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The question is from an exercise in Gilbert Strang's Linear Algebra and its Applications:

Suppose the only eigenvectors of $A$ are multiples of $x=(1,0,0)$. True or false:

(a) $A$ is not invertible.

(b) $A$ has a repeated eigenvalue.

(c) $A$ is not diagonalizable.

(b) has to be true. Since if $A$ has different eigenvalues, then there will be linear independent eigenvectors.

One example that I can come up with for which (a) and (c) are true is $A=\left( \begin{array}{ccc} 0& 1& 0 \\ 0& 0 & 1 \\ 0& 0 & 0 \\ \end{array} \right)$

Here are my questions:

  • Are there any counterexamples for (a) and (c)?
  • What's the underlying picture of this problem?
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    @Qiaochu: Hmm, I'm surprised that the Jordan form comes up in the *appendix* of the Strang's book... And I find that my example is actually a Jordan matrix.2011-08-02

3 Answers 3

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My answer is for a part; let the order of matrix be n then we know the very common relation n-r=1 it implies r=n-1 hence rank is less than n hence not invertible

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In my elementary linear algebra class we did many problems like this without the Jordan Normal Form, etc, which I picked up later. So I will try to give a simpler explanation.

a) Recall that a matrix is non-invertible if it has nonzero vectors in its null space. This also means that those vectors are eigenvectors corresponding to the eigenvalue $0$. So what if $\text{null } A =\text{span }{(1,0,0)}?$ This is the case where an eigenvalue of $A$ is 0. However, you CANNOT conclude this from your givens, because you have no hypothesis on what the eigenvalue of $\text{span }{(1,0,0)}$ is.

b) Now, $A$ is a linear operator on $\mathbb R^3.$ This means that $A$ has a real eigenvalue, because the characteristic equation of $A$ is a cubic, so it has a real root. What if two of those roots were imaginary? Then $A$ would have its one-dimensional eigenspace, as given, but would not have a repeated eigenvalue.

c) This one is true. A matrix is diagonalizable if and only if has $n$ linearly independent eigenvectors, where $n$ is the dimension of the vector space it operates on. This means you need $3$ linearly independent eigenvectors where you only have one! An easy to remember this is that, to diagonalize a matrix $A$, you need to be able to write it as $A = PDP^{-1},$ where $P$ is an $n$ x $n$ matrix whose columns consist of eigenvectors of $A.$ Remember that $P$ is invertible if and only if it has linearly independent columns, and you are done.

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Over the complex numbers, you must have a matrix that is similar to $\left(\begin{array}{ccc} \lambda & 1 & 0\\ 0 & \lambda & 1\\ 0 & 0 & \lambda \end{array}\right),$ where $\lambda$ is the unique eigenvalue; this follows form the theory of Jordan Canonical forms.

Over a non-algebraically closed field, you have two possibilities: either a matrix similar to one as above (if the characteristic polynomial splits as a perfect cube), or else a matrix similar to one of the form $\left(\begin{array}{ccr} \lambda & 0 & 0\\ 0 & 0 & -a\\ 0 & 1 & -b \end{array}\right),$ where $\lambda$ is the eigenvalue, and $x^2 + bx + a$ is an irreducible quadratic polynomial over your field; this follows from the theory of Rational Canonical Forms.

In either case, the matrix is invertible if and only if $\lambda\neq 0$, and the matrix is never diagonalizable.

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    Also in Artin! (Ch. 12)2011-08-05