Nullspace, Column Space, and solution of system given only rref(A)

In summary, the nullspace of matrix A is spanned by the vectors {2, 1, 0, 0, 0} and {-5, 0, -4, -3, 1}, the column space is spanned by the first, third, and fourth columns of the rref(A), and the solutions to Ax = b can be found by solving for the inverse of A and multiplying it by the vector b.
  • #1
nenna
1
0

Homework Statement


Suppose a 3 x 5 matrix A has row-reduced echelon form:
[[1 2 0 0 5]
[0 0 1 0 4]
[0 0 0 1 3]]

a. Describe NS(A)
b. Describe CS(A)
c. Suppose
. [[2]
. [3] [[-2]
A [5] = [4] = b
. [1] [3]]
. [9]]

To be clear, that's the original matrix A times the vector x = {2, 3, 5, 1, 9} to give us the vector b = {-2, 4, 3}.

Find all solutions of the equation Ax = b

Homework Equations


The Attempt at a Solution



I completed a) by finding the basis for the nullspace to be the set of the two following vectors

{2, 1, 0, 0, 0} , {-5, 0, -4, -3, 1}

I'm not really sure how to do part b) since I'm not given the original matrix A, just the rref(A)

I have absolutely no idea how to do part c) since I'm not given the original matrix.

Please Help!
 
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  • #2


Hi there,

For b), you can still describe the column space of A even if you are only given the rref(A). Recall that the columns of the original matrix A are the same as the columns of the rref(A) that contain a leading 1. So in this case, the column space of A would be spanned by the first, third, and fourth column of the rref(A).

For c), you can use the fact that the solution to Ax = b is given by x = A^-1 * b. Since you are given the vector b, you can solve for x by finding the inverse of the original matrix A. The inverse of a matrix can be found by using the Gauss-Jordan elimination method.

Hope this helps! Let me know if you have any other questions.
 

1. What is Nullspace?

Nullspace, also known as the kernel, is the set of all vectors that when multiplied by a matrix A results in the zero vector. In other words, it is the set of all solutions to the equation Ax=0. The nullspace is a subspace of the vector space Rn and is denoted as N(A).

2. What is Column Space?

Column space is the set of all linear combinations of the columns of a matrix A. It represents all the possible outputs that can be produced by multiplying the matrix A by a vector. The column space is a subspace of the vector space Rm and is denoted as Col(A).

3. How are Nullspace and Column Space related?

The nullspace and column space of a matrix A are related through the nullity and rank of the matrix. The nullity is the dimension of the nullspace and the rank is the dimension of the column space. The rank-nullity theorem states that the sum of the nullity and rank is equal to the number of columns in the matrix A. Thus, the nullspace and column space are complementary subspaces.

4. Can you determine the solution of a system of equations from the reduced row echelon form (rref) of a matrix A?

Yes, the rref of a matrix A can be used to determine the solution of a system of equations. The rref can be interpreted as a system of equations where the leading variables correspond to the pivot columns and the non-leading variables correspond to the free variables. The system can then be solved by expressing the non-leading variables in terms of the free variables.

5. How do you find the basis for the nullspace and column space from the rref of a matrix A?

The basis for the nullspace can be found by identifying the free variables in the system of equations represented by the rref of A. The basis will consist of the columns of the original matrix A that correspond to the free variables. The basis for the column space can be found by taking the pivot columns of the rref of A. These columns will form a linearly independent set that spans the column space.

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