Solving Householder Matrix Problem

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In summary: H^2=\left(I_n-2 \dfrac{vv^T}{v^Tv}\right)^2=I_n^2-2I_n\dfrac{vv^T}{v^Tv}+4 \dfrac{vv^T}{v^Tv} \dfrac{vv^T}{v^Tv}=I_n-2\dfrac{vv^T}{v^Tv}+4 \dfrac{vv^Tvv^T}{v^Tv^2}$. Taking into account that $vv^T$ is a matrix of rank 1 we have that its trace is $tr(vv^T)=v^Tv$ so the previous becomes $I_n-2\d
  • #1
Jameson
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Problem: Let $H$ be an $n \times n$ householder matrix given by \(\displaystyle H = I_n-2 \frac{vv^T}{v^Tv}\) for any non-zero $n$-length column vector $v$. Show that $H=H^T$ and $HH^T=I_n$.

Attempt: a) Let's look at $H$ written out in matrix form for an 3x3 matrix. I believe it would look like this.

\(\displaystyle H_{3 \times 3}=\left( \begin{array}{ccc}1-2 \frac{vv^T}{v^Tv} &-2 \frac{vv^T}{v^Tv}&-2 \frac{vv^T}{v^Tv} \\ -2 \frac{vv^T}{v^Tv}&1-2 \frac{vv^T}{v^Tv}&-2 \frac{vv^T}{v^Tv}\\-2 \frac{vv^T}{v^Tv}&-2 \frac{vv^T}{v^Tv}&1-2 \frac{vv^T}{v^Tv} \end{array} \right)\).

Clearly here $H=H^T$. I believe showing this in the general case requires some tricky notation for me. Any ideas to get started on generalizing this?

b) To show $HH^T=I_n$ we start by looking at \(\displaystyle \left(I_n-2 \frac{vv^T}{v^Tv}\right) \left(I_n-2 \frac{vv^T}{v^Tv} \right)^T\)

I'm familiar with rules of matrix multiplication and transpose when products are involved, but when sums are there I'm hoping I can FOIL these terms.

Let's start with the fact from (a) that I haven't proved yet that $H=H^T$. That means that:

\(\displaystyle \left(I_n-2 \frac{vv^T}{v^Tv}\right)^T=\left(I_n-2 \frac{vv^T}{v^Tv}\right)\) so the original product becomes:

\(\displaystyle \left(I_n-2 \frac{vv^T}{v^Tv}\right) \left(I_n-2 \frac{vv^T}{v^Tv}\right) \)

I don't know exactly where to go from here...
 
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  • #2
Jameson said:
Clearly here $H=H^T$. I believe showing this in the general case requires some tricky notation for me. Any ideas to get started on generalizing this?

Using well known properties of tranposition and taking into account that $\dfrac{2}{v^tv}$ is an scalar:

$H ^T= \left(I_n-2 \dfrac{vv^T}{v^Tv}\right)^T=I_n^T-\dfrac{2}{v^tv}(vv^T)^T=I_n^T-\dfrac{2}{v^tv}(v^T)^Tv^T\\=I_n^T-\dfrac{2}{v^tv}(vv^T)=I_n-2 \dfrac{vv^T}{v^Tv}=H$

To show $HH^T=I_n$ use that $HH^T=H^2,$ that $I_n$ commutes with all matrix $n\times n$ and
$(A-B)^2=A^2-2AB+B^2$ if $AB=BA.$
 
  • #3
Here you have a complete proof of $HH^T=I_n$ for if you want to verify your work:

Using that $I_n$ commutes with all $n\times n$ matrix:

$HH^T=H^2= \left( I_n-2 \dfrac{vv^T}{v^Tv}\right)^2=I_n^2-4I_n \dfrac{vv^T}{v^Tv}+4\left (\dfrac{vv^T}{v^Tv} \right)^2\\=I_n-4\dfrac{vv^T}{v^Tv}+4\dfrac{vv^T}{v^Tv}\dfrac{vv^T}{v^Tv}$

Using that $v^Tv$ is an scalar:

$\dfrac{vv^T}{v^Tv}\dfrac{vv^T}{v^Tv}=\dfrac{1}{v^Tv}\dfrac{1}{v^Tv}v(v^Tv)v^T=\dfrac{1}{v^Tv}\dfrac{1}{v^Tv}(v^Tv)(vv^T)=\dfrac{vv^T}{v^Tv}$

So, $HH^T=I_n,$ i.e. $H$ is orthogonal.
 

What is a Householder matrix and why is it important in scientific research?

A Householder matrix is a special type of square matrix used in linear algebra to perform various operations, such as transformation, reflection, and projection. It is important in scientific research because it allows for efficient and accurate computation of complex mathematical problems.

What is the Householder matrix problem and why is it challenging to solve?

The Householder matrix problem refers to the task of finding a Householder matrix that satisfies certain conditions, such as being orthogonal or preserving certain properties of a given matrix. It is challenging to solve because it involves a high degree of mathematical complexity and requires advanced computational techniques.

What are some applications of solving the Householder matrix problem?

Solving the Householder matrix problem has numerous applications in various fields of science, such as data analysis, signal processing, and quantum mechanics. It is commonly used in solving linear systems of equations, eigenvalue problems, and matrix factorization.

What are some commonly used methods for solving the Householder matrix problem?

There are several methods for solving the Householder matrix problem, including QR factorization, Givens rotations, and Householder reflections. Each method has its own advantages and disadvantages, and the choice of method depends on the specific problem at hand.

How can the accuracy and efficiency of solving the Householder matrix problem be improved?

To improve the accuracy and efficiency of solving the Householder matrix problem, advanced numerical techniques such as precision arithmetic and iterative methods can be used. Additionally, parallel computing and computer hardware optimization can significantly speed up the computation process.

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