Day 24:

The Spectral Theorem

Theorem (The spectral theorem part I). If \(A\) is an \(n\times n\) symmetric matrix, and \(A\neq 0\), then \(A\) has a nonzero eigenvalue.

Proposition. Let \(A\) is a symmetric matrix. If \(v\) and \(w\) are eigenvectors of \(A\) with eigenvalues \(\lambda\) and \(\mu\) respectively, and \(\lambda\neq \mu\), then \(v\cdot w = 0\).

Lemma. Assume \(A\) is a symmetric matrix. If \(v\) is a unit norm eigenvector of \(A\) with eigenvalue \(\lambda\neq 0\), then

\[A_{1} = A - \lambda vv^{\top}\]

is a symmetric matrix with \(\text{rank}(A_{1}) \leq \text{rank} (A)-1.\)

Proof. That \(A_{1}\) is symmetric follows from the fact that \((vv^{\top})^{\top} = (v^{\top})^{\top}v^{\top} = vv^{\top}\) and the fact that taking the transpose is linear.

Note that  \[A_{1}v = Av-\lambda vv^{\top}v = \lambda v-\lambda v\|v\|^2  = \lambda v-\lambda v= 0.\] Therefore, \(v\in N(A_{1})\).

Let \(\{e_{1},e_{2},\ldots,e_{k}\}\) be an orthonormal basis for \(N(A)\). Note that every nonzero element of \(N(A)\) is an eigenvector of \(A\) with eigenvalue \(0\). Since \(\lambda\neq 0\) we have\[v\cdot e_{i} = 0\text{  for  }i=1,2,\ldots,k.\] From this we deduce that each \(e_{1},\ldots,e_{k}\) is in \(N(A_{1})\). Moreover, \(\{e_{1},\ldots,e_{k},v\}\) is a linearly independent set in \(N(A_{1}).\) \(\Box\)

Example. Consider the symmetric matrix

\[A =\left[\begin{array}{rrrr} -1 & -3 & 7 & 5\\ -3 & -1 & 5 & 7\\ 7 & 5 & -1 & -3\\ 5 & 7 & -3 & -1 \end{array}\right]\]

 

\[\text{rref}(A-4I) = \begin{bmatrix} 1 & 0 & 0 & 1\\ 0 & 1 & 0 & -1\\ 0 & 0 & 1 & 1\\ 0 & 0 & 0 & 0\end{bmatrix}\]

From this we can see 

\[v = \begin{bmatrix}-1\\ \phantom{-}1\\ -1\\ \phantom{-}1\end{bmatrix}\]

is an eigenvector of \(A\) with eigenvalue \(4\)

Example. Consider the symmetric matrix

\[A =\left[\begin{array}{rrrr} -1 & -3 & 7 & 5\\ -3 & -1 & 5 & 7\\ 7 & 5 & -1 & -3\\ 5 & 7 & -3 & -1 \end{array}\right]\]

 

\[\text{rref}(A-4I) = \begin{bmatrix} 1 & 0 & 0 & 1\\ 0 & 1 & 0 & -1\\ 0 & 0 & 1 & 1\\ 0 & 0 & 0 & 0\end{bmatrix}\]

From this we can see 

\[v = \frac{1}{2}\begin{bmatrix}-1\\ \phantom{-}1\\ -1\\ \phantom{-}1\end{bmatrix}\]

is an eigenvector of \(A\) with eigenvalue \(4.\) In the previous theorem we need an eigenvector with norm \(1.\)

Example continued. 

 

\[A - 4 vv^{\top}= \left[\begin{array}{rrrr} -1 & -3 & 7 & 5\\ -3 & -1 & 5 & 7\\ 7 & 5 & -1 & -3\\ 5 & 7 & -3 & -1 \end{array}\right] - \left[\begin{array}{rrrr} 1 & -1 & 1 & -1\\ -1 & 1 & -1 & 1\\ 1 & -1 & 1 & -1\\ -1 & 1 & -1 & 1\end{array}\right]\]

\[= \left[\begin{array}{rrrr} -2 & -2 & 6 & 6\\ -2 & -2 & 6 & 6\\ 6 & 6 & -2 & -2\\ 6 & 6 & -2 & -2 \end{array}\right]\]

\[\text{rref}(A) = \begin{bmatrix} 1 & 0 & 0 & -1\\ 0 & 1 & 0 & 1\\ 0 & 0 & 1 & 1\\ 0 & 0 & 0 & 0\end{bmatrix}\text{    and    }\text{rref}(A-4vv^{\top}) = \begin{bmatrix} 1 & 1 & 0 & 0\\ 0 & 0 & 1 & 1\\ 0 & 0 & 0 & 0\\ 0 & 0 & 0 & 0\end{bmatrix}\]

\(\text{rank}(A) = 3\)   and   \(\text{rank}(A-4vv^{\top})=2\).

Example continued. 

Note that \[A_{1}:=A-4vv^{\top}= \left[\begin{array}{rrrr} -2 & -2 & 6 & 6\\ -2 & -2 & 6 & 6\\ 6 & 6 & -2 & -2\\ 6 & 6 & -2 & -2 \end{array}\right]\]

 

is a symmetric matrix. Hence, we know that it has a nonzero eigenvalue. In particular \(8\) is an eigenvalue, since

\[\text{rref}(A_{1} - 8I) = \begin{bmatrix} 1 & 0 & 0 & -1\\ 0 & 1 & 0 & -1\\ 0 & 0 & 1 & -1\\ 0 & 0 & 0 & 0\end{bmatrix}\]

From this we see that 

\[w=\frac{1}{2}\begin{bmatrix} 1 & 1 & 1 & 1\end{bmatrix}^{\top}\]

is a unit norm eigenvector of \(A_{1}\) with eigenvalue \(8.\)

Example continued. 


\[A_{1} - 8 ww^{\top}= \left[\begin{array}{rrrr} -2 & -2 & 6 & 6\\ -2 & -2 & 6 & 6\\ 6 & 6 & -2 & -2\\ 6 & 6 & -2 & -2 \end{array}\right] - \begin{bmatrix} 2 & 2 & 2 & 2\\ 2 & 2 & 2 & 2\\ 2 & 2 & 2 & 2\\ 2 & 2 & 2 & 2 \end{bmatrix}\]

\[= \left[\begin{array}{rrrr} -4 & -4 & 4 & 4\\ -4 & -4 & 4 & 4\\ 4 & 4 & -4 & -4\\ 4 & 4 & -4 & -4 \end{array}\right]\]

\[A_{2}: = A - 4vv^{\top} - 8ww^{\top}= \left[\begin{array}{rrrr} -4 & -4 & 4 & 4\\ -4 & -4 & 4 & 4\\ 4 & 4 & -4 & -4\\ 4 & 4 & -4 & -4 \end{array}\right]\]

\[\text{rank}(A_{2}) = 1\]

Example continued. 

\[A_{2}=\left[\begin{array}{rrrr} -4 & -4 & 4 & 4\\ -4 & -4 & 4 & 4\\ 4 & 4 & -4 & -4\\ 4 & 4 & -4 & -4 \end{array}\right]\]

Finally, \(y=\frac{1}{2}\begin{bmatrix} 1 & 1 & -1 & -1\end{bmatrix}^{\top}\) is an eigenvector of \(A_{2}\) with eigenvalue \(-16\). We see that \(A_{2} - (-16)yy^{\top}\) has rank zero, it must be the zero matrix!

\[0 = A_{2} - (-16)yy^{\top} = A_{1} - 8ww^{\top} - (-16)yy^{\top}\]

 

and hence

\[= A - 4vv^{\top} - 8ww^{\top} - (-16)yy^{\top}\]

\[A = 4vv^{\top} + 8ww^{\top} +(-16)yy^{\top}\]

Since \(v,w,\) and \(y\) are eigenvectors with eigenvalues \(4,8,\) and \(-16\) respectively, we see that \(\{v,w,y\}\) is an orthonormal basis for \(C(A)\).

\(z=\frac{1}{2}[1\ -1\ -1\ \ 1]^{\top}\) is a unit norm eigenvector of \(A\) with eigenvalue \(0\). 

\(+ 0zz^{\top}\)

Rank 1 matrices

Suppose \(A\in\mathbb{R}^{m\times n}\) has rank \(1\).

 

This means that \(C(A)\) is a \(1\)-dimensional space, that is, there is a non-zero vector \(x\in\mathbb{R}^{m}\) such that \(C(A) = \operatorname{span}\{x\}\).

 

Every column of \(A\) is a scalar multiple of \(x\):

\[A = \begin{bmatrix} \vert & \vert & & \vert\\ a_{1}x & a_{2}x & \cdots & a_{n}x\\ \vert & \vert & & \vert\end{bmatrix} = xy^{\top}\]

where \(y^{\top}=[a_{1}\ \ a_{2}\ \ \cdots\ \ a_{n}]\).

Proposition. If \(A\) is an \(m\times n\) rank \(1\) matrix, then there are vectors \(x\in\mathbb{R}^{m}\) and \(y\in\mathbb{R}^{n}\) such that \(A = xy^{\top}\).

Rank 1 matrices

It's easy to identify matrices that are rank \(1\):

\[\left[\begin{array}{rrr} 1 & 1 & 1\\ 2 & 2 & 2\end{array}\right]\]

\[\left[\begin{array}{rrr} 1 & 2 & 3\\ 4 & 5 & 6\end{array}\right]\]

\[\left[\begin{array}{rrr} 1 & 2 & 3\\ 2 & 4 & 6\end{array}\right]\]

\[\left[\begin{array}{rrr} 1 & 2 & 0\\ 2 & 4 & 0\end{array}\right]\]

\[\left[\begin{array}{rrr} 0 & 1 & 2\\ 0 & 2 & 4\\ 0 & 4 & 6\end{array}\right]\]

\[\left[\begin{array}{rrr} 1 & 2 & -1\\ 1 & 2 & -1\\ 0 & 0 & 0\end{array}\right]\]

\[\left[\begin{array}{rrr} 1 & 2 & 3\\ 2 & 4 & 6\\ 4 & 8 & 16\end{array}\right]\]

x

x

For each rank 1 matrix, find vectors \(x\) and \(y\) so that the matrix is equal to \(xy^{\top}\).

Which of the following are rank \(1\)?

\[\left[\begin{array}{rrr} 0 & 0 & 0\\ 0 & 0 & 0\\ 0 & 0 & 0\end{array}\right]\]

x

x

Rank 1 matrices

\[\left[\begin{array}{rrr} 1 & 1 & 1\\ 2 & 2 & 2\end{array}\right] = \begin{bmatrix}1\\2\end{bmatrix}\begin{bmatrix}1 & 1 & 1\end{bmatrix}\]

\[\left[\begin{array}{rrr} 1 & 2 & 3\\ 2 & 4 & 6\end{array}\right] = \begin{bmatrix}1\\ 2\end{bmatrix}\begin{bmatrix}1 & 2 & 3\end{bmatrix}\quad\text{or} \quad \begin{bmatrix}2\\ 4\end{bmatrix}\begin{bmatrix}1/2 & 1 & 3/2\end{bmatrix}\]

\[\left[\begin{array}{rrr} 1 & 2 & 0\\ 2 & 4 & 0\end{array}\right] = \begin{bmatrix}1\\ 2\end{bmatrix}\begin{bmatrix}1 & 2 & 0\end{bmatrix}\]

\[\left[\begin{array}{rrr} 1 & 2 & -1\\ 1 & 2 & -1\\ 0 & 0 & 0\end{array}\right] = \begin{bmatrix}1\\ 1\\ 0\end{bmatrix}\begin{bmatrix}1 & 2 & -1\end{bmatrix}\]

Even though it's not rank \(1\):\(\left[\begin{array}{rrr} 0 & 0 & 0\\ 0 & 0 & 0\\ 0 & 0 & 0\end{array}\right] = \begin{bmatrix}0\\ 0\\ 0\end{bmatrix}\begin{bmatrix}0 & 0 & 0\end{bmatrix}\)

Suppose \(V\subset\mathbb{R}^{n}\) is a \(1\)-dimensional subspace.  If \(x\in\mathbb{R}^{n}\) is a unit vector, then \(\{x\}\) is an orthonormal basis for \(V=\operatorname{span}\{x\}\), and \(xx^{\top}\) is the orthogonal projection onto \(V\).

 

Conversely, if \(P\) is an orthogonal projection, and \(\operatorname{rank}(P)=1\), then \(P\) is clearly the orthogonal projection onto \(C(A)\), which is a \(1\)-dimensional subspace. Hence, if we let \(x\) be a unit vector in \(C(A)\), then \(xx^{\top}\) is the orthogonal projection onto \(C(A)\), that is, \(P=xx^{\top}\).

Rank 1 projections

Theorem. A matrix \(P\in\mathbb{R}^{n\times n}\) is a rank 1 projection if and only if there is a unit vector \(x\in\mathbb{R}^{n}\) such that \(P=xx^{\top}\).

\[\left[\begin{array}{rrrr} -1 & -3 & 7 & 5\\ -3 & -1 & 5 & 7\\ 7 & 5 & -1 & -3\\ 5 & 7 & -3 & -1 \end{array}\right] = 4vv^{\top} + 8ww^{\top} +(-16)yy^{\top}+ 0zz^\top\]

Recall that we showed:

Where \(\{v,w,y,z\}\) is an orthonormal basis for \(\mathbb{R}^{4}\).

 

From this we see that our symmetric matrix \(A\) is a linear combination of rank \(1\) projections. Moreover, the ranges of these rank \(1\) projections are orthogonal.

Theorem (The Spectral Theorem Part II). Assume \(A\) is an \(n\times n\) symmetric matrix which is not the zero matrix. There is an orthonormal basis \(\{v_{1},v_{2},\ldots,v_{k}\}\) for \(C(A)\) and scalars \(\lambda_{1},\lambda_{2},\ldots,\lambda_{k}\) such that

\[A = \lambda_{1}v_{1}v_{1}^{\top} + \lambda_{2}v_{2}v_{2}^{\top} + \cdots + \lambda_{k}v_{k}v_{k}^{\top}.\]

Theorem (The Spectral Theorem (Outer Product form). Assume \(A\) is an \(n\times n\) symmetric matrix. There is an orthonormal basis \(\{v_{1},v_{2},\ldots,v_{n}\}\) for \(\R^{n}\) and scalars \(\lambda_{1},\lambda_{2},\ldots,\lambda_{n}\) such that

\[A = \lambda_{1}v_{1}v_{1}^{\top} + \lambda_{2}v_{2}v_{2}^{\top} + \cdots + \lambda_{n}v_{n}v_{n}^{\top}.\]

Proof. Let \(\{v_{k+1},\ldots,v_{n}\}\) be an orthonormal basis for \(N(A)\) and take \(\lambda_{k+1} = \cdots = \lambda_{n} =0.\) \(\Box\)

Example. Consider the symmetric matrix

\[A = \left[\begin{array}{rrr} 3 & 1 & -1\\ 1 & 3 & -1\\ -1 & -1 & 5\end{array}\right]\]

Using the power method we find that \(6\) is an eigenvalue, so we compute:

\[\text{rref}(A-6I) = \left[\begin{array}{rrr} 1 & 0 & \frac{1}{2}\\[0.3ex] 0 & 1 & \frac{1}{2}\\[0.3ex] 0 & 0 & 0\end{array}\right]\]

Hence, \(v_{1} = \frac{1}{\sqrt{6}}[-1\ -1\ \ \ 2]^{\top}\) is a unit norm eigenvector of \(A\) with eigenvalue \(6\).

Set  \[A_{1} = A - 6v_{1}v_{1}^{\top} = \left[\begin{array}{rrr}\!\! 3 & 1 & -1\\\!\! 1 & 3 & -1\\\!\! -1 & -1 & 5\end{array}\right] - \left[\begin{array}{rrr}\!\!1 & 1 & -2\\\!\! 1 & 1 & -2\\\!\! -2 & -2 & 4\end{array}\right] = \left[\begin{array}{rrr}2 & 0 & 1\\ 0 & 2 & 1\\ 1 & 1 & 1\end{array}\right]\]

Example. We continue with

\[A_{1} = \left[\begin{array}{rrr}2 & 0 & 1\\ 0 & 2 & 1\\ 1 & 1 & 1\end{array}\right]\] 

Using the power method is found that \(3\) is an eigenvalue, so we compute:

\[\text{rref}(A_{1}-3I) = \left[\begin{array}{rrr} 1 & 0 & -1\\ 0 & 1 & -1\\0 & 0 & 0\end{array}\right]\]

Hence, \(v_{2} = \frac{1}{\sqrt{3}}[1\ \ 1\ \ 1]^{\top}\) is a unit norm eigenvector of \(A_{1}\) with eigenvalue \(3\).

Set  \[A_{2} = A_{1} - 3v_{2}v_{2}^{\top} = \left[\begin{array}{rrr}2 & 0 & 1\\ 0 & 2 & 1\\ 1 & 1 & 1\end{array}\right] - \left[\begin{array}{rrr} 1& 1 & 1\\ 1 & 1 & 1\\ 1 & 1 & 1\end{array}\right] = \left[\begin{array}{rrr}\!\! 1 & -1 &\phantom{-}0\\\!\! -1 & 1 & 0\\\!\! 0 & 0 & 0\end{array}\right]\]

Example. We continue with

\[A_{2} = \left[\begin{array}{rrr}\!\! 1 & -1 &\phantom{-}0\\\!\! -1 & 1 & 0\\\!\! 0 & 0 & 0\end{array}\right]\] 

Using the power method is found that \(2\) is an eigenvalue, so we compute:

\[\text{rref}(A-2I) = \left[\begin{array}{rrr} 1 & 1 & 0\\ 0 & 0 & 1\\0 & 0 & 0\end{array}\right]\]

Hence, \(v_{3} = \frac{1}{\sqrt{2}}[-1\ \ 1\ \ 0]^{\top}\) is a unit norm eigenvector of \(A_{2}\) with eigenvalue \(2\).

Set  \[A_{3} = A_{2} - 2v_{3}v_{3}^{\top} = \left[\begin{array}{rrr}\!\! 1 & -1 &\phantom{-}0\\\!\! -1 & 1 & 0\\\!\! 0 & 0 & 0\end{array}\right] - \left[\begin{array}{rrr}\!\! 1 & -1 &\phantom{-}0\\\!\! -1 & 1 & 0\\\!\! 0 & 0 & 0\end{array}\right]= \left[\begin{array}{rrr}0 & 0 & 0\\ 0 & 0 & 0\\ 0 & 0 & 0\end{array}\right]\]

Example. Thus, we have

\[A = \left[\begin{array}{rrr} 3 & 1 & -1\\ 1 & 3 & -1\\ -1 & -1 & 5\end{array}\right] = 6v_{1}v_{1}^{\top} + 3v_{2}v_{2}^{\top} + 2v_{3}v_{3}^{\top}\]

\(+\ 3\left(\dfrac{1}{3}\left[\begin{array}{rrr} 1& 1 & 1\\ 1 & 1 & 1\\ 1 & 1 & 1\end{array}\right]\right)\)

\(=6\left(\dfrac{1}{6}\left[\begin{array}{rrr}\!\!1 &\!\! 1 &\!\! -2\\\!\! 1 &\!\! 1 &\!\! -2\\\!\! -2 &\!\! -2 &\!\! 4\end{array}\right] \right) \)

\(+\ 2\left(\dfrac{1}{2}\left[\begin{array}{rrr}\!\! 1 &\!\! -1 &\!\! \phantom{-}0\\\!\! -1 &\!\! 1 &\!\! 0\\\!\! 0 &\!\! 0 &\!\! 0\end{array}\right]\right)\)

Where \(\{v_{1},v_{2},v_{3}\}\) is an orthonormal basis for \(\R^{3}\).

Define the orthogonal matrix

\[Q = \begin{bmatrix} \vert & \vert & \vert\\ v_{1} & v_{2} & v_{3}\\ \vert & \vert & \vert\end{bmatrix} = \left[\begin{array}{rrr} \frac{1}{\sqrt{6}} & \frac{1}{\sqrt{3}} &\!\! -\frac{1}{\sqrt{2}}\\ \frac{1}{\sqrt{6}} & \frac{1}{\sqrt{3}} &\!\! \frac{1}{\sqrt{2}}\\ -\frac{2}{\sqrt{6}} & \frac{1}{\sqrt{3}} &\!\! 0\end{array}\right]\]

Example. 

\[Q\begin{bmatrix} 6 & 0 & 0\\ 0 & 3 & 0\\ 0 & 0 & 2\end{bmatrix}Q^{-1} = \begin{bmatrix} \vert & \vert & \vert\\ v_{1} & v_{2} & v_{3}\\ \vert & \vert & \vert\end{bmatrix} \begin{bmatrix} 6 & 0 & 0\\ 0 & 3 & 0\\ 0 & 0 & 2\end{bmatrix} \begin{bmatrix} - & v_{1}^{\top} & -\\ - & v_{2}^{\top} & -\\ - & v_{3}^{\top} & - \end{bmatrix}\]

 

\(=\begin{bmatrix} \vert & \vert & \vert\\ v_{1} & v_{2} & v_{3}\\ \vert & \vert & \vert\end{bmatrix}  \begin{bmatrix} - & 6v_{1}^{\top} & -\\ - & 3v_{2}^{\top} & -\\ - & 2v_{3}^{\top} & - \end{bmatrix}\)

 

\[ = 6v_{1}v_{1}^{\top} + 3v_{2}v_{2}^{\top} + 2v_{3}v_{3}^{\top} = A\]

Definition. A matrix \(A\) is called orthogonally diagonalizable if there is an orthogonal matrix \(Q\) and a diagonal matrix \(\Lambda\) so that

\[A = Q\Lambda Q^{-1}.\]

Theorem (The Spectral Theorem (Matrix form)). If \(A\) is a symmetric matrix, then \(A\) is orthogonally diagonalizable.

Proof. By the Spectral Theorem (Outer Product form) we have an orthonormal basis \(\{v_{1},\ldots,v_{n}\}\) and scalars \(\lambda_{1},\ldots,\lambda_{n}\) such that

\[A = \lambda_{1}v_{1}v_{1}^{\top} + \lambda_{2}v_{2}v_{2}^{\top} + \cdots + \lambda_{n}v_{n}v_{n}^{\top}.\]

Then we define the orthogonal matrix \(Q\) and the diagonal matrix \(\Lambda\):

\[Q = \begin{bmatrix} | &| &  & |\\ v_{1} & v_{2} & \cdots & v_{n}\\ | &| &  & |\end{bmatrix}\text{  and  }\Lambda = \begin{bmatrix}\lambda_{1} & 0 & \cdots & 0\\ 0 & \lambda_{2} & & \vdots\\ \vdots & & \ddots & \vdots\\ 0 & \cdots & \cdots & \lambda_{n}\end{bmatrix} \]

Then we see

\[Q\Lambda Q^{\top} = \begin{bmatrix} | &| &  & |\\ v_{1} & v_{2} & \cdots & v_{n}\\ | &| &  & |\end{bmatrix} \begin{bmatrix} - & \lambda_{1}v_{1}^{\top} & - \\ - & \lambda_{2}v_{2}^{\top} & - \\ & \vdots & \\ - & \lambda_{n}v_{n}^{\top} & -\end{bmatrix}\]

\[=\lambda_{1}v_{1}v_{1}^{\top} + \lambda_{2}v_{2}v_{2}^{\top} + \cdots + \lambda_{n}v_{n}v_{n}^{\top}=A.\ \Box\]

Example. Find an orthogonal matrix \(Q\) and a diagonal matrix \(\Lambda\) such that

\[Q\Lambda Q^{-1} = \begin{bmatrix} 2 & 2 & 2\\ 2 & 2 & 2\\ 2 & 2 & 2\end{bmatrix}=:A\]

We can see that \(v_{1} = \frac{1}{\sqrt{3}}[1\ 1\ 1]^{\top}\) is an eigenvector of \(A\) with eigenvalue \(6\). Since \(A-6v_{1}v_{1}^{\top}\) is the zero matrix, the only other eigenvalue of \(A\) is zero. 

\[\text{rref}(A) = \begin{bmatrix} 1 & 1 & 1\\ 0 & 0 & 0\\ 0 & 0 & 0\end{bmatrix}\]

and hence \(\left\{\begin{bmatrix} -1\\ \phantom{-}1\\ \phantom{-}0\end{bmatrix},\begin{bmatrix} -1\\ \phantom{-}0\\ \phantom{-}1\end{bmatrix}\right\}\) is a basis for the eigenspace \(N(A)\). 

Using Gram-Schmidt, we find that \(\left\{\frac{1}{\sqrt{2}}\begin{bmatrix} -1\\ \phantom{-}1\\ \phantom{-}0\end{bmatrix},\frac{1}{\sqrt{6}}\begin{bmatrix} -1\\ -1\\ \phantom{-}2\end{bmatrix}\right\}\) is an orthonormal basis for \(N(A)\).

Example. Therefore

\[A=\begin{bmatrix} 2 & 2 & 2\\ 2 & 2 & 2\\ 2 & 2 & 2\end{bmatrix} = \left[\begin{array}{rrr} \frac{1}{\sqrt{3}} &\!\! -\frac{1}{\sqrt{2}} &\!\! \frac{1}{\sqrt{6}}\\\!\! \frac{1}{\sqrt{3}} &\!\! \frac{1}{\sqrt{2}} &\!\! \frac{1}{\sqrt{6}}\\\!\! \frac{1}{\sqrt{3}} &\!\! 0 &\!\! -\frac{2}{\sqrt{6}}\end{array}\right]\begin{bmatrix} 6 & 0 & 0\\ 0 & 0 & 0\\ 0 & 0 & 0\end{bmatrix}\left[\begin{array}{rrr} \frac{1}{\sqrt{3}} &\!\! -\frac{1}{\sqrt{2}} &\!\! \frac{1}{\sqrt{6}}\\\!\! \frac{1}{\sqrt{3}} &\!\! \frac{1}{\sqrt{2}} &\!\! \frac{1}{\sqrt{6}}\\\!\! \frac{1}{\sqrt{3}} &\!\! 0 &\!\! -\frac{2}{\sqrt{6}}\end{array}\right]^{\top}\]

We can also write out the outer product decomposition:

  

We can also write out the outer product decomposition:

\( A= 6\begin{bmatrix}1/\sqrt{3}\\ 1/\sqrt{3}\\ 1/\sqrt{3}\end{bmatrix}\begin{bmatrix} 1/\sqrt{3} & 1/\sqrt{3} & 1/\sqrt{3}\end{bmatrix} + 0\begin{bmatrix}1/\sqrt{2}\\ -1/\sqrt{2}\\ 0\end{bmatrix}\begin{bmatrix} 1/\sqrt{2} & -1/\sqrt{2} & 0\end{bmatrix}\)

\( + 0\begin{bmatrix} -1/\sqrt{6}\\ -1/\sqrt{6}\\ 2/\sqrt{6}\end{bmatrix}\begin{bmatrix} -1/\sqrt{6} & -1/\sqrt{6} & 2/\sqrt{6}\end{bmatrix}\)

Theorem. (The Spectral Theorem) If \(A\in\mathbb{R}^{n\times n}\) is a symmetric matrix, then there is an orthonormal basis \(\{v_{1},\ldots,v_{n}\}\) for \(\mathbb{R}^{n}\) consisting of eigenvectors of \(A\), with associated eigenvalues \(\lambda_{1},\lambda_{2},\ldots,\lambda_{n}\). Moreover, we have the following:

  1. \[A = \sum_{i=1}^{n}\lambda_{i}v_{i}v_{i}^{\top}.\]
  2. \[A = Q\Lambda Q^{\top} = Q\Lambda Q^{-1}.\] where \[Q=\begin{bmatrix} \vert & \vert & & \vert\\ v_{1} & v_{2} & \cdots & v_{n}\\ \vert & \vert & & \vert\end{bmatrix}\quad\text{and}\quad \Lambda = \begin{bmatrix} \lambda_{1} & 0 & \cdots & 0\\ 0 & \lambda_{2} & \cdots & 0\\ \vdots & \vdots & \ddots & \vdots\\ 0 & 0 & \cdots & \lambda_{n}\end{bmatrix},\]

Linear Algebra Day 24

By John Jasper

Linear Algebra Day 24

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