Why does SVD provide the least squares and least norm solution to $ A x = b $? I am studying the Singular Value Decomposition and its properties. It is widely used in order to solve equations of the form $Ax=b$. I have seen the following: When we have the equation system $Ax=b$, we calculate the SVD of A as $A=U\Sigma V^T$. Then we calculate $x'= V \Sigma^{+}U^Tb$. $\Sigma^{+}$ has the reciprocals ($\dfrac{1}{\sigma_i}$) of the singular values in its diagonal and zeros where $\sigma_i=0$.  If the $b$ is in the range of $A$ then it is the solution that has the minimum norm (closest to origin). If it is not in the range, then it is the least-squares solution.
I fail to see how exactly this procedure always produces a $x'$ which is closest to origin if $b$ is in the range of A. (I can see the least-squares solution is an extension of this "closest to origin" property). From a geometric intuitive way if possible, how can we show this property of SVD?
 A: The pseudoinverse solution from the SVD is derived in proving standard least square problem with SVD.
Given $\mathbf{A}x=b$, where the data vector $b\notin\mathcal{N}\left( \mathbf{A}^{*} \right)$, the least squares solution exists and is given by
$$
  x_{LS} = \color{blue}{\mathbf{A}^{\dagger}b} + \color{red}{\left( \mathbf{I}_{n} - \mathbf{A}^{\dagger}\mathbf{A}\right) y}, \quad y\in\mathbb{C}^{n}
$$
where blue vectors are in the range space $\color{blue}{\mathcal{R}\left( \mathbf{A}^{*} \right)}$ and red vectors are in the null space $\color{red}{\mathcal{N}\left( \mathbf{A} \right)}.$
The least squares solution $r^{2}$ minimizes the sum of the squares of the residual errors and is an affine space. That is 
$$
\lVert 
\mathbf{A} x_{LS} (y)
\rVert_{2}^{2} = r^{2}_{min}
$$
for all values of $y$.
What is the vector in this affine space with the smallest length? The length of the solution vectors is 
$$
\lVert
 x_{LS}
\rVert_{2}^{2} 
=
\lVert
 \color{blue}{\mathbf{A}^{\dagger}b} + 
 \color{red}{\left( \mathbf{I}_{n} - \mathbf{A}^{\dagger}\mathbf{A}\right) y}
\rVert_{2}^{2} 
=
\lVert
\color{blue}{\mathbf{A}^{\dagger}b}
\rVert_{2}^{2} 
+
\underbrace{\lVert
 \color{red}{\left( \mathbf{I}_{n} - \mathbf{A}^{\dagger}\mathbf{A}\right) y}
\rVert_{2}^{2}}_{y=\mathbf{0}}
$$
The solution vector of minimum length is $\color{blue}{\mathbf{A}^{\dagger}b}$, the point in the affine space closest to the origin.
A: The resource linked below really helped me understand this. The transformation $A$ can be interpreted in 2D as mapping the unit circle to an elipse. This can be done in a 3 step process using the SVD:


*

*Rotate the unit circle so it can be stretched along its axis

*Stretch each axis to form the ellipse

*Rotate again to align the ellipse with the output space of $A$


To solve for $x$, you reverse this process, starting with $b$.
Least squares comes in when step 2 creates a ellipse with a width of zero. When you're going through this process in reverse, when you get to step 2, un-stretching throws away that dimension with a width of zero. Still, you're left with the closest point to $b$ in the output space of $A$. Continuing through the reversed process gets you to $x'$.
In other words, the transformation $A$ maps the unit circle to a line instead of an ellipse, and you've found the $x$ for which $Ax$ results in the closest point on that line to point $b$.
https://www.cs.cornell.edu/courses/cs3220/2010sp/notes/svd.pdf
A: First, consider the problem $\Sigma x = b$, where
$$
\Sigma = \pmatrix{\sigma_1\\& \ddots\\&&\sigma_r\\ &&&0\\&&&&\ddots\\&&&&&0}
$$
Note that $b$ is only in the range of $\Sigma$ if its entries $b_{r+1},\dots,b_n$ are all zero.  Furthermore, you should be able to convince yourself (geometrically or otherwise) that the least squares solution must be
$$
x = (b_1/\sigma_1,\dots,b_r/\sigma_r,0,\dots,0)^T = \Sigma^+ b
$$
From there, note that
$$
U\Sigma V^T x = b \implies\\
\Sigma (V^T x ) = U^T b
$$
By the above argument, the least squares solution for $(V^T x)$ is given by
$V^T x = \Sigma^+ U^T b$. Noting that $\|V^T x\| = \|x\|$, we can use this to conclude that $x = (V \Sigma ^+ U^T)b$ must be the least squares solution (for $x$).
I hope you find this explanation sufficient.
A: Let's fix the dimensions for the sake of making the example simpler  and say that $A:\mathbb{R}^8\to\mathbb{R}^5$ and that $rank(A)=3$.
$A=U\Sigma V^T$ is the singular value decomposition of $A$ and $e_1, ..., e_5$ and $f_1, ..., f_8$ are the standard bases of $\mathbb{R}^5$ and $\mathbb{R}^8$ (respectively), i.e. $e_1 = [1, 0, 0, 0, 0]^T$ etc. 
So $A(\mathbb{R}^8)$ is a 3-dimensional subspace of $\mathbb{R}^5$. What are the (or some) geometric interpretations of $U$, $\Sigma$ and $V$?
$U$ sends the basis vectors $e_i$ to the column vectors $Ue_i$ of $U$ which give us an orthogonal basis in $\mathbb{R}^5$ such that the first three column vectors span $Im(A)$. You can see this by noting that 
$U\Sigma f_1 = U\sigma_1 e_1 \ne 0$
$U\Sigma f_2 = U\sigma_2 e_2 \ne 0$
$U\Sigma f_3 = U\sigma_3  e_3 \ne 0$
$U\Sigma f_4 = ... = U\Sigma f_8 = 0$
Since $U$ is orthogonal its inverse is $U^T$.
Similarly $V$ sends the basis vectors $f_i$ to the column vectors $Vf_i$ of $V$ which give us an orthogonal basis in $\mathbb{R}^8$ such that the last 5 span $ker(A)$:
$AVf_i = U\Sigma V^TVf_i = U\Sigma f_i \ne 0$ for $i$ = 1, 2, 3 
$AVf_i = U\Sigma V^TVf_i = U\Sigma f_i = 0$ for $i$ = 4 .. 8
And the inverse of $V$ is $V^T$.
$\Sigma$ jams $\mathbb{R}^8$ into $\mathbb{R}^5$by mapping the one-dimensional spaces spanned by each of $f_1, f_2, f_3$ onto those spanned by $e_1, e_2, e_3$ (scaling them by $\sigma_1, \sigma_2, \sigma_3$ in the process) while squashing those spanned by $f_4..f_8$.
It follows that $A$ jams $\mathbb{R}^8$ into $\mathbb{R}^5$ by mapping the one-dimensional spaces spanned by each of $Vf_1, Vf_2, Vf_3$ onto those spanned by $Ue_1, Ue_2, Ue_3$ (scaling them by $\sigma_1, \sigma_2, \sigma_3$ in the process) while squashing those spanned by $Vf_4..Vf_8$.
The key here is that restricted to the space spanned by $Vf_1, Vf_2, Vf_3$ A is an isomorphism onto the space spanned by $Ue_1, Ue_2, Ue_3$, and that $V\Sigma^+U^T$ is the inverse (when restricted to $Ue_1, Ue_2, Ue_3$).
If we have $AX = b$ and $b \in Im(A)$ it follows that there exists a unique $x' \in <Vf_1, Vf_2, Vf_3>$ s.t. $Ax' = b$. Any other solution $x$ in $\mathbb{R}^8$ takes the form $x' + \delta$ for $\delta \in Ker(A)$. Now since we can decompose $\mathbb{R}^8$ into $<Vf_1, Vf_2, Vf_3> \oplus <Vf_4, .., Vf_8>$ we have $\lvert x\rvert^2 = <x' + \delta, x' + delta>  = \lvert x'\rvert^2 + \lvert \delta\rvert^2$ and so $\lvert x\rvert >= \lvert x'\rvert$ - that is, $x'$ is the closest solution to the origin. 
