About orthogonality and nullspaces

In summary, the conversation discusses the relationship between the columns of a matrix A and the nullspace of its transpose A^T. It explains that the nullspace of A^T is orthogonal to the space spanned by the columns of A. This is demonstrated through an example where the nullspace of A^T is shown to be orthogonal to the vectors (1,5) and (3,2). The conversation also mentions the condition for a vector to be in the nullspace of A^T and how it is related to the columns of A.
  • #1
applechu
10
0
Hi:
I see an example about nullspace and orthogonality, the example is following:

$$Ax=\begin{bmatrix} 1 & 3 &4\\ 5 & 2& 7 \end{bmatrix} \times \left[ \begin{array}{c} 1 \\ 1\\-1 \end{array} \right]=\begin{bmatrix} 0\\0\end{bmatrix}$$

The conclusion says the nullspace of [itex]A^T[/itex] is only the zero vector(orthogonal to every vector). I don't know why the columns of A and nullspace of [itex]A^T[/itex] are orthogonal spaces.
I know nullspace is the solution of Ax=0; but in this theorem, why columns of A is related
to nullsapce of [itex]A^T[/itex].
Thanks.
 
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  • #2
Notice the null space of A^T is orthogonal to (1,5), (3,2) and (4,7). So it is orthogonal to the space spanned by the vectors, which is the column space of A.
 
  • #3
[tex]A^T= \begin{bmatrix}1 & 5 \\ 3 & 2 \\ 4 & 7\end{bmatrix}[/tex]
so that the condition that a vector be in the null space of [itex]A^T[/itex] is
[tex]\begin{bmatrix}1 & 5 \\ 3 & 2 \\ 4 & 7\end{bmatrix}\begin{bmatrix}x \\ y \end{bmatrix}= \begin{bmatrix}0 \\ 0 \\ 0 \end{bmatrix}[/tex]
which is the same as
[tex]\begin{bmatrix}x+ 5y \\ 3x+ 2y \\ 4x+ 7y\end{bmatrix}= \begin{bmatrix}0 \\ 0 \\ 0 \end{bmatrix}[/tex]
Do you see now, how the columns of A, which become the rows of AT, are relevant here?
 

Related to About orthogonality and nullspaces

What is orthogonality?

Orthogonality refers to the property of being perpendicular or at right angles to each other. In linear algebra, it is commonly used to describe vectors that are perpendicular to each other.

What is a nullspace?

A nullspace, also known as a kernel, is a set of vectors that when multiplied by a matrix, produce a zero vector. In other words, it is the set of all solutions to the homogeneous equation Ax=0, where A is a matrix.

How are orthogonality and nullspaces related?

Orthogonality and nullspaces are related in the sense that the nullspace of a matrix is the set of all vectors that are orthogonal to the rows of the matrix. This means that the dot product of any vector in the nullspace and any row vector of the matrix will be equal to 0.

What is the significance of orthogonality and nullspaces in linear algebra?

Orthogonality and nullspaces are important concepts in linear algebra because they help us understand the relationships between vectors and matrices. They are also used extensively in applications such as data compression, signal processing, and computer graphics.

How can orthogonality and nullspaces be used in real-world problems?

Orthogonality and nullspaces have many practical applications, such as in finding the shortest distance between a point and a plane, solving systems of linear equations, and finding the best fit line for a set of data points. They are also used in machine learning algorithms and in solving optimization problems.

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