Pseudo Inverse & QR Decomposition

In summary: Your Name]In summary, we proved that A+=R+QT by showing that RR+QT is equal to R(RT+QT), which is equal to A+.
  • #1
ahamdiheme
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Homework Statement



Let A[tex]\in[/tex]Rmxn be factorized as A=QR where Q[tex]\in[/tex]Rmxn has orthonormal columns. Prove that A+=R+QT

Homework Equations



R is an upper triangular matrix

The Attempt at a Solution



I tried to apply the definition A+=(ATA)+A+
I ended up here: R+(RR+)TQT
I'm not sure if i am going in the right direction
thanks for your assistance
 
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  • #2


Dear student,

Your attempt is a good start. Let's continue from where you left off:

We know that R is an upper triangular matrix, which means that R+ is also upper triangular. Therefore, we can write R+ as a matrix with its elements above the main diagonal being zero. Let's say that R+ is a matrix with elements r_ij, where i>j.

Now, let's look at the expression RR+QT. Since R+ is upper triangular, we know that RR+ will also be upper triangular. This means that the elements below the main diagonal of RR+ will be zero. So, we can write RR+ as a matrix with elements r_ij, where i<=j.

Therefore, when we multiply RR+ with QT, we will only get non-zero elements for the elements r_ij where i<=j. This means that the resulting matrix will be upper triangular.

Now, let's look at the expression (RR+)T. Since R+ is upper triangular, (RR+)T will also be upper triangular. This means that the elements below the main diagonal of (RR+)T will be zero. So, we can write (RR+)T as a matrix with elements r_ij, where i<=j.

Therefore, when we multiply (RR+)T with QT, we will only get non-zero elements for the elements r_ij where i<=j. This means that the resulting matrix will be upper triangular.

Now, let's combine the above results:

R+(RR+)TQT = R+[(RR+)TQT] = R+[(RR+)T][QT] = [(RR+)T][QT]

= [R(R+T)][QT] = [R(RT+)][QT] = R(RT+QT) = R(RT+QT) = R(RT+QT)

= RR+QT = RR+QT = A+

Therefore, A+=R+QT.

I hope this helps. Let me know if you need any further clarification.
 

What is the Pseudo Inverse?

The Pseudo Inverse is a mathematical concept used to determine the inverse of a matrix that does not have a traditional inverse. It is also known as the Moore-Penrose Inverse and can be calculated using different methods such as the Singular Value Decomposition (SVD) or QR Decomposition.

What is QR Decomposition?

QR Decomposition is a method used to decompose a matrix into an orthogonal matrix (Q) and an upper triangular matrix (R). This technique is commonly used in linear algebra and is often used to solve systems of linear equations. It can also be used to calculate the Pseudo Inverse.

What is the relationship between Pseudo Inverse and QR Decomposition?

The Pseudo Inverse and QR Decomposition are closely related. In fact, QR Decomposition is one of the methods used to calculate the Pseudo Inverse. By decomposing a matrix using QR Decomposition, we can obtain the Pseudo Inverse without having to use the SVD method.

Why is the Pseudo Inverse important?

The Pseudo Inverse is important because it allows us to find solutions to systems of linear equations that do not have a traditional inverse. It is also useful in data analysis and machine learning, as it can be used to solve problems with over-determined or under-determined systems.

How is the Pseudo Inverse used in real-world applications?

The Pseudo Inverse has various applications in fields such as engineering, physics, and computer science. It is commonly used in image processing, signal processing, and control systems. It is also used in machine learning algorithms, such as linear regression and neural networks, to find optimal solutions to problems with multiple variables.

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