Linear Algebra Four fundamental subspaces small proof.

In summary, to show that C(A) + N(A) = ℝn, we first let x be any vector in C(A) and show that x is also in N(A). Then, we use the fact that A is an nxn matrix to show that N(A) = ℝn. Therefore, C(A) + N(A) = ℝn.
  • #1
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Homework Statement



Given A[itex]\in[/itex] [itex]M[/itex]nxn and A = A2, show that C(A) +N(A) = ℝn.

note: C(A) means the column space of A.
N(A) means the null space of A




Homework Equations



These equations were proved in earlier parts of the problem...

C(A) = {[itex]\vec{x}[/itex][itex]\in[/itex] ℝn such that [itex]\vec{x}[/itex] = [itex]\vec{u}[/itex]-A[itex]\vec{u}[/itex] for some [itex]\vec{u}[/itex] [itex]\in[/itex]ℝn}

N(A) = {[itex]\vec{x}[/itex][itex]\in[/itex]ℝn such that [itex]\vec{x}[/itex] = A[itex]\vec{x}[/itex]}



The Attempt at a Solution



I feel that my attempt is logical and it works, but I'm not sure if the last step I took works, but if anyone could prove me wrong, confirm that I am right, or offer an alternative, that would be cool! My soln is attached as a picture.
 

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  • #2



Your attempt is a good start, but there are a few errors. First, when you wrote "Let x be any vector in ℝn", you should specify that x is in the column space of A, since you are showing that C(A) + N(A) = ℝn. Also, when you wrote "x = u - Au for some u in ℝn", this is not necessarily true. x is in the column space of A, so it can be written as a linear combination of the columns of A, but u may not be in the column space of A.

Instead, let x be any vector in C(A). Then, by definition, x = u - Au for some u in ℝn. Now, since A = A^2, we have A(x) = A(u - Au) = Au - A^2u = Au - Au = 0. This means that x is also in N(A). Therefore, C(A) is a subset of N(A), and we can conclude that C(A) + N(A) = N(A).

To show that N(A) = ℝn, we can use the fact that A is an nxn matrix. This means that the null space of A has dimension n, since n is the number of columns in A. Since the dimension of ℝn is also n, we can conclude that N(A) = ℝn.

Therefore, we have shown that C(A) + N(A) = ℝn, as desired.
 

1. What are the four fundamental subspaces in linear algebra?

The four fundamental subspaces in linear algebra are the column space, row space, null space, and left null space. These subspaces are crucial in understanding the properties of a matrix and its solutions.

2. How do you prove the existence of these four fundamental subspaces?

To prove the existence of the four fundamental subspaces, we use linear algebra techniques such as matrix multiplication, row reduction, and vector spaces. By performing these operations on a matrix, we can show that the four subspaces are orthogonal and span the entire vector space.

3. What is the importance of the four fundamental subspaces in linear algebra?

The four fundamental subspaces are important because they provide a comprehensive understanding of the properties of a matrix. They also help us solve systems of linear equations and understand the rank and nullity of a matrix.

4. Can you give an example of a small proof involving the four fundamental subspaces?

One example of a small proof involving the four fundamental subspaces is proving that the column space and row space of a matrix are orthogonal. This can be done by using the definition of orthogonality and properties of matrix multiplication.

5. How do the four fundamental subspaces relate to each other?

The four fundamental subspaces are interrelated and provide a complete picture of a matrix. The column space and row space are perpendicular complements of each other, while the null space and left null space are also perpendicular complements. Additionally, the dimension of the column space and row space are equal, as well as the dimension of the null space and left null space.

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