Ytukyg

We have the linear system

$$begin{bmatrix} 1 & -1\ 1 & -1end{bmatrix} begin{bmatrix} x\ yend{bmatrix} = begin{bmatrix} 4 \ 6end{bmatrix}$$

which can be rewritten in the form

$$begin{bmatrix} 1\ 1end{bmatrix} eta = begin{bmatrix} 4 \ 6end{bmatrix}$$

where $eta = x - y$. Left-multiplying both sides by $begin{bmatrix} 1\ 1end{bmatrix}^T$, we obtain $2 eta = 10$, or, $eta = 5$. Hence,

$$x - y = 5$$

Thus, there are infinitely many least-squares solutions. One of them is

$$begin{bmatrix} hat x\ hat yend{bmatrix} = begin{bmatrix} 6\ 1end{bmatrix}$$

First, choose points (x,y) that satisfy each equation.

$begin{cases}
x - y = 4, & text{(6,2)} \
x - y = 6, & text{(10,4)}
end{cases}$

Then, proceed as usual

$Ax = begin{bmatrix}
1 & 6 \
1 & 10 \
end{bmatrix} begin{bmatrix}
b \
m \
end{bmatrix} = begin{bmatrix}
2 \
4 \
end{bmatrix}$

$begin{bmatrix}
b \
m \
end{bmatrix} =begin{bmatrix}
5 \
1/2 \
end{bmatrix}$

$y = 1/2x + 5$

Problem statement: underdetermined system

Start with the linear system
$$
begin{align}
mathbf{A} x &= b \
%
left[
begin{array}{cc}
1 & -1 \
1 & -1 \
end{array}
right]
%
left[
begin{array}{c}
x \
y
end{array}
right]
%
&=
%
left[
begin{array}{c}
4 \
6
end{array}
right]
%
end{align}
$$

The system has matrix rank $rho = 1$; therefore, if a solution exists, it will not be unique.

Provided $bnotin color{red}{mathcal{N} left( mathbf{A}^{*} right)}$, we are guaranteed a least squares solution
$$
x_{LS} = left{ xinmathbb{C}^{2} colon lVert mathbf{A} x_{LS} - b rVert_{2}^{2} text{ is minimized} right}
tag{1}
$$

Subspace resolution

By inspection, we see that the row space is resolved as
$$
color{blue}{mathcal{R} left( mathbf{A}^{*} right)} oplus
color{red}{mathcal{N} left( mathbf{A} right)}
=
color{blue}{left[
begin{array}{r}
1 \
-1
end{array}
right]} oplus
color{red}{left[
begin{array}{c}
1 \
1
end{array}
right]}
$$
The column space is resolved as
$$
color{blue}{mathcal{R} left( mathbf{A} right)} oplus
color{red}{mathcal{N} left( mathbf{A}^{*} right)}
=
color{blue}{left[
begin{array}{c}
1 \
1
end{array}
right]} oplus
color{red}{left[
begin{array}{r}
-1 \
1
end{array}
right]}
$$

The coloring indicates vectors in the $color{blue}{range}$ space and the $color{red}{null}$ space.

Finding the least squares solution

Since there is only one vector in $color{blue}{mathcal{R} left( mathbf{A}^{*} right)}$, the solution vector will have the form
$$
color{blue}{x_{LS}} = alpha
color{blue}{left[
begin{array}{r}
1 \
-1
end{array}
right]}
$$
The goal is to find the constant $alpha$ to minimize (1):
$$
color{red}{r}^{2} = color{red}{r} cdot color{red}{r} =
lVert
color{blue}{mathbf{A} x_{LS}} - b
rVert_{2}^{2}
=
8 alpha ^2-40 alpha +52
$$
The minimum of the polynomial is at
$$
alpha = frac{5}{2}
$$

Least squares solution

The set of least squares minimizers in (1) is then the affine set given by
$$
x_{LS} = frac{5}{2}
color{blue}{left[
begin{array}{r}
1 \
-1
end{array}
right]}
+
xi
color{red}{left[
begin{array}{r}
1 \
1
end{array}
right]}, qquad xiinmathbb{C}
$$

The plot below shows how the total error $lVert mathbf{A} x_{LS} - b rVert_{2}^{2}$ varies with the fit parameters. The blue dot is the particular solution, the dashed line homogeneous solution as well as the $0$ contour - the exact solution.

Addendum: Existence of the Least Squares Solution

To address the insightful question of @RodrigodeAzevedo, consider the linear system:

$$
begin{align}
mathbf{A} x &= b \
%
left[
begin{array}{cc}
1 & 0 \
0 & 0 \
end{array}
right]
%
left[
begin{array}{c}
x \
y
end{array}
right]
%
&=
%
left[
begin{array}{c}
0 \
1
end{array}
right]
%
end{align}
$$

The data vector $b$ is entirely in the null space of $mathbf{A}^{*}$:
$bin color{red}{mathcal{N} left( mathbf{A}^{*} right)}$

As pointed out, the system matrix has the singular value decomposition. One instance is:
$$mathbf{A} = mathbf{U}, Sigma, mathbf{V}^{*} = mathbf{I}_{2}
left[
begin{array}{cc}
1 & 0 \
0 & 0 \
end{array}
right]
mathbf{I}_{2}$$
and the concomitant pseudoinverse,
$$mathbf{A}^{dagger} = mathbf{V}, Sigma^{dagger} mathbf{U}^{*} =
mathbf{I}_{2}
left[
begin{array}{cc}
1 & 0 \
0 & 0 \
end{array}
right]
mathbf{I}_{2} = mathbf{A}$$

Following least squares canon, the particular solution to the least squares problem is computed as
$$
color{blue}{x_{LS}} = mathbf{A}^{dagger} b =
color{red}{left[
begin{array}{c}
0 \
0 \
end{array}
right]}
qquad RightarrowLeftarrow
$$
The color collision (null space [red] = range space [blue]) indicates a problem. There is no component of a particular solution vector in a range space!

Mathematicians habitually exclude the $0$ vector a solution to linear problems.

$$A = left [begin{array}{cc}
1 & -1 \
1 & -1 \
end{array} right],
quad b = begin{bmatrix}4\6 end{bmatrix} $$

The linear system

$$begin{bmatrix} 1 & -1\ 1 & -1end{bmatrix} begin{bmatrix} x\ yend{bmatrix} = begin{bmatrix} 4 \ 6end{bmatrix}$$

has no solution. Left-multiplying both sides by $begin{bmatrix} 1 & -1\ 1 & -1end{bmatrix}^T$, we obtain the normal equations

$$begin{bmatrix} 2 & -2\ -2 & 2end{bmatrix} begin{bmatrix} x\ yend{bmatrix} = begin{bmatrix} 10 \ -10end{bmatrix}$$

Dividing both sides by $2$ and removing the redundant equation,

$$x - y = 5$$

Thus, there are infinitely many least-squares solutions. One of them is

$$begin{bmatrix} hat x\ hat yend{bmatrix} = begin{bmatrix} 6\ 1end{bmatrix}$$

The least-squares solution is a solution to the normal equations, not to the original linear system.

Your matrix is just the coefficients of your system of equations. In this case
$$ x-y = 4 $$
$$ x-y = 6 $$
leads to
$$
begin{bmatrix}
1 & -1 \ 1 & -1
end{bmatrix}
begin{bmatrix}
x \ y
end{bmatrix}
=
begin{bmatrix}
4 \ 6
end{bmatrix}
$$
but you should see that there is no solution to this since you can't have $x-y$ be both $4$ and $6$...