basically, you don't need to introduce it in your problem. You just need to find out a split polynomn in C[X] that cancles your matrix ( I think you can find one of degree 2 or 3).
 

1. What is a complex matrix?

A complex matrix is a matrix that contains complex numbers as its entries. A complex number is a number that can be expressed in the form a + bi, where a and b are real numbers and i is the imaginary unit (√-1).

2. What is an upper triangular matrix?

An upper triangular matrix is a square matrix where all the entries below the main diagonal (from top left to bottom right) are zero. The entries on or above the main diagonal can be any complex numbers.

3. How can I determine if a matrix is upper triangular?

To determine if a matrix is upper triangular, you can check if all the entries below the main diagonal are zero. Alternatively, you can use the fact that the determinant of an upper triangular matrix is equal to the product of its diagonal entries.

4. Can a complex matrix be upper triangular?

Yes, a complex matrix can be upper triangular. As long as the entries below the main diagonal are zero, the matrix is considered upper triangular.

5. What are the applications of complex and upper triangular matrices?

Complex and upper triangular matrices have various applications in mathematics, physics, and engineering. They are useful in solving systems of linear equations, in spectral analysis, and in signal processing. They also have applications in quantum mechanics and in computer graphics.

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