Does there exist a 2x2 non-singular matrix with only one 1d eigenspace?

  • #1
zenterix
488
72
Homework Statement
How do we find a matrix ##A## whose only eigenvectors are multiples of ##(1,4)## and that is invertible?
Relevant Equations
Let's try to find a ##2\times 2## matrix. Suppose the matrix ##A## is given by

$$A=\begin{bmatrix}a&b\\c&d\end{bmatrix}$$

Since we have only a single 1d eigenspace for this matrix, there can only be one eigenvalue, ##\lambda##.

We know this eigenvalue cannot be 0 since the matrix is non-singular.
Before going through calculations/reasoning, let me summarize what my questions will be

- In order to obtain the desired matrix, I impose five constraints on ##a,b,c,d,## and ##\lambda##.

- These five constraints are four equations and an inequality. I am not sure how to work with the inequality.

- I can leave the inequality out and solve the system of four equations for five unknowns.

- By guessing values together with the solution to these four equations, I can eventually reach the desired matrix.

- However, in this process of guessing it became clear that one constraint is definitely missing: the equations I have guarantee solutions (ie, matrices) with a sole eigenvalue; but they don't guarantee an eigenspace with only a single dimension.

- I would like to know how to add this constraint.

Now let me go through my reasoning.

$$\begin{vmatrix} a-\lambda & b\\ c & d-\lambda \end{vmatrix}=0\tag{2}$$

$$\lambda^2-\lambda(a+d)+(ad-bc)=0$$

which has discriminant

$$\Delta=(a+d)^2-4(ad-bc)=0\tag{3}$$

We equate ##\Delta## to zero because we there to be a single ##\lambda##.

Thus

$$\lambda=\frac{a+d}{2}\tag{4}$$

We have three further constraints on the variables.

First, ##A## is non-singular if

$$ad-bc\neq 0\tag{5}$$

In addition, ##A(1,4)=\lambda (1,4)## so

$$a+4b=\lambda\tag{6}$$

$$c+4d=4\lambda\tag{7}$$

At this point, equations (3), (4), (5), (6), and (7) are five constraints and we have the five unknowns ##a,b,c,d,## and ##\lambda##.

One of the constraints is an inequality, however.

Suppose I leave out the inequality and just solve (3), (4), (6), and (7). Using Maple, I get the solution

$$a$$

$$b=\frac{d-a}{8}$$

$$c=2(a-d)$$

$$d$$

$$\lambda=\frac{a+d}{2}$$

If I let ##a=d=1## then it turns out that ##b=c=0##. Thus, matrix is the identity matrix.

The problem is that the eigenspace for the sole eigenvalue of ##1## is 2-dimensional not 1-dimensional as desired.

By guessing values, I was able to obtain a matrix with all the desired constraints.

Let ##a=2##, ##d=3##. Then ##b=\frac{1}{8}## and ##c=-2##.

Thus, the matrix is

$$\begin{bmatrix} 2& \frac{1}{8}\\-2 &3\end{bmatrix}$$

We have ##ad-bc=\frac{25}{4}\neq 0## so the matrix is invertible.

The sole eigenvalue is ##\frac{5}{2}## and the eigenspace is the span of ##(1,4)##.

My questions are

1) how do I guarantee that my solution (ie, the matrix ##A##) will have one eigenspace only, and this eigenspace is one-dimensional? What additional constraint do I need in the system of equations?

2) how do I work with a constraint that is an inequality?
 
Last edited:
  • Like
Likes nuuskur
Physics news on Phys.org
  • #2
The eigenspace corresponding to ##\lambda## is the solution space of ##A-\lambda E=0## (i.e ##\mathrm{Ker}(A-\lambda E))##. What you are currently after is that
[tex]
\left(\begin{array}{cc}a-\lambda &b \\ c&d-\lambda \end{array}\right)
[/tex]
is of rank one i.e that the determinant is zero and at least one of the entries in the above is nonzero. Then you further require that
[tex]
\left(\begin{array}{cc} a&b\\c&d \end{array}\right)\left(\begin{array}{c}1\\4 \end{array}\right) = \lambda \left(\begin{array}{c}1\\4 \end{array}\right)
[/tex]
There is no inequality restriction at play here. You correctly deduce that ##\lambda = \frac{a+d}{2}## because there can only be one eigenvalue. So we have
[tex]
\begin{cases}(a-\lambda)(d-\lambda) - bc = 0 \\ a+4b = \lambda \\ c+4d = 4\lambda \\ a+d = 2\lambda \end{cases}
[/tex]
We have four conditions and five variables. Since you didn't restrict the eigenvalue we'll pick one. Since ##A## is nonsingular, we must pick ##\lambda \neq 0##. So let's pick ##\lambda = 1## for instance. Thus, we simplify to
[tex]
\begin{cases}
1-(a+d)+(ad-bc) = 0 \\
a+4b=1 \\
c+4d = 4\\
a+d=2
\end{cases}
[/tex]
We can temporarily ignore the first condition, because it's not linear. The other three conditions give us
[tex]
\left(\begin{array}{cccc|c} 1&4&0&0&1 \\ 0&0&1&4&4 \\ 1&0&0&1&2 \end{array}\right)
[/tex]
The solutions for this are
[tex]
\left(\begin{array}{c}a\\4b\\c\\d \end{array}\right) = \left(\begin{array}{c}2-s \\ -1+s \\ 4-4s \\ s \end{array}\right),\quad s\in F.
[/tex]
So all that's left is to plug it in the nonlinear condition and solve the resulting quadratic and that will determine what ##A## could be if we assumed that ##\lambda =1##. It could also be that the quadratic has no solutions in which case try another ##\lambda##.
edit: It turns out plugging in the solution to the nonlinear condition yields ##0=0##, so pick any ##s## and fire away.

Instead of fixing the eigenvalue, we could also fix one of the entries of ##A## such that ##A-\lambda E## is of rank one.

---

Picking ##s=0## in the above gives us
[tex]
A=\left(\begin{array}{cc} 2 & -\frac{1}{4} \\ 4 & 0\end{array}\right),
[/tex]
which is clearly nonsingular with characteristic polynomial ##(\lambda-1)^2## as expected and the system
[tex]
A-E = \left(\begin{array}{cc}1 & -\frac{1}{4} \\ 4 & -1\end{array}\right)
[/tex]
is clearly of rank one whose solutions are generated by ##(1,4)##.
 
Last edited:
  • Like
Likes fresh_42
  • #3
Do we not have that the Jordan normal form of [itex]A[/itex] is [itex]\begin{pmatrix} \lambda & 1 \\ 0 & \lambda \end{pmatrix}[/itex]? The first basis vector is [itex](1,4)^T[/itex] and the second can be any linearly independent vector - say [itex](0,1)^T[/itex]. Then [tex]
\begin{split}
A \begin{pmatrix} 1 \\ 4 \end{pmatrix} &= \lambda \begin{pmatrix} 1 \\ 4 \end{pmatrix} \\
A \begin{pmatrix} 0 \\ 1 \end{pmatrix} &= \lambda \begin{pmatrix} 0 \\ 1 \end{pmatrix} + \begin{pmatrix} 1 \\ 4 \end{pmatrix} \end{split}[/tex] and [tex]\begin{split}
A\begin{pmatrix} x \\ y \end{pmatrix} &=
A\left(x\begin{pmatrix} 1 \\ 4 \end{pmatrix} + (y - 4x) \begin{pmatrix} 0 \\ 1 \end{pmatrix} \right)\\
&= x\lambda \begin{pmatrix} 1 \\ 4 \end{pmatrix} + (y - 4x) \left( \lambda \begin{pmatrix} 0 \\ 1 \end{pmatrix} + \begin{pmatrix} 1 \\ 4 \end{pmatrix} \right) \end{split} [/tex] giving [tex]
A = \begin{pmatrix} \lambda - 4 & 1 \\ -16 & \lambda + 4 \end{pmatrix}.[/tex]
 
  • Like
Likes nuuskur and fresh_42

Related to Does there exist a 2x2 non-singular matrix with only one 1d eigenspace?

1. Can a 2x2 non-singular matrix have only one 1-dimensional eigenspace?

Yes, it is possible for a 2x2 non-singular matrix to have only one 1-dimensional eigenspace. This occurs when the matrix has a repeated eigenvalue, resulting in only one linearly independent eigenvector.

2. What does it mean for a matrix to be non-singular?

A non-singular matrix is one that has a non-zero determinant, meaning it is invertible. In other words, a non-singular matrix has a unique solution to the equation Ax = b for every non-zero vector b.

3. How can I determine the eigenspaces of a 2x2 matrix?

To find the eigenspaces of a 2x2 matrix, you first need to calculate the eigenvalues by solving the characteristic equation det(A - λI) = 0. Then, for each eigenvalue, find the corresponding eigenvector by solving the equation (A - λI)v = 0.

4. Is it common for a 2x2 non-singular matrix to have only one 1-dimensional eigenspace?

It is not as common for a 2x2 non-singular matrix to have only one 1-dimensional eigenspace compared to having two distinct eigenspaces. However, it is still a valid scenario that can occur depending on the specific eigenvalues of the matrix.

5. What implications does having only one 1-dimensional eigenspace have on the matrix?

Having only one 1-dimensional eigenspace means that the matrix may have less flexibility in terms of diagonalization and decomposition into simpler forms. It can make certain calculations and analyses more challenging compared to matrices with multiple distinct eigenspaces.

Similar threads

  • Calculus and Beyond Homework Help
Replies
8
Views
759
  • Calculus and Beyond Homework Help
Replies
4
Views
858
  • Advanced Physics Homework Help
Replies
3
Views
403
  • Calculus and Beyond Homework Help
Replies
6
Views
1K
  • Calculus and Beyond Homework Help
Replies
19
Views
3K
  • Calculus and Beyond Homework Help
Replies
5
Views
1K
  • Calculus and Beyond Homework Help
Replies
7
Views
3K
  • Calculus and Beyond Homework Help
2
Replies
41
Views
4K
  • Calculus and Beyond Homework Help
Replies
11
Views
2K
  • Calculus and Beyond Homework Help
Replies
3
Views
586
Back
Top