Column Space of Matrix A and ref(A)

In summary, the columns that contain leading ones in the reduced row echelon form of a matrix A form the column space of both A and ref(A). However, the corresponding columns from the original matrix A may not necessarily form the column space of ref(A).
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lkh1986
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Homework Statement


Given a matrix A. So I can reduce A to ref(A). Let's say in ref(A), the columns that contain leading ones are column 1, 3, and 5. True or false:
(a) Columns 1, 3, and 5 from ref(A) form the column space of ref(A).
(b) The corresponding column 1, 3, and 5 from the original matrix A form the column space of matrix A.
(c) Columns 1, 3, and 5 from ref(A) form the column space of the matrix A.
(d) The corresponding column 1, 3, and 5 from the original matrix A form the column space of ref(A).



Homework Equations





The Attempt at a Solution


(a) and (b) are straight forward and hence, both are true. I think (c) is false. Not sure about (d) though.

For (c), I have a specific counter example. I have column space of ref(A) is something like {[1 0 0 0], [0 1 0 0], [0 0 1 0]}, whereas the column space of the original matrix A is {[1 3 2 -1], [-2 2 3 2], [3 1 2 4]}. Notice that the 4th entry for the space spanned by ref(A) will always be 0, but it's possible to have a non-zero value for the space spanned by the column space, if the answers are taken from the original matrix A.
 
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  • #2
When we say that "vectors a, b, and c form as subspace", we mean that they make span the space. Of course, there may be manysets of vectors that span the same subspace. The important fact here is that the column space of A and the coumn space of ref(A) are the same.
 

FAQ: Column Space of Matrix A and ref(A)

What is the column space of a matrix A?

The column space of a matrix A is the span of the columns of A. In other words, it is the set of all possible linear combinations of the columns of A. It is also known as the range of A.

How is the column space of a matrix A related to the row space of A?

The column space and the row space of a matrix A are related by the rank-nullity theorem. This theorem states that the dimension of the column space is equal to the dimension of the row space, which is also equal to the rank of A. Additionally, the nullity of A (the dimension of the null space) is equal to the number of columns of A minus the rank of A.

How can I find the column space of a matrix A?

To find the column space of a matrix A, you can use the process of row reduction (also known as Gaussian elimination) to reduce A to its reduced row-echelon form. The columns of A that contain pivot positions in the reduced row-echelon form will form a basis for the column space.

What is ref(A)?

ref(A) stands for the reduced row-echelon form of a matrix A. It is a specific form that a matrix can be reduced to using row operations. The reduced row-echelon form is useful because it allows us to easily identify the pivot positions and the free variables of a matrix, and it also provides a basis for the column space and the null space of A.

How can I use the column space and the reduced row-echelon form of a matrix A to solve linear systems?

The column space and the reduced row-echelon form of a matrix A can be used to solve linear systems because they provide information about the number of solutions to the system and the values of the variables in those solutions. For example, if the reduced row-echelon form of A has a pivot position in every column, then the system has a unique solution. If the reduced row-echelon form has a free variable, then the system has infinitely many solutions. Additionally, the column space and the row space of A can help us determine if the system is consistent (has at least one solution) or inconsistent (has no solutions).

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