Linear Algebra (Symmetric Matrix)

In summary, the given conversation discusses finding the bases and dimensions of the column space, row space, and left null space of a 3x3 symmetric matrix with a null space of dimension one containing the vector (1,1,1). This can be done by selecting any linearly independent basis for the row space and filling in the rest of the information with the identity matrix. The dimensions of the column space, row space, and left null space are 2, 2, and 1 respectively, and the basis for the row space is given as {[-1 1 0], [-1 - 1]}.
  • #1
MoBaT
5
0
A 3x3 symmetric matrix has a null space of dimension one containing the vector (1,1,1). Find the bases and dimensions of the column space, row space, and left null space.

I understand how to get the Dim of Col(A), Row(A), and Nul(A^T) but how do i get the bases with just knowing the dimension of one vector? How should I approach this?
 
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  • #2
Hey MoBaT.

Can you pick any linearly independent basis?
 
  • #3
chiro said:
Hey MoBaT.

Can you pick any linearly independent basis?

Dosen't say anything against it. I know what the answer is becuase he gave it to us. It was like:

Dim of Col(A) = 2, dim Row(A) = 2, Dim Nul(A^T) = 1
Basis Row(A) = {[-1 1 0], [-1 - 1]}. Because A is symmetric, Col(A) = Row(A) and Nul(A^T) = Nul(A).

I understand everything after the Basis row(A) but do not understand how he got that Row(A)
 
  • #4
Found out how to do it. Pretty much fill in the rest of the information with the identity matrix.
 
  • #5


To find the bases and dimensions of the column space and row space, we can use the fact that the null space of a symmetric matrix is perpendicular to its column space and row space. This means that the vector (1,1,1) is perpendicular to all the columns and rows of the symmetric matrix.

For the column space, this means that the column space is spanned by the two vectors that are perpendicular to (1,1,1). One possible basis for the column space could be (1,0,-1) and (0,1,-1). Therefore, the dimension of the column space is 2.

For the row space, we can use the same approach and find that the row space is also spanned by (1,0,-1) and (0,1,-1). Therefore, the dimension of the row space is also 2.

For the left null space, we can use the fact that the left null space of a matrix A is the null space of its transpose, A^T. Since we know that the null space of A^T is spanned by (1,1,1), the left null space of A is spanned by the vector (1,1,1). Therefore, the dimension of the left null space is 1 and the basis is (1,1,1).

Overall, the bases and dimensions of the column space, row space, and left null space for a 3x3 symmetric matrix with a null space of dimension one containing the vector (1,1,1) are:

- Column space: dimension 2, basis: (1,0,-1), (0,1,-1)
- Row space: dimension 2, basis: (1,0,-1), (0,1,-1)
- Left null space: dimension 1, basis: (1,1,1)
 

Related to Linear Algebra (Symmetric Matrix)

What is a symmetric matrix?

A symmetric matrix is a square matrix where the elements are symmetric about its main diagonal. This means that the element at row i and column j is equal to the element at row j and column i.

What is the importance of symmetric matrices in linear algebra?

Symmetric matrices have several important properties in linear algebra, making them a useful tool for solving equations and performing operations. These include being diagonalizable, having real eigenvalues, and being orthogonal diagonalizable.

How do you determine if a matrix is symmetric?

To determine if a matrix is symmetric, you can check if it is equal to its own transpose. In other words, if A is a matrix and AT is its transpose, then A is symmetric if A = AT.

Can a non-square matrix be symmetric?

No, a non-square matrix cannot be symmetric. The definition of a symmetric matrix requires it to be a square matrix, meaning that it has the same number of rows and columns.

What are some real-world applications of symmetric matrices?

Symmetric matrices have many applications in fields such as physics, engineering, and computer science. They are used to model systems with symmetric properties, such as forces acting on a structure or data encryption algorithms.

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