Pseudoinverse - change of basis?

In summary: The remaining free variables are not in the null-space. They are still in the space of the original matrix A.
  • #1
kviksand81
5
0
Hello,

I was wondering if the pseudoinverse can be considered a change of basis?

If an m x n matrix with m < n and rank m and you wish to solve the system Ax = b, the solution would hold an infinite number of solutions; hence you form the pseudoinverse by A^T(A*A^T)^-1 and solve for x to get the minimum norm solution. And since A was the original basis the pseudoinverse must be a new basis...? Or am I getting it all wrong?

 
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  • #2
I don't understand what you mean by "since A was the original basis the pseudoinverse must be a new basis". A matrix is NOT a "basis". A basis is a collection of vectors, not a matrix or linear transformation. A linear transformation is represented by a matrix using a specific basis but finding the generalized inverse does not change the basis.
 
  • #3
Sorry. From the original problem where m < n, there is not a set of linearly independent vectors to yield a exact solution to the system Ax = b. Then a common technique to solve for a vector that comes as close as possible, would be to form the pseudoinverse, right? This pseudoinverse, is that a new basis or what is it?

Hope that it is more clear what I mean now! :-)

 
  • #4
No, a basis, for a given vector space, is a set of vectors that span the vector space and are independent so what you are describing is not a basis.
 
  • #5
Ok.

If I then solve the system Ax =b where the matrix A (m x n) with rank m by means of the right-hand pseudo inverse A^T(A*A^T)^-1, what I get is the minimum norm solution to the m independent columns of A? Or...?

What happens to the remaining free variables? Are they in the null-space?
 

Related to Pseudoinverse - change of basis?

1. What is a pseudoinverse?

A pseudoinverse is a mathematical tool used in linear algebra that is used to find an approximate inverse of a matrix that does not have an inverse. It is also known as the Moore-Penrose inverse.

2. How is the pseudoinverse different from a regular inverse?

A regular inverse of a matrix only exists for square, non-singular matrices, while a pseudoinverse can be calculated for any matrix. Additionally, the pseudoinverse is a generalization of the regular inverse and can be used to solve systems of linear equations even when the matrix is singular or underdetermined.

3. What is the relationship between pseudoinverse and change of basis?

The pseudoinverse of a matrix can be used to find the change of basis matrix between two vector spaces. This is helpful in solving problems involving transformations between vector spaces with different bases.

4. How is the pseudoinverse calculated?

The pseudoinverse can be calculated using the singular value decomposition (SVD) of a matrix. This involves decomposing the matrix into three components: a unitary matrix, a diagonal matrix, and another unitary matrix. The pseudoinverse is then calculated by taking the inverse of the diagonal matrix and multiplying it with the transpose of the other two matrices.

5. In what applications is the pseudoinverse used?

The pseudoinverse is commonly used in data analysis and machine learning, particularly in solving least squares problems. It is also used in signal processing, control theory, and image processing. Additionally, it has applications in physics, economics, and statistics.

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