Day 32:

Least squares and polynomials

Definition. Given an \(m\times n\) matrix \(A\) with singular value decomposition \[A=U\Sigma V^{\top},\] that is, \(U\) and \(V\) are orthogonal matrices, and \(\Sigma\) is an \(m\times n\) diagonal matrix with nonnegative entries \(\sigma_{1}\geq \sigma_{2}\geq \cdots\geq \sigma_{p}\) on the diagonal, we define the pseudoinverse of \(A\) \[A^{+} = V\Sigma^{+}U^{\top}\] where \(\Sigma^{+}\) is the \(n\times m\) diagonal matrix with \(i\)th diagonal entry \(1/\sigma_{i}\) if \(\sigma_{i}\neq 0\) and \(0\) if \(\sigma_{i}=0\).

Example. Consider \[A = \begin{bmatrix} 2 & 0 & 0 & 0\\ 0 & 1 & 0 & 0\\ 0 & 0 & 0 & 0\end{bmatrix}, \quad\text{then}\quad A^{+} = \begin{bmatrix} 1/2 & 0 & 0\\ 0 & 1 & 0\\ 0 & 0 & 0 \\ 0 & 0 & 0 \end{bmatrix}.\]

Example. Consider \[A = \begin{bmatrix} 1 & 0 & 0 & 0\\ 0 & 2 & 0 & 0\\ 0 & 0 & 0 & 0\end{bmatrix}.\]

Then the singular value decomposition of \(A\) is

\[= \begin{bmatrix} 1 & 0 & 0\\ 0 & 1/2 & 0\\ 0 & 0 & 0\\ 0 & 0 & 0\end{bmatrix}.\]

\[A= \begin{bmatrix} 0  & 1 & 0\\ 1 & 0 & 0\\ 0 & 0 & 1\end{bmatrix}\begin{bmatrix} 2 & 0 & 0 & 0\\ 0 & 1 & 0 & 0\\ 0 & 0 & 0 & 0\end{bmatrix}\begin{bmatrix} 0  & 1 & 0 & 0\\ 1 & 0 & 0 & 0\\ 0 & 0 & 1 & 0\\ 0 & 0 & 0 & 1\end{bmatrix}.\]

Hence, the pseudo inverse of \(A\) is

\[A^{+}= \begin{bmatrix} 0  & 1 & 0 & 0\\ 1 & 0 & 0 & 0\\ 0 & 0 & 1 & 0\\ 0 & 0 & 0 & 1\end{bmatrix}\begin{bmatrix} 1/2 & 0 & 0\\ 0 & 1 & 0\\ 0 & 0 & 0\\ 0 & 0 & 0\end{bmatrix}\begin{bmatrix} 0  & 1 & 0\\ 1 & 0 & 0\\ 0 & 0 & 1\end{bmatrix}.\]

Example. Consider \[A = \begin{bmatrix} 1 & 1\\ 1 & 1\end{bmatrix}.\]

Then the singular value decomposition of \(A\) is

\[= \begin{bmatrix}1/4 & 1/4\\ 1/4 & 1/4\end{bmatrix}.\]

\[A= \left(\frac{1}{\sqrt{2}}\begin{bmatrix} 1 & 1\\ 1 & -1\end{bmatrix}\right)\begin{bmatrix} 2 & 0 \\ 0 & 0\end{bmatrix}\left(\frac{1}{\sqrt{2}}\begin{bmatrix} 1 & 1\\ 1 & -1\end{bmatrix}\right).\]

Hence, the pseudo inverse of \(A\) is

\[A^{+}= \left(\frac{1}{\sqrt{2}}\begin{bmatrix} 1 & 1\\ 1 & -1\end{bmatrix}\right)\begin{bmatrix} 1/2 & 0 \\ 0 & 0\end{bmatrix}\left(\frac{1}{\sqrt{2}}\begin{bmatrix} 1 & 1\\ 1 & -1\end{bmatrix}\right).\]

Example. Consider \[B = \begin{bmatrix} 1 & 0 & 1\\ 0 & 1 & 1\end{bmatrix} = \begin{bmatrix} \frac{1}{\sqrt{2}} & \frac{1}{\sqrt{2}}\\[1ex] \frac{1}{\sqrt{2}} & -\frac{1}{\sqrt{2}}\end{bmatrix}\begin{bmatrix} \sqrt{3} & 0 & 0\\ 0 & 1 & 0\end{bmatrix}\begin{bmatrix} \frac{1}{\sqrt{6}} & \frac{1}{\sqrt{6}} & \frac{2}{\sqrt{6}}\\[1ex] \frac{1}{\sqrt{2}} & -\frac{1}{\sqrt{2}} & 0\\[1ex] \frac{1}{\sqrt{3}} & \frac{1}{\sqrt{3}} & -\frac{1}{\sqrt{3}}\end{bmatrix}.\]

 

Hence, the pseudo inverse of \(B\) is

\[B^{+}=\begin{bmatrix} \frac{1}{\sqrt{6}} & \frac{1}{\sqrt{2}}  & \frac{1}{\sqrt{3}}\\[1ex] \frac{1}{\sqrt{6}}  & -\frac{1}{\sqrt{2}} & \frac{1}{\sqrt{3}}\\[1ex] \frac{2}{\sqrt{6}} & 0 & -\frac{1}{\sqrt{3}}\end{bmatrix}\begin{bmatrix} 1/\sqrt{3} & 0\\ 0 & 1\\ 0 & 0\end{bmatrix}\begin{bmatrix} \frac{1}{\sqrt{2}} & \frac{1}{\sqrt{2}}\\[1ex] \frac{1}{\sqrt{2}} & -\frac{1}{\sqrt{2}}\end{bmatrix}.\]

\[ = \frac{1}{3}\begin{bmatrix} 2 & -1\\ -1 & 2\\ 1 & 1\end{bmatrix}\]

Note: \[BB^{+} = \begin{bmatrix}1 & 0\\ 0 & 1\end{bmatrix} \quad\text{but}\quad B^{+}B = \frac{1}{3}\begin{bmatrix} 2 & -1 & 1\\ -1 & 2 & 1\\ 1 & 1 & 2\end{bmatrix}\]

Let \(A\) be an \(m\times n\) matrix with singular value decomposition

\[A=U\Sigma V^{\top}.\]

Hence

\[A^{+} = V\Sigma^{+}U^{\top}.\]

 

Let \(\operatorname{rank}(A)=r\), then

\[\Sigma = \begin{bmatrix} D & \mathbf{0}\\ \mathbf{0} & \mathbf{0}\end{bmatrix}\quad\text{and}\quad \Sigma^{+} = \begin{bmatrix} D^{-1} & \mathbf{0}\\ \mathbf{0}\ & \mathbf{0}\end{bmatrix}\]

where 

\[D = \begin{bmatrix}\sigma_{1} & 0 & \cdots & 0\\ 0 & \sigma_{2} & & \vdots\\ \vdots & & \ddots & \vdots\\ 0 &\cdots & \cdots & \sigma_{r} \end{bmatrix}\quad \text{and}\quad D^{-1} = \begin{bmatrix}1/\sigma_{1} & 0 & \cdots & 0\\ 0 & 1/\sigma_{2} & & \vdots\\ \vdots & & \ddots & \vdots\\ 0 &\cdots & \cdots & 1/\sigma_{r} \end{bmatrix}\]

and \(\sigma_{1}\geq \sigma_{2}\geq\ldots\geq \sigma_{r}>0\)

 

Note: These could be empty matrices.

Using this we have

\[AA^{+}=U\Sigma V^{\top}V\Sigma^{+}U^{\top} = U\Sigma\Sigma^{+}U^{\top} = U \begin{bmatrix} D & \mathbf{0}\\ \mathbf{0} & \mathbf{0}\end{bmatrix}\begin{bmatrix} D^{-1} & \mathbf{0}\\ \mathbf{0} & \mathbf{0}\end{bmatrix}U^{\top}\]

\[= U \begin{bmatrix} I & \mathbf{0}\\ \mathbf{0} & \mathbf{0}\end{bmatrix}U^{\top} = U(\,:\,,1\colon\! r)U(\,:\,,1\colon\! r)^{\top}\]

Orthogonal projection onto \(C(A)\).

\[A^{+}A=V\Sigma^{+}U^{\top} U\Sigma V^{\top}= V\Sigma^{+}\Sigma V^{\top} = V \begin{bmatrix} D^{-1} & \mathbf{0}\\ \mathbf{0} & \mathbf{0}\end{bmatrix}\begin{bmatrix} D & \mathbf{0}\\ \mathbf{0} & \mathbf{0}\end{bmatrix}V^{\top}\]

\[= V \begin{bmatrix} I & \mathbf{0}\\ \mathbf{0} & \mathbf{0}\end{bmatrix}V^{\top} = V(\,:\,,1\colon\! r)V(\,:\,,1\colon\! r)^{\top}\]

Orthogonal projection onto ???.

If \(A\) has singular value decomposition

\[A=U\Sigma V^{\top},\]

then

\[A^{\top} = (U\Sigma V^{\top})^{\top} = V\Sigma^{\top} U^{\top}.\]

From this we see:

  1. The singular values of \(A\) and \(A^{\top}\) are the same.
  2. The vector \(v\) is a right singular vector of \(A\) if and only if \(v\) is a left singular vector of \(A^{\top}\).
  3. The vector \(u\) is a left singular vector of \(A\) if and only if \(u\) is a right singular vector of \(A^{\top}\).
  4. If \(A\) is rank \(r\), then the first \(r\) columns of \(V\) are an orthonormal basis for \(C(A^{\top})\).

\[A^{+}A = V(\,:\,,1\colon\! r)V(\,:\,,1\colon\! r)^{\top}\]

Orthogonal projection onto \(C(A^{\top})\).

Example. Consider the equation

\[\begin{bmatrix}1 & 1\\ 1 & 2\\ 1 & 3\end{bmatrix}\begin{bmatrix}x_{1}\\ x_{2}\end{bmatrix} = \begin{bmatrix}1\\ 4\\ 9\end{bmatrix}.\]

This is an example of an overdetermined system (more equations than unknowns). Note that this equation has no solution since

\[\text{rref}\left(\begin{bmatrix}1 & 1 & 1\\ 1 & 2 & 4\\ 1 & 3 & 9\end{bmatrix}\right) = \begin{bmatrix} 1 & 0 & 0\\ 0 & 1 & 0\\ 0 & 0 & 1\end{bmatrix}\]

What if we want to find the vector \(\hat{x} = [x_{1}\ x_{2}]^{\top}\) that is closest to being a solution, that is, \(\hat{x}\) is the vector so that \(\|A\hat{x}-b\|\) is as small as possible. This is called the least squares solution to the above equation.

Now, consider a general matrix equation:

\[Ax=b.\]

  • The closest point in \(C(A)\) to \(b\) is \(AA^{+}b\), since \(AA^{+}\) is the orthogonal projection onto \(C(A)\). 
  • Take \(\hat{x} = A^{+}b\), then we have \[A\hat{x} = AA^{+}b\]
  • So \(\hat{x} = A^{+}b\) is a least squares solution to \(Ax=b\).

Note: If \(Ax=b\) has a solution, that is, \(b\in C(A)\), then \(\hat{x}\) is a solution to \(Ax=b\), since \(AA^{+}b=b\).

Example. Consider \[A= \begin{bmatrix} 1 & 0 \\ 0 & 1 \\ 1 & 1\end{bmatrix} = \begin{bmatrix} \frac{1}{\sqrt{6}} & \frac{1}{\sqrt{2}}  & \frac{1}{\sqrt{3}}\\[1ex] \frac{1}{\sqrt{6}}  & -\frac{1}{\sqrt{2}} & \frac{1}{\sqrt{3}}\\[1ex] \frac{2}{\sqrt{6}} & 0 & -\frac{1}{\sqrt{3}}\end{bmatrix}\begin{bmatrix} \sqrt{3} & 0\\ 0 & 1\\ 0 & 0\end{bmatrix}\begin{bmatrix} \frac{1}{\sqrt{2}} & \frac{1}{\sqrt{2}}\\[1ex] \frac{1}{\sqrt{2}} & -\frac{1}{\sqrt{2}}\end{bmatrix}.\]

 

Then, \[A^{+} = \begin{bmatrix} 1 & 0 & 1\\ 0 & 1 & 1\end{bmatrix} = \begin{bmatrix} \frac{1}{\sqrt{2}} & \frac{1}{\sqrt{2}}\\[1ex] \frac{1}{\sqrt{2}} & -\frac{1}{\sqrt{2}}\end{bmatrix}\begin{bmatrix} 1/\sqrt{3} & 0 & 0\\ 0 & 1 & 0\end{bmatrix}\begin{bmatrix} \frac{1}{\sqrt{6}} & \frac{1}{\sqrt{6}} & \frac{2}{\sqrt{6}}\\[1ex] \frac{1}{\sqrt{2}} & -\frac{1}{\sqrt{2}} & 0\\[1ex] \frac{1}{\sqrt{3}} & \frac{1}{\sqrt{3}} & -\frac{1}{\sqrt{3}}\end{bmatrix}.\]

 

\[ = \frac{1}{3}\begin{bmatrix} 2 & -1 & 1\\ -1 & 2 & 1\end{bmatrix}\]

If we tak \(b=[1\ \ 1\ \ 1]^{\top}\), then the equation \(Ax=b\) has no solution. However, the least squares solution is

\[\hat{x} = A^{+}\begin{bmatrix}1\\1\\1\end{bmatrix} = \begin{bmatrix}2/3\\ 2/3\end{bmatrix}.\]

Summary:

  • The vector \(\hat{x}\) that is closest to being a solution to \(Ax=b\), that is, \(\hat{x}\) is the vector so that \(\|A\hat{x}-b\|\) is as small as possible is called the least squares solution to \(Ax=b\).
  • Definition. Given an \(m\times n\) matrix \(A\) with singular value decomposition \[A=U\Sigma V^{\top},\] that is, \(U\) and \(V\) are orthogonal matrices, and \(\Sigma\) is an \(m\times n\) diagonal matrix with nonnegative entries \(\sigma_{1}\geq \sigma_{2}\geq \cdots\geq \sigma_{p}\) on the diagonal, we define the pseudoinverse of \(A\) \[A^{+} = V\Sigma^{+}U^{\top}\] where \(\Sigma^{+}\) is the \(n\times m\) diagonal matrix with \(i\)th diagonal entry \(1/\sigma_{i}\) if \(\sigma_{i}\neq 0\) and \(0\) if \(\sigma_{i}=0\).
  • If \(A^{+}\) is the pseudoinverse of \(A\), then \(A^{+}b\) is a least squares solution to \(Ax=b\).

Example. Consider the collection of points in \(\R^{2}\):

\[(x_{1},y_{1}),(x_{2},y_{2}),\ldots,(x_{n},y_{n})\]

We want to find the polynomial \(p(x)\) of degree \(k\) or less such that \(\displaystyle{\sum_{i=1}^{n}|p(x_{i})-y_{i}|^{2}}\) is as small as possible.

\(k=1\)

\(k=1\)

\(k=1\)

\(k=2\)

\(k=2\)

\(k=2\)

Example. Consider the collection of points in \(\R^{2}\):

\[(x_{1},y_{1}),(x_{2},y_{2}),\ldots,(x_{n},y_{n})\]

We want to find the polynomial \(p(x)\) of degree \(k\) or less such that \(\displaystyle{\sum_{i=1}^{n}|p(x_{i})-y_{i}|^{2}}\) is as small as possible.

Set \[A= \begin{bmatrix}1 & x_{1} & x_{1}^{2} & \cdots & x_{1}^{k}\\[1ex] 1 & x_{2} & x_{2}^{2} & \cdots & x_{2}^{k}\\[1ex] 1 & x_{3} & x_{3}^{2} & \cdots & x_{3}^{k}\\ \vdots & \vdots & \vdots & & \vdots \\ 1 & x_{n} & x_{n}^{2} & \cdots & x_{n}^{k}\\\end{bmatrix} \quad\text{and}\quad b= \begin{bmatrix}y_{1}\\ y_{2}\\ y_{3}\\ \vdots\\ y_{n}\end{bmatrix}\]

If \(\hat{x} = \begin{bmatrix} a_{0}\\ a_{1}\\ \vdots\\ a_{k}\end{bmatrix}\) is the least squares solution to \(Ax=b\), then we see that

\[\|A\hat{x} - b\|^{2}\] is as small as possible among all possible \(\hat{x}\).

 

However

\[A\hat{x}-b = \begin{bmatrix} a_{0}+a_{1}x_{1}+a_{2}x_{1}^{2}+\cdots + a_{k}x_{1}^{k} - y_{1}\\ a_{0}+a_{1}x_{2}+a_{2}x_{2}^{2}+\cdots + a_{k}x_{2}^{k} - y_{2}\\ a_{0}+a_{1}x_{3}+a_{2}x_{3}^{2}+\cdots + a_{k}x_{3}^{k} - y_{3}\\ \vdots\\ a_{0}+a_{1}x_{n}+a_{2}x_{n}^{2}+\cdots + a_{k}x_{n}^{k} - y_{n}\end{bmatrix} = \begin{bmatrix} p(x_{1})-y_{1}\\ p(x_{2})-y_{2}\\ p(x_{3})-y_{3}\\ \vdots\\ p(x_{n})-y_{n}\end{bmatrix}\]

where \[p(x) = a_{0}+a_{1}x+a_{2}x^2+a_{3}x^3+\cdots+a_{k}x^{k}.\]

Therefore,

\[\|A\hat{x} - b\|^{2} = \sum_{i=1}^{n}|p(x_{i})-y_{i}|^{2}\] 

If \(q(x)\) is any polynomial of degree \(\leq k\), say

\[q(x) = c_{0}+c_{1}x+c_{2}x^2+c_{3}x^3+\cdots+c_{k}x^k.\]

Then we set \[z = \begin{bmatrix} c_{0}\\ c_{1}\\ \vdots\\ c_{k}\end{bmatrix}\]

and we see that

\[\|Az-b\|^{2}\geq \|A\hat{x} - b\|^{2},\]

that is,

\[\sum_{i=1}^{n}|q(x_{i})-y_{i}|^{2}\geq \sum_{I=1}^{n}|p(x_{i})-y_{i}|^{2}\]

Example. Consider the collection of points in \(\R^{2}\):

\[(1,1), (0.5,0.25), (1.5,2.25),(0.1,0.01),(2,3.75)\]

\[A = \begin{bmatrix} 1 & 1\\ 1 & 0.5\\ 1 & 1.5\\ 1 & 0.1\\ 1 & 2\end{bmatrix}\quad\text{and}\quad b = \begin{bmatrix} 1\\ 0.25\\ 2.25\\ 0.01\\ 3.75\end{bmatrix}\]

\[\hat{x} = \begin{bmatrix}-0.5791\\ 1.9912\end{bmatrix}\]

Hence \(p(x) = -0.5791+1.9912x\) is the closest linear (degree 1) polynomial to the points, in the sense stated before.

Degree \(1\):

Example. Consider the collection of points in \(\R^{2}\):

\[(1,1), (0.5,0.25), (1.5,2.25),(0.1,0.01),(2,3.75)\]

Degree \(1\):

Example. Consider the collection of points in \(\R^{2}\):

\[(1,1), (0.5,0.25), (1.5,2.25),(0.1,0.01),(2,3.75)\]

\[A = \begin{bmatrix} 1 & 1 & 1\\ 1 & 0.5 & 0.25\\ 1 & 1.5 & 2.25\\ 1 & 0.1 & 0.01\\ 1 & 2 & 4\end{bmatrix}\quad\text{and}\quad b = \begin{bmatrix} 1\\ 0.25\\ 2.25\\ 0.01\\ 3.75\end{bmatrix}\]

\[\hat{x} = \begin{bmatrix} -0.0436 \\ 0.2292\\ 0.8401\end{bmatrix}\]

Hence \(p(x) =  -0.0436+0.2292x+0.8401x^2\) is the closest quadratic (degree 2) polynomial to the points, in the sense stated before.

Degree \(2\):

Example. Consider the collection of points in \(\R^{2}\):

\[(1,1), (0.5,0.25), (1.5,2.25),(0.1,0.01),(2,3.75)\]

Degree \(2\):

\(p(x) =  -0.0436+0.2292x+0.8401x^2\)

Example. Consider the collection of points in \(\R^{2}\):

\[(1,1), (0.5,0.25), (1.5,2.25),(0.1,0.01),(2,3.75)\]

Degree \(3\):

\(p(x) = 0.0248-0.2431x+1.4215x^2-0.1839x^3\)

Example. Consider the collection of points in \(\R^{2}\):

\[(1,1), (0.5,0.25), (1.5,2.25),(0.1,0.01),(2,3.75)\]

Degree \(4\):

Example. Consider the collection of points in \(\R^{2}\):

\[(1,1), (0.5,0.25), (1.5,2.25),(0.1,0.01),(2,3.75)\]

Degree \(5\):

Example. Consider the collection of points in \(\R^{2}\):

 

Example. Consider the collection of points in \(\R^{2}\):

 

Linear polynomial (degree 1)

Example. Consider the collection of points in \(\R^{2}\):

 

Quadratic polynomial (degree 2)

Example. Consider the collection of points in \(\R^{2}\):

 

Degree \(5\) polynomial

Example. Consider the collection of points in \(\R^{2}\):

 

Degree \(10\) polynomial

Example. Consider the collection of points in \(\R^{2}\):

 

Degree \(11\) polynomial

Linear Algebra Day 32

By John Jasper

Linear Algebra Day 32

  • 292