I Proof of Column Extraction Theorem for Finding a Basis for Col(A)

mattTch
Messages
2
Reaction score
0
TL;DR
Let A be an m×n matrix. I am not sure why it's immediately obvious that the set B containing all and only the column vectors of R = RREF(A) which have leading ones, forms a basis for R. In particular, why is it the case that Span(B) = Col(R)? FYI: The linear independence of B is obvious to me.
Theorem: The columns of A which correspond to leading ones in the reduced row echelon form of A form a basis for Col(A). Moreover, dimCol(A)=rank(A).
 
Physics news on Phys.org
Consider:

1. If \alpha : V \to W is a linear map and B = \{b_i\} is a basis for V, then \alpha(B) spans \alpha(V). This follows from linearity of \alpha: If v \in V then v = \sum_i a_ib_i and <br /> \alpha(v) = \alpha\left(\sum _ia_i b_i\right) = \sum_i a_i \alpha(b_i).

2. The ith column of a matrix is the image of the ith standard basis vector.

3. It follows from the definition of RREF that columns which don't have a leading 1 are linear combinations of the columns which do.
 
Why does 3 follow from the definition of RREF?
 
mattTch said:
Why does 3 follow from the definition of RREF?
Can chip in? If I've understood your question this might help.

Consider an example RREF matrix:

##\begin{bmatrix}
1 & 0 & 2 & 0 & 8\\
0 & 1 & 7 & 0 & 3\\
0 & 0 & 0 & 1 & 2\\
0 & 0 & 0 & 0 & 0
\end{bmatrix} ##

Some columns contain a leading '1' (with zeroes for all other elements). These are the basis columns.

Other columns do not contain a leading '1'. These are non-basis columns.

The basis columns here are ##C_1 = \begin{bmatrix}
1\\
0\\
0\\
0
\end{bmatrix}##, ##C_2 = \begin{bmatrix}
0\\
1\\
0\\
0
\end{bmatrix}## and ##C_4 = \begin{bmatrix}
0\\
0\\
1\\
0
\end{bmatrix}##.

From inspection it should be clear that any non-basis column can be constructed as a linear combination of the basis columns, e.g. ##C_5 = 8C_1 + 3C_2 + 2C_4##.

That’s because every non-zero coefficient in a non-basis column is a simple multiple of the ‘1’ in a basis column.
 
Thread 'How to define a vector field?'
Hello! In one book I saw that function ##V## of 3 variables ##V_x, V_y, V_z## (vector field in 3D) can be decomposed in a Taylor series without higher-order terms (partial derivative of second power and higher) at point ##(0,0,0)## such way: I think so: higher-order terms can be neglected because partial derivative of second power and higher are equal to 0. Is this true? And how to define vector field correctly for this case? (In the book I found nothing and my attempt was wrong...

Similar threads

  • · Replies 4 ·
Replies
4
Views
2K
  • · Replies 4 ·
Replies
4
Views
3K
  • · Replies 1 ·
Replies
1
Views
2K
  • · Replies 14 ·
Replies
14
Views
4K
  • · Replies 8 ·
Replies
8
Views
3K
  • · Replies 2 ·
Replies
2
Views
3K
  • · Replies 7 ·
Replies
7
Views
2K
  • · Replies 1 ·
Replies
1
Views
2K
Replies
4
Views
2K
Replies
3
Views
3K