MHB How Can We Find the Minimum 2-Norm of Ax-y Using an Orthogonal Matrix?

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The discussion focuses on finding the minimum 2-norm of the expression \( \|Ax - y\|_2 \) using the decomposition \( A = QR \), where \( Q \) is an orthogonal matrix and \( R \) is an upper triangular matrix. The participants derive that the minimum can be expressed as \( \|(Q^Ty)_{n+1}^m\|_2 \) for any \( y \in \mathbb{R}^m \). They demonstrate the minimization process by rewriting \( Rx \) and applying properties of orthogonal matrices, leading to the conclusion that the minimum occurs when \( Ux = (Q^Ty)_1^n \). The discussion confirms that this approach is valid due to the rank conditions of \( R \) and \( U \).
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Hey! :o

Let $A = QR$, where $Q$ is an orthogonal ($m\times m$)−matrix and $R$ is an upper ($m\times n$)-triangular matrix of rang $n$ ($m>n$).

I want to show that $$\min_{x\in \mathbb{R}^n}\|Ax-y\|_2=\|(Q^Ty)_{n+1}^m\|_2, \ \forall y\in \mathbb{R}^m$$

It is $(a)_k^l=(a_k, \ldots , a_l)^T$ if $a=(a_1, \ldots , a_l)^T\in \mathbb{R}^l$.
I have done the following:

\begin{align*}\min_{x\in \mathbb{R}^n}\|Ax-y\|_2&=\min_{x\in \mathbb{R}^n}\|QRx-y\|_2=\min_{x\in \mathbb{R}^n}\|QRx-QQ^{-1}y\|_2 \\ & =\min_{x\in \mathbb{R}^n}\|Q(Rx-Q^{T}y)\|_2=\min_{x\in \mathbb{R}^n}\|Rx-Q^{T}y\|_2\end{align*}

(We have used here the properties of an orthogonal matrix.)

How could we continue? How can we find that minimum? (Wondering)
 
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Hey mathmari!

We can write $Rx$ as $\binom U0x$ where $U$ is an up upper triangular nxn matrix of rank n, and 0 is a zero matrix, can't we? (Wondering)
It means that:
$$\|Rx-Q^{T}y\|^2
= \|Ux-(Q^{T}y)_1^n\|^2 + \|0-(Q^{T}y)_{n+1}^m\|^2
$$
Can we solve it now? (Wondering)
 
I like Serena said:
Hey mathmari!

We can write $Rx$ as $\binom U0x$ where $U$ is an up upper triangular nxn matrix of rank n, and 0 is a zero matrix, can't we? (Wondering)
It means that:
$$\|Rx-Q^{T}y\|^2
= \|Ux-(Q^{T}y)_1^n\|^2 + \|0-(Q^{T}y)_{n+1}^m\|^2
$$
Can we solve it now? (Wondering)

Ah ok! We have that \begin{align*}\|Rx-Q^{T}y\|_2^2&=\left \|\begin{pmatrix}U \\ 0\end{pmatrix}x-\begin{pmatrix}(Q^T)_1^n \\ (Q^T)_{n+1}^m\end{pmatrix}y\right \|_2^2 =\left \|\begin{pmatrix}Ux-(Q^Ty)_1^n \\ 0-(Q^Ty)_{n+1}^m\end{pmatrix}\right \|_2^2 \\ & =\|Ux-(Q^{T}y)_1^n\|_2^2 + \|(Q^{T}y)_{n+1}^m\|_2^2\end{align*}

Therefore, we get $$\min_{x\in\mathbb{R}^n}\|Rx-Q^{T}y\|^2=\min_{x\in\mathbb{R}^n}\{\|Ux-(Q^{T}y)_1^n\|_2^2 + \|(Q^{T}y)_{n+1}^m\|_2^2\}$$

This is minimized for that $x$ for which it holds $Ux=(Q^{T}y)_1^n$ and so we get $$\min_{x\in \mathbb{R}^n}\|Ax-y\|_2^2=\|(Q^{T}y)_{n+1}^m\|_2^2\Rightarrow \min_{x\in \mathbb{R}^n}\|Ax-y\|_2=\|(Q^{T}y)_{n+1}^m\|_2$$ right? (Wondering)
 
Yep.
And that is possible because $R$ is of rank $n$, and therefore $U$ is as well, making it an invertible matrix. (Nod)
 
I like Serena said:
And that is possible because $R$ is of rank $n$, and therefore $U$ is as well, making it an invertible matrix. (Nod)

Ok! Thanks a lot! (Yes)
 
Here is a little puzzle from the book 100 Geometric Games by Pierre Berloquin. The side of a small square is one meter long and the side of a larger square one and a half meters long. One vertex of the large square is at the center of the small square. The side of the large square cuts two sides of the small square into one- third parts and two-thirds parts. What is the area where the squares overlap?

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