Column space of positive semidefinite matrix

In summary, the column space of a positive semidefinite matrix is the subspace spanned by its columns, representing all possible linear combinations of the columns. It can be found using the Gram-Schmidt process and is significant in various mathematical and scientific fields. The column space cannot be empty and is closely related to the rank of the matrix.
  • #1
td21
Gold Member
177
8
how to prove that
[tex]R(A)=\text{sum of} N(A-\lambda I)[/tex]?

[itex]\lambda[/itex] is nonzero eignevalues of A
 
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  • #2
well i can show that
1)if y[itex]\in[/itex]R(A), then y=Ax=λx.
2)As (A−λI)x=0, x[itex]\in[/itex]N(A−λI).

But how can we show that y[itex]\in[/itex] sum of N(A−λI)?

thanks
 
  • #3
Does this mean y= sum of eignevectors of A?
 
  • #4
anyone?thanks
 
  • #5
Hi td21! :smile:

What about the zero matrix? This is positive semidefinite, and

[tex]R(0)=0~\text{and}~N(0-\lambda I)=N(0)=\text{entire space}[/tex]
 

Related to Column space of positive semidefinite matrix

What is the column space of a positive semidefinite matrix?

The column space of a positive semidefinite matrix is the subspace of the matrix spanned by its columns. It represents all possible linear combinations of the columns of the matrix.

How do you find the column space of a positive semidefinite matrix?

To find the column space of a positive semidefinite matrix, one can use a method called the Gram-Schmidt process. This involves performing a series of orthogonal projections to obtain a set of linearly independent vectors, which form the basis for the column space.

What is the significance of the column space of a positive semidefinite matrix?

The column space of a positive semidefinite matrix is important in many areas of mathematics and science, including linear algebra, optimization, and statistics. It can help in solving systems of linear equations, finding eigenvalues and eigenvectors, and analyzing quadratic forms.

Can the column space of a positive semidefinite matrix be empty?

No, the column space of a positive semidefinite matrix cannot be empty. This is because a positive semidefinite matrix always has at least one non-zero eigenvalue, which corresponds to a non-zero column in the matrix. Therefore, the column space will always contain at least one vector.

How does the column space of a positive semidefinite matrix relate to its rank?

The column space of a positive semidefinite matrix is closely related to its rank. The rank of a matrix is equal to the number of linearly independent columns in the matrix, and the column space contains all possible linear combinations of these columns. Therefore, the dimension of the column space is equal to the rank of the matrix.

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