# The normal equations¶

We seek to solve the general linear least squares problem: Given \(\mathbf{A}\in\mathbb{R}^{m \times n}\) and \(\mathbf{b}\in\mathbb{R}^m\), with \(m>n\), find \(\mathbf{x}\in\mathbb{R}^n\) such that \(\| \mathbf{b} - \mathbf{A}\mathbf{x} \|_2\) is minimized. There is a concise explicit solution to the problem.

In the following proof we make use of the elementary algebraic fact that for two vectors \(\mathbf{u}\) and \(\mathbf{v}\),

If \(\mathbf{x}\) satisfies \(\mathbf{A}^T(\mathbf{A}\mathbf{x}-\mathbf{b})=\boldsymbol{0}\), then \(\mathbf{x}\) solves the linear least squares problem, i.e., \(\mathbf{x}\) minimizes \(\| \mathbf{b}-\mathbf{A}\mathbf{x} \|_2\).

Let \(\mathbf{y}\in \mathbb{R}^n\) be any vector. Then \(\mathbf{A}(\mathbf{x}+\mathbf{y})-\mathbf{b}=\mathbf{A}\mathbf{x}-\mathbf{b}+\mathbf{A}\mathbf{y}\), and

The condition \(\mathbf{A}^T(\mathbf{A}\mathbf{x}-\mathbf{b})=\boldsymbol{0}\) is often written as

called the normal equations. They have a straightforward geometric interpretation, as shown in Fig. 2. The vector in the range (column space) of \(\mathbf{A}\) that lies closest to \(\mathbf{b}\) makes the vector difference \(\mathbf{A}\mathbf{x}-\mathbf{b}\) perpendicular to the range. Thus for any \(\mathbf{z}\), we must have \((\mathbf{A} \mathbf{z})^T(\mathbf{A}\mathbf{x}-\mathbf{b})=0\), which is satisfied if \(\mathbf{A}^T(\mathbf{A}\mathbf{x}-\mathbf{b})=\boldsymbol{0}\).

If we group the left-hand side of the normal equations as \((\mathbf{A}^T\mathbf{A})\,\mathbf{x}\), we recognize (63) as a *square* \(n\times n\) linear system to solve for \(\mathbf{x}\).

## Pseudoinverse and definiteness¶

The \(n\times m\) matrix

is called the pseudoinverse of \(\mathbf{A}\). Mathematically, the overdetermined least squares problem \(\mathbf{A}\mathbf{x}\approx \mathbf{b}\) has the solution \(\mathbf{x}=\mathbf{A}^+\mathbf{b}\). Hence we can generalize our earlier observation: backslash is equivalent mathematically to left-multiplication by the inverse (square case) or pseudoinverse (rectangular case) of a matrix. One may also compute the pseudoinverse directly using pinv, but as with matrix inverses, this is rarely necessary in practice.

The matrix \(\mathbf{A}^T\mathbf{A}\) appearing in the pseudoinverse has some important properties.

For any real \(m\times n\) matrix \(\mathbf{A}\) with \(m\ge n\), the following are true:

\(\mathbf{A}^T\mathbf{A}\) is symmetric.

\(\mathbf{A}^T \mathbf{A}\) is singular if and only if the columns of \(\mathbf{A}\) are linearly dependent. (Equivalently, the rank of \(\mathbf{A}\) is less than \(n\).)

If \(\mathbf{A}^T\mathbf{A}\) is nonsingular, then it is positive definite.

The first part is left as an exercise. For the second part, suppose that \(\mathbf{A}^T\mathbf{A}\mathbf{z}=\boldsymbol{0}\). Note that \(\mathbf{A}^T\mathbf{A}\) is singular if and only if \(\mathbf{z}\) may be nonzero. Left-multiplying by \(\mathbf{z}^T\), we find that

which is equivalent to \(\mathbf{A}\mathbf{z}=\boldsymbol{0}\). Then \(\mathbf{z}\) may be nonzero if and only if the columns of \(\mathbf{A}\) are linearly dependent.

Finally, we can repeat the manipulations above to show that for any nonzero \(n\)-vector \(\mathbf{v}\), \(\mathbf{v}^T(\mathbf{A}^T\mathbf{A})\mathbf{v}=\| \mathbf{A}\mathbf{v} \|_2^2\ge 0\), and equality is not possible thanks to the second part of the theorem.

The definition of the pseudoinverse involves taking the inverse of a matrix and is therefore not advisable to use computationally. Instead, we simply use the definition of the normal equations to set up a linear system, which we already know how to solve. In summary, the steps for solving the linear least squares problem \(\mathbf{A}\mathbf{x}\approx\mathbf{b}\) are:

Compute \(\mathbf{N}=\mathbf{A}^T\mathbf{A}\).

Compute \(\mathbf{z} = \mathbf{A}^T\mathbf{b}\).

Solve the \(n\times n\) linear system \(\mathbf{N}\mathbf{x} = \mathbf{z}\) for \(\mathbf{x}\).

In the last step we can exploit the fact, proved in the AtA theorem, that \(\mathbf{N}\) is symmetric and positive definite, and use Cholesky factorization as in Symmetric positive definite matrices. (The backslash command does this automatically.)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
"""
lsnormal(A,b)
Solve a linear least squares problem by the normal equations.
Returns the minimizer of ||b-Ax||.
"""
function lsnormal(A,b)
N = A'*A; z = A'*b;
R = cholesky(N).U
w = forwardsub(R',z) # solve R'z=c
x = backsub(R,w) # solve Rx=z
return x
end
``` |

## Conditioning and stability¶

We have already used `A\b`

as the native way to solve the linear least squares problem \(\mathbf{A}\mathbf{x}\approx\mathbf{b}\) in Julia. The algorithm employed by the backslash does *not* proceed through the normal equations, because of instability.

The conditioning of the linear least-squares problem relates changes in the solution \(\mathbf{x}\) to those in the data, \(\mathbf{A}\) and \(\mathbf{b}\). A full accounting of the condition number is too messy to present here, but we can generalize from the linear system problem \(\mathbf{A} \mathbf{x} =\mathbf{b}\), where \(m=n\). Recall that the condition number of solving \(\mathbf{A} \mathbf{x}=\mathbf{b}\) is \(\kappa(\mathbf{A})=\|\mathbf{A}\| \cdot \|\mathbf{A}^{-1}\|\).

Provided that the residual norm \(\|\mathbf{b}-\mathbf{A}\mathbf{x}\|\) at the least-squares solution is relatively small, the conditioning of the linear least squares problem is similar. The condition number of the problem generalizes via the pseudoinverse:

These are rectangular matrices, but the induced matrix norm is defined by (42) just as in the square case. If \(\mathbf{A}\) has rank less than \(n\), then \(\kappa(\mathbf{A})=\infty\). The Julia function `cond`

computes condition numbers of rectangular matrices in the 2-norm.

As an algorithm, the normal equations begin by computing the \(n\times n\) system \((\mathbf{A}^T\mathbf{A})\mathbf{x} = \mathbf{A}^T \mathbf{b}\). When these equations are solved, perturbations to the data can be amplified by a factor \(\kappa(\mathbf{A}^T\mathbf{A})\). It turns out that

in the 2-norm. If \(\kappa(\mathbf{A})\) is large, the squaring of it can destabilize the normal equations: while the solution of the least squares problem is sensitive, finding it via the normal equations makes it doubly so.

## Exercises¶

✍ Work out the least squares solution when

\[\begin{split}\mathbf{A} = \begin{bmatrix} 2 & -1 \\ 0 & 1 \\ -2 & 2 \end{bmatrix}, \qquad \mathbf{b} = \begin{bmatrix} 1\\-5\\6 \end{bmatrix}.\end{split}\]✍ Find the pseudoinverse \(\mathbf{A}^+\) of the matrix \(\mathbf{A}=\begin{bmatrix}1&-2&3\end{bmatrix}^T\).

✍ Prove the first statement of the AtA theorem: \(\mathbf{A}^T\mathbf{A}\) is symmetric for any \(m\times n\) matrix \(\mathbf{A}\) with \(m \ge n\).

✍ Prove that if \(\mathbf{A}\) is a nonsingular square matrix, then \(\mathbf{A}^+=\mathbf{A}^{-1}\).

**(a)**✍ Show that for any \(m\times n\) \(\mathbf{A}\) (\(m>n\)) for which \(\mathbf{A}^T\mathbf{A}\) is nonsingular, \(\mathbf{A}^+\mathbf{A}\) is the \(n\times n\) identity.**(b)**⌨ Show using an example in Julia that \(\mathbf{A}\mathbf{A}^+\) is not an identity matrix. (This matrix has rank no greater than \(n\), so it can’t be an \(m\times m\) identity.)✍ Prove that the vector \(\mathbf{A}\mathbf{A}^+\mathbf{b}\) is the vector in the column space (i.e., range) of \(\mathbf{A}\) that is closest to \(\mathbf{b}\), in the sense of the 2-norm.

✍ Show that the flop count for lsnormal is asymptotically \(\sim 2m n^2 + \tfrac{1}{3}n^3\). (In finding the asymptotic count you can ignore terms like \(mn\) whose total degree is less than three.)

⌨ Let \(t_1,\ldots,t_m\) be \(m+1\) equally spaced points in \([0,2\pi]\).

**(a)**Let \(\mathbf{A}_\beta\) be the matrix in (58) that corresponds to fitting data with the function \(c_1 + c_2 \sin(t) + c_3 \cos(\beta t)\). Using the identity (66), make a table of the condition numbers of \(\mathbf{A}_\beta\) for \(\beta = 2,1.1,1.01,\ldots,1+10^{-8}\).**(b)**Repeat part (a) using the fitting function \(c_1 + c_2 \sin^2(t) + c_3 \cos^2(\beta t).\)**(c)**Why does it make sense that \(\kappa\bigl(\mathbf{A}_\beta\bigr)\to \infty\) as \(\beta\to 1\) in part (b) but not in part (a)?✍ ⌨ When \(\mathbf{A}\) is \(m\times n\) with rank less than \(n\), the pseudoinverse is still defined and can be computed using

`pinv`

from`LinearAlgebra`

. However, the behavior in this case is not always intuitive. Let\[\begin{split}\mathbf{A}_s = \begin{bmatrix} 1 & 1 \\ 0 & 0 \\ 0 & s \end{bmatrix}.\end{split}\]Then \(\mathbf{A}_0\) has rank equal to one. Demonstrate experimentally that \(\mathbf{A}_0^+\neq \lim_{s\to 0} \mathbf{A}_s^+\).