Matrix diagonalisation with complex eigenvalues

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misterau
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Homework Statement


Is there a basis of R4 consisting of eigenvectors for A matrix?
If so, is the matrix A diagonalisable? Diagonalise A, if this is possible. If A is not diagonalisable because some eigenvalues are complex, then find a 'block' diagonalisation
of A, involving a 2 × 2 block corresponding to a pair of complex-conjugate eigenvalues.

A=
0 1 0 0
0 0 1 0
0 0 0 1
1 0 0 0

Homework Equations





The Attempt at a Solution



I worked out the eigenvalues 1,-1,i,-i. I also worked out real eigenvectors: (-1,1,-1,1)^T and (1,1,1,1)^T. Whenever I try to work out complex eigenvectors I get no free variables..? How come A is "not diagonalisable because some eigenvalues are complex?" What does it mean by find block disgonalisation?
 
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[tex]A[/tex] is diagonalizable, just not over [tex]\mathbb{R}[/tex]. As for the block-diagonalization, what they mean is that given a real [tex]2 \times 2[/tex] matrix [tex]B[/tex] with eigenvalues [tex]\alpha \pm i \beta[/tex], there exists a real, nonsingular matrix [tex]S[/tex] such that
[tex] S^{-1} B S = \begin{pmatrix}<br /> \alpha & \beta\\<br /> -\beta & \alpha<br /> \end{pmatrix} \textrm{.}[/tex]
This is sometimes called the real canonical form of [tex]B[/tex]. The way you find the matrix [tex]S[/tex] is by first finding some complex eigenvector of [tex]B[/tex] corresponding to the eigenvalue [tex]\alpha + i \beta[/tex], say [tex]\mathbf{w} = \mathbf{u} + i\mathbf{v}[/tex], where [tex]\mathbf{u}[/tex] and [tex]\mathbf{v}[/tex] are real vectors. Then [tex]S[/tex] is the matrix with columns [tex]\mathbf{u}[/tex] and [tex]\mathbf{v}[/tex].

In your case, the real canonical form of the matrix will turn out to be
[tex] \begin{pmatrix}<br /> 1 &0&0&0\\<br /> 0& -1 &0&0\\<br /> 0&0&0& 1\\<br /> 0&0& -1 &0\\<br /> \end{pmatrix} \textrm{.}[/tex]
This is what is meant by block-diagonalization. A basis that puts your original matrix into this form will be [tex]\{ \mathbf{w}_{+1}, \mathbf{w}_{-1}, \mathbf{u}, \mathbf{v} \}[/tex], where [tex]\mathbf{w}_{\lambda}[/tex] indicates an eigenvector corresponding to the eigenvalue [tex]\lambda[/tex] and [tex]\mathbf{u} + i \mathbf{v}[/tex] is an eigenvector with eigenvalue [tex]+ i[/tex].